Breeds Information Animal Science. Dairy Breeds and Selection.
Multivariate factor analysis of genomic correlation matrices in three US dairy cattle breeds
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Transcript of Multivariate factor analysis of genomic correlation matrices in three US dairy cattle breeds
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Multivariate factor analysis of genomic correlation matrices in three
US dairy cattle breeds
1Dipartimento di Scienze Zootecniche, Università di Sassari, Italy 2Animal Improvement Programs Laboratory, ARS, USDA, Beltsville, MD, USA
N. P. P. Macciotta1,* and J. B. Cole2
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
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Chromosomal breeding values: CEBV
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
BTA1 BTA5 BTA9 BTA13 BTA17 BTA21 BTA25 BTA29
Bull1
Bull2
Bull3
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Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
Genome-wide and chromosome-wide (BTA18) correlations can be calculated
Cole et al. 2009. J. Dairy Sci. 92(6):2931–2946.
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Correlation structures differ between chromosomes
These differences may help identify genes with small additive effects that affect several traits
Correlations between genomic values
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
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Issue in population genetics Several proposed tests Common principal components (CPC) PC are used to explain the variance of a
system The covariance should be investigated
Comparison of genetic correlation (covariance) matrices
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
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The factor model
Dipartimento di Scienze ZootecnicheUniversità degli Studi di Sassari
A small number of latent variables can explain the covariance structure of the original data
S = BB’ + S = (Co)variance matrix of original dataB = (Co)variance between original data and latent factors
= Specific variance matrix
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Compare correlation matrices of genomic (GEBV) and chromosome-specific (CEBV) within and across three US cattle breeds
Apply multivariate factor analysis
Can matrices for a large number of traits be compared algorithmically?
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
Aim of the work
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GEBV from 3 breeds of US cattleHolstein 63,615Brown Swiss 2,038Jersey 8,084
23 traits (production and functional)
GEBV and CEBV from December 2010 run
Genomewide (GW) and chromosomal (CHRW) correlation matrices
Materials and methods
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
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Traits analyzed
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YieldMilkFatFat percentProteinProtein percentConformationDairy formFinal scoreFoot angle
Fore udderattachment
Front teatplacement
Rear legs (side)Rear udder heightRump angleRump widthStatureStrength
Teat lengthUdder cleftUdder depthHealth and fitnessDaughter pregnancy
rateNet meritSomatic cell scoreProductive life
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Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
Factor Analysis
Genome-wideGEBV correlation matrices
Within-breedcomparison
GW vs. CHRW
Breed comparison
Chromosome wideCEBV correlation matrices
BTA6, BTA14, BTA18
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BS GWDairy_form 0.589 0.577 0.109 -0.200 -0.147 -0.164
Dtr_Preg_Rate -0.383 -0.357 -0.025 0.419 0.155 0.395
Fat 0.934 0.127 0.003 0.089 0.192 0.037
Fat_Percent -0.0199 0.034 -0.044 -0.049 0.930 -0.020
Final_score 0.344 0.705 0.471 0.104 -0.097 0.134
Foot_angle 0.088 0.258 0.427 0.619 0.032 -0.092
Fore_udder_att 0.127 0.684 0.183 0.238 0.010 0.342
Front_teat_pla 0.067 0.567 0.191 0.053 -0.014 0.464
Milk 0.900 0.102 0.029 0.114 -0.367 0.047
Net_Merit 0.638 0.124 -0.181 0.574 0.092 0.415
Pro_Percent -0.009 -0.043 0.050 0.047 0.916 -0.009
Prod_Life 0.142 0.130 -0.224 0.637 -0.082 0.569
protein 0.950 0.083 0.055 0.144 0.0478 0.047
Rear_legs_side -0.129 -0.054 -0.097 -0.769 0.120 0.128
Rear_ud_height 0.297 0.765 0.160 0.125 -0.020 0.118
Rump_angle 0.120 -0.545 0.104 0.143 0.00099 0.219
Rump_width -0.0854 0.169 0.808 -0.128 0.003 0.114
SCS -0.0626 -0.116 0.239 -0.612 -0.092 -0.156
Stature 0.287 0.385 0.724 0.036 -0.002 -0.132
Strength -0.039 -0.033 0.904 0.025 0.008 0.019
teat_length -0.045 -0.174 -0.036 0.026 0.033 -0.756
Udder_cleft 0.138 0.513 0.027 0.196 0.030 0.089
Udder_depth -0.141 0.638 0.159 0.435 0.086 0.323
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Summarizing
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Breed nVariance explained Meaning
Brown Swiss
6 0.74 Yield, udder, body size, functional, composition, teat lenght
Holstein 7 0.81 Udder, body size, functional, yield, composition
Jersey 6 0.75 Udder, yield, body size, composition, functional
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Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
Some comparisons between breeds GW
Brown Swiss YieldDairy_form 0.589Fat 0.934Milk 0.900Net_Merit 0.638Protein 0.950Expl. var. 0.17
Holstein YieldFat 0.828milk 0.928Protein 0.933Expl. var.
