Multivariate factor analysis of genomic correlation matrices in three US dairy cattle breeds

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

http://www.uniss.it/php/proiettoreImmagini.php?cat=398&item=2&itemImm=6032&xmlImm=/xml/testi/testi6032.xml&xml=/xml/immagini/immagine1162.xml&pagina=0&singpag=0

Chromosomal breeding values: CEBV



-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




Genome-wide and chromosome-wide (BTA18) correlations can be calculated

Cole et al. 2009. J. Dairy Sci. 92(6):2931–2946.


Correlation structures differ between chromosomes

These differences may help identify genes with small additive effects that affect several traits

Correlations between genomic values




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




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


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?



Aim of the work


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




Traits analyzed



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




Factor Analysis

Genome-wideGEBV correlation matrices

Within-breedcomparison

GW vs. CHRW

Breed comparison

Chromosome wideCEBV correlation matrices

BTA6, BTA14, BTA18


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

Summarizing



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




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




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


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




Some comparison within breed: BS



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


Some comparison within breed: JE



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


Some comparison within breed: BS



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


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)



Implications



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?

Acknowledgements



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

Multivariate factor analysis of genomic correlation matrices in three US dairy cattle breeds

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