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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #318511

Title: Practical implications for genetic modeling in the genomics era

item Vanraden, Paul

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/16/2015
Publication Date: 3/1/2016
Citation: Van Raden, P.M. 2016. Practical implications for genetic modeling in the genomics era. Journal of Dairy Science. 99(3):2405-2412.

Interpretive Summary: Genetic models convert raw data into the predictions of genetic merit used in genomic selection. Many differing models and algorithms can combine trait phenotypes, animal pedigrees, and rapidly expanding numbers of genotypes into accurate and unbiased predictions of performance. Because hundreds of thousands or millions more variants can be imputed from a few thousand genotyped variants, computation has become a more limiting factor when choosing models. Genomic preselection, short generation intervals, and rapidly changing input data make modeling and validation more difficult. Researchers and models need to adapt to these new breeding programs.

Technical Abstract: Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, including genetic by environmental interactions and correlations among traits, and accounting for nonadditive inheritance or nonnormal distributions. Data included phenotypes and pedigrees during the last century and genotypes within the last decade. The genomic data can include single nucleotide polymorphisms, quantitative trait loci, insertions, deletions, and haplotypes; total numbers of known variants have increased rapidly from thousands to hundreds of thousands to millions. Nonlinear models have been used to account for the nonnormal distribution of genomic effects, but reliability is usually better than linear models only for traits influenced by major genes. Numbers of available genotyped animals have also increased rapidly from a few thousand in 2009 to over 1 million in 2015. Most are young females and will contribute to estimating allele effects in the future, but only about 150,000 have phenotypes so far. Traditional animal models may become biased by genomic preselection because Mendelian sampling of phenotyped progeny and mates is no longer expected to average 0. Single-step models that combine pedigree and genomic relationships can account for such selection, but approximations are required for affordable computation. Traditional animal models may include all breeds and crossbreds, but most genomic evaluations are still computed within breed. Inclusion of inbreeding, heterosis, dominance, and interactions can improve precision. Multitrait genomic models may be preferred for traits with many missing records or when foreign records are included as pseudo-observations, but most countries use multitrait traditional evaluations followed by single-trait genomic evaluations. Researchers must choose from many available models and explain how the models work so that breeders can more confidently apply the predictions in their selection programs.