Post on 21-Aug-2020
1
GENETIC AND ENDOCRINOLOGICAL STUDIES
OF FEMALE REPRODUCTIVE DYSFUNCTION
DOCTOR OF PHILOSOPHY
In
Zoology
By
IRFANA LIAQAT Roll No: 0024-PHD-GCU-Z-09
Session 2009-2012
Department of Zoology
GC University, Lahore
2
This work is submitted as a dissertation in partial fulfillment
of the requirement for the degree of
DOCTOR OF PHILOSOPHY
In
Zoology
By
IRFANA LIAQAT Roll No: 0024-PHD-GCU-Z-09
Session 2009-2012
Department of Zoology,
Government College University,
Lahore 54000,
Pakistan.
3
DECLARATION
I, Irfana Liaqat Roll No. 24-GCU-PHD-Z-09, student of Doctor of Philosophy in
the subject of Zoology, hereby declare that the matter printed in the thesis titled
Genetic and Endocrinological Studies of Female Reproductive Dysfunction is my
own work and has not been printed, published and submitted as research work, thesis
or publication in any form in any University, Research Institution etc. in Pakistan or
abroad.
Dated
Irfana Liaqat
4
RESEARCH COMPLETION CERTIFICATE
It is certified that research work contained in this thesis titled “Genetic and
Endocrinological Studies of Female Reproductive Dysfunction” has been carried
out and completed by Irfana Liaqat Roll No. 24-GCU-PHD-Z-09 under my
supervision.
Dated
Dated
Supervisor
Prof. Dr. Nusrat Jahan
Co- Supervisor
Prof. Dr. Khalid Parvez Lone
Chairperson
Department Of Zoology
GC University Lahore
Controller of Examination
GC University Lahore
5
6
Acknowledgements
Above all is the Almighty ALLAH; I give thanks to him for it is He who has
brought me thus far and to his Last and Beloved Prophet (Sallallallhu Alaihi
Wasallam) for whom this wide and huge universe was assembled and who is the
pioneer of education and research.
I would like to acknowledge my gratitude for everyone who has helped me
along my Ph. D thesis work.
First, I must thank Prof. Dr. Nusrat Jahan, the Chairperson, Department of
Zoology, GC University Lahore. What an amazing person my supervisor is! You have
put such a lot of time and effort in to me - I couldn’t have wished for a better role
model. The road was not always smooth, but we persevered. To her I am truly
grateful. Her knowledge, sagacity and wonderful memory have helped me a lot. I am
very grateful to have her as my supervisor and I hope her enthusiasm towards science
will continue to inspire all her students. I want to express my deepest thanks to my
supervisor. Who has helped and supported me all along my research work at the GC
University Lahore.
I also pay gratitude to Prof. Dr. Khalid Parvez Lone the Chairperson of
Department of Physiology and Cell Biology, University of Health Science, Lahore. He
was never hesitant to lend an ear, or give advice, and if he had the resources, they
were mine to use. But for his kind cooperation which he gave me in my hard times, I
would not have been able to do this effort. Your kindness and generosity has meant so
much. I want to express my deepest thanks to my co-supervisor.
A huge thank-you to Prof. Dr. Hugh S. Taylor, Professor and Chair
Department of Obstetrics, Gynecology and Reproductive Sciences Yale University
USA, for your support and guidance throughout this project. You are an amazing
teacher, your enthusiasm and positivity was much appreciated.
I acknowledge Higher Education commission Pakistan for funding my PHD
Indigenous fellowship as well as for providing International Research Support
Initiative Program (IRSIP) Scholarship. You have made so much difference to my life
over the last few years, and without you this thesis would never have been finished.
The shadows of the past have turned into a future that I can begins to look forward to
without anxiety, a future I never believed possible.
7
My heartiest thanks are due to Dr. Andrew Pakstis for patiently teaching me
the sequencing techniques and for his excellent technical assistance in genetic and
statistical analysis. You remained encouraging and positive throughout my analysis
while my self-doubt ran rampant. Thanks to everyone in the Taylor Lab, Yale School
of medicine, Graciela Krikun, Flannery Clare, Haniya, Levent, Sihyu, Hongling
Du, Yuping Zhou for assistance with the time course experiment and the training in
genetic studies.
I am grateful to my all teachers for giving me enormous help at G C
University Lahore. I am also thankful to all lab and departmental staff for their
devotive and cooperative behavior. I am in debt to Dr. Shafaqat Rehmani for
providing me an opportunity to work at WTO Labs. Thanks to everyone, Imran
Haider, Ali Imran, Asghar and Wadood in the office at GC University Lahore.
I have special thanks for my friends and colleagues for giving me enormous
help and coverage. I have to say a huge thank you to my friends Najiya, Andleeb,
Riffat, Shaista, Sonia. Asma, Mehwish, Tariq, and Ijaz. Without their support I
could not possibly have reach this point. I know my words will be embarrassed to
describe Epical favor. I am also indebted to many others who have contributed
directly or indirectly to my research work.
Thank you to my parents for your belief in the value education. I would also
like to thank my parents, my family, especially my father and my brothers, Rafaqat,
Asif and Azam for all of their support throughout my life. Without them I would not
be here. I particularly wish to thank Fateh Naseeb for being absolutely amazing
during the tough times. You have supported me emotionally throughout the last few
years.
Finally, one special person deserves my deepest gratitude and love. I want to
thank my mother who had brought me up to the person.
IRFANA LIAQAT
8
SUMMARY
Endometriosis is one of the major threats to women’s health that leads toward
infertility. The inheritable predisposition to endometriosis develops a growing interest
in identifying the genes and genomic variants involved in the pathogenesis of
endometriosis. Polycystic ovary syndrome (PCOS) is the major cause of anovulatory
infertility. The genetic basis of PCOS is not well understood, it is a common
metabolic and endocrine disorder. The current study aimed to investigate for the first
time, the possible genomic variants and single nucleotide polymorphism (SNP) in
selective genes associated with endometriosis and PCOS in Pakistani women from the
Punjab region. DNA samples from fifty-two genetically unrelated endometriosis
patients and fifty-two controls were analyzed by direct sequencing to determine the
polymorphisms of estrogen receptor alpha ESR1 (rs2234693, rs9340799), estrogen
receptor beta ESR2 (rs4986938), progesterone receptor PGR (rs1042838,
rs10895068), interleukin 10 IL10 (rs1800871, rs1800872 and rs1800896) and follicle
stimulating hormone receptor FSHR (rs6166, rs6165) genes. Genetically unrelated
PCOS patients and controls, 96 each, were selected on the basis of their Body Mass
Index (BMI) and degree of obesity. The polymorphisms of different loci on
adiponectin ADIPOQ (rs2241766, rs1501299 and rs2241767), insulin receptor INSR
(rs1799817, rs1799815 of rs2059806 and rs2229429), FSHR (rs6164, rs6165),
Follicle stimulating hormone beta FSHB (rs6169), Luteinizing hormone
choriogonadotropin receptor LHCGR (rs61996318 and rs111834744), Luteinizing
hormone beta LHB (rs1800447 and rs4002462), ESR1 (rs2234693, rs9340799) and
ESR2 (rs4986938) genes were analyzed by direct sequencing. The rs4986938 of ESR2
and rs1042838 of PGR gene show strong association (p = 0.006, OR: 1.360; CI: 0.16-
0.80; p < 0.003, OR = 1.935, 95% CI = 0.10-0.62) with endometriosis. The genotype
and allele frequency of ESR1 gene polymorphisms were distributed similarly among
patients and control groups (p > 0.050). The allele A of rs1800872 (p < 0.006, OR =
2.379, 95% CI = 0.23-0.75), T of rs1800871 (p = 0.010, OR = 0.443, 95% CI = 0.19-
0.92) and G of rs1800896 ((p = 0.007, OR = 1.435, 95% CI = 0.24-0.77) at IL10 gene
were strongly associated with the predisposition of endometriosis in Pakistani women.
Significant (p < 0.001) associations were observed within the genotype frequencies,
allele frequencies and multi-SNP haplotype analysis of the genetic polymorphisms of
FSHR gene with endometriosis. This study identified new single nucleotide
9
polymorphisms (SNPs) at positions +349 A/G in ADIPOQ, +1638 T/C in INSR and
+657 del/T in LHCGR genes associated with PCOS (p < 0.005) in Pakistani women.
The polymorphisms in the ADIPOQ (r2= 0.78), ESR1 (r
2= 0.48) and FSHR (r
2= 0.76)
genes were in strong (p < 0.001) linkage disequilibrium. The highly significant (p =
0.030, OR = 0.436, 95% CI = 0.21-0.89) SNP of ADIOPQ gene associated with
PCOS was rs2241766 when compared with their controls. In INSR gene the most
common haplotypes CGT, CAC and CAT were found to be more prevalent in the
PCOS than controls (p < 0.001). The FSHR and LHB gene polymorphism seems to be
stable loci in Pakistani PCOS women (p > 0.050). Whereas, the rs6169 of FSHB gene
was strongly (p= 0.020, OR= 0.606, 95% CI= 0.40-0.91) associated with PCOS in this
subpopulation. The rs111834744 of LHCGR (p < 0.001, OR = 1.270, 95% CI = 0.18-
0.42), rs2232693 of ESR1 and rs4986938 of ESR2 genes were significantly (p <
0.005) associated with the onset of PCOS in Pakistani women. Significant (p < 0.005)
associations were observed within the genotype frequencies, allele frequencies and
multi-SNP haplotype analysis of most polymorphisms studied. Serum progesterone
level was lower in endometriosis patients while the serum testosterone and FSH titres
were higher in PCOS women when compared with their controls (p < 0.001). The
current findings suggest that the functional promoter polymorphism of IL10 gene
ATG genotype may contribute in the risk of endometriosis. Current study provides the
novel and new to science associations of ADIPOQ, INSR, FSHB, LHCGR, ESR1 and
ESR2 genes with PCOS in Pakistani women. The genetic variants of above mentioned
gene loci might influence the fertility status of endometriosis and PCOS patients. This
suggests that the susceptible loci for endometriosis and PCOS lie within or very close
to the chromosomal regions spanning these genes.
10
TABLE OF CONTENTS
Page No.
LIST OF TABLES .................................................................................................................... i
LIST OF FIGURES ................................................................................................................. iv
LIST OF ABBREVIATIONS ................................................................................................ viii
CHAPTER 1
INTRODUCTION.................................................................................................................. 1-13
1.1 Infertility ...............................................................................................................................1
1.1.1 Causes of infertility in women ........................................................................................1
1.2 Endometriosis ........................................................................................................................2
1.3 Polycystic ovary syndrome ...................................................................................................6
CHAPTER 2
REVIEW OF LITERATURE ............................................................................................... 14-33
2.1 Endometriosis .....................................................................................................................14
2.1.1 Estrogen receptor alpha (ESR1) .....................................................................................14
2.1.2 Estrogen receptor beta (ESR2) .......................................................................................17
2.1.3 Interleukin 10 (IL10) .....................................................................................................19
2.1.4 Progesterone receptor (PGR) .........................................................................................19
2.2 PCOS ...................................................................................................................................22
2.2.1 Adiponectin (ADIPOQ) .................................................................................................22
2.2.2 Insulin receptor (INSR) ..................................................................................................25
2.2.3 Follicle stimulating hormone receptor (FSHR) .............................................................27
2.2.4 Follicle stimulating hormone beta (FSHB)....................................................................31
2.2.5 Estrogen receptor alpha (ESR1) .....................................................................................31
2.2.6 Estrogen receptor beta (ESR2) .......................................................................................32
2.2.7 Luteinizing hormone choriogonadotropin receptor (LHCGR) ......................................32
2.2.8 Luteinizing hormone beta (LHβ) ...................................................................................32
11
CHAPTER 3
MATERIALS AND METHODS ......................................................................................... 34-54
3.1 Sampling size and study design .........................................................................................34
3.2 Subjects ...............................................................................................................................34
3.3 Sample selection ..................................................................................................................34
3.3.1 Inclusion criteria ............................................................................................................34
3.3.2 Exclusion criteria ...........................................................................................................35
3.3.3 Selection of controls ......................................................................................................35
3.4 Physical examination ..........................................................................................................37
3.4.1 Determination of Height, Weight and BMI ...................................................................37
3.4.2 Physical examinations and interviews ...........................................................................37
3.5 Blood sampling and Storage ..............................................................................................37
3.6 Genotyping and DNA sequencing .....................................................................................38
3.6.1 Genomic DNA extraction ..............................................................................................38
3.6.2 Candidate Genes ............................................................................................................39
3.6.3 Single nucleotide polymorphism selection ....................................................................39
3.6.4 Primer designing ............................................................................................................46
3.6.5 PCR Analysis.................................................................................................................49
3.6.6 Gel electrophoresis and verification of PCR products ..................................................49
3.6.7 Purification of PCR product ..........................................................................................49
3.6.8 DNA sequencing ...........................................................................................................50
3.6.9 DNA sequencing data interpretation .............................................................................50
3.7 Hormonal Analysis.................................................................................................................51
3.7.1 Follicle stimulating hormone .........................................................................................51
3.7.2 Testosterone ...................................................................................................................52
3.7.3 Progesterone ..................................................................................................................52
3.8 Statistical analysis ..............................................................................................................53
12
3.9 Haplotype and linkage disequilibrium analysis...............................................................53
CHAPTER 4
RESULTS ............................................................................................................................. 55-128
4.1 Population characteristics .................................................................................................55
4.2 Genotype, allele frequencies and Haplotype ....................................................................55
4.3 Endometriosis .....................................................................................................................60
4.3.1 Estrogen receptor alpha (ESR1) .....................................................................................60
4.3.2 Estrogen receptor beta (ESR2) .......................................................................................68
4.3.3 Interleukin 10 (IL10) .....................................................................................................70
4.3.4 Progesterone receptor (PGR) .........................................................................................77
4.3.5 Follicle stimulating hormone receptor (FSHR) .............................................................81
4.4 PCOS ...................................................................................................................................86
4.4.1 Adiponectin (ADIPOQ) .................................................................................................86
4.4.2 Insulin receptor (INSR) ..................................................................................................93
4.4.3 Follicle stimulating hormone receptor (FSHR) .............................................................99
4.4.4 Follicle stimulating hormone beta (FSHB)..................................................................104
4.4.5 Luteinizing hormone choriogonadotropin receptor (LHCGR) ....................................107
4.4.1 Luteinizing hormone beta (LHB) .................................................................................111
4.4.2 Estrogen receptor alpha (ESR1) ...................................................................................116
4.4.3 Estrogen receptor beta (ESR2) .....................................................................................123
4.5 Hormonal analysis ...........................................................................................................123
4.5.1 Follicle stimulating hormone (FSH) ............................................................................123
4.5.2 Testosterone .................................................................................................................123
4.5.3 Progesterone ................................................................................................................123
CHAPTER 5
DISCUSSION ..................................................................................................................... 129-137
5.1 Population characteristics ...............................................................................................129
5.2 Endometriosis .................................................................................................................130
13
5.3 PCOS ..............................................................................................................................133
CHAPTER 6
REFERENCES ................................................................................................................... 138-162
CHAPTER 7
ANNEXURES ..................................................................................................................... 163-178
LIST OF PUBLICATIONS AND PRESENTATIONS .................................................. 179-180
14
LIST OF TABLES
Table No. Title Page No.
Table 2.1 Summary of association studies between ESR1 gene polymorphisms
(rs9340799 and rs2234693) and endometriosis.
16
Table 2.2 Summary of association studies between ESR2 (rs4986938) gene
polymorphisms and endometriosis.
18
Table 2.3 Summary of association studies between IL10 (rs1800896, rs1800871
and rs1800872) gene polymorphisms and endometriosis.
20
Table 2.4
(a)
Summary of association studies between ADIPOQ (rs2241766) gene
polymorphisms and PCOS.
23
Table 2.4
(b)
Summary of association studies between ADIPOQ (rs1501299) gene
polymorphisms and PCOS.
24
Table 2.5 Summary of association studies between INSR (rs1799817 and
rs2059806) gene polymorphisms and PCOS.
26
Table 2.6 Summary of association studies between FSHR (rs6165) gene
polymorphisms and PCOS.
29
Table 2.7 Summary of association studies between FSHR (rs6166) gene
polymorphisms and PCOS.
30
Table 3.1 Genotyping panel for endometriosis candidate genes. 40
Table 3.2 Genotyping panel for PCOS candidate genes. 41
Table 3.3 Primers, PCR program, annealing temperatures and PCR nucleotide
product size of endometriosis polymorphisms.
42
Table 3.4 Primer, annealing temperature, PCR program and product size of
PCOS polymorphisms.
44
Table 4.1 Anthropometric characteristics of PCOS, endometriosis and controls. 56
Table 4.2 Anthropometric characteristics of women with endometriosis and
controls, the values given are mean ± S.D. The mean values of two
groups were compared by “t” test.
57
15
Table 4.3 Anthropometric characteristics of women with PCOS and controls.
The values given are mean ± S.D. The mean values of two groups
were compared by “t” test.
58
Table 4.4 Frequency distribution of single nucleotide polymorphisms of ESR1
gene with risk of endometriosis in Pakistani women.
64
Table 4.5 Multi-SNPs haplotype frequency estimates for endometriosis and
controls at ESR1 gene.
66
Table 4.6 Frequency distribution of single nucleotide polymorphism of ESR2
gene with risk of endometriosis in Pakistani women.
69
Table 4.7 Frequency distribution of single nucleotide polymorphisms of IL10
gene with risk of endometriosis in Pakistani women.
73
Table 4.8 Multi-SNPs haplotype frequency estimates for endometriosis and
controls at IL10 gene.
75
Table 4.9 Frequency distribution of single nucleotide polymorphisms of PGR
gene with risk of endometriosis in Pakistani women.
79
Table 4.10 Multi-SNPs haplotype frequency estimates for endometriosis and
controls at PGR gene
80
Table 4.11 Frequency distribution of single nucleotide polymorphisms of FSHR
gene with risk of endometriosis in Pakistani women.
84
Table 4.12 Multi-SNPs haplotype frequency estimates for endometriosis and
controls at FSHR gene.
85
Table 4.13 Frequency distribution of single nucleotide polymorphisms of
ADIPOQ gene with risk of PCOS in Pakistani women.
90
Table 4.14 Multi-SNPs haplotype frequency estimates for PCOS and controls at
ADIPOQ gene.
91
Table 4.15 Frequency distribution of single nucleotide polymorphisms of INSR
gene with risk of PCOS in Pakistani women.
95
Table 4.16 Multi-SNPs haplotype frequency estimates for PCOS and controls at
INSR gene.
97
Table 4.17 Frequency distribution of single nucleotide polymorphisms of FSHR
gene with risk of PCOS in Pakistani women.
102
Table 4.18 Multi-SNPs haplotype frequency estimates for PCOS and controls at
FSHR gene.
103
Table 4.19 Frequency distribution of single nucleotide polymorphisms of FSHB
gene with risk of PCOS in Pakistani women.
106
16
Table 4.20 Frequency distribution of single nucleotide polymorphisms of
LHCGR gene with risk of PCOS in Pakistani women.
109
Table 4.21 Multi-SNPs haplotype frequency estimates for PCOS and controls at
LHCGR gene.
110
Table 4.22 Frequency distribution of single nucleotide polymorphisms of LHB
gene with risk of PCOS in Pakistani women.
114
Table 4.23 Multi-SNPs haplotype frequency estimates for PCOS and controls
at LHB gene.
115
Table 4.24 Frequency distribution of single nucleotide polymorphisms of ESR1
gene with risk of PCOS in Pakistani women.
118
Table 4.25 Multi-SNPs haplotype frequency estimates for PCOS and controls
at ESR1 gene.
121
Table 4.26 Frequency distribution of single nucleotide polymorphisms of ESR2
gene with risk of PCOS in Pakistani women.
124
17
LIST OF FIGURES
Figure No. Title Page No
Figure 1.1 Pathogenesis of endometriosis; hormone and endometrial
inflammatory related manifestation.
3
Figure 1.2 Pathogenesis of PCOS; hormone and metabolic syndrome
related manifestation.
8
Figure 3.1 Diagrammatic and schematic presentation of research layout. 36
Figure 3.2 The sequence highlighted blue represent the forward and
reverse primers and arrows (purple) shows SNP (a-d).
48
Figure 4.1 Clinical manifestation of anthropometric characteristics in
endometriosis compared with control along with percentage
symptoms.
59
Figure 4.2 Clinical manifestation of anthropometric characteristics in
PCOS compared with control along with percentage
symptoms.
59
Figure 4.3 Two sequence BLAST alignment of ESR1 gene
polymorphisms.
62
Figure 4.4 Sequence chromatogram showing homozygous and
heterozygous form of T/C base substitution of rs2234693.
62
Figure 4.5 Multi-SNPs haplotype percent frequency distribution for
endometriosis and controls at ESR1 gene.
63
Figure 4.6 Pairwise linkage disequilibrium of ESR1 gene in endometriosis
and controls.
67
Figure 4.7 Two sequence BLAST alignment of IL10 gene
polymorphisms.
71
Figure 4.8 Sequence chromatogram showing homozygous and
heterozygous form of C/T base substitution of rs1800871.
71
Figure 4.9 Multi-SNPs haplotype percent frequency distribution for
endometriosis and controls at IL10 gene.
72
Figure 4.10 Pairwise linkage disequilibrium of IL10 gene in endometriosis
and controls.
76
18
Figure 4.11 Multi-SNPs haplotype percent frequency distribution for
endometriosis and controls at PGR gene.
78
Figure 4.12 Pairwise linkage disequilibrium of PGR gene in endometriosis
and controls.
80
Figure 4.13 Two sequence BLAST alignment of FSHR gene
polymorphisms.
82
Figure 4.14 Sequence chromatogram showing homozygous and
heterozygous form of A/G base substitution of rs6166.
82
Figure 4.15 Multi-SNPs haplotype percent frequency distribution for
endometriosis and controls at FSHR gene.
83
Figure 4.16 Pairwise linkage disequilibrium of FSHR gene polymorphisms
in endometriosis and controls.
85
Figure 4.17 Two sequence BLAST alignment of ADIPOQ gene
polymorphisms.
88
Figure 4.18 Sequence chromatogram showing homozygous and
heterozygous form of T/G base substitution of rs2241766.
88
Figure 4.19 Multi-SNPs haplotype percent frequency distribution for
PCOS and controls at ADIPOQ gene.
89
Figure 4.20 Pairwise linkage disequilibrium of ADIPOQ gene in PCOS
and controls.
92
Figure 4.21 Multi-SNPs haplotype percent frequency distribution for
PCOS and controls at INSR gene.
94
Figure 4.22 Pairwise linkage disequilibrium of INSR gene in PCOS and
controls.
98
Figure 4.23 Multi-SNPs haplotype percent frequency distribution for
PCOS and controls at FSHR gene.
101
Figure 4.24 Pairwise linkage disequilibrium of FSHR gene in PCOS and
controls.
103
Figure 4.25 Two sequence BLAST alignment of FSHB gene
polymorphisms.
105
Figure 4.26 Sequence chromatogram showing homozygous and
heterozygous form of T/C base substitution of rs6169.
105
19
Figure 4.27 Multi-SNPs haplotype percent frequency distribution for
PCOS and controls at LHCGR gene.
108
Figure 4.28 Pairwise linkage disequilibrium of LHCGR gene in PCOS and
controls.
110
Figure 4.29 Two sequence BLAST alignment of LHB gene
polymorphisms.
112
Figure 4.30 Sequence chromatogram showing homozygous and
heterozygous form of A/G base substitution of rs4002462.
112
Figure 4.31 Multi-SNPs haplotype percent frequency distribution for
PCOS and controls at LHB gene.
113
Figure 4.32 Pairwise linkage disequilibrium of LHB gene in PCOS and
controls.
115
Figure 4.33 Multi-SNPs haplotype percent frequency distribution for
PCOS and controls at ESR1 gene.
117
Figure 4.34 Pairwise linkage disequilibrium of ESR1 gene in PCOS and
controls.
122
Figure 4.35 Serum concentration of FSH in women with PCOS and
controls. Values given are mean in mIU/ml of FSH ± S.D. The
asterisk shows the significant difference between 2 groups
according to the “t” test.
125
Figure 4.36 Comparison of mean FSH values in women with PCOS and
controls according to rs6166 genotype of FSHR gene. . The
asterisk shows the significant difference between 2 groups
according to the “t” test.
125
Figure 4.37 Comparison of mean FSH values in women with PCOS and
controls according to rs6165 genotype of FSHR gene.
126
Figure 4.38 Comparison of mean FSH values in women with PCOS and
controls according to rs6169 genotype of FSHB gene.
126
Figure 4.39 Serum concentration of Testosterone in women with PCOS
and controls. Values given are mean in ng/ml of testosterone ±
S.D. The asterisk shows the significant difference between 2
groups according to the “t” test.
127
20
Figure 4.40 Serum concentration of progesterone in women with
endometriosis controls. Values given are mean in ng/ml of
progesterone ± S.D. The asterisk shows the significant
difference between 2 groups according to the “t” test.
128
21
LIST OF ABBREVIATIONS
ADIPOQ Adiponectin
ASRM American Society for Reproductive Medicine
BMI Body mass index
bp Base pair
Chi Sq Chi square
CI Confidence interval
CVD Cardio vascular disease
ddNTPs Dideoxy terminators
dNTP Deoxynuleotide
E het Estimated heterozygosity
EDTA Ethylenediaminetetraacetic acid
E-M Expectation-maximization
ESHRE European Society of Human Reproduction and Embryology
ESR1 Estrogen receptor alpha
ESR2 Estrogen receptor beta
Exp. Het Expected Heterozygosity
FSH Follicle stimulating hormone
FSHB Follicle stimulating hormone beta
FSHR Follicle stimulating hormone receptor
ftp File transfer protocol
Global LD
LR Chi Sq
Global likelihood ratio chi-square
hCG Chorionic Gonadotropin
HGVS Human Genome Variation Society
HWE Hardy–Weinberg equilibrium
IL10 Interleukin 10
INSR Insulin receptor
LD Linkage disequilibrium
LH Luteinizing hormone
LHB Luteinizing hormone beta
22
LHCGR Luteinizing hormone choriogonadotropin receptor
MgCl2 Magnesium chloride
NCBI National Center for Biotechnology Information
OD Ovulatory dysfunction
OR Odd ratios
p Significance
PCOS Polycystic ovary syndrome
PCR Polymerase chain reaction
PGR Progesterone receptor
PID Pelvic inflammatory disease
S.E.E Standard error of estimation
SD Standard deviation
SDS Sodium dodecyl sulphate
SNP Single nucleotide polymorphism
T2 DM Type 2 diabetes mellitus
TE Tris-EDTA
UCSC University of California, Santa Cruz
WBCs White blood cells
WHR Waist and hip ratio
χ2 Chi square
23
CHAPTER 1
INTRODUCTION
The need for research related to female reproduction is one of the most
significant issues presently faced by the health sector in Pakistan today.
1.1 Infertility
Infertility is the most prevalent health disorders in young adults worldwide;
approximately 8-10% of couples are unable to conceive during their reproductive age
(Inhorn, 2003). This refers to the biological inability to conceive after regular
unprotected intercourse (Makar and Toth, 2002). Infertility can be immunological,
psychological, resulting from certain surgery or blockage, or be associated with
defined abnormalities in the gametes. In 20% of couples the cause of infertility is
unexplained (Uehara et al., 2001). According to a study carried out in a Gynaecology
and Obstetrics Department at the Federal Government Services Hospital, Islamabad,
Pakistan, the estimated infertility rate in the observed population from the last three
years was 7 % (Shaheen et al., 2010).
A complete physical examination of both partners is required for infertility
evaluation. Six factors have been reported to be responsible for the cause of infertility.
These include male factor, cervical factor, endometrial-uterine factor, tubal factor,
peritoneal factor and ovulatory factor (Clark and Keefe, 1989).
1.1.1 Causes of infertility in women
The major causes of infertility comprise ovulatory dysfunction (OD), tubal
damage and endometriosis in women. According to World Health Organization
(WHO) criteria, OD is classified in to various categories including low gonadotropins,
hyperprolactinaemia, and primary ovarian failure (Lunenfeld and Insler, 1974). The
function of female reproductive organs is remarkably influenced by many genetic
factors. Different studies on various populations investigated the susceptible genes for
female reproductive disorders such as endometriosis, polycystic ovary syndrome
(PCOS), ovulation disorder, pelvic inflammatory disease (PID), premature ovarian
failure, pelvic adhesions and benign uterine fibroids (Escobar-Morreale et al., 2005).
24
1.2 Endometriosis
Endometriosis is a common cause of infertility and pelvic pain. This disease
affects approximately 7-10% of all women worldwide during their reproductive age
(Baldi et al., 2008) with substantial annual health costs (Simoens et al., 2007) and
health burdens for individuals (Jones et al., 2002). Endometriosis accounts for $22
billion annually in US healthcare costs (Simoens et al., 2007). Clinically the major
concern of endometriosis is its predisposition of infertility. It was expected that 25-
50% of endometriosis women are infertile (Gao et al., 2006). Whereas 25-30% of
women have developed endometriotic lesions and identified for infertility (Missmer
and Cramer, 2003; Giudice and Kao, 2004). Many genetic factors strongly influence
the reproductive functions of female and different studies investigated the susceptible
and candidate genes involved in the female reproduction and fertility (Missmer and
Cramer, 2003; Giudice and Kao, 2004; Montgomery et al., 2008).
