Design and Analytical Issues in family-based longitudinal ... · Design and Analytical Issues in...
Transcript of Design and Analytical Issues in family-based longitudinal ... · Design and Analytical Issues in...
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Design and Analytical Issues in family-based longitudinal cohort studies: The FHS
Vasan S. Ramachandran, MD BU and NHLBI’s Framingham Heart Study
Boston University School of Medicine
No Conflicts to Disclose
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Omni study 2, N~400 2002
Framingham Heart Study Longitudinal Community-Based Family Study
2016
Third Gen cohort N~4000, 1576 spouse pairs, 3514 biological offspring
2002
Offspring cohort N = 5124 1972 2016
Original cohort N = 5209 men and women (ages 28-62) 1644 spouse pairs, 596 extended families
1948 2016
Omni study 1, N = 506 1995 2016
32nd
9th
4th
2016 New Offspring Spouses, N=100
Year/Exam
3rd
3rd
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Framingham Heart Study
• Longitudinal - Lifetime measures & Lifestyle measures
• Deeply Phenotyped - FHS is ~ The Human Phenome Project
• Extensive Genetic/Genomic Resources - Unique tissue resources
• Family-based study
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FHS: Dense Phenotypic Characterization
Dementia Stroke Depression
Eye Hearing Cardiac
Pulmonary
Renal Vascular
Osteoporosis
Osteoarthritis
Cancer
Diabetes
Proteome
Alcohol
Aging
Endocrine
Metabolome Transcriptome Methylome
CMS
Funded grants
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Framingham Heart Study Cohort Retention
COHORT ENROLLED
% Exam
or update or dead
Lost to date
ORIGINAL 5209 99.6 19
OFFSPRING 5124 99.8 15
Gen 3 4095 99 0
NOS 103 99.5 0
OMNI 1 507 99.2 30
OMNI 2 410 98.9 17
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FHS is a Family-based Study
Familial Relationships in EA subjects
Number of Pairs Relationship 6477 Parent-offspring 5530 Siblings 1267 Grandparent-grandchild 6849 Avuncular 306 Half siblings
8882 3rd degree 4503 4th degree 3876 5th degree
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FHS design and statistical issues
• Correlated observations - Multilevel modeling
• Related individuals - Family-based analysis
• Longitudinal observations - Lifecourse approach, trajectories - Lifetime risk - Antecedent RF profile - Pooled repeated observations
• Risk functions
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FHS design and statistical issues
• Temporal Trends
• Follow-up to extinction - Competing causes
• Genetic Studies - Linkage and Association analyses,
including FBAT
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Genomic Resources
• Marshfield genome scan (~400 markers/every 10cM)
• GWAS: 100K, 550K imputed to 1000K, 5M Omni
• Exome Chip Illumina v1.0 • Whole Exome Sequences • Whole Genome Sequences • MediSeq • Candidate genes/SNPs
Access locally if working at BU Or through dbGaP http://www.ncbi.nlm.nih.gov/gap
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SABRe CVD Initiative: Resources
• High-throughput technology to measure - Project 1: Discovery proteomics, metabolomics & lipomics - Project 2: Targeted immunoassays - Project 3: Gene expression profiling - Project 4: microRNA profiling
Gene Expression Methylome Metabolome miRNA
Protein Biomarkers*
5,622 2,726 2,650 5,718 7,315 *170 immunoassay proteins.
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FHS design and statistical issues
• Correlated observations - Multilevel modeling
• Related individuals - Family-based analysis
• Longitudinal observations - Lifecourse approach, trajectories - Lifetime risk - Antecedent RF profile - Pooled repeated observations
• Risk functions
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• Level 1 = individual observations
• Level 2 = clustering units (participant) - 2 types of variability (between participant
and within participant)
• Level 3 = Type of cohort (Original, Offspring, Third Generation)
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FHS: Multilevel modeling of Echo traits
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130
150
170
190
210
230
250
35 40 45 50 55 60 65
Mea
n LV
Mas
s (g
)
Age
Men (high RF burden)
Women (high RF burden)
Men (low RF burden)
Women (low RF burden)
Longitudinal tracking of LV mass
Lieb et al. Circulation, 2009; 119:3085
Cumulative risk factor burden and longitudinal LV mass tracking
Low CVD risk factor burden – • Non-smoker • Non-hypertensive systolic BP of 130 mm Hg; not on antihypertensive treatment • Free of diabetes • BMI of 25 kg/m²
High CVD risk factor burden – • Current smoker • Hypertensive systolic BP of 140 mm Hg; not on antihypertensive treatment • With diabetes • BMI of 30.0 kg/m² (women) and 27.5 (men)
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FHS design and statistical issues
• Correlated observations - Multilevel modeling
• Related individuals - Family-based analysis
• Longitudinal observations - Lifecourse approach, trajectories - Lifetime risk - Antecedent RF profile - Pooled repeated observations
• Risk functions
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JAMA Original Contribution | February 27, 2002 Residual Lifetime Risk for Developing Hypertension in Middle-aged Women and Men: The Framingham Heart Study Ramachandran S. Vasan, MD; Alexa Beiser, PhD; Sudha Seshadri, MD; Martin G. Larson, ScD; William B. Kannel, MD; Ralph B. D'Agostino, PhD; Daniel Levy, MD
