Improvement of Prediction Ability for Genomic Selection of Dairy Cattle by Including Dominance Effects

被引:70
作者
Sun, Chuanyu [1 ]
VanRaden, Paul M. [2 ]
Cole, John B. [2 ]
O'Connell, Jeffrey R. [3 ]
机构
[1] Natl Assoc Anim Breeders, Columbia, MO USA
[2] USDA, Agr Res Serv, Anim Genom & Improvement Lab, Beltsville, MD USA
[3] Univ Maryland, Sch Med, Baltimore, MD 21201 USA
基金
美国农业部;
关键词
BREEDING VALUE PREDICTION; MILK-PRODUCTION TRAITS; VARIANCE; INFORMATION; PEDIGREE; VALUES; YIELD; REGRESSION; HEALTH; PLANT;
D O I
10.1371/journal.pone.0103934
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Dominance may be an important source of non-additive genetic variance for many traits of dairy cattle. However, nearly all prediction models for dairy cattle have included only additive effects because of the limited number of cows with both genotypes and phenotypes. The role of dominance in the Holstein and Jersey breeds was investigated for eight traits: milk, fat, and protein yields; productive life; daughter pregnancy rate; somatic cell score; fat percent and protein percent. Additive and dominance variance components were estimated and then used to estimate additive and dominance effects of single nucleotide polymorphisms (SNPs). The predictive abilities of three models with both additive and dominance effects and a model with additive effects only were assessed using ten-fold cross-validation. One procedure estimated dominance values, and another estimated dominance deviations; calculation of the dominance relationship matrix was different for the two methods. The third approach enlarged the dataset by including cows with genotype probabilities derived using genotyped ancestors. For yield traits, dominance variance accounted for 5 and 7% of total variance for Holsteins and Jerseys, respectively; using dominance deviations resulted in smaller dominance and larger additive variance estimates. For non-yield traits, dominance variances were very small for both breeds. For yield traits, including additive and dominance effects fit the data better than including only additive effects; average correlations between estimated genetic effects and phenotypes showed that prediction accuracy increased when both effects rather than just additive effects were included. No corresponding gains in prediction ability were found for non-yield traits. Including cows with derived genotype probabilities from genotyped ancestors did not improve prediction accuracy. The largest additive effects were located on chromosome 14 near DGAT1 for yield traits for both breeds; those SNPs also showed the largest dominance effects for fat yield (both breeds) as well as for Holstein milk yield.
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页数:18
相关论文
共 38 条
[1]  
ASHWELL MS, 2002, P 7 WORLD C GEN APPL, V31, P123
[2]   Novel Use of Derived Genotype Probabilities to Discover Significant Dominance Effects for Milk Production Traits in Dairy Cattle [J].
Boysen, Teide-Jens ;
Heuer, Claas ;
Tetens, Jens ;
Reinhardt, Fritz ;
Thaller, Georg .
GENETICS, 2013, 193 (02) :431-+
[3]   Genomic breeding value prediction: methods and procedures [J].
Calus, M. P. L. .
ANIMAL, 2010, 4 (02) :157-164
[4]   Distribution and location of genetic effects for dairy traits [J].
Cole, J. B. ;
VanRaden, P. M. ;
O'Connell, J. R. ;
Van Tassell, C. P. ;
Sonstegard, T. S. ;
Schnabel, R. D. ;
Taylor, J. F. ;
Wiggans, G. R. .
JOURNAL OF DAIRY SCIENCE, 2009, 92 (06) :2931-2946
[5]   Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary US Holstein cows [J].
Cole, John B. ;
Wiggans, George R. ;
Ma, Li ;
Sonstegard, Tad S. ;
Lawlor, Thomas J., Jr. ;
Crooker, Brian A. ;
Van Tassell, Curtis P. ;
Yang, Jing ;
Wang, Shengwen ;
Matukumalli, Lakshmi K. ;
Da, Yang .
BMC GENOMICS, 2011, 12
[6]   Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers [J].
Crossa, Jose ;
de los Campos, Gustavo ;
Perez, Paulino ;
Gianola, Daniel ;
Burgueno, Juan ;
Luis Araus, Jose ;
Makumbi, Dan ;
Singh, Ravi P. ;
Dreisigacker, Susanne ;
Yan, Jianbing ;
Arief, Vivi ;
Banziger, Marianne ;
Braun, Hans-Joachim .
GENETICS, 2010, 186 (02) :713-U406
[7]  
*CTR BIOINF COMP B, 2013, BOS TAUR ASS
[8]   Mixed Model Methods for Genomic Prediction and Variance Component Estimation of Additive and Dominance Effects Using SNP Markers [J].
Da, Yang ;
Wang, Chunkao ;
Wang, Shengwen ;
Hu, Guo .
PLOS ONE, 2014, 9 (01)
[9]   Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding [J].
de los Campos, Gustavo ;
Hickey, John M. ;
Pong-Wong, Ricardo ;
Daetwyler, Hans D. ;
Calus, Mario P. L. .
GENETICS, 2013, 193 (02) :327-+
[10]  
Duangjinda M, 2001, J ANIM SCI, V79, P2997