Recently, the genetic polymorphisms have been suggested as genetic
biomarkers of disease (Falconer et al., 2007; Vietri et al., 2007). The human genome
project discovered the single nucleotide polymorphism (SNP) as new markers and this
opened new novel ways to establish the genetic association of complex disorders with
candidate and susceptible genes. A mutation that results in base substitution is called
single nucleotide polymorphism. SNPs in protein coding region could be classified as
non-synonymous and synonymous; the former results in missense mutation that
causes change in amino acid or may be nonsense mutation resulting in codon
termination. While the synonymous mutations are silent and do not change the amino
acid. SNPs in promoter region can cause increased or reduced expression of gene. In
addition SNPs in intron region alter the transcription rate due to mutation of
regulatory elements. The average occurrence of SNPs in genome is every 1.9 kb
whereas 1.42 million SNPs had been mapped. More than 60,000 SNPs were
represented in untranslated and exon regions (Marth et al., 2001).
The pathogenesis of endometriosis remains enigmatic (Figure 1.1). It is a
condition in which endometrial tissue implants and grows outside the uterus and
subsequently affects the function of ovaries, uterus and fallopian tubes resulting in
reduced fertility (Sasson and Taylor, 2008). The primary symptoms of endometriosis
include infertility, abdominal and pelvic pain, back pain, dysmenorrhea, dysuria,
dyschezia and dyspareunia (Giudice and Kao, 2004). Among several hypotheses
25
Figure 1.1: Pathogenesis of endometriosis; hormone and endometrial
inflammatory related manifestation.
Genetics Lifestyle and Environment
Endometriosis
Hormone driven
manifestation
Infertility
Low
Progesterone
Hyper activated
endometrial inflammatory
system
Prostaglandins
Excess estrogen Endometrium
out growths
Pelvic pain
Dysmenorrhea
Back pain
Cytokines
Ovarian cancer
Inflammatory
response
resistance
IL10, FSHR,ESR1,PGR,
ESR2 genes etc.
Dyspareunia
Dysuria Growth factor
26
proposed to explain the endometriosis pathogenesis the retrograde mensuration is
widely accepted (Sasson and Taylor, 2008). Endometriosis exists in at least three
distinct forms: peritoneal endometriosis, rectovaginal endometriotic nodules and
endometriomas. These three types might be variants of the similar pathological
process; however it is likely that they are caused by distinct mechanisms (Brosens,
2004). Endometriosis is a complex disease that is not likely due to a single heritable
genetic defect. Endometriosis was indicated as risk factor for multiple malignancies
especially ovarian cancer in numerous epidemiological studies (Borgfeldt and Ellika,
2004; Kobayashi et al., 2007).
The development of endometriotic lesions includes genetic predisposition and
environmental factors (Sasson and Taylor, 2008). Endometriosis is recognized as
polygenic multifactorial disease with heritable tendencies (Simpson and Bischoff,
2002; Simpson et al., 2003). The candidate and susceptible genes responsible for
pathogenesis of endometriosis have been investigated in various genetic association
studies (Hadfield et al., 2001; Vigano et al., 2003).
In recent years various lines of genetic association studies have explore the
association between genetic polymorphisms with the development of endometriosis
(Luisi et al., 2006; Chun et al., 2012; Gallegos-Arreola et al., 2012), although the
exact genes involved in the pathogenesis and susceptibility of endometriosis
development are unknown.
Endometriosis is clearly a hormone dependent disease that is characterized by
responsiveness to estrogens, prostaglandins, metalloproteinases, cytokines,
chemokines and resistance to progesterone (Giudice and Kao, 2004; Borghese et al.,
2010). Estrogen is responsible for persistent growth of endometriotic tissue; however
the cytokines and prostaglandins are significant in the pathogenesis and development
of endometriosis (Jabbour et al., 2006). The large quantities of prostaglandin E2
produced by stromal cells induce local estrogen production and biosynthesis in
women with endometriosis and impair pelvic pain (Soysal et al., 2004). The
aromatase converted cholesterol into estradiol locally in endometriosis (Bulun et al.,
2005). Due to the multiple contributors and pathways leading to disease, genes
encoding inflammatory mediators, sex hormones and enzymes involved in
metabolism and biosynthesis have been investigated for their putative role in
endometriosis. For example, the inflammatory mediator interleukin-10 (1q31-q32,
IL10); sex hormone receptors, estrogen receptor alpha (6q25.1, ESR1), estrogen
27
receptor beta (14q23.2, ESR2), and progesterone receptor (11q22-q23, PGR)
(Arvanitis et al., 2003; Hsieh et al., 2005; Kim et al., 2005; De Carvalho et al., 2007;
Lee et al., 2007) have been studied by different laboratories on various populations.
Recently, the interest has focused on identifying genetic polymorphisms in various
genes underlying susceptibility to endometriosis (Falconer et al., 2007).
Estrogen and progesterone receptors mediated the pathological and
physiological activities of the sex steroid hormons. SNPs in ESR1 (rs2234693 and
rs9340799), ESR2 (rs4989638) and PGR (rs10895068) genes were associated with
expression and alteration of these sex steroid receptors (Gomes et al., 2006).
Endometriosis seems to be inhibited by progesterone (Young and Lessey, 2010) and
progression of disease is stimulated by estrogen (Bulun et al., 2000; Ness, 2003).
Gurates and Bulun, (2003) emphasized the potential involvement of PGR in the
molecular mechanism underlying the development of endometriosis. Indeed the
particular genes regulated by progesterone showed abnormal expression and defect in
function of endometrium in women with endometriosis. Moreover, alternation of PGR
gene expression was demonstrated in endometrium tissue (Gurates and Bulun, 2003;
Kao et al., 2003). Estrogen and progesterone are gonadotrophic hormones and are
essential for human reproduction. Alteration in any of these genes causes infertility
including endometriosis (Gurates and Bulun, 2003).
Similarly, many immunological factors are reported to be associated with the
pathogenesis of endometriosis (Wu and Ho, 2003). An important immune-modulatory
cytokine is IL10 was recognized for its function to inhibit activation and function of
T-cells, macrophages and monocytes. IL10 acts as to limit and terminate the
inflammatory response (Moore et al., 2001). The polymorphisms, rs1800872,
rs1800871 and rs180089 of interleukin 10 gene in the promoter region and their
relationship with endometriosis have been analyzed in recent years (Xie et al., 2009;
Riiskjaer et al., 2011), although the role of these polymorphisms in disease
development is not recognized. A study on Taiwanese patients with endometriosis
reported the association of IL10 gene polymorphism with this disease (Juo et al.,
2009).
The polymorphism in follicle stimulating hormone receptor (FSHR) gene in
relation with endometriosis is least studied. The polymorphism rs6166 is most studied
as compared to polymorphism rs6165 (Mohiyiddeen and Nardo, 2010). The amino
acid coded by this region is present in extracellular domain of follicle stimulating
28
hormone (FSH) protein binding region (Simoni et al., 2002). This is the fundamental
region for FSH mediated events of signal transduction therefore affects the hormone
binding ability of the receptor (Kene et al., 2005). The homozygous polymorphism
GG at 680 of FSHR enhanced aromatase activity which induce more production of
estrogens and stimulate the proliferation of endometriotic tissues (Wang et al., 2011).
In addition, the lower risk of endometriosis was investigated with non-synonymous
SNPs of FSHR gene including homozygous GG (Ser/Ser) and GA (Ser/Asn) at 680 in
Taiwan population (Wang et al., 2011).
A study on Taiwanese Chinese females suggests that the non-synonymous
SNP rs6165 of FSHR gene mediated the extracellular recognition events which
modulate the risk and development of endometriosis. Moreover, the conversion of
680Ser from 680Asn as a result of A to G of rs6166 showed significant association with
endometriosis suggests that phosphorylation of the cytoplasmic residue may be
important for normal signaling pathways against the development of endometriosis
(Wang et al., 2012).
1.3 Polycystic ovary syndrome (PCOS)
PCOS is a heterogeneous ovarian disorder associated with obesity,
anovulation, hyperandrogenism, hirsutism, and infertility that affect 6-10 % of women
at reproductive age worldwide (Goodarzi et al., 2011) and accounts for 70% cases of
anovulatory infertility (Brassard et al., 2008). Stein and Leventhal first described the
PCOS in 1935 (Stein and Leventhal, 1935). The etiology of PCOS is still not clear.
Some genetic and hormonal factors have been implicated for the pathogenesis of
PCOS including altered ovarian synthesis of steroids, hyperinsulinemia, aberrant
folliculogenesis, abnormal secretion of gonadotropin and neuroendocrine
abnormalities (Goodarzi et al., 2011). Among different criteria for the diagnosis of
PCOS the most widely used was proposed by Rotterdam (2004). This includes any
form of hyperandrogenemia either clinical (hirsutism and acne) or endocrine (high
level of androgens), oligomenorrhea and the ultrasound examination of polycystic
ovary. These three major criteria make PCOS a heterogeneous disease. The diagnosis
can be made if 2 out of 3 criteria are met (Rotterdam, 2004).
Both environmental and genetic factors make PCOS a heterogeneous disorder
(Figure 1.2). The multifactorial etiology of PCOS is underpinned by multiple genetic
architecture that initiated to elucidate in recent years (Fauser et al., 2011). An
29
interaction between environmental factors and polymorphisms of various genes
makes PCOS a complex polygenic disorder (Luque-Ramírez et al., 2006; Vink et al.,
2006). However the molecular genetic mechanisms and inheritance pattern of PCOS
is not fully known (Valdés et al., 2008). PCOS is an endocrinological disorder
therefore the genes involved in the sex hormones or their regulators have been studied
for polymorphisms (Simoni et al., 2008). Different studies provide evidences that
PCOS is a familial disorder (Legro et al., 1998; Ehrmann, 2005). This indicates the
significance of genetic component and involvement of multiple gene variants
contributing the metabolic and endocrine effects in PCOS (Diao et al., 2004; Roldan
et al., 2004; Ehrmann, 2005).
A number of studies investigated the association of various candidate genes
with PCOS while the pathogenesis of diseases is not accepted with any of the gene
universally (Nam and Straus, 2007; Unluturk et al., 2007; Urbanek, 2007). The
heritable basis and genetic etiology of PCOS has also been indicated by clinical
evidences (Legro et al., 1998; Vink et al., 2006).
The candidate and susceptible genes for PCOS includes: polymorphisms of
genes encoding regulatory proteins and sex hormones like follicle stimulating
hormone beta (FSHB), follicle stimulating hormone receptor (FSHR), estrogen
receptor alpha (ESR1) and estrogen receptor beta (ESR2) (Tong et al., 2000; de Castro
et al., 2004; Jun et al., 2006; Yang et al., 2006;) and variants of genes encoding
protein expressions involved in insulin resistance like adiponectin (ADIPOQ), insulin
receptor gene (INSR), (Xita et al., 2005; Jin et al., 2006). A number of genetic
variants were implicated in the susceptibility of PCOS, whereas no single marker was
associated repeatedly and conclusively with the disorder (Urbanek, 2007).
PCOS is a metabolic syndrome, therefore adiponectin was considered as
potential candidate gene for PCOS. Adiponectin possesses insulin sensitizing, anti-
atherosclerotic and anti-inflammatory properties, accumulating evidence suggest that
adiponectin has key roles in regulating female reproductive functions (Brochu-
Gaudreau et al., 2010). The ADIPOQ gene consists of 17 kb, consisting of two introns
and three exons located on chromosome 3q27 (Hu et al., 1996). Adiponectin is a
protein produced by an active endocrine organ the adipose tissue (Magkos and
Sidossis, 2007). The region 3q27 of ADIPOQ gene has been revealed as susceptibility
locus for metabolic syndromes including obesity (Kissebah et al., 2000) and insulin
resistance (Francke et al., 2001).
30
Figure 1.2: Pathogenesis of PCOS; hormone and metabolic syndrome
related manifestation.
Genetics Lifestyle and
Environment
PCOS
Hormone driven
manifestation
Insulin
Hyperandrogenism
oligo- or
anovulation
Metabolic syndrome
manifestation
Acne
Hirsutism
Androgens Adiposity
Diabetes
Cardio vascular
diseases
Obesity
Irregular
menstrual cycle
Infertility
Hyperinsulinemia
Insulin resistance
ADIPOQ, INSR, FSHR,
FSHB, ESR1, LHCGR,
LHB, ESR2 genes etc.
31
Among adipose protein the adiponectin modulate insulin activity and
represents a connection between adipocity and insulin resistance (Stefan et al., 2002).
The PCOS women showed abdominal adiposity and obesity due to occurrence of
hyperinsulinemia and insulin resistance (Dunaif, 2006). The women with PCOS
showed reduced fertility linked with metabolic syndrome including hyperinsulinemia
and insulin resistance (Tosca et al., 2008). PCOS women exhibit more susceptibility
for metabolic syndrome when compared with general population (Cussons et al.,
2008).
To date, the identified SNPs in adiponectin gene is 13 however among the
multiple polymorphisms of ADIPOQ, the two polymorphisms rs2241766 in exon 2
and rs1501299 in intron 2 attracted the most consideration. These two SNPs were
involved in alteration of serum level of adiponectin (Xita et al., 2004; Pollin et al.,
2005). To some extent these polymorphisms of adiponectin may determine the
phenotypic expression of certain metabolic syndrome. A study reported the
interaction between steroid hormone synthesis and adiponectin polymorphisms
(Meigs et al., 2005). However the gene expression or function of adiponectin under
the influence of these polymorphisms is not completely known, as rs1501299 is
intronic polymorphism while rs2241766 is synonymous polymorphism (Xita et al.,
2004).
Insulin resistance is defined as decrease in insulin mediated glucose
consumption. It is found in 50-90% PCOS women while its prevalence is 10-25% in
general population (Goodarzi et al., 2005). The beta (β) cells of pancreas produce
insulin which affects the various organs in the body. In female reproductive system
insulin enhances the growth of ovaries and stimulates the production and secretion of
androgens from ovaries; this inhibits ovarian follicle apoptosis and results in cyst
formation (Baillargeon and Nestler, 2006).
Insulin stimulates ovarian androgen production that affects ovarian
steroidogenic responses to luteinizing hormone and follicle-stimulating hormone.
Women develop PCOS because of hypersensitivity of the intra-ovarian insulin
androgen signaling pathway (Baillargeon and Nestler, 2006). In PCOS women the
major cause of insulin resistance is defect in post-binding region of INSR signiling
transduction pathway that reduced the autophosphorylation of tyrosine kinase domain
(Azziz, 2002). The insulin receptor gene located on chromosome 19p13.2 (Urbanek et
al., 2005) and the exon 17-21of INSR gene codes for tyrosine kinase residue of the
32
receptor required for insulin signal transduction. To date, among the different SNPs
detected in the region of exon 17 of INSR gene, the significant association was shown
by the rs1799817 with PCOS (Panz et al., 1996; Siegel et al., 2002). The substitution
from C to T allele at exon 17 was associated with the defect of tyrosine kinase domain
of INSR in PCOS women possibly due to its effects on the auto phosphorylation of the
INSR function (Lee et al., 2008). This also showed that polymorphism in INSR gene
may be associated with the patients of PCOS who are obese.
Follicle-stimulating hormone (FSH) is a pituitary glycoprotein that plays an
important role during folliculogenesis by promoting the proliferation and
differentiation of granulosa cells and maturation and development of follicles. The
effect of FSH is mediated by binding to a specific follicle stimulating hormone
receptor (FSHR) that is situated on the granulosa cells of the ovary (Moyle and
Campbell, 1996). Because of this important role of FSHR in the signaling
transmission of FSH, the FSHR gene may be an important candidate gene for PCOS.
A previous genome-wide association study in Chinese women with PCOS identified a
region on chromosome 2p16.3 that encodes the FSH receptor (FSHR) gene as a
reproducible PCOS susceptibility locus (Shi et al., 2012). The FSHR gene contains
two polymorphisms in exon 10 and change in amino acids at position N680S and
A370T. The rs6165 (A370T) is in the extracellular domain of FSHR gene which is the
high affinity binding site of FSH hormone (Du et al., 2010; Dolfin et al., 2011). The
polymorphisms of FSHR gene are in linkage disequilibrium therefore the haplotype of
these polymorphisms affected the FSH level (Orio et al., 2006). A number of genetic
studies showed the association between polymorphisms of FSHR gene and PCOS
(Orio et al., 2006; Bon-Hee et al., 2010).
FSHR is crucial for reproduction and mutations in this gene greatly affect the
fertility. It has been previously reviewed that mutations in FSHR cause loss of
function or gain of function in various cases (Lussiana et al., 2008; Thompson et al.,
2008; Salvi and Pralong, 2010). The mutations of FSHR gene inhibit the proper
ovarian function and causes infertility (Meduri et al., 2003; Kuechler et al., 2010). In
human the infertility including hypergonadotrophic, hypogonadism, amenorrhea and
ovarian failure is caused by inactivating mutations in the FSHR gene (Doherty et al.,
2002; Allen et al., 2003; Meduri et al., 2003; Kuechler et al., 2010). The mutations
found in any region of FSHR gene cause suppression in hormone binding capacity
and abnormality in signal transduction (Rannikko et al., 2002; Tranchant et al., 2011).
33
Estrogens play an important role in the function and development of the
reproduction system. The estrogen action was mediated by two specific high affinity
receptors, the ESR1 and ESR2, both of which belong to superfamily of nuclear
receptor and act as ligand activated transcription factor. Both receptors are necessary
for the proper functioning of the hypothalamic-pituitary-ovarian axis. Both ESR1 and
ESR2 are expressed in the human ovary, ESR2 is the main type of receptor and its
activation enhances folliculogenesis and ovulation (Hegele-Hartung et al., 2004).
Furthermore, the expression of ESR2 is lower in follicle derived from women with
PCOS compared with healthy women, while the ESR1 expression is markedly
increased in theca cells of polycystic ovaries, causing alteration in the ESR1/ESR2
ratio in PCOS and possibly abnormal follicular development (Jakimiuk et al., 2002).
Based on these observations, variants of ESR1 and ESR2 may be implicated in the
pathogenesis of PCOS. A number of polymorphic sites in ESR1 and ESR2 have been
identified with the most widely studied being rs2234693, rs9340799 polymorphism in
ESR1 and rs4986938 in ESR2.
The LHCGR gene encodes a receptor for luteinizing hormone (LHR) and
human chorionic gonadotropin (hCG) and mutations in LHCGR was associated with
infertility (Yariz et al., 2011). In PCOS women the polymorphisms of LHR gene
effect the ovarian androgen production and concentration of luteinizing hormone
(LH) which is expressed in the theca and granulose cells that leads toward anovulation
(Norman et al., 2007). Different studies investigated that mutations in LHR gene and
luteinizing hormone beta (LHβ) gene altered their structure and function by activating
and inactivating the bioactivity, moreover this cause amenorrhea, anovulation and
PCOS in women (Huhtaniemi and Themmen 2005; Huhtaniemi and Alevizaki 2006).
There were strong evidences that polymorphisms of LHβ and LHR genes involved in
the development of PCOS, however the inconsistent results were observed in different
populations and loci. LHβ gene polymorphism in exon 3 was examined to be mutant
in Singapore Chinese women with menstrual disorders (Ramanujam et al., 1999). A
Korean study found no associtaion between LHβ gene polymorphisms with PCOS
(Kim et al., 2001). However the women with LHR mutations often show infertility
and amenorrhea (Themmen, 2005). A genome wide association study (GWAS) in
Han Chinese women observed strong association between LHR gene polymorphism
and PCOS (Chen et al., 2011).
34
In most of the anovulated PCOS women the level of gonadotropic and
estrogen remains normal (Broekmans et al., 2006). The women with PCOS showed
frequent elevation in serum LH concentration and the LH to FSH ratio (Rebar et al.,
1976). The most common biochemical feature of PCOS is hyperandrogenemia
(Goodarzi et al., 2005). The high level of circulating androgen was observed in 80-
90% of PCOS women with oligomenorrhea (Hull, 1987; Goodarzi et al., 2005).
About 60% of PCOS women are hirsute due to hyperandrogenemia. This is the most
common clinical sign for PCOS phenotype (Azziz et al., 2009). Snehalatha reported
that Asian Indians have higher insulin resistance both with lean Body Mass Index
(BMI) and central obesity (Snehalatha et al., 2003).
The reproductive function is regulated by FSH, luteinizing hormone (LH) and
chorionic gonadotropin hormones; these are functionally and evolutionary related
hormones (Pierce and Parson, 1981). The elevated level of insulin stimulates the
granulosa cells to secrete FSH that results in ovarian steroidogenesis regulation and
growth of follicular cyst. As FSH plays important role in oogenesis and follicular
development, the increased FSH level leads to maturation of follicles (Simoni et al.,
1997; Sudo et al., 2002). Biochemical measurements of testosterone showed elevated
level of hormone in PCOS toward the upper limit than normal and cause
hyperandrogenism (Cho et al., 2008). The LH and FSH receptors mediate the function
in gonads, as these hormones are produced in a pulsatile manner by the anterior lobe
of pituitary gland (Dalkin et al., 2001; Ascoli et al., 2002; Dias et al., 2002). In
women the FSH stimulate estrogen production in ovaries and required for follicle
maturation (Mcgee and Hsueh, 2000). In females the LH stimulate the synthesis of
progesterone, ovulation and androgen in the theca cells (Moyle and Campbell, 1996).
Some studied have reported significant association of genetic variants with
infertility while others have not found any relationship. So far the evidence of an
association between PCOS and endometriosis with specific genetic polymorphism
giving consistent results in different populations is missing or very weak. Therefore,
large scale, well defined case control studies are required to validate these
associations in poly cystic ovary syndrome and endometriosis employing the
improved methodology for SNP detection.
In view of the important roles already identified for ESR1, ESR2, IL10, PGR,
and FSHR in endometriosis, we studied polymorphisms previously associated with
endometriosis at these loci to determine whether there is evidence for similar
35
associations in women from a previously unstudied distinct population. We are not
aware of any previous endometriosis association studies reported in Pakistani women;
here we identify conservation of these associations in this population. PCOS is one of
the major threats to women’s health that leads to anovulatory infertility, amenorrhoea,
hirsutism and oligomenorrhoea. No research evidence examining the role of genetic
polymorphisms in PCOS has been reported previously for Pakistani women. We
elucidate, for the first time in Pakistani women of the Punjab the putative functional
significance of different polymorphisms on the ADIPOQ, INSR, FSHR, FSHB,
LHCGR, LHB, ESR1 and ESR2 genes in the development of PCOS.
With this comprehensive database of information provided by the present
study about the reproductive endocrinology and genetics the possible association
between single nucleotide polymorphisms (SNPs) of genes related with PCOS and
endometriosis are likely to be identified. This will provide a starting point for
functional and biological studies to develop better diagnosis and treatment for these
debilitating diseases. There is no therapeutic treatment for the control of the
endometriosis and PCOS. The identification of genetic risk factors for female
reproductive disorders will be beneficial for the early prediction of the disease that
will potentially lead to the better diagnosis and treatment. The present project is
designed to evaluate the indigenous SNP database of genes and role of sex steroids
involved in reproductive disorders.
Aims and Objectives
The major aims and objectives of this research Project are:
1. To determine the genetic and endocrinological causes of female reproductive
disorders like endometriosis and PCOS.
2. To find the reasons of genetic disorder linked with gene SNPs for inflammation, sex
hormone regulation and steroid biosynthesis associated with endometriosis and
PCOS.
3. To compare the serum level of sex steroids and gonadotropins in diagnosed women
patients of the above mentioned syndromes with their controls.
36
CHAPTER 2
REVIEW OF LITERATURE
Infertility is a common public health problem in developing countries. The
pathogenesis of diseases is better understood due to genetic studies, this help to
improve our ability to treatment for patients (Simoni et al., 2008).
2.1 Endometriosis
Endometriosis is strongly linked with infertility, ovarian maturation, implantation and
fertilization affected by growth factors and alteration in the production of different
cytokines (Vassiliadis et al., 2005).The symptoms of endometriosis depends on the
localization of the implants, this includes dyspareunia, dysmenorrhea, dysuria,
dyschezia, chronic pelvic pain and infertility (Bulun et al., 2000; Missmer and
Cramer, 2003; Marques et al., 2004). Moreover, many inflammatory mediator
response factors and immunologic factors were investigated for their significant role
in the development and pathogenesis of endometriosis (Wu and Ho, 2003).
2.1.1 Estrogen receptor alpha (ESR1)
Endometriosis is a multifactorial and polygenic disease and associated with genetic
factors and complex interaction of cytokine activation, hormones and immuno-
inflammatory process (Vigano et al., 1998). A study on Japanese population observed
the association of ESR2 gene polymorphism with endometriosis while no association
with ESR1 SNPs (Wang et al., 2004).
Hsieh et al., (2007) elucidated the association of ESR1 -351 A/G and -397 T/C
polymorphisms of 112 women with endometriosis and 110 controls in Taiwan
population. ESR1 mutant related genotype revealed higher percentage in affected
women when compared with control. The genotype proportion AA/AG/GG of ESR1 -
351 was 26.8/57.1/16.1 in endometriosis women while 33.6/64.6/1.8 in control group.
The genotype proportion TT/TC/CC of ESR1 -397 was 24.1/60.7/15.2 in
endometriosis group and 54.5/40/5.5 in control group. They conclude that both
polymorphisms were correlated with the pathogenesis and susceptibility of
endometriosis.
37
A study on 214 Chinese women with endometriosis and 160 controls investigated no
significant association with the genotype frequency of PvuII, although the XbaI of
ESR1 showed distinct variations in allele and genotype frequency suggesting that this
SNP was associated significantly with endometriosis in studied Chinese population
(Xie et al., 2009). The data of association studies of ESR1 gene with endometriosis in
various populations is presented in Table 2.1.
38
Table 2.1: Summary of association studies between ESR1 gene polymorphisms (rs9340799 and rs2234693) and endometriosis.
a. rs9340799
Sr.
No
Population Endometriosis Control Endometriosis
(n)
Control
(n)
p Association Reference
Allele frequency
A Allele
n (%)
G Allele
n (%)
A Allele
n (%)
G Allele
n (%)
1 Japan 194 (79.5) 50 (20.5) 262 (76.6) 80 (23.4) 122 171 0.402 Negative Wang et al., 2004
2 Taiwan 124 (55.4) 100 (44.6) 145 (65.9) 75 (34.1) 112 110 0.005 Positive Hsieh et al., 2007
3 China 316 (73.83) 112 (26.17) 260 (81.25) 60 (18.75) 214 160 0.017 Positive Xie et al., 2009
b. rs2234693
Sr.
No
Population Endometriosis Control Endometriosis
(n)
Control
(n)
p Association Reference
Allele frequency
A Allele
n (%)
G Allele
n (%)
A Allele
n (%)
G Allele
n (%)
1 Japan 145 (59.9) 97 (40.1) 182 (52.9) 162 (47.1) 121 172 0.09 Negative Wang et al., 2004
2 Taiwan 122 (54.5) 102 (45.5) 164 (74.5) 56 (25.5) 112 110 0.005 Positive Hsieh et al., 2007
3 China 246 (57.48) 182 (42.52) 204 (63.75) 116 (36.25) 214 160 0.083 Negative Xie et al., 2009
39
2.1.2 Estrogen receptor beta (ESR2)
The rs1042838 (G/A) polymorphism of ESR2 gene and its association with the risk of
endometriosis were investigated in Korean women. In this study 239 cases of
endometriosis and 287 controls were assessed by restriction fragment length
polymorphism analysis. The allele G and A frequency were not different in cases and
control group 87.4%, 12.6% vs. 85.5%, 14.5% respectively. This suggests that the
polymorphism was not associated with the pathogenesis of endometriosis in Korean
population (Lee et al., 2007).
In Brazilian women the genotype and allele frequencies showed significant difference
for rs1042838 at +1730 position, G/A polymorphism of ESR2 gene between
endometriosis and controls. The frequencies of genotypes AA, GA and GG of ESR2
gene was 1.9%, 47.2% and 50.9% in women with endometriosis while 1.4%, 24.3%
and 74.3% in controls respectively (Bianco et al., 2009). The inconsistency in results
may be due to different ethnic origin and genetic background. Genetic factors
involved in pathogenesis of disease varied in different populations.
The ESR2 +1730 G/A polymorphism were also investigated in a case control study on
another group of infertile Brazilian women. This study includes 201 women with
endometriosis and control group of 206 healthy women. A statistically significant (p =
0.003) difference was observed in endometriosis infertile women and control
regarding to the isolated frequencies of polymorphism (Christofolini et al., 2011). The
allele frequencies of ESR2 gene associated with endometriosis in different population
is given in Table 2.2.
40
Table 2.2: Summary of association studies between ESR2 (rs4986938) gene polymorphisms and endometriosis.
Sr.
No
Population Endometriosis Control Endometriosis
(n)
Control
(n)
p Association Reference
Allele frequency
G allele
n (%)
A allele
n (%)
G allele
n (%)
A allele
n (%)
1 Japan 230 (90.5) 24 (9.5) 309 (85.8) 51 (14.2) 127 180 0.08 Negative Wang et al., 2004
2 Brazil 161 (74.5) 55 (25.5) 363 (86.4) 57 (13.6) 108 210 0.003 Positive Bianco et al., 2009
3 Brazil 216 (79.4) 56 (20.6) 368 (88.0) 50 (12.0) 136 209 0.003 Positive Zulli et al., 2010
41
2.1.3 Interleukin 10 (IL10)
The promoter region of IL10 gene spans 5 kb upstream from transcription start site,
both the promoter and coding region contain several polymorphisms (Lazarus et al.,
2002). The IL10 gene was recognized on chromosome 1q31-32 (Kim et al., 1992;
Opdal, 2004).
The associations of IL10 promoter polymorphisms with endometriosis have been
investigated in various populations. In Japanese population no significant difference
was examined in genotype and allele frequencies at position -592 (rs1800872) and -
1082 (rs1800896) in endometriosis and controls (Kitawaki et al., 2002). In addition
the frequency of haplotype GCC at rs1800872, rs1800871 and rs1800896 (-592 A>C,
-819 T>C and -1082 A>G) is about 50% in European females, 20% in African
females while lowest below 5% in Asian female (Meenagh et al., 2002). In contrast, a
significant association of endometriosis with A allele at -592 position of rs1800872 in
the promoter region of IL10 was investigated in Taiwanese population (Hsieh et al.,
2003).