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Lifetime Risk of HTN Statistical Methods
• Modified Kaplan-Meier method - cumulative incidence (CI)
» age-specific incidence • fA =hA SA-1
A » F = ∑ fj j=A min
- CI adjusted for competing risk
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Unadjusted Cumulative Incidence of HTN defined as { Stage I or RX}
Men and Women Free of HTN to age 65
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20
Years
Cum
ulat
ive
Inci
denc
e (%
)
Exam 3Exam 14
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Lifetime Risk of HTN Data for Men & Women, Baseline Age 65 Yrs
F/U Cumulative Incidence HTN (%) 1952-1975 1976-1998
5 years 36 45 10 years 59 68 15years 73 82 20 years 81 89
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Lifetime Risk of CHF Framingham Heart Study
0%
10%
20%
40 50 60 70 80 90
Cum
ulat
ive
Ris
k
Attained Age
0%
10%
20%
40 50 60 70 80 90
Attained Age
Men Women
• Lloyd-Jones Circulation 2002;106:3068
20.6% 20.2%
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FHS design and statistical issues
• Correlated observations - Multilevel modeling
• Related individuals - Family-based analysis
• Longitudinal observations - Lifecourse approach, trajectories - Lifetime risk - Antecedent RF profile - Pooled repeated observations
• Risk functions
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Study Sample Framingham Heart Study
Original Cohort; Age >50 yrs; no CHF
1948
21 15 17 16 18 19 20 22 24 23 14 11 5 7 6 8 9 10 12 13 4 3 2 1
Follow-up
Included participants if 3+ measures of BP, BMI and other variables in recent / remote periods
Baseline Examination 1970
recent 1960-70
remote 1950-60
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Effect of Baseline or Antecedent BP on HF Events, Adjusted for Baseline Risk Factors except BP
Hazards Ratio per SD Increment (95% CI)
Current BP SBP 1.26 (1.13-1.40)* DBP 0.93 (0.84-1.04) PP 1.21 (1.11-1.31)*
Previous BP SBP 1.36 (1.22-1.53)* DBP 0.97 (0.87-1.09) PP 1.31 (1.19-1.43)*
Remote BP SBP 1.24 (1.11-1.39)* DBP 1.02 (0.91-1.13) PP 1.24 (1.13-1.36)*
‡ P<0.05, † P<0.01, * P<0.001 Adjusted for standard RF including intercurrent MI, Age stratified
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Effect of Antecedent BP on HF Events
Adjusted for Baseline Risk Factors including BP
Hazards ratio per SD (95% CI)
Previous BP
SBP 1.31 (1.11-1.55)† DBP 1.02 (0.88-1.19) PP 1.33 (1.14-1.54)*
Remote BP SBP 1.17 (1.04-1.31)‡ DBP 1.05 (0.93-1.18) PP 1.17 (1.06-1.31)†
‡ P<0.05, † P<0.01, * P<0.001 Adjusted for standard RF including intercurrent MI, Age stratified
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Effect of Baseline or Antecedent BMI on HF
Without & with adjustment for Baseline BMI
* P<0.001
Hazards Ratio per Unit (95% CI)
No Adjustment for Current BMI Current BMI 1.05 (1.03-1.07)* Previous BMI 1.06 (1.04-1.08)* Remote BMI 1.07 (1.05-1.10)*
Adjusted for Current BMI Hazards Ratio per Unit
(95% CI) Previous BMI 1.15 (1.08-1.23)* Remote BMI 1.09 (1.05-1.14)*
Adjusted for standard RF including intercurrent MI, Age stratified
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1.24 1.25
1.13
0.80.91.01.11.21.31.4
Current SBP
Prior SBP
Remote SBP
1.191.25
1.16
0.8
0.9
1.0
1.1
1.2
1.3
1.4
Current PP
Prior PP
Remote PP
0.94 0.98 0.95
0.60.70.80.91.01.11.21.31.4
Current DBP
Prior DBP
Remote DBP
1.22 1.231.28
0.80.91.01.11.21.31.4
Current BMI
Prior BMI
Remote BMI
Effect of Baseline or Antecedent BP & BMI on HF
Comparison of single random measure
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FHS design and statistical issues
• Correlated observations - Multilevel modeling
• Related individuals - Family-based analysis
• Longitudinal observations - Lifecourse approach, trajectories - Lifetime risk - Antecedent RF profile - Pooled repeated observations
• Risk functions
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FHS design and statistical issues
• Temporal Trends
• Follow-up to extinction - Competing causes
• Genetic Studies - Linkage and Association analyses,
including FBAT
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FHS design and statistical issues
• Temporal Trends
• Follow-up to extinction - Competing causes
• Genetic Studies - Linkage and Association analyses,
including FBAT
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Lee D et al. N Engl J Med 2006;355:138-147
Parental Heart Failure & Risk of CHF/LV Remodeling in Offspring
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This manuscript highlights use of FHS menarche data in a consortium that identified genes implicated in BMI & various diseases, including rare disorders of puberty. Signals in imprinted regions identified for first time.
Nature, October 2014
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Imputation of Ungenotyped FHS Participants in Informative Families
Using 8998 Framingham Heart Study (FHS) participants genotyped with Affymetrix 550K SNPs, we imputed genotypes of the same set of SNPs for an additional 3121 participants We developed a novel algorithm for splitting large pedigrees for imputation and found a plausible imputation quality filtering threshold based on FHS. We could employ WGS in a selected 60-75% of FHS participants, using imputation, to have a whole genome in >>10K participants.
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Thank you