1. BayesAS and GBLUP models
for genomic selection in Mink
T. Villumsen1, G. Su 1, Z. Cai 1, B. Guldbrandtsen 1, T. Asp 1, G. Sahana 1, M.S. Lund 1
1 Department of Molecular Biology and Genetics, Aarhus University (DK).
Conclusions:
Multiple trait models generally didn’t perform better than single trait models for our mink data
BayesAS models allowing heterogeneous (co)variance over the genome were superior to GBLUP models
Questions:
Does a multiple trait model perform better than a than single trait model?
Is a model that accounts for heterogeneous (co)variance over the genome (BayesAS)superior to a GBLUP model for our mink data?
Results:
Data: Foulum brown line
28,336 SNP-markers in 391 scaffolds
2013
Accuracy of prediction in the 5-fold cross validation for 2014
200 Males
Geno- & Phenotypes
800 Females
Geno- & Phenotypes
Trait
No of Animals
ST-GBLUP
MT-GBLUP
ST-BayesAS
MT-BayesAS
Live grad.
Weight1
752
0.490
0.5001
0.516
0.5231
Quality
0.685
0.694
0.695
0.706
Density
0.393
0.462
0.450
0.459
Silky
0.820
0.818
0.844
0.843
Dried skins
P_length
639
0.480
0.489
0.618
0.624
P_quality
665
0.227
0.234
0.270
0.258
P_density
0.299
0.302
0.357
0.362
P_silky
0.138
0.135
0.197
0.194
6000 Progeny
Live- and Peltgrading
2014
200 Males
Geno- & Phenotypes
400 Females
Geno- & Phenotypes
6000 Progeny
Live- and Peltgrading
840 Genotyped
Models:
MT-GBLUP:
MT-BayesAS:
Hierarchial model, handle correlation between SNP effects:
Not a clear tendency for MT-models to be better than ST-models
BayesAS superior to GBLUP models
BayesAS GEBV increased more for pelt traits than live grading traits
All traits analyzed with weight in MT-analyses. Results for weight: analyzed with P_length AU
AARHUS
UNIVERSITY
DEPARTMENT OF MOLECULAR BIOLOGY AND GENETICS