A study on Chinese patients with endometriosis revealed the higher frequency of C
allele of rs1800872 and rs1800871 as compared to controls. No difference was
observed in allele frequency at position -1082 (rs1800896) (Zhang et al., 2007).
The genomic variants of IL10 gene in promoter region was studied in 214 women
with endometriosis and 160 controls in China. The variations in allele frequency was
observed at -592 and -819 regions of IL10 and seems associated with endometriosis
while the polymorphism at -1082 region was conserved with no difference in allele
frequency (Xie et al., 2009). The allele frequencies of various polymorphisms of IL10
gene associated women with endometriosis in various populations are shown in Table
2.3.
2.1.4 Progesterone receptor (PGR)
The progesterone receptor mediates the action of progesterone, encoded by
progesterone receptor gene (PGR) on chromosome 11q22-q23 consisting of seven
introns and eight exons (Gurates and Bulun, 2003).
42
Table 2.3: Summary of association studies between IL10 (rs1800896, rs1800871 and rs1800872) gene polymorphisms and endometriosis.
a. rs1800896
Sr.
No
Population Endometriosis Control Endometriosis
(n)
Control
(n)
p Association Reference
Allele frequency
A allele
n (%)
G allele
n (%)
A allele
n (%)
G allele
n (%)
1 Japan 310 (96.9) 10 (3.1) 379 (96.7) 13 (3.3) 160 196 0.885 Negative Kitawaki et al., 2002b
2 China 402 (93.2) 26 (6.0) 300 (93.7) 20 (6.2) 214 160 0.921 Negative Xie et al., 2009
3 Denmark 92 (46.0) 108 (54.0) 326 (45.5) 390 (54.5) 100 358 >0.050 Negative Riiskjaer et al., 2011
b. rs1800871
Sr.
No
Population Endometriosis Control Endometriosis
(n)
Control
(n)
p Association Reference
Allele frequency
T allele
n (%)
C allele
n (%)
T allele
n (%)
C allele
n (%)
1 China 278 (64.95) 150 (35.0) 232 (72.5) 88 (27.5) 214 160 0.028 Positive Xie et al., 2009
2 Denmark 40 (20.0) 160 (80.0) 552 (22.9) 164 (77.1) 100 358 >0.050 Negative Riiskjaer et al., 2011
43
Table Continued……
c. rs1800872
Sr.
No
Population Endometriosis Control Endometriosis
(n)
Control
(n)
p Association Reference
Allele frequency
A allele
n (%)
C allele
n (%)
A allele
n (%)
C allele
n (%)
1 Japan 214 (66.9) 106 (33.1) 233 (59.4) 159 (40.6) 160 196 0.041 Positive Kitawaki et al., 2002b
2 China 278 (64.9) 150 (35.0) 232 (72.5) 88 (27.5) 214 160 0.028 Positive Xie et al., 2009
3 Denmark 40 (20.0) 160 (80.0) 164 (22.9) 552 (77.1) 100 358 >0.050 Negative Riiskjaer et al., 2011
44
2.2 PCOS
The association of various genetic variants with PCOS was studied in different
ethnicities.
2.2.1 Adiponectin (ADIPOQ)
The association of PCOS and polymorphism in adiponectin gene was investigated in
several studies. In 2001, 2 independent groups identified that adiponectin effects the
insulin sensitivity (Berg et al., 2001; Yamauchi et al., 2001).
The Japanese population was examined for the association of rs2241766 (T45G)
polymorphism with increased risk of type 2 diabetes mellitus (Hara et al., 2002). A
significant association was investigated between T45G polymorphism with insulin
resistance and obesity in an Italian population (Menzaghi et al., 2002). PCOS being
metabolic syndrome show insulin resistance and type 2 diabetes mellitus (T2 DM); it
is suggested that the polymorphism of adiponectin gene was related with the
development of T2 DM (Filippi et al., 2004).
Conversely an association was observed between PCOS and adiponectin
polymorphism in various studies. Panidis et al., (2004) observed low frequency of T
allele and high frequency of G allele at T45G position of rs2241766 adiponectin gene
in Greek population of obese PCOS women; however this difference was not
statistically significant (p > 0.050).
Haap et al. (2005) investigated the adiponectin gene polymorphism with higher
frequency of T to G substitution at +45 position of rs2241766 in PCOS women as
compared to control. Studies provide the evidences that the polymorphism in
adiponectin gene in women with PCOS showed abnormalities in reproductive system
as well as high risk of T2 DM, obesity and cardio vascular disease (CVD) when
compared with normal control (Kadowaki and Yamauchi, 2005). The association of
ADIPOQ gene with PCOS in various populations is given in Table 2.4.
45
Table 2.4 (a): Summary of association studies between ADIPOQ (rs2241766) gene polymorphisms and PCOS.
Sr.
No
Population PCOS Control PCOS
(n)
Control
(n)
p Association Reference
Allele frequency
T allele
n (%)
G allele
n (%)
T allele
n (%)
G allele
n (%)
1 Spain 54(37.5) 90(62.5) 28 (19.4) 56 (66.6) 72 42 0.004
Positive San Millan et al., 2004
2 Spain 53 (34.9) 99 (65.1) 29 (36.3) 51 (63.7) 76 40 0.201 Negative Escobar-Morreale et al., 2005
3 Finland 74(25.9) 212(74.1) 160 (32.9) 330 (67.3) 143 245 0.005
Positive Heinonen et al., 2005
4 Greece 73(36.5) 127(63.5) 103 (32.5) 177 (63.2) 100 140 0.061
Negative Xita et al., 2005
5 Spain 53(34.9 99(65.1) 29 (19.08) 51 (63.7) 76 40 0.050
Positive Escobar-Morreale et al., 2006
6 China 82(34.2) 158(65.8) 108 (45.0) 132 (55.0) 120 120 0.050
Positive Zhang et al.,2008
7 Korea 93 (32.3) 195 (67.7) 135 (42.5) 183 (57.5) 144 159 0.010
Positive Li et al., 2011
8 Iran 101(27.9) 261(72.1) 101(27.9) 261(72.1) 181 181 0.52
Negative Ranjzad et al., 2012
46
Table 2.4 (b): Summary of association studies between ADIPOQ (rs1501299) gene polymorphisms and PCOS.
Sr.
No
Population PCOS Control PCOS
(n)
Control
(n)
p Association Reference
Allele frequency
T allele
n (%)
G allele
n (%)
T allele
n (%)
G allele
n (%)
1 Greece 217 (82.2) 47 (17.8) 179 (89.5) 21 (10.5) 132 100 0.050 Positive Panidis et al., 2004
2 Spain 118 (81.9) 26 (18.0) 70 (83.3) 14 (16.6) 72 42 0.005 Positive San Millan et al., 2004
3 Germany 84 (79.2) 22 (20.7) 940 (86.7) 144 (13.2) 53 542 0.005 Positive Haap et al., 2005
4 Finland 267 (93.3) 19 (6.6) 466 (95.1) 24 (4.9) 143 245 0.402 Negative Heinonen et al., 2005
5 Greece 177 (88.5) 23 (11.5) 242 (86.4) 38 (13.5) 100 140 0.004 Positive Xita et al., 2005
6 Spain 130 (85.5) 22 (14.5) 65 (81.3) 15 (18.8) 76 40 0.201 Negative Escobar-Morreale et al., 2005
7 Finnish 267(93.4) 19(6.6) 466(95.1) 24(4.8) 143 245 0.601 Negative Heinonen et al., 2005
8 Spain 130 (85.5) 22 (14.4) 65 (81.2) 15 (18.7) 76 40 0.004 Positive Escobar-Morreale et al., 2006
9 China 168 (70.0) 72 (30.0) 190 (79.1) 50 (20.8) 120 120 0.020 Positive Zhang et al., 2008
10 Turkey 160 (83.3) 32 (16.6) 164 (88.1) 22 (11.8) 96 93 0.004 Positive Demirci et al., 2010
11 Korea 217 (75.3) 71 (24.7) 228 (71.7) 90 (28.3) 144 159 0.350 Negative Li et al., 2011
12 Iran 296(81.8) 66(18.2) 322(88.9) 40(11.1) 181 181 0.005 Positive Ranjzad et al., 2012
47
In Finnish population no difference was reported in genotype and allele frequencies of
rs2241766 ADIPOQ gene variants between PCOS and control group (Heinonen et al.,
2005). A study conducted on Greek population had detected no difference in genotype
frequencies and association with the development of PCOS (Xita et al., 2005). A
study on women of Spain confirmed the same findings with no association of PCOS
and rs2241766 polymorphism when compared with their control (San Millian et al.,
2004; Escobar-Morreale et al., 2006). An association was observed between SNPs of
adiponectin gene rs2241766 and rs1501299 in Han Chinese PCOS women than
controls (Zhang et al., 2008).
2.2.2 Insulin receptor (INSR)
Insulin receptor gene consists of 22 exons (Seino et al., 1990). Insulin resistance and
hyperinsulinemia was observed in PCOS women with polymorphisms in the region of
exon 17-21 of INSR gene. This region codes for tyrosine kinase residue of the
receptor necessary for insulin transduction pathway (Krook et al., 1994). The patients
with PCOS were examined with several polymorphisms in exons and introns of INSR
gene (Siegel et al., 2002).
The evidences showed that the susceptible loci for PCOS are present in the INSR gene
at chromosome 19 suggested that it might be the susceptible region for disease (Tucci
et al., 2001). According to Azziz, insulin receptor has post binding defect in
signalling pathway that considered as one of the major contributor in pathogenesis of
PCOS (Azziz, 2002). The polymorphisms in INSR gene bring mild change in function
of the gene which may contribute in the development of PCOS (Siegel et al., 2002).
Insulin resistance has metabolic consequences and play important role in development
and pathogenesis of PCOS (Azziz, 2002; Ehrmann, 2005).
48
Table 2.5: Summary of association studies between INSR (rs1799817 and rs2059806) gene polymorphisms and PCOS.
(a) rs1799817
Sr.
No
Population PCOS Control PCOS
(n)
Control
(n)
p Association Reference
Allele frequency
T allele
n (%)
C allele
n (%)
T allele
n (%)
C allele
n (%)
1 Korea 130(37.4) 218(62.6) 75(40.3) 111(59.6) 174 93 >0.050 Negative Lee et al., 2007
2 Korea 79(29.9) 185(70.0) 68(34) 132(66.0) 132 100 >0.050 Negative Lee et al., 2008
3 India 125(34.7) 235(65.2) 80(27.7) 208(72.2) 144 180 >0.050 Negative Mukherjee et al., 2009
4 Turkey 66 (75.0) 22 (25.0) 71 (71.0) 29 (29.0) 44 50 0.538 Negative Unsal et al., 2009
5 Iran 88 (24.3) 274 (75.7) 79 (21.8) 283 (78.2) 181 181 0.427 Negative Ranjzad et al., 2012
(b) rs2059806
1
Iran 265 (73.2) 97 (26.8) 256 (70.7) 106 (29.3) 181 181 0.456 Negative Ranjzad et al., 2012
49
PCOS being metabolic syndrome is also called as insulin resistant disease (Rotterdam,
2004; Legro et al., 2004).
Insulin resistance has an important role in the pathogenesis and metabolic
consequences of PCOS (Azziz, 2002; Legro et al., 2004; Ehrmann, 2005). Legro et al,
(2004) described that 50-70% of PCOS women exhibits insulin resistance and insulin
insensitivity that leads toward the hyperandrogenism; a major contributor of PCOS
symptoms and sign.
2.2.3 Follicle stimulating hormone receptor (FSHR)
FSH is a pituitary glycoprotein responsible for folliculogenesis by activating the
maturation of follicles; this cause granulosa cells to proliferate and trigger the
synthesis of aromatase which is an androgen converting enzyme (Gharib et al., 1990;
Moyle and Campbell, 1996). The FSH receptor (FSHR) which belongs to G-protein
coupled receptor mediates the action of FSH signal transduction (Segaloff and Ascoli,
1993). The first inactivated mutation in FSHR gene with ovarian failure was observed
in some Finnish women (Aittomaki et al., 1995). The substitution of A to G of rs6165
polymorphism causes change of threonine (ACT) codon to alanine (GCT) at 307
coding position of protein. The replacement of G with A of rs6166 SNP at coding
position 680 leads to change of amino acid serine (AGT) codon to asparagine (AAT)
(Aittomaki et al., 1995).
The worldwide screening of patients and control in different ethnicities for FSHR
gene explore two non-synonymous SNPs in exon 10. These SNPs in the coding region
had been identified for frequency of >30% in the normal population. The rs6165 at
position 919 showed substitution of A to G changing the amino acid threonine to
alanine. Whereas the rs6166 at position 2039 replaced G with A, this change the
amino acid sequence from serine to asparagine (Aittomaki et al., 1995).
The two polymorphisms N680S and A307T of FSHR gene in exon 10 were in linkage
disequilibrium. The polymorphism A307T of FSHR gene is located in the high
affinity binding site of FSH hormone in extracellular domain of (Davis et al., 1995).
This site affects the signal transduction and hormone trafficking (Hipkin et al., 1995).
The FSHR gene consist of 10 exons in which the largest one encodes for intracellular
domain while the remaining nine smaller encodes for extracellular domain (Simoni et
al., 1997).
50
In a study of Japanese PCOS women a significant increase was observed in the
Ala307Thr frequency with 66.7% compared with 43.5% in normal ovulating women
(Sudo et al., 2002).
The alteration in the amino acid affects the function of receptors of FSHR protein at
post-translational modifications (de Castro et al., 2004). The genetic association
between women with PCOS and FSHR gene polymorphisms has been studied
previously (Tong et al., 2001; Orio et al., 2006)
The candidate genes involved in insulin metabolism, gonadotropin secretions,
steroidogenesis and inflammation were studied in the previous years. The
susceptibility genes for PCOS have been explored and identified in women of Spain
(Escobar-Morreale et al., 2005). However the results of various studies were so far
inadequate (Diamanti- Kandarakis et al., 2006).
The existence of association of Ala307Thr polymorphism with PCOS was not
confirmed in Turkish women (Unsal et al., 2009) and in women with Caucasian
origin (Valkenburg et al., 2009). However a study on Chinese women reported
significant association of Ala307Thr polymorphism of FSHR gene with PCOS (du et
al., 2010). The associations of PCOS and polymorphisms of FSHR gene have been
studied extensively in various ethnicities (de Koning et al., 2006; Jun et al., 2006;
Simoni et al., 2008; Achrekar et al., 2009; Livshyts et al., 2009; Overbeek et al.,
2009; Valkenburg et al., 2009; Du et al., 2010; Gu et al., 2010; Kuijper et al., 2010;
Rendina et al., 2010), however the consistency of these findings are not without
contradictions.
Previous genetic studies revealed that the genes involved in the hormone metabolism
and energy regulation, such as INSR, LH and CYP19 play important role in
pathogenesis of PCOS (Rajkhowa et al., 1995; Kahsar-Miller et al., 2004; Witchel et
al., 2005; Unsal et al., 2009).
Mohiyiddeen and Nardo (2010) reviewed 15 studies and examine the polymorphism
of FSHR gene; this review suggest that the homozygous serine variant was associated
with higher FSH level in all ovulatory patients but three studies did not showed
consistent results with these findings (Falconer et al., 2005; Klinkert et al., 2006;
Loutradis et al., 2006). More recent studies could not confirm the association of
homozygous serine variant with the FSH concentration at follicular phase as was
51
Table 2.6: Summary of association studies between FSHR (rs6165) gene polymorphisms and PCOS.
Sr.
No
Population PCOS Control PCOS
(n)
Control
(n)
p Association Reference
Allele frequency
A allele
n (%)
G allele
n (%)
A allele
n (%)
G allele
n (%)
1 UK 92 (49.5) 94 (50.5) 41 (40.2) 61 (59.8) 93 51 >0.050 Negative Conway et al., 1999
2 Singapore 162 (65.3) 86 (34.7) 314 (66.5) 158 (33.5) 124 236 >0.050 Negative Tong et al.., 2001
3 Japan 18 (50.0) 18 (50.0) 219 (65.2) 117 (34.8) 18 168 >0.050 Negative Sudo et al., 2002
4 Italian 43 (43.0) 57 (57.0) 43 (43.0) 57 (57.0) 50 50 >0.050 Negative Orio et al., 2006
5 Caucasian 496 (50.1) 494 (49.9) 3064 (50.0) 3060 (50.0) 495 3062 >0.050 Negative Valkenburg et al., 2009
6 Turkey 51 (58.0) 37 (42.0) 57 (57.0) 43 (43.0) 44 50 >0.050 Negative Unsal et al., 2009
7 China 72 (65.5) 38 (34.5) 117 (63.6) 67 (36.4) 55 92 0.007 Positive Du et al., 2010
8 China 228(29.7) 540(70.3) 483(31.4) 1053(68.8) 384 768 0.390 Negative Fu et al., 2013
52
Table 2.7: Summary of association studies between FSHR (rs6166) gene polymorphisms and PCOS.
Sr.
No
Population PCOS Control PCOS
(n)
Control
(n)
p Association Reference
Allele frequency
A allele
n (%)
G allele
n (%)
A allele
n (%)
G allele
n (%)
1 UK 92 (49.5) 94 (50.5) 41 (40.2) 61 (59.8) 93 51 >0.050 Negative Conway et al., 1999
2 Singapore 162 (65.3) 86 (34.7) 314 (66.5) 158 (33.5) 124 236 >0.050 Negative Tong et al.., 2001
3 Japan 18 (50.0) 18 (50.0) 219 (65.2) 117 (34.8) 18 168 >0.050 Negative Sudo et al., 2002
4 Italian 43 (43.0) 57 (57.0) 43 (43.0) 57 (57.0) 50 50 >0.050 Negative Orio et al., 2006
5 Caucasian 496 (50.1) 494 (49.9) 3064 (50.0) 3060 (50.0) 495 3062 >0.050 Negative Valkenburg et al., 2009
6 Turkey 51 (58.0) 37 (42.0) 57 (57.0) 43 (43.0) 44 50 >0.050 Negative Unsal et al., 2009
7 China 72 (65.5) 38 (34.5) 117 (63.6) 67 (36.4) 55 92 0.007 Positive Du et al., 2010
8 China 228(29.7) 540(70.3) 483(31.4) 1053(68.8) 384 768 0.390 Negative Fu et al., 2013
53
observed previously (Kuijper et al., 2010; Genro et al., 2012; Mohiyiddeen et al.,
2012).
2.2.4 Follicle stimulating hormone beta (FSHB)
The non-coding region of FSHB gene comprises less number of polymorphism but
consider as highly frequent alleles in human. However the variants of this gene are in
strong linkage disequilibrium (Simoni et al., 2008).
2.2.5 Estrogen receptor alpha (ESR1)
Both ESR1 and ESR2 genes were involved in the physiological action on estrogen
production in the ovary (Byers et al., 1997; Drummond et al., 1999). The alternation
in the expression of ESR1 and ESR2 genes may cause anomalous development of
follicles (Jakimiuk et al., 2002).
Estrogen play substantial role in the function and development of reproductive
system. The estrogen action is mediated by two specific receptors, the ESR1 and
ESR2 which belongs to nuclear receptor family. Both ESR1 and ESR2 are required for
the proper function of hypothalamus pituitary ovarian axis. Estrogen acts on the
ESR1 to regulate the cyclic process of gonadotropin release in the hypothalamic
pituitary axis (Hillisch et al., 2004), and enhance the process of folliculogenesis and
ovulation by its action on ESR2 in the ovary (Hegele-Hartung et al., 2004). The ESR1
gene found on chromosome 6q25.1and consists of 1 intron and 8 exons spanning 140
Kb, contains two SNPs r2234693 and rs9340799 (Ale´ssio et al., 2007).
Kim et al, (2010) in Korean population observed significant difference in allele and
genotype frequencies between women with PCOS and controls. The “A” allele was
significantly different with 11.7% in PCOS and 19.1% in control. The study
concludes that the ESR2 polymorphism was significantly associated with the
pathogenesis of PCOS (Kim et al., 2010).
A study conducted on Greece population showed the association of rs9340799
polymorphism of ESR1 gene with FSH level in PCOS women. The association of
allele A was observed with low serum FSH level indicates the contribution of this
genomic variant with reduced development of follicles (Nectaria et al., 2012).
54
2.2.6 Estrogen receptor beta (ESR2)
ESR2 gene spans approximately 62 Kb, consists of 9 exons and resides on
chromosome 14q23.1 (Pettersson and Gustafsson, 2001).
2.2.7 Luteinizing hormone choriogonadotropin receptor (LHCGR)
LHR mediate the effect of LH and this is expressed in granulosa and theca cells
(Balen, 1993). LHCGR belongs to G-protein receptor located on 2p16.3 chromosome
(Atger et al., 1995). A single non synonymous mutation at G1052A of LHβ replaces
amino acid glycine with serine in exon 3 (Tapanainen et al., 1999). The LHR gene
comprises of 10 introns and 11 exons (Ascoli et al., 2002). The anomalous signalling
of LH is believed to enhance the production of ovarian androgen in women with
PCOS and leads toward anovulation (Norman et al., 2007).
The LHCGR is expressed in various tissues including testis, ovaries and some non-
gonadal tissues (Rahman and Rao, 2009). In Han Chinese PCOS women the two
susceptible loci were identified at 9q33.3 and 2p21 encoding for LHCGR and FSHR
gene respectively in a genome wide association study (Chen et al., 2011). The
LHCGR gene comprises a receptor for chorionic gonadotropin (HCG) and luteinizing
hormone (LH) and mutations in these receptors were related with infertility (Yariz et
al., 2011).
Studies have investigated that the mutations in LHR and LHβ gene may alter the
function or structure of LHR and LH, either inactivating or activating their bioactivity.
The mutations in LHR cause amenorrhea, anovulation, infertility and PCOS in women
(Themmen and Huhtaniemi, 2000; Themmen, 2005; Huhtaniemi and Themmen,
2005; Huhtaniemi and Alevizaki, 2006).
2.2.8 Luteinizing hormone beta (LHβ)
The convincing evidences suggest the LHR and LHB gene as the genetic determinant
of PCOS. Whereas, the studies on different populations showed inconsistent results
with polymorphisms of this gene. The mutations in LHβ gene Trp8Arg and Ile15Thr
polymorphisms were associated with the PCOS women of Japan and UK whereas the
low frequency of mutations was observed in North European obese PCOS women
(Rajkhowa et al., 1995; Suganuma et al., 1995; Tapanainen et al., 1999). A study on
Singapore Chinese women speculated that G1052A mutation of LHβ gene affect the
function of gonads and this polymorphism was related with menstrual disorder. The A
55
allele (4%) was present only in women with menstrual irregularities and absent in
controls (Ramanujam et al., 1999). Kim et al. (2001) found no homozygous
polymorphic variant at G1052A of LHβ in 108 Korean women with PCOS. The
secretion of androgens was mediated by LH in theca cells of ovaries and help in
follicles maturation (Laven et al., 2002). The LHR was over-expressed in theca and
granulosa cells of women with PCOS (Jakimiuk et al., 2001).
These findings suggest that different regional and ethnical groups may express
different genotypes of G1052A polymorphism of LHβ. A genome-wide association
study on Han Chinese women with PCOS investigated significant association with
LHR gene polymorphisms (Chen et al., 2011).
A study conducted on Chinese women showed that the frequency of heterozygous and
homozygous variants were 3.2% (10/315) and 1.0% (3/315) respectively at G1052A
polymorphism of LHβ in PCOS. In contrast no polymorphism (0/212) was observed
at G1052A of LHβ gene in control. The mutant allele A of LHβ gene was significantly
associated (p = 0.001) with PCOS (Liu et al., 2012).
In PCOS women the LHR is expressed in theca cells, the LHR function is altered by
multiple polymorphisms in the LH receptor (Jakimiuk et al., 2001). LHR stimulates
the ovarian theca cells to secrete androgens which consequently arrest follicular
maturation (Laven et al., 2002). The PCOS women have normal limits of FSH level
(Laven et al., 2002).
56
CHAPTER 3
MATERIALS AND METHODS
3.1 Sampling size and study design
This research has been approved by the research ethics Committee Board of Advance
Studies and Research (BASR) of Government College University Lahore as well as
from infertility clinics of obstetrics/gynecology teaching hospitals. This was a Case
control analytical study and the sample size was calculated by the following formula
(Lwanga and Lemeshow, 1991).
n = z2
α/2 π (1- π)/e2
3.2 Subjects
The diagnosed female subjects with known infertility (endometriosis and PCOS) were
recruited from the outpatient’s infertility clinics of Gynecology /Obstetrics wards of
tertiary hospitals (Sir Ganga Ram hospital Lahore and Lady Willington hospital
Lahore). The women were referred from clinics, hospitals and physicians from all
over the Punjab province. The written informed consent on a prescribed performa was
obtained from all subjects (Annexure I). The anthropometric data of all the subjects
were collected and every individual was assessed after registering their medical
history on specially designed subject data sheets (Annexure II) (Sue et al., 2007). The
healthy Pakistani women from the Punjab with regular menses and history of
spontaneous conception volunteered as controls.
3.3 Sample selection
There were separate cases for endometriosis and PCOS.
Ninety-six (96) Pakistani women with PCOS.
Fifty-two (52) Pakistani women with endometriosis.
One hundred forty eight (148) healthy Pakistani women as control.
3.3.1 Inclusion criteria:
Age 20 to 40 years.
Females married for at least 1 year.
57
The diagnosed cases of PCOS according to Rotterdam ESHRE/ASRM-
Sponsored PCOS Consensus Workshop Group, 2004 (Rotterdam, 2004).
Therefore, diagnostic criteria included women who displayed two of the
following three criteria: clinical and/or biochemical signs of
hyperandrogenism, oligo- or anovulation and polycystic ovary morphology.
The patients of endometriosis were diagnosed clinically (e.g. pelvic pain,
dysmenorrhea and infertility).
The reproductively active age group was taken.
3.3.2 Exclusion criteria:
Known causes of congenital adrenal hyperplasia, hyperandrogenism, Cushing
syndrome, hyperprolactinemias, adrenal tumor and virilizing ovarian tumor
were excluded.
Hyperthyroidism, Pregnancy, Smoking, Hypertension.
Intake of any medications including hormonal treatment in last 1 year.
Use of anti-obesity drugs, anti-diabetic, glucocorticoids.
Any neoplastic, metabolic, Cardiovascular, or other relevant medical illness.
Inflammatory conditions of pelvis and abdomen.
On treatment of infertility i.e. IVF.
Laparotomy/laparoscopy.
3.3.3 Selection of controls
Females of age 20 to 40 year married for more than 1 year.
BMI matched healthy controls with regular menstruation.
No symptoms of hyperandrogenism, e.g. hirsutism, Virilism.
History of spontaneous conception.
Had not taken any hormonal contraception.
Agreeable to participate in the study.
58
Figure 3.1 Diagrammatic and schematic presentation of research layout.
Blood Sampling
(Cases=150, Control= 150
DNA extraction
AGTGTGGC
ATGCYAGC
AGGACAGA
SNP selection
Primer designing
PCR
Amplification
PCR product
Purification
Gel Electrophoresis
PCR product
Sequencing
analysis
Sequence
chromatogram
Sampling
Anthropometric
data (n=500)
Age, Family history, weight, height,
waist and hip ratio, physical
examination, pelvic ultrasonography,
patient’s medical, gynecological and
surgical history
Data Tabulation and
Statistical Analysis
Sequence reading in Chromas
program, Data analysis by Chi
square test & HAPLO program
Serum
Hormonal Analysis
FSH, Testosterone,
Progesterone
59
3.4 Physical examination
3.4.1 Determination of weight, height and Body Mass Index
The standing height of each subject was measured using a portable Standiometer,
whereas weight was determined with a digital weight scale nearest to 0.1 cm. The
Body Mass Index (BMI) of every subject was calculated by using the equation
demonstrated by Cole et al. (1997):
BMI = Weight in kilogram / (Height in meter) 2
3.4.2 Physical examinations and interviews
All participants completed a structured questionnaire covering family history,
education level, weight and height, waist and hip ratio (WHR), patient’s medical,
gynecological and surgical history. Complete physical examination and pelvic
ultrasonography was conducted. All the eligible controls were matched on the bases
of age, residential area and were genetically unrelated to minimize possible selection
bias. Hence all subjects had the same similar socioeconomic status and ethnicity. All
subjects were recruited by gynecologists after reviewing patient’s medical records to
meet with eligibility criteria.
3.5 Blood sampling and Storage
The blood samples (5-10ml) were drawn from each patient and were stored at 4 oC
after dividing into sub-samples until further process for genetic and hormonal
analysis. The sub-samples of blood for molecular genetic studies were collected in
tubes containing ethylenediaminetetraacetic acid (EDTA) as an anticoagulant.
Whereas the blood samples for serum separation were collected in gel coated tubes.
The serum was stored at -80 oC for further analysis. All the collected blood samples
were transported to laboratory on ice.
60
3.6 Genotyping and DNA sequencing
3.6.1 Genomic DNA extraction
Genomic DNA of all subjects including endometriosis, PCOS and healthy controls
were extracted from the blood using a modified standard extraction method
(Sambrook et al., 1989). (The recipe of solutions prepared for DNA extraction was
presented in Annexure III).
The protocol followed for genomic DNA isolation consists of following steps.
1) In 500 µl of blood 500-700 µl of Tris-EDTA (TE) buffer was added, mixed the
buffer by inverting tubes ups and down several times. The tubes were left at room
temperature for 10-15 min.
2) Centrifugation was performed at 25 oC with 13500 rpm for 5-10 min.
3) Supernatant was discarded leaving the white blood cells (WBCs) pellet at the
bottom of 1.5 ml eppendorf. The pellet was broken by gentle tapping and vortexing.
