Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection
Tóm tắt
A dataset was simulated and distributed to participants of the QTLMAS XII workshop who were invited to develop genomic selection models. Each contributing group was asked to describe the model development and validation as well as to submit genomic predictions for three generations of individuals, for which they only knew the genotypes. The organisers used these genomic predictions to perform the final validation by comparison to the true breeding values, which were known only to the organisers. Methods used by the 5 groups fell in 3 classes 1) fixed effects models 2) BLUP models, and 3) Bayesian MCMC based models. The Bayesian analyses gave the highest accuracies, followed by the BLUP models, while the fixed effects models generally had low accuracies and large error variance. The best BLUP models as well as the best Bayesian models gave unbiased predictions. The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance. The current comparison suggests that Bayesian analyses on haplotypes or SNPs are the most promising approach for Genomic selection although the BLUP models may provide a computationally attractive alternative with little loss of efficiency. On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection.
Tài liệu tham khảo
Fernando RL, Grossman M: Marker assisted selection using best linear unbiased prediction. Genet Sel Evol. 1989, 21: 467-477. 10.1051/gse:19890407.
Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker map. Genetics. 2001, 157: 1819-1829.
Schaeffer LR: Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet. 2006, 123: 218-223. 10.1111/j.1439-0388.2006.00595.x.
Gianola D, Fernando RL, Stella A: Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics. 2006, 173: 1761-1776. 10.1534/genetics.105.049510.
Haldane JBS: The combination of linkage values and the calculation of distances between the loci of linked factors. J Genet. 1919, 8: 299-309. 10.1007/BF02983270.
NCBI 2005: National Centre for Biotechnology Information, MGI genetic map of the mouse genome (Mus musculus). [http://www.ncbi.nlm.nih.gov/Genomes/]
Hayes BJ, Goddard ME: The distribution of the effects og genes affecting quantitative traits in livestock. Genet Sel Evol. 2001, 33: 209-229. 10.1051/gse:2001117.
McKay SD, Schnabel RD, Murdoch BM, Matukumalli LK, Aerts J, Coppieters W, Crews D, Dias Neto E, Gill CA, Gao C, Mannen H, Stothard P, Wang Z, Van Tassell CP, Williams JL, Taylor JF, Moore SS: Whole genome linkage disequilibrium maps in cattle. BMC Genetics. 2007, 8: 74-10.1186/1471-2156-8-74.
Sargolzaei M, Schenkel FS, Jansen GB, Schaeffer LR: Extent of Linkage Disequilibrium in Holstein Cattle in North America. J Dairy Sci. 2008, 91: 2106-2117. 10.3168/jds.2007-0553.
de Ross AP, Hayes BJ, Spelman RJ, Goddard ME: Linkage disequilibrium and persistence of phase in Holstein-Fresian, Jersey and Angus cattle. Genetics. 179 (3): 1503-1512. 10.1534/genetics.107.084301.
Zukowski K, Suchochi T, Gontarek A, Szyda J: The impact of single nucleotide polymorphism selection on prediction of genomewide breeding values. BMC Proceedings. 2009, 3 (Suppl 1): S13-10.1186/1753-6561-3-s1-s13.
Hastie T, Tibshirani R, Friedman J: The elements of statistical learning. 2003, New York, Springer
Macciotta PP, Gaspa G, Steri R, Pieramati C, Carnier P, Dimauro C: Pre-selection of the most significant SNPs for the estimation of genomic breeding values. BMC Proceedings. 2009, 3 (Suppl 1): S14-10.1186/1753-6561-3-s1-s14.
Habier D, Fernando RL, Dekkers JCM: The impact on relationship information on genome-assisted breeding values. Genetics. 2007, 177: 2389-2397.
Pimentel ECG, König S, Schenkel FS, Simianer H: Comparison of statistical procedures for estimating polygenic effects using dense genome-wide marker data. BMC Proceedings. 2009, 3 (Suppl 1): S12-10.1186/1753-6561-3-s1-s12.
Calus MPL, de Roos APV, Veerkamp RF: Estimating genomic breeding values from the QTL-MAS Workshop data using single SNP regression and the haplotype/IBD approach. BMC Proceedings. 2009, 3 (Suppl 1): S10-10.1186/1753-6561-3-s1-s10.
Villumsen TM, Janss L: Bayesian genomic selection: the effect of haplotype length and priors. BMC Proceedings. 2009, 3 (Suppl 1): S11-10.1186/1753-6561-3-s1-s11.
Villumsen TM, Janss L, Lund MS: The importance of haplotype length and heritability using genomic selection in dairy cattle. J Anim Breed Genet. 2008, Published online: 24 Sep 2008; doi 10.1111/j.1439-0388.2008.00747.x
Andrew J, Chang SZ, Eichler EE: Structural Variation of the human genome. Annual Review of Genomics and Human Genetics. 2006, 7 (1): 407-10.1146/annurev.genom.7.080505.115618.
Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES: A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics. 2006, 38: 203-208. 10.1038/ng1702.