0.12
Jersey Yield
Dairy_form
0.565
Fat 0.870
Milk 0.813
Net_Merit 0.838
Prod_Life 0.575
Protein 0.923
Expl. var 0.18
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Dipartimento di Scienze Zootecniche
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Some comparisons between breed GW
Brown Swiss FunctionalFoot_angle 0.619Net_Merit 0.574Prod_Life 0.637Rear_legs_side
-0.769
SCS -0.612Expl. var. 0.12
Holstein Functional
Dtr_Preg_Rate 0.861Net_Merit 0.842Prod_Life 0.937SCS -0.746Expl. var. 0.14
Jersey FunctionalDtr_Preg_Rate 0.699Prod_Life 0.576SCS -0.763Expl. var 0.12
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Some considerations
Similar meaning of factors extracted in the different breeds
Some differences in their structure
More defined in the Holstein compared to the other two breeds
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
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Some comparison within breed: BS
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
GW YieldDairy_form
0.589
Fat 0.934Milk 0.900Net_Merit 0.638Protein 0.950Expl. var. 0.17
GWA Composition
Fat_Percent 0.930Pro_Percent 0.916Expl. var. 0.09
BTA6 F1Dairy_form 0.699Dtr_Preg_Rate -0.657Fat 0.778Fat_Percent -0.610Milk 0.936Net_Merit 0.504Pro_Percent -0.589Protein 0.695Udder_depth -0.541Expl. var 0.21
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Some comparison within breed: JE
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
GW YieldDairy_form
0.565
Fat 0.870
Milk 0.813
Net_Merit 0.838
Prod_Life 0.575
Protein 0.923
Expl. var 0.18
GW Composition
Fat_Percent 0.947Pro_Percent 0.908Expl. Var. 0.11
BTA14 F2Dairy_form 0.841Final score 0.632Front_teat_pla 0.677Protein 0.695SCS 0.768Expl. var 0.17
BTA14 F3Fat 0.889Fat_Percent 0.989Milk -0.886Pro_Percent 0.983Expl. var 0.17
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Some comparison within breed: BS
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
GW FunctionalFoot_angle 0.619Net_Merit 0.574Prod_Life 0.637Rear_legs_side -0.769SCS -0.612Expl. var. 0.12
BTA18 F4Dtr_Preg_Rate 0.904Net_Merit 0.668Prod_Life 0.766SCS -0.766Expl. Var. 0.12
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Some considerations Some changes in the factor structure
moving from GW to CHRW
Overlapping yield and composition factors (BTA6 in BS)
Different behaviour of protein (BTA14 in JE)
Inclusion of a fertility trait in the functional factor (BTA18 for BS)
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
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Implications
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
Multivariate factor analysis can characterise the correlation structure of GEBV and CEBV
Identification of differences in genetic correlations among traits across the genome and on individual chromosomes
Observed changes in the correlation structure may be indications of genomic regions affecting multiple traits?
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Acknowledgements
Dipartimento di Scienze Zootecniche
Università degli Studi di Sassari
Research developed within the non funded cooperative agreement 58-1265-1-033FN between Animal Improvement Programs labortory, ARS, USDA and Università di Sassari, Dipartimento di Scienze Zootecniche