After adding 1 ml of TE buffer the step 2 was repeated until the WBCs pellet became
clear or light pink.
4) The broken clear pellet was dissolved into 375 µl of 3M sodium acetate, 25 µl of
10% sodium dodecyl sulphate (SDS) and 10 µl of proteinase K (10 µg/µl) and
incubated at 37 oC overnight in a shaking water bath.
5) One volume of chilled chloroform: isoamyl alcohol (24:1) was added into three
volumes of digested pellet, mixed by gently inverting eppendorfs ups and down until
a milky emulsion was formed and centrifuged at 25 oC with 13500 rpm for 5-10 min.
6) Three layers can be visualized after centrifugation. The upper layer contains DNA,
the lower layer chloroform: isoamyl alcohol and the middle whitish layer proteins.
The DNA layer was carefully transferred into a new labeled eppendorf avoiding
contamination from other layers, mixed with equal volume of chilled absolute ethanol
by gently inverting eppendorfs ups and down until DNA threads became visible, left
at room temperature for 10 min and centrifuged at 25 oC with 13500 rpm for 5-10
min.
7) Supernatant was discarded and the DNA pellet was washed with 500-1000 µl
chilled 70% ethanol by centrifuging at 25o C with 13500 rpm for 5-10 min.
8) Ethanol was discarded and the DNA pellet was dried by air keeping eppendorfs
open inside the laminar air flow at room temperature for 15-30 min.
61
9) The dried DNA pellet was dissolved into 30-50 µl low TE buffer, incubated in a
shaking water bath at 70 oC for 30 min, cooled at room temperature and spinned by
brief centrifugation.
10) DNA was stored in duplicate at -20oC and -80
oC after assessing its quality and
quantity by nanodrop (Thremoscientific, Nanodrop 2000) and gel electrophoresis.
11) All the DNA samples (endometriosis, PCOS and control) were extracted in
duplicate to avoid the mishandling waste and troubleshooting with optimization.
3.6.2 Candidate Genes
Various candidate genes were selected from three metabolic pathways that have been
implicated in the etiology of endometriosis and PCOS (Table 3.1, 3.2). These genes
map to distinct chromosomal locations. The common variants in these regions,
1q32.1, 6q25.1, 11q22.1, 2p21, 3q27.3, 6q25.1, 11p14.1, 15q21.2, 19q13.2, and
19p13.33 were analyzed. The single nucleotide polymorphisms (SNPs) of ESR1,
ESR2, PGR, IL10, FSHR, FSHB, LHCGR, LHB, ADIPOQ and INSR genes were
selected (Table 3.1, 3.2). The NCBI reference sequence assembly was used for
chromosome position, gene ID and gene symbol GRCh37.p5 along with build 37.3.
Nomenclature guidelines suggested by the Human Genome Variation Society
(HGVS) was followed, the coding reference position of the relevant DNA used along
with the Ensemble accession file is shown in Table 3.1 and 3.2.
3.6.3 Single nucleotide polymorphism (SNPs) selection
All SNPs were selected for their functional relevance to PCOS and endometriosis
after searching databases of “University of California, Santa Cruz” (UCSC)
(https://genome.ucsc.edu/), “National center for biotechnology information” (NCBI)
(http://www.ncbi.nlm.nih.gov) and prior publications. The SNPs were searched using
the NCBI website (http://www.ncbi.nlm.nih.gov/snp/). The search for endometriosis
susceptible SNPs has been focused on genes encoding inflammatory mediators, sex
hormones, enzymes involved in metabolism and steroid biosynthesis. The susceptible
and candidate genes for PCOS involved in insulin sensitivity, steroid biosynthesis,
detoxification and sex hormone regulation were selected.
66
Table 3.1: Genotyping panel for endometriosis candidate genes.
Marker locus Candidate
genes
Gene
symbol
Gene
ID
Ensemble accession *SNP ID *Chr Genomic
position
Coding DNA
reference
position
Gene
mode
Protein
position
Residue
change
Sex Steroid
hormone
Estrogen receptor α ESR1 2099 ENST00000440973 rs2234693 6q25.1 g.152163335 c.453-397T>C Intron
rs8179176 g.152163334 c.453-398C>T Intron
rs9340799 g.1521633381 c.453-351A>G Intron
Estrogen receptor β ESR2 2100 ENST00000557772 rs4986938 14q23.2 g.64699816 c.1859G>A UTR-3
Progesterone
receptor
PGR 5241 ENST00000325455 rs10895068 11q22.1 g.101000214 c.-413G>A UTR-5
rs1042838 g.100933412 c.1978G>T missense 660 Val-Leu
Inflammatory
mediators
Interleukin 10 IL10 3586 ENSG00000136634 rs1800872 1q32.1 g.206946407 c. -592 A>C UTR-5
rs1800871 g.206946634 c.-819C>T UTR-5
rs1800894 g.206946666 c.886G>A UTR-5
rs1800896 g.206946897 c.-1082A>G UTR-5
Gonadotropin
action
Follicle-stimulating
hormone receptor
FSHR 2492 ENST00000406846 rs6166 2p16.3 g.49189921 c.2039G>A missense 680 Ser-Asn
rs6165 g.49191041 c.919G>A missense 307 Ala-Thr
Assembly used for chromosome position, gene ID and gene symbol is GRCh37.p5 along build 37.3
SNP: Single nucleotide polymorphism; * Chr: Chromosome
67
Table 3.2: Genotyping panel for PCOS candidate genes.
Assembly used for chromosome position, gene ID and gene symbol is GRCh37.p5 along build 37.3; * Chr: Chromosome; SNP: Single nucleotide polymorphism
Marker locus Candidate gene Gene
symbol
Gene
ID
Ensemble accession SNP ID *Chr Genomic
position
Coding DNA
reference
position
Gene
mode
Protein
position
Residue
change
Steroid
hormone
Estrogen receptor α ESR1 2099 ENST00000440973 rs2234693 6q25.1 g.152163335 c.453-397T>C Intron
rs8179176 g.152163334 c.453-398C>T Intron
rs9340799 g.1521633381 c.453-351A>G Intron
Estrogen receptor β ESR2 2100 ENST00000557772 rs4986938 14q23.2 g.64699816 c.1859G>A UTR-3
Gonadotropin
action
Follicle-stimulating
hormone beta
FSHB 2488 ENST00000417547 rs6169 11p14.1 g.30255185 c.228C>T cds-Synon 76 Tyr-Tyr
Follicle-stimulating
hormone receptor
FSHR 2492 ENST00000406846 rs6166 2p16.3 g.49189921 c.2039G>A missense 680 Ser-Asn
rs6165 g.49191041 c.919G>A missense 307 Ala-Thr
Luteinizing
hormone
LHCGR 3973 ENST00000294954 rs61996318 2p21 g.48941162 c.568C>A missense 190 Gln-Lys
choriogonadotropin
receptor
rs111834744 g.48941073 c.605+52delT intron
Luteinizing
hormone beta
LHB 3972 ENST00000221421 rs1800447 19q13.33 g.49519905 c.82T>C missense 28 Trp-Arg
rs4002462 g.49519997 c.16-26T>C intron
Insulin action Adiponectin ADIPOQ 9370 ENST00000412955 rs2241766 3q27.3 g.186570892 c.45T>G intron
rs1501299 g.186571123 c.214+62G>T missense 190 Gln-Lys
rs2241767 g.186571196 c.214+135A>G intron
Insulin receptor INSR 3643 ENST00000341500 rs1799817 19p13.2 g.7125297 c.3219C>T cds-Synon 1085 HiS-His
rs1799815 g.7125519 c.2997C>T cds-Synon 1011 Tyr-Tyr
rs2059806 g.7166376 c.1650G>A cds-Synon 550 Ala-Ala
rs2229429 g.7166388 c.1638C>T missense 546 Asp-Glu
68
Table 3.3: Primers, PCR program, annealing temperatures and PCR nucleotide product size of endometriosis polymorphisms.
Gene
ID
*SNPs ID Primer sequence Annealing
temperature
(◦C)
PCR Program
(35 cycles)
Product size
(*bp)
ESR1
(2099)
rs2234693 F: CTGCCACCCTATCTGTATC
R: ACCCTGGCGTCGATTATCT
53 ⁰C 95⁰C 15min, 95⁰C 45 Sec
53⁰C 45 Sec, 72⁰C 1 min
1366
rs9340799 F: CTGCCACCCTATCTGTATC
R: ACCCTGGCGTCGATTATCT
53 ⁰C 95⁰C 15min, 95⁰C 45 Sec
53⁰C 45 Sec, 72⁰C 1 min
1366
ESR2
(2100)
rs4986938 F: CGGCAGAGGACAGTAAAAGC
R: TGAGAGTTGGGAAGGTGGAG
58 ⁰C 95⁰C 15min, 94⁰C 45 Sec
58⁰C 45 Sec, 72⁰C 45 Sec
213
IL10
(3586)
rs1800872 F: GGACAGCTGAAGAGGTGGAA
R: GAGGGGGTGGGCTAAATATC
60 ⁰C 95⁰C 15min, 94⁰C 45 Sec
60⁰C 45 Sec, 72⁰C 1 min
166
rs1800871
F: TCATTCTATGTGCTGGAGATGG
R: TGGGGGAAGTGGGTAAGAGT
60 ⁰C 95⁰C 15min, 94⁰C 45 Sec
60⁰C 45 Sec, 72⁰C 45 Sec
208
rs1800896
F: TTCCCCAGGTAGAGCAACAC
R: GATGGGGTGGAAGAAGTTGA
56 ⁰C 95⁰C 15min, 94⁰C 45 Sec
56⁰C 45 Sec, 72⁰C 45 Sec
238
PGR
(5241)
rs10895068 F: GTACGGAGCCAGCAGAAGTC
R: GAGGACTGGAGACGCAGAGT
56 ⁰C 95⁰C 15min, 94⁰C 45 Sec
56⁰C 45 Sec, 72⁰C 1 min
220
rs1042838 F: TTCGAAACTTACATATTGATGACCA
R: CACTTAAAATAACAAAAACAACAAAAG
56 ⁰C 95⁰C 15min, 94⁰C 45 Sec
56⁰C 45 Sec, 72⁰C 45 Sec
180
69
Table continues……………..
FSHR
(2492)
rs6166 F: GCAAGTGTGGCTGCTATGAA
R: GTGACATACCCTTCAAAGGC
55 ⁰C 95⁰C 15min, 95⁰C 1min
55⁰C 1 min, 72⁰C 1 min
231
rs6165 F: CCTGCACAAAGACAGTGATG
R: TGGCAAAGACAGTGAAAAAG
55 ⁰C 95⁰C 15min, 95⁰C 1min
55⁰C 1 min, 72⁰C 1 min
577
*SNP: Single nucleotide polymorphism
F: Forward; R: Reverse
*bp: base pair
70
Table 3.4: Primer, annealing temperature, PCR program and product size of PCOS polymorphisms.
Gene
ID
*SNPs ID Primer sequence Annealing
temperature
(◦C)
PCR Program
(35 cycles)
Product
size
(*bp)
ADIPOQ
(9370)
rs2241766 F:GAAGTAGACTCTGCTGAGATGG
R:TATCAGTGTAGGAGGTCTGTGATG
56 ⁰C 95⁰C 15min, 93⁰C 45 Sec
56⁰C 30 Sec, 72⁰C 45 Sec
372
rs1501299
F: CCTGGTGAGAAGGGTGAGAA
R: AGATGCAGCAAAGCCAAAGT
56 ⁰C 95⁰C 15min, 93⁰C 45 Sec
56⁰C 30 Sec, 72⁰C 45 Sec
241
rs2241767
F: CCTGGTGAGAAGGGTGAGAA
R: AGATGCAGCAAAGCCAAAGT
56 ⁰C 95⁰C 15min, 93⁰C 45 Sec
56⁰C 30 Sec, 72⁰C 45 Sec
241
INSR
(3643)
rs1799817
F: CCAAGGATGCTGTGTAGATAAG
R: TCAGGAAAGCCAGCCCATGTC
56 ⁰C 95⁰C 15min, 93⁰C 45 Sec
56⁰C 30 Sec, 72⁰C 45 Sec
317
rs1799815 F: CCAAGGATGCTGTGTAGATAAG
R: TCAGGAAAGCCAGCCCATGTC
56 ⁰C 95⁰C 15min, 93⁰C 45 Sec
56⁰C 30 Sec, 72⁰C 45 Sec
317
rs2059806
F: CGGTCTTGTAAGGGTAACTG
R: GAATTCACATTCCCAAGACA
62 ⁰C 95⁰C 15min, 93⁰C 45 Sec
62⁰C 30 Sec, 72⁰C 45 Sec
324
rs2229429
F: CGGTCTTGTAAGGGTAACTG
R: GAATTCACATTCCCAAGACA
62 ⁰C 95⁰C 15min, 93⁰C 45 Sec
62⁰C 30 Sec, 72⁰C 45 Sec
324
FSHR
(2492)
rs6166 F: GCAAGTGTGGCTGCTATGAA
R: GTGACATACCCTTCAAAGGC
55 ⁰C 95⁰C 15min, 95⁰C 1min
55⁰C 1 min, 72⁰C 1 min
231
71
Table continues…………….
rs6165
F: CCTGCACAAAGACAGTGATG
R: TGGCAAAGACAGTGAAAAAG
55 ⁰C 95⁰C 15min, 95⁰C 1min
55⁰C 1 min, 72⁰C 1 min
577
FSHB
(2488)
rs6169 F: TGTTAGAGCAAGCAGTATTCAATTTCT
R: GTATGTGGCCTGAAATGTCCACTGATC
60 ⁰C 95⁰C 15min, 94⁰C 1min
60⁰C 30 Sec, 72⁰C 1 min
357
LHCGR
(3973)
rs61996318 F: TGATGGTGGTGGTGATGATG
R: GGTTTCTAGCCAGCCAGTTG
60 ⁰C 95⁰C 15min, 94⁰C 1 min
60⁰C 40 Sec, 72⁰C 1 min
379
rs111834744 F: TGATGGTGGTGGTGATGATG
R: GGTTTCTAGCCAGCCAGTTG
60 ⁰C 95⁰C 15min, 94⁰C 1 min
60⁰C 40 Sec, 72⁰C 1 min
379
LHB
(3972)
rs1800447 F: GTCTCAGACCTGGGTGAAGC
R: GCACAGATGGTGGTGTTGAC
58 ⁰C 95⁰C 15min, 94⁰C 45 Sec
58⁰C 45 Sec, 72⁰C 45 Sec
185
rs4002462 F: GTCTCAGACCTGGGTGAAGC
R: GCACAGATGGTGGTGTTGAC
58 ⁰C 95⁰C 15min, 94⁰C 45 Sec
58⁰C 45 Sec, 72⁰C 45 Sec
186
ESR1
(2099)
rs2234693 F: CTGCCACCCTATCTGTATC
R: ACCCTGGCGTCGATTATCT
53 ⁰C 95⁰C 15min, 95⁰C 45 Sec
53⁰C 45 Sec, 72⁰C 1 min
1366
rs9340799 F: CTGCCACCCTATCTGTATC
R: ACCCTGGCGTCGATTATCT
53 ⁰C 95⁰C 15min, 95⁰C 45 Sec
53⁰C 45 Sec, 72⁰C 1 min
1366
ESR2
(2100)
rs4986938 F: CGGCAGAGGACAGTAAAAGC
R: TGAGAGTTGGGAAGGTGGAG
58 ⁰C 95⁰C 15min, 94⁰C 45 Sec
58⁰C 45 Sec, 72⁰C 45 Sec
213
*SNP: Single nucleotide polymorphism; F: Forward; R: Reverse; *bp: base pair
72
3.6.4 Primer designing
The forward and reverse primers were designed using primer 3
(http://gmdd.shgmo.org/primer3/?seqid=47) website. The sequences of the PCR
primers, forward and reverse sets (Figure 3.2), annealing temperature, cycling
conditions and product size are shown in Table 3.3 and 3.4. The validity of primers
was checked through NCBI database.
(a) SNPs and primer sequence of ESR1 gene
TGAAACTCCTTGCTAAGAGTCTGCTTTGTCAGGTCTGAATTCACTTAACCAGTCTTT
GCTTTGTTGGACTTCTCTCTGCCACCCTATCTGTATCATCATCCTCTCCATAAACCT
TTCTACTTAAAGCATTTTACTTCCTTATTTTCTTGGTTTTCCTAGAATCTCCTTACT
GTTCATTTTCAGCTTCCTTTCTGTGTTCCTCTTCTCTTCCTACATTTTTTTTTAGCT
TTCTACTTTCTTAAAGCATTTTACTTCCTTATTTTCTTGGTTTTCCTAGAATTTTCT
TACTGTTCATTTTCAGTTTCCTTTCTGTGTTCCTCTGATTGTCTCTCTTTCTACATT
TTTTTTTTCTGTGTTCCTCTGATTTTCACGCAGTCTGGAGTTGTCATGATCAATCAT
AGCCTACTGCAGCCTCGACATCCTAGGCTCAAGTGATTCTCCCACCTCAGCCTTACA
AGTAGCTAGGACTACAGTCACACATCACCATTCTCAGCTAATTTTTTTAAGAAGCAT
TTTTATAGAGATGGAGTCTTGCTATATTGTGCAGGCTGGGCTCAAACTACAGGGCTT
AAACAATTCTCCTGCTTTGGCCTCCCAAAGTGCTGGGATTCCAGGCATGAACCACCA
TGCTCAGTCTCTACATGTTCCTAAAGAGGAGTTTTGAATATTGAAGAACAGTATTTT
CAAATTACATTATTCAAGTTATAAAAACTGATATCCAGGGTTATGTGGCAATGACGT
AAAAATTTGAATTGTTATTTTTTTGACACATGTTCTGTGTTGTCCATCAGTTCATCT
GAGTTCCAAATGTCCCAGCTGTTTTATGCTTTGTCTCTGTTTCCCAGAGACCCTGAG
TGTGGTCTAGAGTTGGGATGAGCATTGGTCTCTAATGGTTCTGAAATAATTGTATAT
TCCTGCAAAAACATTAAGTCTATTAGAAACCAGCTAATTTCATTTTGTCATTTTTAT
AGGTAACATATTCTGGTGCAGGTAGTATGTTTTTAAAACAAGTTTGCAATAAACAAT
TTCCCCTCAAGGTTAATATAATAGGCAACACCTTTTGCTGCAACAGACGGCAAGAGG
TAATGAAAGATTAGCTTACATTATGATTCATTATTTCAAAATGTCAGGATAAAGTGG
ATCTGCTGCATCTCCCAGAGAGTGCATGTTTTGCTTTTCTAATGTTAATGGATTTAC
TGTTTTTTTCCCCCCAGGCCAAATTCAGATAATCGACGCCAGGGTGGCAGAGAAAGA
TTGGCCAGTACCAATGACAAGGGAAGTATGGCTATGGAATCTGCCAAGGAGACTCGC
TACTGTGCAGTGTGCAATGACTATGCTTCAGGCTACCATTATGGAGTCTGGTCCTGT
GAGGGCTGCAAGGCCTTCTTCAAGAGAAGTATTCAAGGTAATAGTGTGTTGAAAACG
ACTTCTATTTTTGATCCTATGAGCAGATCCTAAGAGCCAAAGCGACTGGACTGGACT
73
(b) SNPs and primers sequences of IL10 gene promoter region
GACTATAGAGTGGCAGGGCCAAGGCAGAGCCCAGGCCTCCTGCACCTAGGTCAGTGT
TCCTCCCAGTTACAGTCTAAACTGGAATGGCAGGCAAAGCCCCTGTGGAAGGGGAAG
GTGAAGGCTCAATCAAAGGATCCCCAGAGACTTTCCAGATATCTGAAGAAGTCCTGA
TGTCACTGCCCCGGTCCTTCCCCAGGTAGAGCAACACTCCTCGCCGCAACCCAACTG
GCTCCCCTTACCTTCTACACACACACACACACACACACACACACACACACACACACA
CACAAATCCAAGACAACACTACTAAGGCTTCTTTGGGAAGGGGAAGTAGGGATAGGT
AAGAGGAAAGTAAGGGACCTCCTATCCAGCCTCCATGGAATCCTGACTTCTTTTCCT
TGTTATTTCAACTTCTTCCACCCCATCTTTTAAACTTTAGACTCCAGCCACAGAAGC
TTACAACTAAAAGAAACTCTAAGGCCAATTTAATCCAAGGTTTCATTCTATGTGCTG
GAGATGGTGTACAGTAGGGTGAGGAAACCAAATTCTCAGTTGGCACTGGTGTACCCT
TGTACAGGTGATGTAATATCTCTGTGCCTCAGTTTGCTCACTATAAAATAGAGACGG
TAGGGGTCATGGTGAGCACTACCTGACTAGCATATAAGAAGCTTTCAGCAAGTGCAG
ACTACTCTTACCCACTTCCCCCAAGCACAGTTGGGGTGGGGGACAGCTGAAGAGGTG
GAAACATGTGCCTGAGAATCCTAATGAAATCGGGGTAAAGGAGCCTGGAACACATCC
TGTGACCCCGCCTGTACTGTAGGAAGCCAGTCTCTGGAAAGTAAAATGGAAGGGCTG
CTTGGGAACTTTGAGGATATTTAGCCCACCCCCTCATTTTTACTTGGGGAAACTAAG
GCCCAGAGACCTAAGGTGACTGCCTAAGTTAGCAAGGAGAAGTCTTGGGTATTCATC
CCAGGTTGGGGGGACCCAATTATTTCTCAATCCCATTGTATTCTGGAATGGGCAATT
TGTCCACGTCACTGTGACCTAGGAACACGCGAATGAGAACCCACAGCTGAGGGCCTC
TGCGCACAGAACAGCTGTTCTCCCCAGGAAATCAACT
(c) SNPs and primers sequence of FSHB gene
AATCCTGTCATGTTTTTCATCATTTTTCTATGCTAAAATTCAAAGTTCCTTTATATTTTGAA
AAATAGTTAATATTTTGATATAGCCATAGGAAGTAAGAAAAGAAATTACTTGTATTTTCTGG
AAGATTTCAAGAACAATTTAGAAATGTAAATAGCATATAGGTCATTTATGAGGTCATGTTTT
AATGGGTAAATGTTAGAGCAAGCAGTATTCAATTTCTGTCTCATTTTGACTAAGCTAAATAG
GAACTTCCACAATACCATAACCTAACTCTCTTCTTAAACTCCTCAGGATCTGGTGTATAAGG
ACCCAGCCAGGCCCAAAATCCAGAAAACATGTACCTTCAAGGAACTGGTATACGAAACAGTG
AGAGTGCCCGGCTGTGCTCACCATGCAGATTCCTTGTATACATACCCAGTGGCCACCCAGTG
TCACTGTGGCAAGTGTGACAGCGACAGCACTGATTGTACTGTGCGAGGCCTGGGGCCCAGCT
ACTGCTCCTTTGGTGAAATGAAAGAATAAAGATCAGTGGACATTTCAGGCCACATACCCTTG
TCCTGAAGGACCAAGATATTCAAAAAGTCTGTGTGTGTGCAATGTGCCCAGGGGACAAACCA
CTGGATCAGGGGATTCAGACTCTACTGATCCCTGGTCTACTGGCAGAGGGAACTCTGGGAAT
74
TGAGAGTGCTGGGGGCCAGGACTCCATCATGATTCAGCTCTATATTCCTAGGTCTGATTTCA
TAAGGTTT
(d) SNPs and primers sequence of INSR gene
GTAGCATCATTCCATAGCAAGGGTCTGAAATCAGACAAGAAGGATGGGGATGCAGGT
TTGCCTCAGGACATATTGGCCAGGATCTTGGACCAGTTGTGGCTCCTTCCTTGAGTC
TCTGCCATGCCCTCTCCATGGGTGCAGATGCCTGTCCTGTTCTCGGCCATATGCCCA
GTGCCCGGCATGGGTCCTGGATCACAGAACTCATTTCATGAGTGTTTTCGAGGGGGT
TTGGGTGAGGGCTTGGGTGGAAGGTGGCTGCAGACCCCCAAGGGATCCTCCAAGGAT
GCTGTGTAGATAAGTAAGAAGTAGTGTTTCCATGCTCTGTGTACGTGCCGGACGAGT
GGGAGGTGTCTCGAGAGAAGATCACCCTCCTTCGAGAGCTGGGGCAGGGCTCCTTCG
GCATGGTGTATGAGGGCAATGCCAGGGACATCATCAAGGGTGAGGCAGAGACCCGCG
TGGCGGTGAAGACGGTCAACGAGTCAGCCAGTCTCCGAGAGCGGATTGAGTTCCTCA
ATGAGGCCTCGGTCATGAAGGGCTTCACCTGCCATCACGTGGTGAGTCCAGTGGGGG
TGGGACATGGGCTGGCTTTCCTGACCCTTCCCTTTCTCTGCCTCCTCCTCCTGCACA
GAGCGACAGAGGACACAGGGTGTAACCTCCTACCCACCCCTCACTCCACTAAGCTTC
CCATCCTAGAGGTGTGAGGAGGGATCATTCCCTTCTCAAATCCCATCCTCTCCCACT
TCGCCTGGAACCAGACTCTATCACTGTCTCATCACTTTCTGGAATGTTTCATTGCTT
TCCAGAATCTTTCATCACTTTC
Figure 3.2: The sequence highlighted blue represent the forward and reverse
primers and arrows (purple) shows SNP (a-d).
75
3.6.5 PCR Analysis
PCR was carried out in a total volume 25 μl reaction mixtures consisting of 50-100 ng
of DNA template, 10 μmol of each primer, 10 mM dNTP , 10X PCR buffer
containing15 mM MgCl2 and 5 U/μl Hotstart Taq DNA polymerase (Qiagen, Hilden
Germany). A DNA-free control was also run at parallel conditions.
PCR reaction mixture
Volume of reaction
Single reaction 96 reactions
Master mix
10X PCR buffer including
MgCl2
2.5ul 240ul
Primer forward 0.5 ul 48 ul
Primer reverse 0.5 ul 48 ul
dNTPs 0.5 ul 48 ul
Hot star Taq polymerase 0.5 ul 48 ul
Total master mix 4.5 ul 432 ul
Template DNA Variable according to the
concentration
Molecular grade water Adjustable
Total volume of reaction 25 ul
3.6.6 Gel electrophoresis and verification of PCR products
PCR products were visualized by ethidium bromide staining on a 2 % agarose gel
(Sigma Aldrich, St.Louis Missouri) (Annexure IV). The identities of the PCR
products were verified by sequencing. The gel electrophoresis of optimized PCR
products of selected genes was presented in Annexure V.
3.6.7 Purification of PCR product
The amplification PCR products were purified with Qiaquick PCR purification kit
(Qiagen, Hilden Germany). The PCR product was purified from nucleotides, primers,
salts and polymerases using Qiaquick spin columns. (The reagent preparation was
given in Annexure VI).
76
Procedure
In 25 µl of PCR sample 125 µl of PB buffer was added and mixed. The QIAquick
spin column was placed in a provided 2 ml collection tube. The DNA of sample
bound to the QIAquick column after centrifugation for 30–60 seconds. The flow-
through was discarded and the QIAquick column was placed back into the same tube.
The membrane bounded DNA was washed by adding 750 µl buffer PE to the
QIAquick column and centrifuge for 30–60 seconds. The flow-through was discarded
and placed the QIAquick column back in the same tube then centrifuged the column
for an additional 1 minute. The QIAquick column was placed in a clean 1.5 ml micro-
centrifuge tube. The DNA was eluted with DEPC treated water (pH 7.0–8.5) to the
center of the QIAquick membrane and centrifuged the column for 1 minute. The DNA
concentration and purity were measured using an absorbance ratio of 260/280 nm by
nanodrop (Thremoscientific, Nanodrop 2000).
3.6.8 DNA sequencing
The purified products were subject to direct sequencing with either the forward or
reverse primers. DNA sequencing was carried out with Applied Biosystems, 3730xL
capillary instruments at Keck Biotechnology Resource Laboratory Yale University
USA. The Sanger chemistry for this platform involves dideoxy terminators (ddNTPs)
tagged with a difference fluorescent dye (Big Dye Terminators), along with AmpliTaq
polymerase. The sample undergoes cycle sequencing in a thermo-cycler and then the
product is bead purified with Beckman Coulter’s Clean SEQ particles (Applied
Biosystem, New Haven CT).
3.6.9 DNA sequencing data interpretation
The electrophoretic chromatogram data were received as a computer readable
sequence file through file transfer protocol (ftp). For each gene the sequence data was
assembled using the DNA star software to ensure the sequence counting.
Polymorphisms were identified by using the Laser gene DNA Star sequence counting
program and were confirmed manually. Each genomic variant was verified only if it
was observed with forward and reverse primer. All the sequence entries were BLAT
using UCSC (https://genome.ucsc.edu/cgi-bin/hgBlat?command=start) to confirm the
related relevance of SNPs with PCOS and endometriosis in prior publications.
77
Then specificity of the selected optimized DNA fragment of all the SNPs were
validated against human genome from NCBI database by two sequence nucleotide
alignment BLAST.
(http://blast.stva.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch&BLAST_SP
EC=blast2seq&LINK_LOC=align2seq).
3.7 Hormonal Analysis
The hormonal analysis of gonadotropins (FSH), sex steroids (Testosterone and
progesterone) hormones of the samples was measured using specific commercially
available ELISA kits according to the manufacturer’s instructions.
3.7.1 Follicle stimulating hormone (FSH)
The hormone, FSH was measured in PCOS women and controls by an ELISA kit
(Monobind Lake forest, CA, USA), that uses an enzyme immunoassay performed in
micro-titre plate. With every plate a standard curve was also run and the absorbance
of the unknown were read against this standard curve. The standards S0-S5 have the
following concentrations of FSH (0, 5, 10, 25, 50 and 100 mIU/ml). All the samples
and standards were run in duplicate.
Expected values for the FSH ELISA test system (mIU/ml)
Follicular phase 3.0-12.0
Mid cycle 8.0-22.0
Luteal phase 2.0-12.0
The cross-reactivity of the FSH Monobind ELISA test system to selected substances
was evaluated by adding the interfering substances to a serum matrix at various
concentrations.
Substance Cross reactivity Concentration
Follitropin (FSH) 1.0000 -
Lutropin Hormone (hLH) <0.0001 1000 ng/ml
Chorionic Gonadotropin (hCG) <0.0001 1000 ng/ml
Thyrotropin (TSH) <0.0001 1000 ng/ml
78
3.7.2 Testosterone
The elevated level of testosterone was measured in PCOS and control group to assess
the phenotype of the disease. Competitive Enzyme Immunoassay (EIA) was
performed to measure the serum level of testosterone by using an ELISA kit
(Monobind, Lake forest, CA, USA). Testosterone concentration in the sample is
calculated based on a series of standard. The concentration of standards S1-S5 was 0,
0.1, 0.5, 1.0, 2.5 and 5 ng/ml. Intra and inter-assay coefficient of variance have been
determined at multiple points on the standard curve.
Expected values for the testosterone ELISA test system (ng/ml)
Female 0.2-0.95
Male 2.5-10.0
The cross-reactivity of the testosterone Monobind ELISA test system to selected
substances was evaluated by adding the interfering substances to a serum matrix at
various concentrations.
3.7.3 Progesterone
Progesterone hormone was measured in endometriosis women and control group
using an ELISA kit (Monobind, Lake forest, CA, USA). A standard curve was plotted
and progesterone concentrations in samples were determined by interpolation the
standard curve.
Expected values for the testosterone ELISA test system (ng/ml)
Follicular phase 0.15-1.40
Luteal phase 2.0-25.0
Substance Cross reactivity
Testosterone 1.0000
Dihydotestosterone 0.0178
Cortisol <0.0001
Progesterone <0.0001
Estradiol <0.0001
79
The cross-reactivity of the testosterone Monobind ELISA test system to selected
substances was evaluated by adding the interfering substances to a serum matrix at
various concentrations.
3.8 Statistical analysis
A simple chi-square test was used to test for Hardy–Weinberg equilibrium in the
cases and controls. The odd ratios and confidence interval was calculated using
SigmaPlot 11.0. The mean of various anthropometric parameters was compared by
student t-test. The maximum likelihood haplotype frequency estimates and standard
errors were computed using the HAPLO software (Hawley & Kidd, 1995) which
implements the expectation-maximization (E-M) algorithm of Dempster et al. (1977).
3.9 Haplotype and linkage disequilibrium analysis
The estimation of haplotype frequencies was carried out by employing the
FORTRAN software called HAPLO described in Hawley and Kidd (1995). This set
of computer programs applies the Expectation-Maximization (EM) algorithm of
Dempster et al (1977) which has been widely applied to estimation problems that can
only be solved by iterative calculations. The HAPLO calculations for this thesis
project were run using a Linux compiler on the Louise cluster at the Yale University
Biomedical High Performance Computing Center which is supported by NIH grants
RR19895 and RR029676-01.
“HAPLO is designed to compute haplotype frequency estimates from phenotype data
based on samples of unrelated individuals. The EM algorithm is a generalized
iterative maximum likelihood approach to estimation that is useful when data are
Substance Cross reactivity
Progesterone 100.00
Testosterone 0.015
Androstenedione 0.158
Dihydotestosterone 0.006
Cortisol 0.005
Estradiol 0.004
80
ambiguous and/or incomplete. The next three paragraphs quoted from Hawley and
Kidd (1995) presents the key ideas underlying the estimation of the haplotype
frequencies and standard errors.”
"The EM algorithm is an iterative process; each iteration gives a set of frequency
estimates that converge to Table maximum likelihood estimates. The iterations start
with all haplotypes (alleles) at equal frequency. It is easy to show that the frequency
estimates for haplotypes that are not definitely “observed,” i.e., required to explain a
phenotype, will go to zero."
"The HAPLO program optionally estimates standard errors in two ways. First a
jackknife procedure is used. Estimates of all haplotype frequencies are recalculated
with each individual in turn removed from the data set. For each haplotype the
standard deviation of those frequency estimates is an estimate of the standard error of
the original frequency estimate. This estimate of the standard error takes into account
the added uncertainty in the data because of the ambiguity and missing data as well as
the sampling error. The second estimate of the standard error of each haplotype
frequency applies the formula for the binomial standard error assuming all haplotypes
were directly observed and counted. This assumption can be correct or a very close
approximation in some instances and in any case the binomial standard error estimate
serves as a lower bound check on the jackknife estimates, which can become
inaccurate when the number of observations is small."
"The use of a genotype-phenotype correspondence list gives users great flexibility in
defining the haplotype system. Because the user defines the correspondence, it is not
necessary to supply any explicit information about the component polymorphic
systems. Definitions need to be entered only for the observed phenotypes and
whatever genotypes may be implied; it is not necessary to define all the possible
combinations of alleles. It is possible to define phenotypes that make use of any
available phase information or that cover cases where data on the component systems
are incomplete. Individuals with an ambiguous phenotype that is partially or fully
resolved are included in the analysis by simply defining a new phenotype that
corresponds to only the single or few remaining genotype(s). Individuals with missing
data are included by defining a phenotype that corresponds to the set of genotypes that
includes all phenotypes at the missing site but only those possible for the typed sites."
81
CHAPTER 4
RESULTS
4.1 Population characteristics
The selected anthropometric characteristics of the study population are summarized in
Table 4.1-4.3 for endometriosis, PCOS and controls women. The women of
reproductive active age group between 20 to 40 years were selected. No significant
difference (p > 0.05) was observed in endometriosis, PCOS and controls regarding
Body Mass Index (BMI), Waist/Hip Ratio (WHR) and mean age of menarche, they
were well matched on all other demographic characteristics (Table 4.2, 4.3). The
proportion of obese women was 96.87 % vs. 95.83% within the PCOS and control
group respectively (p > 0.05) (Table 4.3).
The clinical manifestation of various anthropometric characteristics associated with
endometriosis and PCOS (primary, secondary infertility, pelvic pain and
dysmenorrhea; oligomenorrhea, hirsutism, infertility and obesity) were represented in
Figure 4.1, 4.2. The women volunteered as control were without oligomenorrhea,
hirsutism, dysmenorrhea and infertility.
4.2 Genotype, allele frequencies and Haplotype
The genotype distribution and relative allele frequencies of all the studied
polymorphism of ninety-six PCOS patients, fifty-two endometriosis and one hundard-
fourty eight controls were investigated in the female population of Pakistan. The
Tables in the preceding section illustrates the number of relative genotypes (n),
percentage of genotype (%), allele frequencies and their percentage, estimated
heterozygosity (E het), chi square value (χ2) and odd ratios (OR) at 95% confidence
interval (CI) along with the p-values. The genotype frequencies of all the single
nucleotide polymorphisms fit the Hardy-Weinberg principle in all endometriosis,
PCOS and controls; non-significant p values suggest that alleles are in equilibrium.
No noteworthy deviations of Hardy-Weinberg ratios were observed for all of the
SNPs studied and tested separately in the affected and control groups since the
expected and observed genotype frequencies were not significantly different using a
“p” value of 1%. The genotype and allele frequencies of the polymorphisms in the
study were determined by simple allele and genotype counting.
82
Table 4.1: Anthropometric characteristics of PCOS, endometriosis and controls.
Population
characteristics
PCOS (n= 96) Endometriosis (n= 52) Controls (n= 148)
n (%) n (%) n (%)
Education
Illiterate 37 (38.5) 19 (36.5) 47 (31.8)
Up to primary 11 (11.5) 8 (15.4) 23 (15.5)
Higher secondary 33 (34.4) 18 (34.6) 56 (37.8)
Graduate and above 16 (16.7) 7 (13.5) 22 (19.9)
Age of patients
20-30 years 47 (48.9) 21 (40.4) 63 (42.6)
31-40 years 49 (51.1) 31 (59.6) 85 (57.4)
Duration of
marriage
01-05 years 35 (36.4) 25 (48.0) 67 (45.3)
06-10 years 31 (32.2) 17 (32.7) 54 (36.5)
11-15 years 14 (14.6) 8 (15.4) 17 (11.4)
16 and above 16 (16.6) 2 (3.8) 10 (6.7)
Primary Infertility 59 (61.5) 30 (57.6) 0
Secondary
Infertility 37 (38.5) 22 (42.3) 0
Family H/O
Hirsutism 23 (23.9) 4 (9.6) 9 (6.0)
Obesity 43 (44.8) 11 (21.2) 29 (30.2)
Menstrual problems 23 (23.9) 7 (13.5) 5 (5.2)
Thyroid
abnormalities 3 (3.13)
0 0 (0)
DM 39 (40.6) 9 (17.3) 11 (11.5)
Infertility 23 (23.9) 2 (3.8) 1 (1.04)
PCOS 19 (19.8) 1 (1.9) 0
Endometriosis
13 (25)
Patient H/O
Diabetes Mellitus 11 (11.5) 0 16 (10.8)
Infertility 86 (89.6) 49 (94.2) 0
Weight gain 88 (91.7) 5 (9.6) 13 (8.8)
Hirsutism 89 (92.7) 0 0
Facial hair 90 (93.75) 2 (3.9) 4 (2.7)
Acne 70 (72.9) 7 (13.5) 9 (6.0)
Oligomenorrhea 88 (91.7) 0
Diagnosis
Ultrasound, Clinically 87 (90.6) 37 (71.1) -
Clinically 9 (9.3) 15 (28.8)
Age of Menarche
11-13 year 88 (91.7) 47 (90.3) 131 (88.5)
14 to onward 8 (8.3) 5 (9.6) 17 (11.5)
83
Table Continued…
Pattern of cycle
Regular 9 (9.4) 23 (44.2) 131 (88.5)
Irregular 87 (90.6) 29 (55.7) 11 (7.5)
History of pain
Pelvic pain 23 (24.0) 43 (82.7) 11 (7.4)
Back pain 27 (28.1) 44 (84.5) 44 (29.7)
Dysmenorrhea 11 (11.5) 37 (71.2) 23 (15.5)
Dyschezia 5 (5.2) 27 (51.9) 5 (3.4)
Dysuria 7 (7.3) 11 (21.2) 4 (2.7)
Dyspareunia 11 (11.5) 31 (59.6) 19 (12.8)
Table 4.2: Anthropometric characteristics of women with endometriosis and
controls, the values given are mean ± S.D. The mean values of two groups were
compared by “t” test.
Characteristics Endometriosis
(n= 52)
Controls
(n= 52)
p
Mean ± S.D Mean ± S.D
Age (year) 30.25 ± 5.46 30.59 ± 4.95 > 0.05
BMI (Kg/m2) 25.12 ± 2.72 25.57 ± 2.08 > 0.05
WHR 0.80 ± 0.04 0.79 ± 0.06 > 0.05
Gynaecological history n (%) n (%)
Primary Infertility 30 (57.69) 0 < 0.001
Secondary Infertility 22 (42.31) 0 < 0.001
Pelvic pain 49 (94.23) 12 (23.07) < 0.001
Dysmenorrhea 50 (96.15) 6 (11.53) < 0.001
84
Table 4.3: Anthropometric characteristics of women with PCOS and controls.
The values given are mean ± S.D. The mean values of two groups were compared
by “t” test.
Characteristics PCOS (n= 96) Controls (n= 96)
Mean ± S.D Confidence
Level 95%
Mean ± S.D Confidence
Level 95%
p
Age (Year) 26.87 ± 4.42 0.89 26.02 ± 3.52 0.561 > 0.05
BMI (Kg/m2) 31.10 ± 1.47 0.83 30.49 ± 1.66 0.742 > 0.05
WHR 1.16 ± 0.07 0.01 1.12 ± 0.05 0.011 > 0.05
Age of Menarche
(Year)
13.09 ± 1.06 0.21 13.07 ± 1.52 0.022 > 0.05
Gynecological history
Characteristics n= 96 % n= 96 % p
Oligomenorrhea
(Less than 9 menses
per year)
88 91.67 0 0 < 0.001
H/O irregular
menses with weight
gain
85 88.54 0 0 < 0.001
Family history
Hirsutism 23 23.96 0 0.00 < 0.001
Obesity 43 44.79 29 30.21 < 0.001
Menstrual problems 23 23.96 5 5.21 < 0.001
Thyroid
abnormalities
3 3.13 0 0.00 < 0.001
DM 39 40.63 11 11.46 < 0.001
Infertility 23 23.96 1 1.04 < 0.001
PCOS 19 19.79 0 0.00 < 0.001
Subject Symptoms
Infertility 86 89.58 0 0.00 < 0.001
Obesity 93 96.87 92 95.83 > 0.05
Hirsutism 89 92.71 0 0.00 < 0.001
Facial hair 90 93.75 0 0.00 < 0.001
Acne 70 72.91 0 0.00 < 0.001
85
Figure 4.1: Clinical manifestation of anthropometric characteristics in
endometriosis compared with control along with percentage symptoms.
Figure 4.2: Clinical manifestation of anthropometric characteristics in PCOS
compared with control along with percentage symptoms.
86
4.3 Endometriosis
The SNPs and genetic variations of ESR1, ESR2, PGR, IL10 and FSHR genes
associated with the diagnosis of endometriosis with infertility were studied.
4.3.1 Estrogen receptor alpha (ESR1)
The contribution of the genomic variants of ESR1 was studied for two SNPs
rs2234693 and rs9340799. Both ESR1 polymorphisms are in intron 1 and separated
by only 51 base pairs. The rs2234693 polymorphism is characterized by a T→C
transition 397 nucleotide upstream in the intron 1. The rs9340799 polymorphism
marks an A→G transition 351 nucleotides upstream in intron 1. The mutations of
ESR1 gene in two sequence BLAST alignment are shown in Figure 4.3. The sequence
chromatogram showed homozygous and heterozygous form of T/C base substitution
of rs2234693 (Figure 4.4). The genotype distribution and relative allele frequencies
for ESR1 gene are shown in Table 4.4. The frequency of the rs2234693 “C” allele was
43.3% in the patients and 35.6 % in the control group. No significant differences were
observed for comparative genotype distribution and relative allele frequencies of SNP
rs2232693 between endometriosis and controls (p = 0.301, OR = 1.381, 95% CI =
0.79-2.41, χ2
= 0.99). We had also found no statistically significant differences in the
genotypes and allele frequencies between endometriosis and control group for
rs9340799 (p = 0.220, OR = 1.425, 95% CI = 0.82-2.47, χ2
= 1.26). A novel SNP
rs11155815 was found during the screening of DNA sequencing map. Two were
homozygous for rs11155815 T allele, 16 were heterozygous and there were 34
subjects homozygous for C allele. The heterozygotes CT were 30.8% in patients with
endometriosis and 21.2% in controls. As the homozygous TT genotype and
heterozygous CT genotype was rare both in endometriosis and controls, the TT and
CT were combined together and are referred as variants genotype of this new SNP.
The frequency of variant genotype was different in both groups. The frequency of the
rs11155815 “T” allele was 19.2% in the patients and 12.5% in the control group.
Comparison of frequencies between patients and control groups show no statistically
significant difference between them (p = 0.150, OR = 1.833, 95% CI = 0.87-3.87, χ2
=
2.02). The SNPs rs8179176, rs11155816, rs4986936 and rs4986937 were not
significantly associated with endometriosis (Table 4.4). This suggested that the
87
genetic variants of ESR1 gene were not related in the occurrence of endometriosis in
Pakistani women. Interestingly, the haplotype analysis showed distinct variations
among endometriosis and control groups. The frequency of haplotypes “GAT, AAC
and GGC” was 0.301, 0.091 and 0.164 respectively in endometriosis whereas the
haplotypes “GAC, AAT and GGT” were common in controls with frequencies 0.301,
0.035 and 0.264 respectively (Table 4.5; Figure 4.5). The overall haplotype
distributions in the two groups differ at a p-value of less than 0.005. Also the highly
significant value (p < 0.005, χ2
= 24.3) of genetic heterogeneity test showed that the
haplotype frequency estimates for the population samples being compared are from
the same population. The pairwise linkage disequilibrium (LD) or Delta-squared
value for the ESR1 gene is shown in Figure 4.6.
88
(a) Alignment of two sequences BLAST (Endometriosis)
Query 493 CCAATGCTCATCCCAACTCTAGACCACACTCAGGGTCTCTGGGAAACAGAGACAAAGCAT 552
||||||||||||||||||| ||||||||||||||||||||||||||||||||||||||||
Sbjct 327 CCAATGCTCATCCCAACTCCAGACCACACTCAGGGTCTCTGGGAAACAGAGACAAAGCAT 386
Query 553 AAAACAGCTGGGACATTTGGAACTCAGATGAACTGATGGACAACACAGAACATGTGTCAA 612
||||| ||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 387 AAAACGGCTGGGACATTTGGAACTCAGATGAACTGATGGACAACACAGAACATGTGTCAA 446
(b) Alignment of two sequences BLAST (Control)
Query 480 GAACCATTAGAGACCAATGCTCATCCCAACTCTAGACCACACTCAGGGTCTCTGGGAAAC 539
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 316 GAACCATTAGAGACCAATGCTCATCCCAACTCTAGACCACACTCAGGGTCTCTGGGAAAC 375
Query 540 AGAGACAAAGCATAAAACAGCTGGGACATTTGGAACTCAGATGAACTGATGGACAACACA 599
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 376 AGAGACAAAGCATAAAACAGCTGGGACATTTGGAACTCAGATGAACTGATGGACAACACA 435
Figure 4.3: Two sequence BLAST alignment of ESR1 gene polymorphisms.
Figure 4.4: Sequence chromatogram showing homozygous and heterozygous
form of T/C base substitution of rs2234693.
C
T
89
Figure 4.5: Multi-SNPs haplotype percent frequency distribution for
endometriosis and controls at ESR1 gene.
30.1
17.4 16.8 16.4
9.1
5.94.3
0
26.8
31
26.4
3.3 3.24.9
0.9
3.5
0
5
10
15
20
25
30
35
GAT GAC GGT GGC AAC AGT AGC AAT
Pe
rce
nt
Fre
qu
en
cy (
%)
Haplotypes
Affected
Control
90
Table 4.4: Frequency distribution of single nucleotide polymorphisms of ESR1 gene with risk of endometriosis in Pakistani women.
SNPs ID Genotype/allele
frequencies
Endometriosis
(n= 52)
Controls
(n= 52)
OR (95% CI) χ2 p HWE
n % n %
rs2234693 TT 17 32.7 22 42.3 0.662
(C>T) CT 25 48.1 23 44.2 (0.29-1.47) 0.65 0.410 >.05
Intron 1 CC 10 19.2 7 13.5
Allele frequency
C allele 45 43.3 37 35.6 1.381
T allele 59 56.7 67 64.4 (0.79-2.41) 0.99 0.301 >.05
E(HET) 49 46
rs9340799 AA 15 28.8 20 38.5 0.686
(G>A) GA 25 48.1 24 46.2 (0.30-1.56) 0.48 0.480 >.05
Intron 1 GG 12 23.1 8 15.4
Allele frequency
G allele 49 47.1 40 38.5 1.425
A allele 55 52.9 64 61.5 (0.82-2.47) 1.26 0.220 >.05
E(HET) 50 47
rs11155815 CC 34 65.4 40 76.9 0.567
(C>T) CT 16 30.8 11 21.2 (0.24-1.34) 1.17 0.270 >.05
Intron 1 TT 2 3.8 1 1.9
Allele frequency
C allele 84 80.8 91 87.5 1.833
T allele 22 19.2 13 12.5 (0.87-3.87) 2.02 0.150 >.05
Table continues……………
91
E(HET) 31 22
rs8179176 CC 41 78.8 48 92.3 0.285
(C>T) CT 11 21.1 4 7.7 (0.08-0.95) 3.69 0.060 >.05
Intron 1 TT 1 1.9 0 0
Allele frequency
C Allele 96 92.3 100 96.5 0.295
T Allele 13 12.5 4 3.8 (0.09-0.93) 3.46 0.050 >.05
E(HET) 20 7
rs11155816 TT 46 88.5 46 88.5 1
(C>T) TC 5 9.6 6 11.5 (0.30-3.31) 0.09 0.750 >.05
Intron 1 CC 1 1.9 0 0
Allele frequency
C Allele 7 6.7 6 5.8 1.212
T Allele 97 93.3 98 94.2 (0.35-4.10) 0.05 1.000 >.05
E(HET) 13 11
rs4986936 TT 46 88.5 51 98.1 0.150
(G>A) CT 5 9.6 1 1.9 (0.01-1.29) 2.45 0.110 >.05
Intron 1 CC 1 1.9 0 0
Allele frequency
G Allele 7 6.7 1 99 6.280
A Allele 97 93.3 103 1 (0.88-45.02) 7.28 0.070 >.05
E(HET) 9 2
rs4986937 GG 46 88.5 48 92.3 0.640
(C>T) AG 6 11.5 4 7.7 (0.17-2.41) 0.11 0.730 >.05
92
Table Continue…...
Intron 1 AA 0 0 0 0
Allele frequency
G Allele 98 94.2 100 96.2 1.531
A Allele 6 5.8 4 3.8 (0.41-5.59) 0.11 0.701 >.05
SNPs ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg equilibrium; χ2: Chi square.
Table 4.5: Multi-SNPs haplotype frequency estimates for endometriosis and controls at ESR1 gene.
Groups Haplotype frequencies ± S.E.E Exp.
Het
Global LD
LR ChiSq
p Genetic
Heterogeneity
Test
GAT GAC GGT GGC AAC AGT AGC AAT Chi Sq p
Endometriosis 0.301
±
0.037
0.174
±
0.017
0.168
±
0.016
0.164
±
0.010
0.091
±
0.015
0.059
±
0.033
0.043
±
0.023
0.000
±
0.000
81.00 4.70 0.050
24.30 0.005 Control 0.268
±
0.028
0.310
±
0.067
0.264
±
0.039
0.033
±
0.060
0.032
±
0.049
0.049
±
0.062
0.009
±
0.029
0.035
±
0.018
75.70 10.40 0.050
Exp. Het: Expected Heterozygosity; Global LD LR Chi Sq: Global likelihood ratio chi-square; Chi Sq: Chi square; S.E.E: Standard error of estimation; p: Significance
93
ESR1 Endometriosis
rs4986936
rs11155815
rs8179176
rs2234693
rs11155816
rs9340799
rs4986937
Figure 4.6: Pairwise linkage disequilibrium of ESR1 gene in endometriosis and
controls.
ESR1 Control
rs4986936
rs11155815
rs8179176
rs2234693
rs11155816
rs9340799
rs4986937
.10
.20
.13
.10
.20 .30
.16
.12 .16 .12
.11
.02
.16
.11
.25
.26
.15 .10 .22
.48
.12
.22
.10
.02
.02
.03 .03
.02
.12 .11 .12
.02
.02
.02
.02
.02
.02
.02 .02 .02
.16
.02
.02
.02
94
4.3.2 Estrogen receptor beta (ESR2)
The ESR2 gene was studied for polymorphism rs4986938 which is a G→A change at
position 1859 in the 3′-untranslated region (3′-UTR) of exon 8. The genotype
frequency of G/A polymorphism in endometriosis group was significantly different
from that of controls. The mutant allele “A” was in significantly higher frequency
among the endometriosis (38.5%) compared to the controls (20.2%) yielding an odds
ratio of 2.470 (p = 0.006, OR: 1.360; CI: 0.16-0.80; χ2
= 7.52). As the homozygous
GG genotype was more frequent in controls (63.5%) compared with (38.5%) in
endometriosis (Table 4.6). The heterozygous GA genotype and the AA were
combined together and are referred as variants genotype of rs4986938. The frequency
of variant genotype of rs4986938 was different in both groups (32/52 vs. 19/52 in
endometriosis and controls respectively).
95
Table 4.6: Frequency distribution of single nucleotide polymorphism of ESR2 gene with risk of endometriosis in Pakistani
women.
SNP ID Genotype/allele
frequencies
Endometriosis
(n= 52)
Controls
(n= 52)
OR (95% CI) χ2 p HWE
n % n %
rs4986938 GG 20 38.5 33 63.5 1.360
(G>A) AG 24 46.2 17 32.7 (0.16-0.80) 5.54 0.010 >.05
UTR-3 AA 8 15.4 2 3.8
Allele frequency
G allele 64 61.5 83 79.8 2.470
A allele 40 38.5 21 20.2 (0.20-0.75) 7.52 0.006 >.05
E(HET) 47 32
SNP ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg equilibrium; χ2: Chi square.
96
4.3.3 Interleukin 10 (IL10)
To determine whether the IL10 SNP loci were associated with endometriosis; we
initially compared the single-point genotype and allele distribution of these SNPs in
the endometriosis and control groups. The mutations of IL10 gene in two sequence
BLAST alignment for endometriosis and controls are shown in Figure 4.7. The
sequence chromatogram showed homozygous and heterozygous form of T/C base at
rs1800871 (Figure 4.8). We observed an association of IL10 gene polymorphism and
endometriosis group, the genotype distribution and relative allele frequencies of the
inflammation mediator gene are shown in Table 4.7. The IL10 rs1800872 mutant
allele “A” was significantly overexpressed (2.379 folds) in women with endometriosis
than controls (44.2% vs. 25%; p < 0.006, OR = 2.379, 95% CI = 0.23-0.75). We found
highly significant association between different genotypes (Table 4.7) with
endometriosis group. In SNP rs1800871 the frequency of T allele was higher in
endometriosis patient (38.5% vs. 26%; p = 0.010, OR = 0.443, 95% CI = 0.19-0.92).
In the IL10 PCR products DNA sequencing map, a new SNP rs1800894 was found at
genomic position 206946666 in which the substitution is G to A. No significant
association was found in both groups with this SNP (Table 4.7). In rs1800896 a
statistically significant (p = 0.007) higher frequency of the “G” allele was observed in
patients than in controls (Table 4.7). The frequency of “A” (51.9% in endometriosis
and 71.2% in controls) and “G” (48.1% in endometriosis and 28.8% in control) allele
differ significantly in endometriosis and control groups (p = 0.007, OR = 1.435, 95%
CI = 0.24-0.77).
Haplotype estimation analysis of three SNPs genotype data by means of maximum
likelihood method revealed that estimated overall haplotype frequencies in
endometriosis and control groups differ significantly (Table 4.8). Eight major
haplotypes of IL10 were present in the study population and the frequency of most
common haplotypes is shown in Figure 4.9. In single haplotype association the ATG,
CTG and ACG are more frequent in the endometriosis group, while the haplotype
CCA, CCG and CTA were found frequent in the control group. The other low
frequencies haplotypes showed borderline difference. The genetic heterogeneity test
showed a strong significance (p < 0.005, χ2
= 22.2) between endometriosis and control
groups. The pairwise LD analysis between four loci revealed week R-squared or
Delta-squared value in both the groups (Figure 4.10).
97
(a) Alignment of two sequences BLAST (Endometriosis)
Query 13 AATTCTCAGTTGGCACTGGTGTACCCTTGTACAGGTGATGTAACATCTCTGTGCCTCAGT 72
||||||||||||||||||||||||||||||||||||||||||| ||||||||||||||||
Sbjct 46 AATTCTCAGTTGGCACTGGTGTACCCTTGTACAGGTGATGTAATATCTCTGTGCCTCAGT 105
Query 73 TTGCTCACTATAAAATAGAGACGGTAGGGGTCATGGTGAGCACTACCTGACTAGCATATA 132
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 106 TTGCTCACTATAAAATAGAGACGGTAGGGGTCATGGTGAGCACTACCTGACTAGCATATA 165
(b) Alignment of two sequences BLAST (Control) Query 15 AATTCTCAGTTGGCACTGGTGTACCCTTGTACAGGTGATGTAATATCTCTGTGCCTCAGT 74
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 46 AATTCTCAGTTGGCACTGGTGTACCCTTGTACAGGTGATGTAATATCTCTGTGCCTCAGT 105
Query 75 TTGCTCACTATAAAATAGAGACGGTAGGGGTCATGGTGAGCACTACCTGACTAGCATATA 134
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 106 TTGCTCACTATAAAATAGAGACGGTAGGGGTCATGGTGAGCACTACCTGACTAGCATATA 165
Figure 4.7: Two sequence BLAST alignment of IL10 gene polymorphisms.
Figure 4.8: Sequence chromatogram showing homozygous and heterozygous
form of C/T base substitution of rs1800871.
T
TC
98
Figure 4.9: Multi-SNPs haplotype percent frequency distribution for
endometriosis and controls at IL10 gene.
18.6
16.5
12
16.9
7.58.7
15.4
4.4
36.8
19.5
16.1 16.5
4.22.6 2.5 1.8
0
5
10
15
20
25
30
35
40
CCA CCG CTA ACA ACG CTG ATG ATA
Perc
en
t fr
eq
uen
cy (
%)
Haplotypes
Affected
Control
99
Table 4.7: Frequency distribution of single nucleotide polymorphisms of IL10 gene with risk of endometriosis
in Pakistani women.
SNPs ID Genotype/allele
frequencies
Endometriosis
(n= 52)
Controls
(n= 52)
OR (95% CI) χ2 p HWE
n % n %
rs1800872 CC 17 32.7 29 55.8 0.385
(C>A) AC 24 46.2 20 38.5 (0.17-0.85) 4.71 0.030 >.05
UTR-5 AA 11 21.2 3 5.8
Allele frequency
C allele 58 55.8 78 75 2.379
A allele 46 44.2 26 25 (0.23-0.75) 7.67 0.006 >.05
E(HET) 47 38
rs1800871 CC 19 36.5 30 57.7 0.422
(C>T) TC 24 46.2 20 38.5 (0.24-0.80) 3.86 0.049 >.05
UTR-5 TT 9 17.3 2 3.8
Allele frequency
C allele 62 61.5 80 74 0.443
T allele 42 38.5 24 26 (0.19-0.92) 6.41 0.010 >.05
E(HET) 48 38
rs1800894 GG 44 84.6 48 92.3 0.357
(G>A) AG 7 13.5 4 7.7 (0.12-1.60) 0.85 0.357 >.05
UTR-5 AA 1 1.9 0 0
Allele frequency
100
Table continues…………
G allele 95 91.3 100 96.2 0.543
A allele 9 8.7 4 3.8 (0.15-1.91) 0.43 0.501 >.05
rs1800896 AA 14 26.9 26 50 0.368
(G>A) AG 26 50 22 42.3 (0.16-0.83) 4.91 0.027 >.05
UTR-5 GG 12 23.1 4 7.7
Allele
frequency
A allele 54 51.9 74 71.2 1.435
G allele 50 48.1 30 28.8 (0.24-0.77) 7.33 0.007 >.05
E(HET) 50 41
SNPs ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg Equilibrium; χ2: Chi square.
101
Table 4.8: Multi-SNPs haplotype frequency estimates for endometriosis and controls at IL10 gene.
Groups Haplotype frequencies ± S.E.E Exp.
Het
Global
LD LR
ChiSq
p Genetic
Heterogeneity
Test
CCA CCG CTA ACA ACG CTG ATG ATA
ChiSq
p
Endometriosis 0.186
±
0.029
0.165
±
0.038
0.120
±
0.031
0.169
±
0.042
0.075
±
0.028
0.087
±
0.025
0.154
±
0.027
0.044
±
0.030
84.70 4.90 0.050
22.20 0.050 Control 0.368
±
0.031
0.195
±
0.062
0.161
±
0.016
0.165
±
0.081
0.042
±
0.058
0.026
±
0.035
0.025
±
0.052
0.018
±
0.057
78.40 2.00 0.050
Exp. Het: Expected Heterozygosity; Global LD LR Chi Sq: Global likelihood ratio chi-square; Chi Sq: Chi square; S.E.E: Standard error of estimation; p: Significance
102
IL 10 Endometriosis
IL10 Control
rs1800872
rs1800871
rs1800894
rs1800896
rs1800872
rs1800871
rs1800894
rs1800896
Figure 4.10: Pairwise linkage disequilibrium of IL10 gene in endometriosis and
controls.
.11
.02 .04
.08
.07
.04
.02
.05 .04
.04
.02
.04
103
4.3.4 Progesterone receptor (PGR)
PGR gene was analyzed for rs1042838 in exon 4 and rs10895068 in promoter region
by direct sequencing. In SNP rs1042838 the genotype and allele frequencies showed
distinct variations (Table 4.9) in endometriosis group. In contrast little variations were
found in control group. The frequency of “T” (22.1% in endometriosis and 6.7% in
control) and “G” (77.9% in endometriosis and 93.3% in controls) allele differ
significantly in endometriosis and control groups (p = <0.003, OR = 1.935, 95% CI =
0.10-0.62). The PGR rs1042838 mutant allele “T” was significantly overexpressed
(1.935 folds) in women with endometriosis than controls (p < 0.003). We studied
another polymorphism in rs10895068, no significant difference in genotype and allele
frequencies was observed between endometriosis and control groups (p = 0.210)
(Table 4.9).
Much stronger associations were observed when the two SNPs were considered
together as haplotypes. When the estimated haplotype frequencies were compared
between endometriosis and control, the most common haplotype GG (0.875 vs. 0.679
in controls and endometriosis respectively) was found to be more prevalent in the
control group (Figure 4.11) whereas the TG (0.205 vs. 0.067 in endometriosis and
controls respectively), GA (0.099 vs. 0.058 in endometriosis and controls
respectively) was more common among endometriosis patients (Table 4.10). The
genetic heterogeneity test comparing the affected and control haplotype frequencies
overall differ from each other at p-value = 0.010 significance level. The pairwise LD
analysis revealed week LD for the pairs of loci in the endometriosis and control group
(Figure 4.12).
104
Figure 4.11: Multi-SNPs haplotype percent frequency distribution for
endometriosis and controls at PGR gene.
67.9
20.5
9.9
1.7
87.5
6.7 5.8
00
10
20
30
40
50
60
70
80
90
100
GG TG GA TA
Perc
en
t fr
eq
uen
cy (
%)
Haplotypes
Affected
Control
105
Table 4.9: Frequency distribution of single nucleotide polymorphisms of PGR gene with risk of endometriosis in Pakistani women.
SNPs ID Genotype/allele
frequencies
Endometriosis
(n= 52)
Controls
(n= 52)
OR (95% CI) χ2 p HWE
n % n %
rs1042838 GG 32 61.5 45 43.3 1.249
(G>T) TG 17 32.7 7 6.7 (0.09-0.65) 7.20 0.007 >.05
Exon 4 TT 3 5.8 0 0
Allele frequency
G allele 81 77.9 97 93.3 1.935
T allele 23 22.1 7 6.7 (0.10-0.62) 8.76 0.003 >.05
E(HET) 34 13
GG 41 78.8 46 88.5 0.482
rs10895068 AG 10 19.2 6 11.5 (0.16-1.40) 1.12 0.280 >.05
(G>A) AA 1 1.9 0 0
UTR Allele frequency
G allele 92 88.5 98 94.2 1.130
A allele 12 11.5 6 5.8 (0.17-1.30) 1.52 0.210 >.05
E(HET) 20 11
SNPs ID: single nucleotide polymorphisms identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg Equilibrium; χ2: Chi square.
106
Table 4.10: Multi-SNPs haplotype frequency estimates for endometriosis and controls at PGR gene.
Groups Haplotype frequencies ± S.E.E Exp. Het Global LD
LR ChiSq
p Genetic
Heterogeneity
Test
GG TG GA TA Chi Sq p
Endometriosis 0.679±0.046 0.205±0.040 0.099±0.029 0.017±0.013 48.90 0.40 0.050
12.60 0.010 Control 0.875±0.032 0.067±0.025 0.058±0.023 0.000±0.000 22.70 0.90 0.050
Exp. Het: Expected Heterozygosity; Global LD LR Chi Sq: Global likelihood ratio chi-square; Chi Sq: Chi square; S.E.E: Standard error of estimation; p: Significance
Figure 4.12: Pairwise linkage disequilibrium of PGR gene in endometriosis and controls
PGR Endometriosis
PGR Endometriosis rs
108950
68
rs10428
38
rs108950
68
rs10428
38
.24
.0 .0
.06
.0 .0
107
4.3.5 Follicle stimulating hormone receptor (FSHR)
The two common polymorphisms of the FSHR gene in exon 10 were screened among
104 individuals (52 endometriosis and 52 controls). The mutations of FSHR gene in
two sequence BLAST alignment are shown in Figure 4.13. The sequence
chromatogram showed homozygous and heterozygous form of A/G base substitution
of rs6166 (Figure 4.14).The genotype, allele and haplotype frequencies of both the
polymorphisms were agreed with the Hardy-Weinberg equilibrium in endometriosis
and controls. The distribution of allele and genotype frequency along with odd ratios
and significance for association with endometriosis are shown in Table 4.11. In SNP
rs6166 A>G, the frequencies of genotype AA, AG and GG were 46.2% (24/52),
44.2% (23/52) and 9.6 % (5/52) in women with endometriosis while 11.5% (6/52),
44.2% (23/52) and 44.2 % (23/52) in healthy controls respectively. Moreover, we
observed that in SNP rs6166 the women with endometriosis had significantly higher
frequency of allele A in comparison with control (68.3% vs. 33.7%; OR. 4.240, 95%
CI. 2.37- 7.57, p < 0.001). In SNP rs6165 A>G the frequencies of genotype AA, AG
and GG were 46.2% (24/52), 42.3% (22/52) and 11.5 % (6/52) in the women with
endometriosis while 13.5% (7/52), 48.1% (25/52) and 38.5 % (20/52) in healthy
controls respectively. In rs6165 the frequency of allele A was 67.3 vs. 37.5 in
endometriosis and in controls respectively (OR = 3.430, 95% CI = 1.94-6.07, p <
0.001).
The frequency of AA haplotype was 45.5% and 16.3% in patients and controls.
Interestingly the frequency of GG haplotype was inverse, 9.9% and 45.2% was
observed in patients and controls respectively Figure 4.15. The haplotypes AA and
GG showed distinct difference both in women with endometriosis and controls (Table
4.12). The pairwise LD value was 0.76 for both sites. This r2 value suggests that these
two polymorphisms are in strong linkage disequilibrium (Figure 4.16).
108
(a) Alignment of two sequences BLAST (Endometriosis)
Query 93 TCTTCAGCTCCCAGAGTCACCAGTGGTTCCACTTACATACTTGTCCCTCTAAGTCATTTA 152
|||||||||||||||||||||| |||||||||||||||||||||||||||||||||||||
Sbjct 62 TCTTCAGCTCCCAGAGTCACCAATGGTTCCACTTACATACTTGTCCCTCTAAGTCATTTA 121
Query 153 GCCCAAAACTAAAACACAATGTGAAAATGTATCTGAGTATTGAATGATAATTCAGTCTTG 212
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 122 GCCCAAAACTAAAACACAATGTGAAAATGTATCTGAGTATTGAATGATAATTCAGTCTTG 181
(b) Alignment of two sequences BLAST (Control) Query 95 TTCAGCTCCCAGAGTCACCAGTGGTTCCACTTACATACTTGTCCCTCTAAGTCATTTAGC 154
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 66 TTCAGCTCCCAGAGTCACCAGTGGTTCCACTTACATACTTGTCCCTCTAAGTCATTTAGC 125
Query 155 CCAAAACTAAAACACAATGTGAAAATGTATCTGAGTATTGAATGATAATTCAGTCTTGCC 214
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 126 CCAAAACTAAAACACAATGTGAAAATGTATCTGAGTATTGAATGATAATTCAGTCTTGCC 185
Figure 4.13: Two sequence BLAST alignment of FSHR gene polymorphisms.
Figure 4.14: Sequence chromatogram showing homozygous and heterozygous
form of A/G base substitution of rs6166.
A
AG
109
Figure 4.15: Multi-SNPs haplotype percent frequency distribution for
endometriosis and controls at FSHR gene.
45.5
21.8 22.8
9.9
16.3
21.2
17.3
45.2
0
5
10
15
20
25
30
35
40
45
50
AA GA AG GG
Perc
en
t fr
eq
uen
cy (
%)
Haplotypes
Affected
Control
110
Table 4.11: Frequency distribution of single nucleotide polymorphisms of FSHR gene with risk of endometriosis in Pakistani women.
SNPs ID Genotype/allele
frequencies
Endometriosis
(n= 52)
Controls
(n= 52)
OR (95% CI) χ2 p HWE
n % n %
rs6166 AA 24 46.2 6 11.5 6.570
(A>G) AG 23 44.2 23 44.2 (2.39-18.01) 13.53 0.001 >.05
Exon 10 GG 5 9.6 23 44.2
Allele frequency
A Allele 71 68.3 35 33.7 4.240
G Allele 33 31.7 69 66.3 (2.37- 7.57) 23.56 0.001 >.05
E(HET) 43 45
rs6165 AA 24 46.2 7 13.5 5.511
(A>G) AG 22 42.3 25 48.1 (2.09-14.46) 11.76 0.001 >.05
Exon 10 GG 6 11.5 20 38.5
Allele frequency
A Allele 70 67.3 39 37.5 3.430
G Allele 34 32.7 65 62.5 (1.94-6.07) 17.35 0.001 >.05
E(HET) 44 47
SNPs ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg Equilibrium; χ2: Chi square.
111
Table 4.12: Multi-SNPs haplotype frequency estimates for endometriosis and controls at FSHR gene.
Groups Haplotype frequencies ± S.E.E Exp.
Het
Global LD
LRChiSq
p Genetic
Heterogeneity
Test
AA GA AG GG Chi Sq p
Endometriosis 0.455±0.049 0.218±0.041 0.228±0.041 0.099±0.029 68.90 0.00 >0.05
38.60 0.001 Control 0.163±0.051 0.212±0.061 0.173±0.054 0.452±0.049 69.90 1.00 >0.05
Exp. Het: Expected Heterozygosity; Global LD LR Chi Sq: Global likelihood ratio chi-square; Chi Sq: Chi square; S.E.E: Standard error of estimation; p: Significance
FSHR Endometriosis
FSHR Control rs
616
6
rs6165
rs6169
rs6166
rs6165
rs6169
Figure 4.16: Pairwise linkage disequilibrium of FSHR gene polymorphisms in endometriosis and controls.
.11
.04 .76
.04
.01 .13
112
4.4 PCOS
The SNPs of ADIPOQ, INSR, FSHR, FSHB, LHCGR, LHB, ESR1 and ESR2 genes
were investigated in PCOS Pakistani women.
4.4.1 Adiponectin (ADIPOQ)
To determine whether the adiponectin SNP loci at exon 2 (45T→G) and Intron 2
(276G→T) were associated with PCOS; we initially compared the single-point
genotype and allele distribution of these SNPs in the PCOS and control groups. The
mutations of ADIPOQ gene in two sequence BLAST alignment are shown in Figure
4.17. The sequence chromatogram showed homozygous and heterozygous form of
T/C base substitution of rs2241766 (Figure 4.18). We observed an association of
ADIPOQ gene polymorphism and PCOS group, the gene and allele frequencies are
given in Table 4.13. Seventy five subjects were homozygous for rs2241766 T allele,
20 were heterozygous GT and there was 1 subject homozygous for G in PCOS group.
The ADIPOQ rs2241766 “TT” genotype compared with “TG and GG” genotypes was
associated with a 3.725 folds increased risk for PCOS in the study population (p <
0.001, OR = 3.725, 95% CI = 1.98-6.97). Furthermore, the ADIPOQ rs2241766
mutant allele “T” was significantly overexpressed (3.345 folds) in women with PCOS
(88.5%) than controls (69.8%), (p < 0.001, OR = 3.345, 95% CI = 1.95-5.74). In
rs1501299 the homozygous TT genotype and heterozygous TG genotype was
infrequent both in PCOS and controls (Table 4.13), the TT and TG was combined
together as referred variants genotype of rs1501299. The frequency of variant
genotype of rs1501299 was different in both groups. We did find a moderate
significant association between different genotypes (Table 4.13) in both the groups. In
SNP rs1501299 the frequency of T allele was higher in PCOS patient (23.4% vs.
15.1%, p = 0.050, OR = 0.581, 95% CI = 0.34-0.97). In the ADIPOQ PCR products
DNA sequencing map, we found a new SNP rs2241767 in ADIPOQ gene at +345
coding reference gene positions in intron 2, in which the substitution is A to G. In
PCOS group, there were 2 homozygous “GG” of this new SNP and heterozygous
frequency was higher in PCOS than that in controls (26% vs. 14.6%; p = 0.030, OR =
0.436, 95% CI = 0.21-0.89). On 3q27.3, the most significant SNP was rs2241766
which locates in the exon 2 of ADIPOQ gene.
113
Haplotype estimation analysis of three SNPs genotype data by means of maximum
likelihood method revealed that estimated overall haplotype frequencies in PCOS and
controls groups differ significantly (Table 4.14). Seven major haplotypes of ADIPOQ
were present in the study population (Figure 4.19). The frequency of most common
haplotypes TGA, TTA, GGA and GGG was 63.9%, 22.1%, 0.00% and 10.9 % in
PCOS; while 53.7%, 12.0%, 24.2% and 3.0% in controls groups (p < 0.001, LR Chi
Sq = 79.100, 3.200 respectively in PCOS and controls). Whereas in single haplotype
association the TGA, TTA and GGG are more frequent in PCOS group, while the
haplotype GGA was found only in controls group (Figure 4.19). The other low
frequencies haplotypes showed borderline difference. So the null hypothesis that the
haplotype frequency distributions of the two groups are the same is rejected strongly.
The genetic heterogeneity test showed a strong significance (p < 0.001, χ2
= 69.4)
between PCOS and controls. In order to study the linkage disequilibrium between
exon 2 and intron 2 loci, we performed pairwise LD analysis between rs2241766,
rs1501299 and rs2241767. The R-squared or Delta-squared value for the PCOS
between rs2241766 and rs2241767 was 0.78 (p < 0.001) and for the Control group
was 0.06 (p < 0.001). This revealed very strong LD for the pair of loci which are
located approximately 300 bp from each other and haplotype correlate well in the
PCOS group as compared to the control group (Figure 4.20). The polymorphisms in
the ADIPOQ were in linkage disequilibrium.
114
(a) Alignment of two sequences BLAST (PCOS)
Query 105 GCTCAGGATGCTGTTGCTGGGAGCTGTTCTACTGCTATTAGCTCTGCCCGGTCATGACCA 164
||||||||||||||||||||||||||||||||||||||||||||||||||| ||||||||
Sbjct 79 GCTCAGGATGCTGTTGCTGGGAGCTGTTCTACTGCTATTAGCTCTGCCCGGGCATGACCA 138
Query 165 GGAAACCACGACTCAAGGGCCCGGAGTCCTGCTTCCCCTGCCCAAGGGGGCCTGCACAGG 224
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 139 GGAAACCACGACTCAAGGGCCCGGAGTCCTGCTTCCCCTGCCCAAGGGGGCCTGCACAGG 198
(b) Alignment of two sequences BLAST (Control)
Query 105 GCTCAGGATGCTGTTGCTGGGAGCTGTTCTACTGCTATTAGCTCTGCCCGGTCATGACCA 164
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 75 GCTCAGGATGCTGTTGCTGGGAGCTGTTCTACTGCTATTAGCTCTGCCCGGTCATGACCA 134
Query 165 GGAAACCACGACTCAAGGGCCCGGAGTCCTGCTTCCCCTGCCCAAGGGGGCCTGCACAGG 224
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 135 GGAAACCACGACTCAAGGGCCCGGAGTCCTGCTTCCCCTGCCCAAGGGGGCCTGCACAGG 194
Figure 4.17: Two sequence BLAST alignment of ADIPOQ gene polymorphisms.
Figure 4.18: Sequence chromatogram showing homozygous and heterozygous
form of T/G base substitution of rs2241766.
T
TG
115
Figure 4.19: Multi-SNPs haplotype percent frequency distribution for PCOS and
controls at ADIPOQ gene.
63.9
22.1
0
10.9
1.8 0.5 0.8
53.7
12
24.2
3 4.2 2.90
0
10
20
30
40
50
60
70
TGA TTA GGA GGG TGG GTA TTG
Perc
en
t fr
eq
uen
cy (
%)
Haplotypes
Affected
Control
116
Table 4.13: Frequency distribution of single nucleotide polymorphisms of ADIPOQ gene with risk of PCOS in Pakistani women.
SNPs ID Genotype/allele
frequencies
PCOS
(n= 96)
Controls
(n= 96)
OR (95% CI) χ2 p HWE
n % n %
rs2241766 TT 75 78.1 47 49 3.725
(T>G) GT 20 20.8 40 41.7 (1.98-6.97) 16.39 0.001 >.05
Exon 2 GG 1 1 9 9.4
Allele frequency
T Allele 170 88.5 134 69.8 3.345
G Allele 22 11.5 58 30.2 (1.95-5.74) 19.34 0.001 >.05
E(HET) 20 42
rs1501299 GG 56 58.3 69 71.9 0.548
(G>T) TG 35 36.5 25 26 (0.30-1.00) 3.30 0.060 >.05
Intron 2 TT 5 5.2 2 2.1
Allele frequency
T Allele 45 23.4 29 15.1 0.581
G Allele 147 76.6 163 84.9 (0.34-0.97) 3.77 0.050 >.05
E(HET) 36 26
rs2241767 AA 69 71.9 82 85.4 0.436
(A>G) GA 25 26 14 14.6 (0.21-0.89) 4.46 0.030 >.05
Intron 2 GG 2 2.1 0 0
Allele frequency
A Allele 163 86.5 178 92.7 1.262
G Allele 29 13.5 14 7.3 (0.22-0.86) 5.13 0.020 >.05
117
Table 4.14: Multi-SNPs haplotype frequency estimates for PCOS and controls at ADIPOQ gene.
Groups Haplotype frequencies ± S.E.E Exp.
Het
Global LD
LR ChiSq
p Genetic
Heterogeneity
Test
TGA TTA GGA GGG TGG GTA TTG Chi Sq p
PCOS 0.639
±
0.035
0.221
±
0.030
0.000
±
0.000
0.109
±
0.023
0.018
±
0.010
0.005
±
0.005
0.008
±
0.006
53.10 79.10 0.001
69.40 0.001 Control 0.537
±
0.039
0.120
±
0.027
0.242
±
0.031
0.030
±
0.017
0.042
±
0.017
0.029
±
0.003
0.000
±
0.000
63.60 3.20 0.050
Exp. Het: Expected Heterozygosity; Global LD LR Chi Sq: Global likelihood ratio chi-square; Chi Sq: Chi square; S.E.E: Standard error of estimation; p: Significance
118
ADIPOQ PCOS
ADIPOQ Control
rs2241766
rs1501299
rs2241767
rs2241766
rs1501299
rs2241767
Figure 4.20: Pairwise linkage disequilibrium of ADIPOQ gene in PCOS and
controls.
.78
.06 .04
.04
.01 .03
119
4.4.2 Insulin receptor (INSR)
INSR gene was analyzed for rs1799817, rs1799815 of exon 17 and SNPs rs2059806,
rs2229429 of exon 8 by direct sequencing. This revealed presence of association of
these polymorphisms with PCOS as shown in Table 4.15. In SNP rs1799817 a silent
polymorphism C/T SNP at 1085 site (His-His) was observed. The genotype and allele
frequencies are shown in Table 4.15. In contrast the frequency of “C” (76% in PCOS
and 57.8% in controls) and “T” (24% in PCOS and 42.2% in controls) allele differ
significantly in PCOS and control groups (p < 0.001, OR = 2.316, 95% CI = 1.45-
3.59). The frequency of CC genotype was overrepresented as compared to CT_TT
genotype in PCOS (p < 0.001, OR = 2.935, 95% CI = 1.62-5.29). We also observed
another novel polymorphism in exon 17 rs1799815. The frequency of CC, CT and TT
genotypes is shown in Table 4.15. The frequency of “T” and “C” allele differ
significantly between PCOS and control group (p = 0.05, OR = 0.261, 95% CI = 0.07-
0.95). We analyzed exon 8 for polymorphism rs2059806 (G/A). In SNP rs2059806
the genotype and allele frequency of GG, GA and AA in PCOS and controls is shown
in Table 4.15. The frequency of “A” allele was high in PCOS group as compared to
controls (24% vs. 16.1%, p = 0.030, OR = 0.520, 95% CI = 0.34-0.92) and was
statistically significant. During the screening of exon 8 we found another SNP
rs2229429. The genotype distribution of rs2229429 C/T polymorphism in PCOS
group was significantly different from that of controls.
120
Figure 4.21: Multi-SNPs haplotype percent frequency distribution for PCOS and
controls at INSR gene.
121
Table 4.15: Frequency distribution of single nucleotide polymorphisms of INSR gene with risk of PCOS in Pakistani women.
SNPs ID Genotype/allele
frequencies
PCOS
(n= 96)
Controls
(n= 96)
OR (95% CI) χ2 p HWE
n % n %
rs1799817 CC 56 58.3 31 32.3 2.935
(C>T) TC 34 35.4 49 51 (1.62-5.29) 12.1 0.001 >.05
Exon 17 TT 6 6.3 16 16.7
Allele frequency
C Allele 146 76 111 57.8 2.316
T Allele 46 24 81 42.2 (1.45-3.59) 13.6 0.001 >.05
E(HET) 36 49
rs1799815 CC 86 89.5 93 96.8 0.277
(C>T) TC 9 9.3 3 3.1 (0.07-1.04) 2.97 0.080 >.05
Exon 17 TT 1 1.9 0
Allele frequency 0
C Allele 181 94.2 189 98.4 0.261
T Allele 11 5.7 3 1.6 (0.07-0.95) 3.63 0.050 >.05
E(HET) 6 3
rs2059806 GG 54 58.7 67 72.8 0.557
(G>A) AG 35 38 27 29.3 (0.30-1.00) 3.21 0.070 >.05
Exon 8 AA 7 7.6 2 2.2
122
Table continues……………
Allele frequency
G Allele 143 76 161 83.9 0.520
A Allele 49 24 31 16.1 (0.34-0.92) 4.56 0.030 >.05
E(HET) 37 29
rs2229429 CC 33 38 57 59.4 0.358
(C>T) CT 45 46.8 35 36.4 (0.19-0.64) 11.06 0.001 >.05
Exon 8 TT 18 18.7 4 4.1
Allele frequency
C Allele 111 57.8 149 77.6 0.395
T Allele 81 42.2 43 22.4 (0.25-0.61) 16.31 0.001 >.05
E(HET) 49 35
SNPs ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg Equilibrium; χ2: Chi square.
123
Table 4.16: Multi-SNPs haplotype frequency estimates for PCOS and controls at INSR gene.
Exp. Het: Expected Heterozygosity; Global LD LR Chi Sq: Global likelihood ratio chi-square; Chi Sq: Chi square; S.E.E: Standard error of estimation; p: Significance
Groups Haplotype frequencies ± S.E.E Exp.
Het
Global LD
LR Chi Sq
p Genetic
Heterogeneity
Test
CGC CGT TGC CAC TGT TAC CAT TAT Chi Sq p
PCOS 0.308
±
0.033
0.27
±
0.032
0.139
±
0.025
0.105
±
0.022
0.044
±
0.015
0.027
±
0.019
0.078
±
0.019
0.029
±
0.012
79.30 2.30 1.000
54.60 0.001 Control 0.401
±
0.045
0.101
±
0.034
0.214
±
0.038
0.076
±
0.021
0.122
±
0.031
0.086
±
0.021
0.000
±
0.00
0.000
±
0.000
75.50 11.70 0.050
124
INSR PCOS
INSR Control
rs1799817
rs2059806
rs2229429
rs1799817
rs2059806
rs2229429
Figure 4.22: Pairwise linkage disequilibrium of INSR gene in PCOS and
controls.
.07
.06 .04
.04
.01 .03
125
A similar trend toward association with features of insulin resistance was observed for
the “TT and CT” genotype at position 1638 (Table 4.15). There was also a significant
difference in the “C” (57.8% in PCOS vs. 77.6% in controls) and “T” (42.2% in PCOs
vs. 22.4% in controls) allele frequencies between the two groups (p < 0.001, OR =
0.395, 95% CI = 0.25-0.61).
Single point analysis was expanded to pair of loci haplotype analysis to depict the
estimated haplotype frequencies of the three SNPs of INSR gene of unknown phase in
the PCOS and control groups. Much stronger associations were observed when the
three SNPs were considered together as haplotypes. The SNP rs1799815 was
excluded from haplotype analysis due to low variations. When the estimated
haplotype frequencies were compared between PCOS and controls, the most common
haplotype CGC (0.401 vs. 0.308 in controls and PCOS respectively) and TGC (0.214
vs. 0.139 in controls and PCOS respectively) was found to be more prevalent in the
control group, whereas the CGT (0.270 vs. 0.101 in PCOS and controls respectively),
CAC (0.105 vs. 0.076 in PCOS and controls respectively) and CAT (0.078 vs. 0.000
in PCOS and controls respectively) was more common among PCOS patients (Table
4.16; Figure 4.21). So the null hypothesis that the haplotype frequency distributions of
the two groups are the same is rejected strongly. The genetic heterogeneity test
showed strong association (p < 0.001, χ2
= 54.6). In order to study the extent of
linkage disequilibrium between exon 17 and exon 8 loci, we performed pairwise LD
analysis. The pairwise LD analysis revealed week LD for the pairs of loci in the
PCOS and control group (Figure 4.22).
4.4.3 Follicle stimulating hormone receptor (FSHR)
Genotype distribution of genes encoding sex hormones and hormones regulator SNPs
rs6166 and rs6165 are shown in Table 4.17. The allele “A” and “G” frequencies of
SNP rs6166 were not different between PCOS and controls (p = 0.350, OR = 1.232,
95% CI = 0.85-1.84). We also did not find any statistically significant differences in
the genotypes and allele frequencies between PCOS and controls for rs6165 in exon
10 (p = 0.470, OR = 1.181, 95% CI = 0.79-1.76). Haplotype-specific tests for
association were also performed. Four major haplotypes of FSHR were present in the
study population (Table 4.18). The haplotype frequencies were significantly different
between the patients and controls for the two SNPs as shown in Figure 4.23.
126
The haplotype “AA” (0.504 vs. 0.305 in PCOs and controls) and “GG” (0.431 vs.
0.326 in PCOs and controls) were more common in PCOS while haplotype “GA”
(0.022 vs. 0.179 in PCOs and controls) and “AG” (0.043 vs. 0.190 in PCOs and
controls) were frequent in controls (p < 0.001, LR Chi Sq=117.4, 6.1 respectively in
PCOS and controls). The genetic heterogeneity test gives a p-value of <0.001 (Table
4.18). To calculate the extent of LD in pairwise combinations of the SNPs, we
calculate LD value. The pairwise LD values were 0.76 (p < 0.001) for both sites
which are situated in exon 10 within only hundreds of bases of each other (Figure
4.24).
127
Figure 4.23: Multi-SNPs haplotype percent frequency distribution for PCOS and
controls at FSHR gene.
128
Table 4.17: Frequency distribution of single nucleotide polymorphisms of FSHR gene with risk of PCOS in Pakistani women.
SNPs ID Genotype/allele
frequencies
PCOS
(n= 96)
Controls
(n= 96)
OR (95% CI) χ2 p HWE
n % n %
rs6166 AA 29 30.2 24 25 0.777
(A>G) AG 47 48.9 47 48.9 (0.40-1.45) 0.41 0.510 >.05
Exon 10 GG 20 20.8 25 26
Allele frequency
A Allele 105 54.7 95 49.5 1.232
G Allele 87 45.3 97 50.5 (0.85-1.84) 0.85 0.350 >.05
E(HET) 50 50
rs6165 AA 27 28.1 22 22.9 1.310
(A>G) AG 47 48.9 49 51 (0.68-2.52) 0.44 0.500 >.05
Exon 10 GG 22 22.9 25 26
Allele frequency
A Allele 101 52.6 93 48.4 1.181
G Allele 91 47.4 99 51.6 (0.79-1.76) 0.51 0.470 >.05
E(HET) 50 50
SNPs ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg Equilibrium; χ2: Chi square.
129
Table 4.18: Multi-SNPs haplotype frequency estimates for PCOS and controls at FSHR gene.
Groups Haplotype frequencies ± S.E.E Exp.
Het
Global LD
LR Chi Sq
p Genetic
Heterogeneity
Test
AA GG GA AG Chi Sq p
PCOS 0.504±0.036 0.431±0.036 0.022±0.028 0.043±0.028 55.70 117.40 0.001
58.30 0.001 Control 0.305±0.042 0.326±0.038 0.179±0.037 0.190±0.035 73.30 6.10 0.050
Exp. Het: Expected Heterozygosity; Global LD LR Chi Sq: Global likelihood ratio chi-square; Chi Sq: Chi square; S.E.E: Standard error of estimation; p: Significance
FSHR PCOS
FSHR Control
rs6166
rs6165
rs6169
rs6166
rs6165
rs6169
Figure 4.24: Pairwise linkage disequilibrium of FSHR gene in PCOS and controls.
.03
.04 .76
.04
.01 .07
130
4.4.4 Follicle stimulating hormone beta (FSHB)
FSHB gene was analyzed for SNP rs6169 T/C in exon 3. The mutations of FSHB gene
in two sequence BLAST alignment are shown in Figure 4.25. The sequence
chromatogram showed homozygous and heterozygous form of T/C base substitution
of rs6169 (Figure 4.26).Genotype and allele frequencies were significantly different
for PCOS and controls. A statistically significant (p < 0.005) higher frequency of the
“T” allele was observed in patients than in controls (Table 4.19). The frequency of
“C” (34.4% in PCOS and 46.4% in controls) and “T” (65.6% in PCOS and 53.6% in
controls) allele differ significantly in PCOS and control groups (p= 0.020, OR= 0.606,
95% CI= 0.40-0.91). The frequency of TT genotype was overrepresented as compared
to CT_CC genotype in PCOS (p = 0.020, OR = 2.094, 95% CI = 1.14-3.83).
131
(a) Alignment of two sequences BLAST (PCOS) Query 94 CTCAGGATCTGGTGTATAAGGACCCAGCCAGGCCCAAAATCCAGAAAACATGTACCTTCA 153
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 61 CTCAGGATCTGGTGTATAAGGACCCAGCCAGGCCCAAAATCCAGAAAACATGTACCTTCA 120
Query 154 AGGAACTGGTATACGAAACAGTGAGAGTGCCCGGCTGTGCTCACCATGCAGATTCCTTGT 213
||||||||||||| ||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 121 AGGAACTGGTATATGAAACAGTGAGAGTGCCCGGCTGTGCTCACCATGCAGATTCCTTGT 180
(b) Alignment of two sequences BLAST (Control) Query 94 CTCAGGATCTGGTGTATAAGGACCCAGCCAGGCCCAAAATCCAGAAAACATGTACCTTCA 153
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 61 CTCAGGATCTGGTGTATAAGGACCCAGCCAGGCCCAAAATCCAGAAAACATGTACCTTCA 120
Query 154 AGGAACTGGTATACGAAACAGTGAGAGTGCCCGGCTGTGCTCACCATGCAGATTCCTTGT 213
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 121 AGGAACTGGTATACGAAACAGTGAGAGTGCCCGGCTGTGCTCACCATGCAGATTCCTTGT 180
Figure 4.25: Two sequence BLAST alignment of FSHB gene polymorphisms.
Figure 4.26: Sequence chromatogram showing homozygous and heterozygous
form of T/C base substitution of rs6169.
T
TC
132
Table 4.19: Frequency distribution of single nucleotide polymorphisms of FSHB gene with risk of PCOS in Pakistani women.
SNP ID Genotype/allele
frequencies
PCOS
(n= 96)
Controls
(n= 96)
OR (95% CI) χ2 p HWE
n % n %
rs6169 TT 42 43.7 26 27 2.094
(T>C) CT 42 43.7 51 53.1 (1.14-3.83) 5.12 0.020 >.05
Exon 3 CC 12 13.5 19 19.7
C Allele 66 34.4 89 46.4 0.606
T Allele 126 65.6 103 53.6 (0.40-0.91) 5.24 0.020 >.05
E(HET) 45 50
SNPs ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg Equilibrium; χ2: Chi square.
133
4.4.5 Luteinizing hormone choriogonadotropin receptor (LHCGR)
LHCGR gene on region 2p16.3 was analyzed for rs61996318. Heterozygotes CA were
greater in patients (16.6%) than controls (7.2%). As the homozygous AA genotype
and heterozygous CA genotype was rare both in cases and controls (Table 4.20), the
AA and CA were combined together and are referred as variants genotype of
rs6199318. The frequency of variant genotype of rs6199318 was different in both
groups. We did find a significant association between different genotypes (Table
4.20). In SNP rs61996318 the genotype frequencies CC, CA and AA were 82.2%
(79/96), 16.6% (16/96), 1.0% (01/96) in PCOS whereas 92.7% (89/96), 7.2% (07/96)
and 0% (0/96) in controls. Although the genotype and allele frequencies showed little
variations but these were statistically significant (p = 0.030, OR = 1.366, 95% CI =
0.14-0.89).
A novel SNP in LHCGR
In LHCGR PCR product DNA sequencing map, we found a new SNP at 48941073
nucleotide position, where there was a loss of base “A” in which the gene sequence
changes at AGCC A GAG. In PCOS group, there were 28 homozygous of this new
SNP and in controls there were 6. Heterozygotes D/T was greater in patients (50%)
than controls (36.4%) which increased the risk of disease. A statistically significant
higher frequency of the “missing” allele was observed in patients than in controls
(54.2% vs. 24.5%, p < 0.001, OR = 1.270, 95% CI = 0.18-0.42). As the homozygous
D/D genotype and heterozygous D/T genotype was prevalent in PCOS (Table 4.20),
the D/D and D/T was combined together and referred as variants genotype of this new
SNP. The frequency of variant genotype of new SNP was different in both groups
(76/96 vs. 41/96 in PCOS and controls respectively). The frequency of D/D genotype
was overrepresented as compared to TT_DT genotype in PCOS (p < 0.001, OR =
1.220, 95% CI = 0.11-0.43).
The haplotype analysis revealed that the most common haplotype “TC” was frequent
in controls as compared with PCOS group (72.4% vs. 40.9% in controls and PCOS
respectively) (Figure 4.27). Whereas the other three nonzero haplotypes; DC (0.497
vs. 0.239 in PCOS and controls), TA (0.049 vs. 0.031 in PCOS and controls) and DA
(0.045 vs. 0.006 in PCOS and controls) were more common in PCOS group (Table
4.21). The genetic heterogeneity test showed strong significance (p < 0.001,
134
Figure 4.27: Multi-SNPs haplotype percent frequency distribution for PCOS and
controls at LHCGR gene.
135
Table 4.20: Frequency distribution of single nucleotide polymorphisms of LHCGR gene with risk of PCOS in Pakistani women.
SNPs ID Genotype/allele
frequencies
PCOS
(n= 96)
Controls
(n= 96)
OR (95% CI) χ2 p HWE
n % n %
rs61996318 CC 79 82.2 89 92.7 0.365
(C>A) CA 16 16.6 7 7.2 (0.14-0.92) 3.86 0.050 >.05
Exon AA 1 1 0 0
Allele frequency
C Allele 174 90.6 185 96.4 1.366
A Allele 18 9.4 7 3.6 (0.14-0.89) 4.28 0.030 >.05
E(HET) 17 7
rs111834744
TT 20 20.8 55 57.2 1.220
(D>T) DT 48 50 35 36.4 (0.11-0.43) 20.3 0.001 >.05
Intron DD 28 29.1 6 6.2
Allele frequency
T Allele 88 45.8 145 75.5 1.270
D Allele 104 54.2 47 24.5 (0.18-0.42) 34.22 0.001 >.05
E(HET) 50 37
SNPs ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg Equilibrium; χ2: Chi square.
136
Table 4.21: Multi-SNPs haplotype frequency estimates for PCOS and controls at LHCGR gene.
Groups Haplotype frequencies ± S.E.E Exp. Het Global LD
LR ChiSq
p Genetic
Heterogeneity
Test
TC DC TA DA Chi Sq p
PCOS 0.409±0.036 0.497±0.035 0.049±0.015 0.045±0.016 58.10 0.20 0.05
40.80 0.001 Control 0.724±0.034 0.239±0.035 0.031±0.010 0.006±0.015 47.70 0.10 0.05
Exp. Het: Expected Heterozygosity; Global LD LR Chi Sq: Global likelihood ratio chi-square; Chi Sq: Chi square; S.E.E: Standard error of estimation; p: Significance
Figure 4.28: Pairwise linkage disequilibrium of LHCGR gene in PCOS and controls.
LHCGR PCOS
LHCGR PCOS
rs61996318
rs111834744
rs61996318
rs111834744
.26
0 0
.04
0 0
137
χ2
= 40.8) between the PCOS and control groups. The pairwise LD values were week
for both sites (Figure 4.28).
4.4.6 Luteinizing hormone beta (LHB)
The LHB was analyzed for two polymorphisms rs1800447 and rs4002462. The
mutations of LHB gene in two sequence BLAST alignment are shown in Figure 4.29.
The sequence chromatogram showed homozygous and heterozygous form of T/C base
substitution of rs4002462 (Figure 4.30). Overall, there was no statistically significant
(p > 0.05) difference in the distribution of either polymorphism between PCOS and
controls, the genotype frequency and allele frequency showed little variations among
both groups. This indicate that individual polymorphism of LHB may not be
associated with increased risk of PCOS (Table 4.22). Haplotype-specific tests for
association were also performed. Three major haplotypes of LHB were present in the
study population (Table 4.23).
However the haplotype analysis showed that the most common haplotype “AC”
(94.3% vs. 86.5%) was more frequent in controls than in PCOS (Figure 4.31).
Whereas, the haplotypes “AT and GC” were frequent in PCOS group (Table 4.23).
The genetic heterogeneity test showed a border line significance among the two
groups (p = 0.05, χ2
= 6.7). The pair wise LD analysis revealed no significant
association (Figure 4.32).
138
(a) Alignment of two sequences BLAST (PCOS) Query 94 CTGTTTCTTTCAGTTTCTTCCCATTCCTAAACCCTCTCCTCCCTCCTTCAGCAAACTATA 153
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 60 CTGTTTCTTTCAGTTTCTTCCCATTCCTAAACCCTCTCCTCCCTCCTTCAGCAAACTATA 119
Query 154 TGGAAATGGATTTGAAGAAGTAAAAAGTCATGCATTCAATGGGACGACACTGACTTCACT 213
|||||||||||||||||||||| |||||||||||||||||||||||||||||||||||||
Sbjct 120 TGGAAATGGATTTGAAGAAGTAGAAAGTCATGCATTCAATGGGACGACACTGACTTCACT 179
(b) Alignment of two sequences BLAST (Control) Query 99 TCTTTCAGTTTCTTCCCATTCCTAAACCCTCTCCTCCCTCCTTCAGCAAACTATATGGAA 158
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 65 TCTTTCAGTTTCTTCCCATTCCTAAACCCTCTCCTCCCTCCTTCAGCAAACTATATGGAA 124
Query 159 ATGGATTTGAAGAAGTAGAAAGTCATGCATTCAATGGGACGACACTGACTTCACTGTAAG 218
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 125 ATGGATTTGAAGAAGTAGAAAGTCATGCATTCAATGGGACGACACTGACTTCACTGTAAG 184
Figure 4.29: Two sequence BLAST alignment of LHB gene polymorphisms.
Figure 4.30: Sequence chromatogram showing homozygous and heterozygous
form of A/G base substitution of rs4002462.
G
AG
139
Figure 4.31: Multi-SNPs haplotype percent frequency distribution for PCOS and
controls at LHB gene.
140
Table 4.22: Frequency distribution of single nucleotide polymorphisms of LHB gene with risk of PCOS in Pakistani women.
SNPs ID Genotype/allele
frequencies
PCOS
(n= 96)
Controls
(n= 96)
OR (95% CI) χ2 p HWE
n % n %
rs1800447 CC 80 83.3 88 91.7 0.450
(C>T) TC 15 15.6 8 8.3 (0.18-1.40) 2.33 0.140 >.05
Exon TT 1 1 0 0
Allele frequency
A Allele 183 95.3 189 98.4 0.323
G Allele 9 4.7 3 1.6 (0.08-1.21) 2.15 0.120 >.05
E(HET) 9 3
rs4002462 AA 87 90.6 93 96.9 0.312
(A>G) AG 9 9.4 3 3.1 (0.08-1.11) 2.22 0.130 >.05
Intron GG 0 0 0 0
Allele frequency
A Allele 175 91.1 184 95.8 0.448
G Allele 17 8.9 8 4.2 (0.18-1.06) 2.74 0.090 >.05
E(HET) 16 8
SNPs ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg Equilibrium; χ2: Chi square.
141
Table 4.23: Multi-SNPs haplotype frequency estimates for PCOS and controls at LHB gene.
Groups Haplotype frequencies ± S.E.E Exp. Het Global LD
LR ChiSq
p Genetic
Heterogeneity
Test
AC AT GC ChiSq p
PCOS 0.865±0.025 0.088±0.020 0.046±0.015 24.10 0.30 1.000
6.70 0.050 Control 0.943±0.017 0.042±0.014 0.016±0.009 10.90 0.30 1.000
Exp. Het: Expected Heterozygosity; Global LD LR Chi Sq: Global likelihood ratio chi-square; Chi Sq: Chi square; S.E.E: Standard error of estimation; p: Significance
Figure 4.32: Pairwise linkage disequilibrium of LHB gene in PCOS and controls.
LHB PCOS
LHB PCOS rs
1800447
rs4002462
rs1800447
rs4002462
.08
0 0
.04
0 0
142
4.4.6 Estrogen receptor alpha (ESR1)
The contribution of the genomic variants of ESR1 was studied for seven SNPs. The
genotype distribution and relative allele frequencies for ESR1 gene polymorphisms
are shown in Table 4.24. Twenty two subjects were homozygous for rs2234693 C
allele, 47 were heterozygous and there were 27 subjects homozygous for T allele. The
frequency of the rs2234693 “C” allele was 47.4% in the PCOS and 32.8 % in the
control group. Significant differences were observed for comparative genotype
distribution and relative allele frequencies of SNP rs2232693 between PCOS and
controls (p < 0.005, OR = 1.845, 95% CI = 1.22-2.79, χ2
= 7.90). We had also find
statistically significant differences in the genotypes and allele frequencies between
PCOS and control group for rs9340799 (p < 0.010, OR = 1.688, 95% CI = 0.96-2.19,
χ2
= 5.74).
A new SNP rs8179176 was found during the screening of DNA sequencing map. Two
were homozygous for new SNP T allele, 23 were heterozygous and there were 71
subjects homozygous for C. Heterozygotes CT were greater in patients (24%) than
controls (10.4%). As the homozygous TT genotype and heterozygous CT genotype
was rare both in cases and controls, the AA and GA were combined together and are
referred as variants genotype of this new SNP. The frequency of variant genotype of
was different in both groups. The frequency of the rs8179176 “T” allele was 14.1 %
in the patients and 5.2% in the control group. Comparison of frequencies between
patients and control groups show statistically difference between them (P < 0.006, OR
= 1.336, 95% CI= 0.15-0.71, χ2
= 7.66). This suggested that these genetic variants are
related in the occurrence of PCOS in Pakistani women. However the genotype and
allele frequencies of SNPs rs11155816, rs4986936 and rs4986937 were not
significantly different between PCOS women and controls (Table 4.24).
Interestingly, the haplotype analysis showed distinct variations among PCOS and
control groups. Two haplotypes “AT and GC” are more frequent in PCOS whereas
the other two haplotypes “AC and GT” are common in controls (Table 4.25; Figure
4.33). Also the genetic heterogeneity test showed a highly significant association
between PCOS and controls (p < 0.001, χ2
= 75.5). LD value between rs2234693 and
rs9340799 was 0.46 (p < 0.001) in PCOS group which are located 51 bp from each
other. This showed a strong LD for the pair of loci and haplotype correlate well in the
PCOS group as compared to the control group (Figure 4.34).
143
Figure 4.33: Multi-SNPs haplotype percent frequency distribution for PCOS and
controls at ESR1 gene.
144
Table 4.24: Frequency distribution of single nucleotide polymorphisms of ESR1 gene with risk of PCOS in Pakistani women.
SNPs ID Genotype/allele
frequencies
PCOS
(n= 96)
Controls
(n= 96)
OR (95% CI) χ2 p HWE
n % n %
rs2234693 TT 27 28.1 43 44.8 0.482
(C>T) CT 47 49 43 44.8 (0.26-0.87) 5.05 0.020 >.05
Intron 1 CC 22 22.9 10 10.4
Allele frequency
C Allele 91 47.4 63 32.8 1.845
T Allele 101 52.6 129 67.2 (1.22-2.79) 7.90 0.005 >.05
E(HET) 49 45
rs9340799 AA 28 29.2 42 43.8 0.529
(G>A) GA 47 49 43 44.8 (0.29-0.96) 3.80 0.050 >.05
Intron 1 GG 21 21.9 11 11.5
Allele frequency
G Allele 89 46.4 65 33.9 1.688
A Allele 103 53.6 127 66.1 (0.96-2.19) 5.74 0.010 >.05
E(HET) 49 45
rs8179176 CC 71 74 86 89.6 0.330
(C>T) CT 23 24 10 10.4 (0.40-0.73) 6.85 0.009 >.05
Intron 1 TT 2 2.1 0 0
Allele frequency
C Allele 165 85.9 182 94.8 1.336
T Allele 27 14.1 10 5.2 (0.15-0.71) 7.66 0.006 >.05
145
Table continues………………..
E(HET) 24 10
rs11155816 CC 81 84.4 87 90.6 0.559
(C>T) TC 14 14.6 9 9.4 (0.23-1.34) 1.19 0.210 >.05
Intron TT 1 1 0 0
Allele frequency
T Allele 16 8.3 9 4.7 1.848
C Allele 176 91.7 183 95.3 (0.79-4.29) 1.54 0.215 >.05
E(HET) 15 9
rs11155815
TT 66 68.8 75 78.1 0.616
(C>T) CT 28 29.2 20 20.8 (0.32-1.17) 1.70 0.180 >.05
Intron CC 2 2.1 1 1
Allele frequency
C Allele 160 83.3 170 88.5 0.647
T Allele 32 16.7 22 11.5 (0.36-1.16) 1.75 0.186 >.05
E(HET) 28 20
rs4986936 TT 86 89.6 94 97.9 0.183
(G>A) CT 10 10.4 2 2.1 (0.03-0.85) 4.35 0.031 >.05
Intron CC 0 0 0 0
Allele frequency
C Allele 10 5.2 2 99 5.22
T Allele 182 94.8 190 1 (1.12-24.14) 4.21 0.040 >.05
E(HET) 10 2
146
Table continues………………..
rs4986937 GG 89 92.7 93 96.9 0.41 >.05
(C>T) AG 7 7.3 3 3.1 (0.10-1.63) 0.95 0.310
AA 0 0 0 0
Allele frequency
G Allele 185 96.4 189 98.4 0.42
A Allele 7 3.6 3 1.6 (0.10-1.64) 0.92 0.336 >.05
E(HET) 7 3
SNPs ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg Equilibrium;
χ2: Chi square.
147
Table 4.25: Multi-SNPs haplotype frequency estimates for PCOS and controls at ESR1 gene.
Groups Haplotype frequencies ± S.E.E Exp. Het Global LD
LR ChiSq
p Genetic
Heterogeneity
Test
AT AC GT GC Chi Sq p
PCOS 0.479±0.036 0.073±0.035 0.084±0.020 0.364±0.019 62.60 62.30 0.001
75.50 0.001 Control 0.384±0.042 0.267±0.029 0.267±0.038 0.082±0.039 70.30 2.70 0.060
Exp. Het: Expected Heterozygosity; Global LD LR Chi Sq: Global likelihood ratio chi-square; Chi Sq: Chi square; S.E.E: Standard error of estimation; p: Significance
148
ESR1 PCSO
rs4986936
rs11155815
rs8179176
rs2234693
rs11155816
rs9340799
rs4986937
ESR1 Control
rs4986936
rs11155815
rs8179176
rs2234693
rs11155816
rs9340799
rs4986937
Figure 4.34: Pairwise linkage disequilibrium of ESR1 gene in PCOS and
controls.
.10
.22
.13
.10
.20 .06
.10
.12 .11 .1
.11
.02
.16
.11
.04
.26
.15 .10 .22
.41
.12
.22
.10
.02
.06
.10 .03
.02
.12 .11 .14
.04
.05
.02
.02
.04
.02
.02 .02 .02
.20
.02
.02
.02
149
4.4.7 Estrogen receptor beta (ESR2)
The ESR2 gene was studied for polymorphism rs4986938 which is a G→A change at
position 1859 in the 3′-untranslated region (3′-UTR) of exon 8. The genotype
frequency of G/A polymorphism in PCOS group was significantly different from that
of controls. The mutant allele “A” was in significantly higher frequency among the
cases (41.7%) compared to the controls (27.6%) yielding an odds ratio of 0.534 (p =
0.005, 95% CI = 0.35-0.81, χ2
= 7.78). As the homozygous GG genotype was more
frequent in controls (52.1%) compared with (34.4%) in PCOS (Table 4.26). The
heterozygous GA genotype and the AA were combined together and are referred as
variants genotype of rs4986938. The frequency of variant genotype of rs4986938 was
different in both groups (63/96 vs. 46/96 in PCOS and controls respectively).
4.5 Hormonal analysis
The results regarding the hormonal levels in controls, endometriosis and PCOS
women are given in the following paragraphs.
4.5.1 Follicle stimulating hormone (FSH)
FSH levels (mIU/ml) in controls and PCOS women were different from each
other. The difference between the 2 groups was significant statistically (p < 0.005).
The detail can be seen in Figure 4.35. Different genotypes, i.e, AA, AG and GG seem
to affect the FSH levels in PCOS group; details are given in Figure 4.36-4.38.
4.5.2Testosterone
The testosterone (ng/ml) titres were lower in controls group (p < 0.001) as compared
to women with PCOS. The profile of testosterone is presented in Figure 4.39.
4.5.3 Progesterone
Serum progesterone (ng/ml) was lower in endometriosis women as compared with
controls (p < 0.001) (Figure 4.40).
150
Table 4.26: Frequency distribution of single nucleotide polymorphisms of ESR2 gene with risk of PCOS in Pakistani women.
SNP ID Genotype/allele
frequencies
PCOS
(n= 96)
Controls
(n= 96)
OR (95% CI) χ2 p HWE
n % n %
rs4986938 GG 33 34.4 50 52.1 0.482
(G>A) GA 46 47.9 39 40.6 (0.27-0.86) 5.43 0.020 >.05
UTR-3 AA 17 17.7 7 7.3
Allele frequency
G Allele 112 58.3 139 72.4 0.534
A Allele 80 41.7 53 27.6 (0.35-0.81) 7.78 0.005 >.05
E(HET) 48 41
SNPs ID: single nucleotide polymorphism identity; OR: Odd ratios; CI: Confidence interval; p: Significance; HWE: Hardy Weinberg Equilibrium; χ2: Chi square.
151
Figure 4.35: Serum concentration of FSH in women with PCOS and controls.
Values given are mean in mIU/ml of FSH ± S.D. The asterisk shows the significant
difference between 2 groups according to the “t” test.
Figure 4.36: Comparison of mean FSH values in women with PCOS and controls
according to rs6166 genotype of FSHR gene. . The asterisk shows the significant difference
between 2 groups according to the “t” test.
152
Figure 4.37: Comparison of mean FSH values in women with PCOS and controls
according to rs6165 genotype of FSHR gene.
Figure 4.38: Comparison of mean FSH values in women with PCOS and controls
according to rs6169 genotype of FSHB gene.
153
Figure 4.39: Serum concentration of Testosterone in women with PCOS and
controls. Values given are mean in ng/ml of testosterone ± S.D. The asterisk shows the
significant difference between 2 groups according to the “t” test.
154
Figure 4.40: Serum concentration of progesterone in women with endometriosis
controls. Values given are mean in ng/ml of progesterone ± S.D. The asterisk shows
the significant difference between 2 groups according to the “t” test.
155
CHAPTER 5
DISCUSSION
Infertility is a common public health problem in developing countries.
However a limited data is available about the prevalence of infertility. Generally it is
believed that more than 70 million couples affect from infertility worldwide (Boivin
et al., 2007). The pathogenesis of diseases is better understood on the base of genetic
studies, which help to improve the treatment of patients (Simoni et al., 2008).
This is the first integrated study on the single nucleotide polymorphism of ESR1,
ESR2, PGR, IL10, ADIPOQ, INSR, FSHR, FSHB, LHCGR and LHB genes, to identify
genetic factors which might have an impact on the etiology of the endometriosis and
PCOS in Pakistani women. All SNPs in this study were chosen for their functional
relevance to endometriosis and PCOS after searching databases and previous
publications. Study of genomic variants and genes contributing in molecular
mechanism develop understanding of disease pathogenesis involving DNA sequence
among individuals. Current studies provide novel and detailed information on
polymorphism of various genes in Pakistani subpopulation of women. Since this is the
first study on Pakistani women, the present data will be compared with the results of
all the polymorphisms investigated with similar studies conducted on other
populations.
5.1 Population characteristics
The excess body fat is commonly measured in terms of Body Mass Index
(BMI). The BMI classified as overweight 25 kg/m2 < BMI < 30 kg/m
2 and obese with
BMI > 30 kg/m2 (Lo et al., 2006). In current study the women with PCOS were obese
when compared with respective controls. The mean BMI was 31.10 kg/m2 in PCOS
women and 30.49 kg/m2 in controls but this difference were not statistically
significant (p > 0.05). The prevalence of obesity in PCOS women varied (BMI >
30kg/m2) across different Asian countries reported by several authors (Al-Azemi et
al., 2004; Lam et al., 2005; Haq et al., 2007; Kurioka et al., 2007; Li et al., 2007; Shi
et al., 2007). Obesity, mainly the abdominal phenotype may be responsible for
hyperinsulinemia and associated with insulin resistance in PCOS women (Broekmans
et al., 2006; Lo et al., 2006; Norman et al., 2007). In USA the PCOS constitutes a
significant economic and health burden estimated over $4 billion with 12.2% for
156
infertility care, >30.1% costs hormonal related treatment due to menstrual
dysfunction, 40.5% for diabetes mellitus 2 and 14.2% for hirsutism treatment (Azziz
et al., 2005). The published trials on different populations showed the prevalence and
prediction of metabolic syndrome from 33.4 to 47.3% in PCOS women (Apridonidze
et al., 2005; Dokras et al., 2005; Ehrmann et al., 2005). The ethnicity of PCOS
women showed variations in hormonal, metabolic and clinical characteristics.
5.2 Endometriosis
In spite of high prevalence the molecular mechanism involved in the
development of endometriosis puzzled many investigators for years and still remain
enigmatic. Endometriosis is considered as sex hormone dependent disease that affects
women of reproductive age and regresses after menopause (Kitawaki et al., 2002a).
The interaction between multiple genes and environmental factors is involved in the
pathogenesis of endometriosis. The identification of genes or genetic polymorphisms
involved in susceptibility of endometriosis has attracted a growing interest in recent
years (Falconer et al., 2007). In view of the involvement of various genes, present
study explores the association between genetic polymorphisms with the predisposition
of endometriosis.
Estrogen receptor genes harbour certain DNA sequence variants that may
contribute to the risk of infertility. In the present study no association was observed in
genotype and allele frequencies between genetic variants rs2234693 and rs9340799 of
ESR1 gene polymorphisms and endometriosis (p > 0.050). In contrast, distinct
variations were observed in haplotype frequencies (p < 0.005) indicated that the
polymorphisms in ESR1 gene may affect the other regions of the gene in study
population. During screening of the ESR1 gene the other five genetic polymorphisms
rs11155815, rs8179176, rs11155816, rs4986936 and rs4986937 were also
investigated. These genetic variants were more frequent in women with endometriosis
than control however these were statistically not significant (p ≥ 0.050). These
findings were in agreement with the prior studies when compared with other
populations (Table 2.1). A study on Japanese women failed to detect an association
between ESR1 gene polymorphisms and endometriosis (Matsuzaka et al., 2012). In
Estonian women no association was observed in genetic variants of ESR1 rs2234693
with endometriosis (Lamp et al., 2011). In the present study the polymorphisms
rs2234693 and rs9340799 may act through linkage disequilibrium (LD = 0.48; p =
157
0.005) for a functional sequence variation in the gene of endometriotic women. By
contrast a highly significant (p = 0.006) difference was observed in the genotype and
allele frequencies of rs4986938 of ESR2 gene polymorphism in the endometriosis
compared with control group. A study on Japanese population showed that the ESR2
gene polymorphism was associated with an increased risk of endometriosis (Wang et
al., 2004). In Brazilian women, the polymorphism in ESR2 gene has been associated
with the risk of endometriosis development, irrespective to the stage of endometriosis
(Bianco et al., 2009). However a study conducted on Korean women with
endometriosis and controls suggests that the polymorphism rs4986938 of ESR2 gene
may not be associated with the severity of disease (Lee et al., 2007). The allele
frequencies of rs4986938 of ESR2 gene was significantly (p = 0.003) associated with
endometriosis in women from Brazil population (Bianco et al., 2009; Zulli et al.,
2010).
In the progesterone receptor gene two polymorphisms, rs10895068 in the
promoter region and rs1042838 in exon 4 were studied. A highly significantly (p =
0.003) association between endometriosis and progesterone receptor gene rs1042838
was observed in present population. Both polymorphisms of PGR gene are present in
the coding region of genome so it is predictable that these polymorphisms are
associated with the susceptibility of endometriosis. In current study the low level of
progesterone in serum was observed with endometriosis patient when compared with
their controls (p < 0.001). This elevation in serum progesterone level indicates the fact
that the production of this hormone was affected by PGR gene. Previously a German
study revealed that both these polymorphisms influenced the function of progesterone
receptors (Romano et al., 2007). A study on Dutch population investigated an
association of rs10895068 polymorphism (p < 0.050) with endometriosis (van Kaam
et al., 2007). However, no association with any of the variant rs1042838 and
rs10895068 polymorphism was observed with the susceptibility of endometriosis in a
pooled analysis from Australia and India (Treloar et al., 2005).
Interleukin 10 is the chief immune-modulatory cytokine and function to
terminate and limit the inflammatory responses (Moore et al., 2001). The associations
of IL10 promoter polymorphism and endometriosis have been investigated in various
populations.
158
The genotype and allele frequencies at each 4 SNPs of IL10 gene
polymorphisms rs1800872, rs1800871, rs1800894 and rs1800896 of promoter region
differ significantly (p ≤ 0.005) in endometriosis women when compared with control
group. In IL10 gene, multi-SNP haplotypes at each 3 SNPs (rs1800872, rs1800871
and rs1800896) differ significantly (p = 0.005) in endometriosis women compared
with their controls, suggesting that “ATG” genotype may contribute to the risk of
disease. Interestingly this haplotype was not reported in any previous population
suggesting that “ATG” was a novel haplotype in Pakistani women with
endometriosis. The findings of present study in terms of association are in line with
the previous studies. A study on Chinese women with endometriosis revealed that the
patients having rs1800871 polymorphism had a higher IL10 level when compared
with controls (Zhang et al., 2007). A recent study suggests that the ACC/ACC
haplotype, which is known to be a “low-producer” of IL10, is associated with
endometriosis (Riiskjaer et al., 2011). However in present study population the
ATG/ATG haplotype in the promoter region is related to the risk of endometriosis. In
Pakistani women the frequency of GCC is 16.5 % in endometriosis and 19.5 % in
controls. In addition the frequency of haplotype GCC is about 50% in European
females, 20% in African females while lowest below 5% in Asian female that is
(Meenagh et al., 2002).
No statistically significant (p > 0.050) difference was observed in allele and
genotype frequencies of rs1800872 and rs1800896 in Japanese patients with
endometriosis compared with controls (Kitawaki et al., 2002b). In contrast, a
significant association of endometriosis with rs1800872 in the promoter region of
IL10 was found in Taiwanese population (Hsieh et al., 2003). The frequency of C
allele of rs1800872 was lower in endometriosis patients (55.8 %) than in controls (78
%). A study on Chinese patients with endometriosis revealed the higher frequency of
C allele at rs1800872 as compared to controls (Zhang et al., 2007). Moreover, the
polymorphism in promoter region of IL10 gene was also studied in Chinese women
with endometriosis compared with controls. The distinct variations (p < 0.050) in
allele frequency were observed at rs1800872 and rs1800871 of IL10 gene, this seems
to be associated with endometriosis while the polymorphism rs1800896 was
conserved with no difference in allele frequency (Xie et al., 2009). These findings
were in agreement with the prior studies when compared with other populations
(Table 2.3).
159
5.3 PCOS
In current study the polymorphisms of various genes ADIPOQ, INSR, FSHR,
FSHB, LHCGR, LHB, ESR1 and ESR2 and their association with the development of
PCOS was investigated. PCOS is the second reproductive disorder studied in the
present research.
In this study, the association of three individual SNPs rs2241766, rs1501299 and
rs2241767 of ADIPOQ gene along with their haplotypes with susceptibility to PCOS
was studied. The rs2241766 has synonymous variation in the exon 2 (Gly-Gly)
whereas rs1501299 and rs2241767 are located in intron. The allele frequencies at each
of 3 above mentioned SNPs rs2241766, rs1501299 and rs2241767 (p < 0.001) as well
as multi-SNP haplotypes differ significantly (p < 0.001) between PCOS patients and
controls, suggesting that these SNPs may contribute to the risk development of PCOS.
Current findings are in line with previous studies (Panidis et al., 2004; San Millan et
al., 2004; Zhang et al., 2008) showing a significant association between the ADIPOQ
rs2241766 polymorphism and risk of PCOS. In addition, “T” allele of rs2241766 was
more frequent in PCOS women compared with control. However in a study on Han
Chinese women “G” allele was related to an increased risk of PCOS, this may be due
to difference in ethnicities (Demirci et al., 2010). In present study “TT” genotype of
ADIPOQ genetic variant rs2241766 was associated with a 3.725 folds increased risk
for PCOS patients similar with a study in Iran where 1.93-fold increase risk for PCOS
with the same genotype was observed (Ranjzad et al., 2012). A previous study on the
same gene ADIPOQ revealed that there are two strong LD blocks, one LD block
reside in the putative promoter and other appears to extend over the region between
rs2241766 and rs1501299 (Heid et al., 2006). In current study strong LD value (r2 =
0.78; p < 0.001) in ADIPOQ gene in Pakistani women with PCOS supports previous
finding. In addition, a new SNP at 186571196 genomic position was also investigated,
the heterozygous gene frequency was 26.0 % in PCOS women than 14.6 % in
controls. Moreover, rs2241767 showed strong linkage disequilibrium with rs2241766
present in the coding region of genome. The rs2241767 was not observed in any
population previously. The distribution of allele frequencies of rs2241766 and
rs1501299 polymorphisms of ADIPOQ gene in present study were in agreement with
the prior studies (Table 2.4a and 2.4b).
160
PCOS is a heterogeneous disorder; the patients of PCOS have an increased
susceptibility of type 2 diabetes as insulin resistance and glucose tolerance (San
Millan et al., 2004). In the present study a novel substitution of T→C within the
rs1799817 exon 17 of INSR gene with significantly different allele and genotype
frequencies (p = 0.001) was observed. Previous studies of exon 17 have shown that a
C→T substitution was associated with PCOS (Siegel et al., 2002). In current study,
the frequency of mutant homozygous “CC” genotype was significantly higher (58.3
%) in women with PCOS compared with (32.3 %) in controls (p = 0.001)).
Interestingly the frequency of “T” allele was significantly higher 42.2 % in control
group than 24 % in PCOS (p < 0.001). However one previous study reported the T/C
polymorphism associated with PCOS and decreased insulin sensitivity in Chinese
population (Jin et al., 2006). In present study another novel SNP, rs1799815 was
detected during the screening of exon 17. Whereas, no other novel polymorphism was
observed in a study on Indian PCOS women other than rs1799817 in exon 17 of INSR
gene (Mukherjee et al., 2009). The exon 8 of INSR gene is rarely studied for
association of polymorphism with PCOS. Furthermore, the association of genetic
variant rs2229429 in exon 8 of INSR gene with PCOS risk was also investigated
currently, which was not analysed previously in any study. A highly significant
difference (p = 0.001) in the genotype and allele frequencies was observed among the
PCOS and control group for this polymorphism. The haplotype frequencies showed a
clear difference in control group and PCOS women where the haplotype “CAT”
(0.078) and “TAT” (0.029) was observed only in PCOS women (p = 0.001).
The polymorphism of FSHR rs6166 and rs6165 was not responsible for any
significant association (p > 0.050) with PCOS in the current studied population. The
frequency of non-synonymous polymorphisms rs6166 and rs6165 in coding region of
exon 10 of FSH gene was greater than 30% in normal population. In a previous study
on Italian women no significant association was studied in FSHR gene
polymorphisms rs6166 and rs6165 in PCOS group (Orio et al., 2006). Similar results
in these two polymorphisms were reported in Chinese Singapore patients with PCOS
(Tong et al., 2001). However the haplotype analysis showed distinct variations
between the PCOS and control group. In present findings two haplotypes GG (43.1
%) and AA (50.4 %) were more frequent in PCOS than 32.6 % and 30.5 %
respectively in controls (p < 0.001)). This suggest that at least one susceptible locus
161
for PCOS lies within or very close to the region spanning rs6166 and rs6165 in the
FSHR gene in Pakistani women. This explained that the haplotype analysis has strong
effect than individual genetic markers in association analysis; therefore haplotype
analysis takes into account the correlation between these markers (Dudbridge, 2003).
Both the polymorphisms of FSHR gene were in strong linkage disequilibrium, which
shows that they may act as haplotype rather than individual marker. The two SNPs of
FSHR gene rs6166 and rs6165 are in linkage disequilibrium but most focused is on
rs6165 variant. Several studies demonstrate the effect of these polymorphisms on
reproduction and influence on FSHR functional activity (Simoni et al., 2008).
This study found that in rs6169 polymorphism of FSHB gene the allele
frequency and homozygosity was significantly higher (p = 0.020) in patients with
PCOS when compared with controls. The FSHB rs6169 polymorphism was
previously found to be associated with obese PCOS patients in Southeast Asian
population (Liao et al., 1999; Tong et al., 2000). A more recent study in Italy revealed
that the joint conjunction of genetic polymorphisms in FSHR and FSHB demonstrated
that serum FSH level may be affected by these polymorphisms (La Marca et al.,
2013). Currently serum FSH level was higher in PCOS women as compared to
controls (p < 0.005). However, the previous studies revealed that the PCOS women
either have normal limits of FSH level or there is slight increase in serum FSH level
when compared with their controls (Laven et al., 2003).
The polymorphism rs61996318 of LHCGR gene seems to be a stable locus in
Pakistani women (p > 0.050), and it was not related with the development and
pathogenesis of PCOS. A similar finding was observed in Chinese Han women
suggesting that LHR rs6168318 polymorphism may be highly conserved in the
studied subpopulation (Liu et al., 2012). A novel SNP of LHCGR was found in
current study where there is a loss of base “A”. The frequency of missing base was
significantly higher (54.2 %) in PCOS women than (24.5 %) in controls (p < 0.001).
This showed that this novel SNP had a strong relation with the development and
susceptibility of PCOS in Pakistani women.
In present study two polymorphisms rs1800447 and rs4002462 in LHB gene
were also studied. Both of these LHB polymorphisms in Pakistani women were not
associated with PCOS (p > 0.050). Similar findings were observed in a previous study
where polymorphisms of LHB gene were not associated with PCOS and infertility in
162
various populations (Lamminen et al., 2002). However, another study on Finland,
Netherland, United Kingdom and United State showed that the silent mutation of the
LHB genomic variant may have influenced the polymorphism rs1800447 which might
lead to ovulatory disorder (Tapanainen et al., 1999). In PCOS women the LH is
overexpressed in theca cells and its function is altered by multiple polymorphisms in
the LHRCGR gene (Jakimiuk et al., 2001). LH stimulates the ovarian theca cells to
secrete androgens which consequently arrest follicular maturation (Laven et al.,
2002). Mutations in LHB subunit gene are rarely studied. The allelic frequency of this
variant varies extensively between different ethnic groups. The wide occurrence of
this polymorphism in populations with different evolutionary histories and wide
frequency variability may imply that this polymorphism represent an ancient form of
LH (Lamminen & Hutaniemi 2001).
In the present study the polymorphisms rs2234693 and rs9340799 of ESR1 gene
may act through linkage disequilibrium (41 %) for a functional sequence variation in
the gene (p = 0.001). A significant (p = 0.005) difference was observed in genotype
and allele frequencies of rs2234693 and rs9340799 polymorphisms in PCOS and
control group. In a recent study on these polymorphisms in Greek women; no
difference in genotype frequency was observed among PCOS and controls, however
the association of rs2234693 and rs9340799 with insulin resistance and FSH level
suggests the possible contribution of these genomic variants to the phenotype of
PCOS (Nectaria et al., 2012). The present study provides data showing that the two
polymorphisms may be associated with PCOS, but the mechanism underlying this
association remains to be elucidated. In addition rs2234693 and rs9340799
polymorphisms do not result in amino acid change; the observed effect may be due to
linkage disequilibrium among these polymorphisms. The present study suggests the
association of ESR2 rs4986938 polymorphism with the PCOS (p = 0.005). A previous
study evaluating the rs4986938 in women with ovulatory dysfunction found a higher
frequency of this polymorphism in PCOS patients than in controls (Sundarrajan et al.,
2001). In our study the strong LD value (p < 0.001) and genetic heterogeneity test (p
< 0.001) of ADIPOQ, FSHR and ESR1 genes in PCOS group revealed that these
polymorphisms may act through linkage disequilibrium for a functional sequence
variation in the gene.
163
Reproduction in vertebrates is under the control of hormonal regulation by
hypothalamus-pituitary-gonadal axis. Gonadotropins and steroids control the process
of reproduction. Androgens are correlated with gonadal development. In present study
the serum level of FSH and testosterone was higher (p = 0.005) in PCOS women. The
increase level of testosterone presents a classic picture of PCOS symptoms in study
population (i.e hirsutism, acne, weight gain and obesity) while the serum level of
progesterone was lower in endometriosis women when compared with normal control
(p = 0.005).
In conclusion, this study demonstrates that genetic variation in the studied
genes is associated with the diagnosis of endometriosis and PCOS with infertility in
Pakistani (Punjabi) women in a fashion similar to reports in other ethnic groups. The
consistent and new finding of these associations suggests a biologically plausible role
for genetic polymorphisms in the etiology of endometriosis and PCOS. To conclude,
endometriosis and PCOS are complex genetic disorders and previous studies have
investigated many genes for their possible role as susceptibility loci. This is the first
report to explore in Pakistani women from Punjab the association between different
polymorphisms of the ESR1, ESR2, PGR, IL10, ADIPOQ, INSR, FSHR, FSHB,
LHCGR and LHB genes and the development of endometriosis and PCOS. This study
provides evidence that susceptibility loci for endometriosis and PCOS lie within or
very close to the chromosomal region spanning these genes. By investigating, the
functional role of these SNPs in Pakistani women and building up more information’s
underlying the genetic basis for PCOS; the onset of disease in the individuals at risk
can be delayed by changing the life style that will reduce expenditures to control the
disease and its complications. Further this information could also be beneficial for the
prevention of other long term complications such as obesity, diabetes and heart
diseases. Moreover, samples from other Provinces should be investigated for any
change in SNP to get the true picture of the country.
164
CHAPTER 6
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189
Annexure I
3.2 CONSENT FORM
Genetic and endocrinological studies of female reproductive dysfunction
GC University Lahore
I __________________, wife/daughter of ________________ hereby, fully agree to
participate in the above mentioned study under ID #__________________________
I understand that the study is designed to add to the medical knowledge. I have been
informed about possible risks/discomforts involved and had the opportunity to ask
any question about the study. I agree to give my medical history and blood sample.
All the information collected in the data sheet will be kept confidential and will only
be used solely for research purposes. I have no objection if in case the data obtained
from my investigation is published in any scientific journal.
________________
Patient’s Signature
Date: ____________
190
Annexure II
3.2 SUBJECT DATA SHEET
Patient Identity No.: _______________ Date: ______________
Name: _______________________ Age: ______________
W/O or D/O: __________________ DOB: ______________
Phone Number: _________________ Cast: ______________
Postal Address:
_____________________________________________________________________
_____________________________________________________________________
Marital Status: Married__________ Unmarried___________
Pregnant: Yes ___________ No__________ No. of children: _______________
General physical examination:
Height: _________ Weight: ________ Kg BMI: _____________
Waist circumference: _____________ Cm Hip circumference: __________
Cm
WHR: _________________ Pulse: ___________ Blood pressure: ____________
Cardiovascular system: _____________ Central Nervous system: ____________
Family H/O:
Smoking ________ HTN ______ DM ______
IHD _______ PCOS _____ Endometriosis
_____
Did you have any history of? Yes No
Diabetes Mellitus (DM) ___ ___
Hypertension ( HTN) ___ ___
Smoking ___ ___
Alcohol ___ ___
Hyperthyroidism ___ ___
Any hospitalization in past ___ ___
Ischemic heart (IHD) ___ ___
Any concurrent medical illness ___ ___
Any surgery in past ___ ___
(Hysterectomy, Oophorectomy)
191
Gynecological history
Menstrual history:
Age of Menarche: ____________ Pattern of cycle: Regular ___ Irregular_______
Less than 9 menses per year: Yes _____ No _____
H/O irregular menses with weight gain: Yes _____ No _____
Pelvic pain: __________ Back pain: ____________
Dysmenorrhea: _________ Abnormal vaginal discharge: ________
Dyschezia: ____________ Dysuria: _______________ Dyspareunia ____________
Have you had a physician give you a medical diagnosis of Polycystic Ovarian
Syndrome (PCOS)? YES__________ NO ___________
If yes, how was the diagnosis made? ________________
Do you have a history of any of the following?
Endometriosis _______ Uterine fibroids________
Ovarian cysts________ Breast lumps (or fibrocystic breast disease)
________
Do you have a family history of any of the following?
PCOS _______ Excessive hair growth_______
Infertility_______ Obesity_______
Menstrual problems _______ Thyroid abnormalities_______
Hormonal problems_______ Miscarriage_______
Which symptoms you are currently experiencing as a result of your PCOS?
Infertility_______ Anxiety_______
Weight gain_______ Insulin resistance_______
Food cravings _______ Facial hair_______
Specifically: _______ Mood swings_______
Acne _______ Depression_______
Which of the following treatments for PCOS have you attempted?
Low carbohydrate diet_______ Acne medication_______
General healthy eating_______ Psychological counseling_______
Insulin sensitizing medications_______ Nutritional counseling_______
Oral Contraceptive_______ Antidepressants_______
Exercise/Personal Trainer_______ Mood stabilizing medication ______
Other_______
192
Medications to regulate period_______ Please describe:
Did you take any contraceptic medicine in past Yes ______ No ______
If yes, for how long _____________
Did you take any other hormonal medication in past Yes ______ No ______
If yes, for how long _____________
Did you take any anti-obesity drugs Yes ______ No ______
If yes, for how long _____________
Did you take any anti-diabetic medicine Yes ______ No ______
If yes, for how long _____________
Any history of miscarriage:
How many previous pregnancies were attempted ______________?
Successful pregnancies ______________________
How many previous attempted pregnancies were without success
______________
History of hirsutism: _____________ History of Acne: _____________
History of obesity: _______________ History of prostin: _________________
Pattern of obesity: Generalized ___________Visceral __________ Buffalo hump
_______
Ovarian ultrasound:
Rt ovary: ___________ Lt ovary: ___________Size: _________ Morphology:
__________
Any cyst: _______________ If cysts present then blood flow: ____________
Investigations
Pelvic scan: ________ Laparoscopic findings: ________USG _______ TVS _____
Rectosigmoidoscopy _________ Cystoscopy _________ CA-125: ______________
CT SCAN (pelvis/abdomen) ___________ MRI. Pelvis/abdomen ____________
Laboratory tests
Fasting glucose: _______ Fasting insulin: ________Serum Estradiol: ___________
Serum Testosterone: _____________ Serum LH: _________________
Serum FSH: _________ Serum Progesterone __________ Serum Prolactin _______
193
QUESTIONNAIRE FOR DIAGNOSIS PCOS (Sue et al., 2007)
-*If score is equal or more than 2, PCOS is present
* If score is less than 2, not consistent with diagnosis of PCOS
1. Between ages of 16-40 years about how long was your average menstrual cycle
Less than 25 days
25-35
36-60
More than 60
Totally variable
*1 score for 36 days onward
2. During your menstruation years (not including your pregnancy period) did you have a
tendency to grow dark, coarse hair on your
Upper lip
Chin
Breast
Back
Belly
Upper arm
Upper thigh
Chest b/w breast
* For more than 3 sites _______ I score
3. Were you ever overweight and obese b/w ages of 16-40 years
Yes __________ 1 score
No ________ 0
4. Between ages of 16-40 years, have you ever noticed a milky discharge from your
nipples ( not including during pregnancy or recent child birth)
Yes __________ - 1
No ___________ 0
194
Annexure III
3.6.1 Preparation of solutions
1L 1M Tris-HCl
Weigh 121.1g tris base (2-Amino-2-hydroxymethyl-propane-1,3-diol), dissolve into
700 ml distilled H2O, adjust pH to 7.4-8.0, raise final volume to 1L, autoclave and “if
possible” filter the solution and store at room temperature.
1L 3M sodium acetate
Weigh 246.1 × 3g sodium acetate, dissolve into distilled H2O, adjust pH to 5.2, raise
final volume to 1L, autoclave and “if possible” filter the solution and store at room
temperature.
1L 10M NaOH
Weigh 40 × 10g sodium hydroxide (NaOH), dissolve into 700 ml distilled H2O, raise
final volume to 1L and store at room temperature.
1L 1M HCl
Measure 86.2 ml of 11.6M HCl, dilute into 913.8 ml distilled H2O and store at room
temperature. Precaution Add acid into distilled H2O not distilled H2O into acid.
1L 0.5M EDTA
Weigh 372.2/2g ethylene-diamine-tetraacetic acid (EDTA), dissolve into 700 ml
distilled H2O, adjust pH to 8.0 by using NaOH pellets or 10M NaOH solution, raise
final volume to 1L, autoclave and “if possible” filter the solution and store at room
temperature. Note The dissolution of EDTA is pH-sensitive. EDTA will start
dissolving on stirrer only when pH is increased towards 8.0 by the pellet-wise or
drop-wise addition of NaOH into the starting mixture. Once EDTA solution becomes
clear, stop adding NaOH. If excessive NaOH is added during EDTA dissolution, pH
may become greater than 8.0, which will then be adjusted to 8.0 by using 1M HCl.
1L TE buffer (10 mM Tris-HCl, 2 mM EDTA, pH 8.0)
Dilute 10 ml of 1M Tris-HCl solution and 4 ml of 0.5M EDTA solution into 500 ml
distilled H2O, raise final volume to 1L and store at room temperature.
50 ml 10% SDS
Dissolve 5g sodium dodecyl sulphate (SDS) into 45 ml distilled H2O, heat up to 68oC
for dissolution, adjust pH to 7.2 by using 1M HCl and “if possible” filter the solution
195
and store at room temperature. Note Don’t use fume hood while preparing SDS
solution.
10 ml 10 µg/µl proteinase K
Dissolve 100 mg proteinase K into 10 ml distilled H2O, make aliquots of 100 µl and
store at -20oC.
500 ml chloroform: isoamyl alcohol (24:1)
Mix 480 ml chloroform into 20 ml isoamyl alcohol and store at 4oC.
500 ml 70% ethanol
Mix 350 ml absolute ethanol into 150 ml distilled H2O and store at 4oC.
100 ml low TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0)
Dilute 1 ml of 1M Tris-HCl solution and 200 µl of 0.5M EDTA solution into 50 ml
distilled H2O; raise final volume to 100 ml and store at room temperature.
196
Annexure IV
3.6.3 Agarose Gel Electrophoresis
The lengths of optimized DNA fragments of PCR products were quantify and
visualized through agarose gel electrophoresis.
TAE Buffer 1L
4.84 g Tris Base
1.14 ml Glacial Acetic Acid
2 ml 0.5M EDTA (pH 8.0)
The Tris base was added to 500 ml of distilled H2O. After Adding acetic acid and
EDTA the solution was mixed. The volume was made 1 liter with more distilled
water. For convenience a concentrated stock of TAE buffer of 50X was prepared and
before use it was diluted with distilled water to 1X concentration.
1L 10X TBE buffer
Dissolve 108g tris-borate, 55g boric acid and 40 ml of 0.5M EDTA solution into 700
ml distilled H2O, raise final volume to 100 ml and store at room temperature.
1L 1XTBE buffer
Dilute 100 ml of 10× TBE buffer into 900 ml distilled H2O and store at room
temperature.
10 ml 10 µg/µl ethidium bromide (EtBR)
Dissolve 100 mg EtBR into 10 ml distilled H2O or low TE buffer, make aliquots of 1
ml, cover tubes with aluminum foil and store at room temperature. Note EtBR is a
strong mutagen and protection from exposure is needed.
50 ml 6× loading dye
Dissolve 0.125g bromophenol blue (0.25%), 0.125g xylene cyanol FF (0.25%) and 15
ml of 100% glycerol (30%) or 20g sucrose (40%) or 7.5g Ficoll type 400 (15%) into
distilled H2O for making final volume up to 50 ml and store at 4oC or -20
oC.
197
Preparing the Agarose gel
Take 100 ml TAE Buffer to the 250 ml flask and add 1 g of Agarose powder. The
flask was heated in a microwave until the solution became clear. The solution was
cooled to about 50-55°C and added 2 l of Ethidium Bromide in 100 ml solution,
swirling the flask occasionally to cool evenly. Place the combs in the gel casting tray.
Poured the melted agarose solution into the casting tray and cooled it until it is solid
(it should appear milky white). Carefully pull out the combs. Placed the gel in the
electrophoresis chamber.
Loading the gel
Added 1 l of Sample Loading Dye to 5l of PCR product on a thin film and mix it.
The order of the samples loading was recorded. Carefully pipette 6 l of each sample
Loading Buffer mixture into separate wells in the gel. Pipette 2 l of the DNA ladder
into at least one well of on the gel.
Running the gel
Placed the lid on the gel box, connecting the electrodes. Connected the electrode
wires to the power supply, making sure the positive (red) and negative (black) were
correctly connected. Turned on the power supply, about 100 volts for 30 minutes. The
power run until the blue dye approaches the end of the gel. Remove the lid of the
electrophoresis chamber. Carefully removed the tray and gel with DNA bands were
observed in gel doc system (BioRad, Gel doc system)
198
Annexure V
3.6.6 Optimization of selected fragments of DNA of various genes
Figure 3.1: PCR product size: LHCGR 379 bp; FSHR
577 bp (base pairs); column 1: DNA ladder; Column 2-
16: PCR products of different DNA samples
199
Figure 3.2: PCR product size: FSHB 577 bp (base pairs);
column 1: DNA ladder; Column 2-15: PCR products of
different DNA samples
200
Figure 3.3: PCR product size: ADIPOQ 241 bp (base pairs);
column 1: DNA ladder; Column 2-15: PCR products of
different DNA samples
201
Figure 3.4: PCR product size: INSR 317 bp (base pairs);
column 1: DNA ladder; Column 2-16: PCR products of
different DNA samples
202
Figure 3.5: PCR product size: PGR 238 bp (base pairs);
column 1: DNA ladder; Column 2-8: PCR products of
different DNA samples
203
Figure 3.6: PCR product size: IL10 208 bp (base pairs);
column 1: DNA ladder; Column 2-8: PCR products
of different DNA samples
204
Annexure VI
3.6.7 PCR purification reagents preparation
• Add molecular grade ethanol (98–100%) to Buffer PE
• Add 1:250 volume pH indicator I to Buffer PB (add 600 μl pH
indicator I to 150 ml Buffer PB). The yellow color of Buffer PB with pH indicator I
indicates a pH of 7.5.
205
LIST OF PUBLICATIONS AND PRESENTATIONS FROM THESIS
PUBLICATIONS
Liaqat, I., Jahan, N., Krikun, G. and Taylor, H.S. (2014). Genetic Polymorphisms
in Pakistani Women with Polycystic Ovary Syndrome. Reproductive Sciences.
22(3): 347-357. (DOI: 10.1177/1933719114542015).(IF. 2.23)
Liaqat, I., Jahan, N., Lone, K.P, Pakstis, A. and Taylor, H.S. (2013). Genetic
polymorphisms associated with endometriosis in Pakistani women. Journal of
Endometriosis. 5(4): 134 – 143. DOI:10.5301/je.5000165 (H. INDEX. 7).
Liaqat, I., Jahan, N., Lone, K.P, and Taylor, H.S. (2015). Genetic polymorphisms
of INSR and ADIPOQ genes associated with PCOS Pakistani population.
(Submitted to Plos One. Manuscript Number. Pone-S-15-20751: IF 3.730)
ORAL PRESENTATIONS
Oral presentation entitled “Genetic polymorphisms in adiponectin and insulin receptor
genes in Pakistani women with polycystic ovary syndrome”. 34th Pakistan Congress
of Zoology (International). Feb. 25-27, 2014. CBGP-46.
Oral presentation entitled “Endometriosis and Genetic Polymorphisms in Pakistani
Women”. IBPHR (International). May6-8, 2014. (BcGc-1406).
POSTER PRESENTATION
Poster presentation entitled Genetic Polymorphisms Associated with Endometriosis in
Pakistani Women at "Transforming Women's Health via Translational Investigation:
A Global Perspective". SGI, Florence Italy. March 25-28, 2014. (F-246).
ABSTRACTS
Liaqat, I., Jahan, N., Loneb, K. P., Pakstis, A., and Taylor, H. S. (2014). Genetic
Polymorphisms Associated with Endometriosis in Pakistani Women. Reproductive
Sciences. 21(3):318A-318A.
Liaqat, I., Jahan, N., Lone, K.P., Pakstis, A. and Taylor, H.S. (2014). Genetic
polymorphisms in adiponectin and insulin receptor genes in Pakistani women with
polycystic ovary syndrome. Abstracts of 34th Pakistan Congress of Zoology
(International). Feb. 25-27, 2014. CBGP-46
206
Liaqat, I., Jahan, N., Lone, K.P., Batool, A. and Taylor, H.S. (2014). Endometriosis
and Genetic Polymorphisms (FSHR, IL10, ESR1) in Pakistani Women. IBPHR
(International). May6-8, 2014. (BcGc-1406).