Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy

Abstract

Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCp) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCp to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored.T HE principle of genomic selection is to estimate simultaneously the effect of all markers in a training population consisting of phenotyped and genotyped individuals (Meuwissen et al. 2001). Genomic estimated breeding values (GEBVs) are then calculated as the sum of estimated marker effects for genotyped individuals in a prediction population. Fitting all markers simultaneously ensures that marker-effect estimates are unbiased, small effects are captured, and there is no multiple testing.Current genomic prediction models usually use only a single phenotypic trait. However, new varieties of crops and animals are evaluated for their performance on multiple traits. Crop breeders record phenotypic data for multiple traits in categories such as yield components (e.g., grain weight or biomass), grain quality (e.g., taste, shape, color, nutrient content), and resistance to biotic or abiotic stress. To take advantage of genetic correlation in mapping causal loci, multi-trait QTL mapping methods have been developed using maximum-likelihood (Jiang and Zeng 1995) and Bayesian (Banerjee et al. 2008; Xu et al. 2009) methods. Calus andVeerkamp (2011) recently presented three multiple-trait genomic selection (MT-GS) models: ridge regression (GBLUP), BayesSSVS, and BayesCp. The authors ranked the performances of these MT-GS methods (BayesSSVS . BayesCp . GBLUP) based on simulated traits under a single genetic architecture. Genetic correlation was shown to be a key factor determining the MT-GS advantage over single-trait genomic selection (ST-GS). A few issues for these MT-GS methods still need attention. First, genetic architectu...

Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population

“…Using such a predictor trait may result in an improvement of the trait of interest at a low cost. Also in genomic selection, an additional gain in accuracy can be achieved using predictor traits, as it was shown using both deterministic (Calus et al, 2013) and empirical simulations Jia and Jannink, 2012). Both empirical studies had predictor traits measured on both the reference animals and evaluated animals.…”

Do Molecular Markers Inform About Pleiotropy?

“…For instance, Yang et al (2010) suggested using whole-genome regressions (WGRs) (Meuwissen et al 2001) to assess the proportion of variance of a trait or disease risk that can be explained by a regression of phenotypes on common SNPs or genomic heritability and a related parameter, the "missing heritability." More recently, WGR models have been extended for the analysis of systems of multiple traits, so the concept of genomic correlation also has entered into the picture (Jia and Jannink 2012;Lee et al 2012). For instance, Maier et al (2015) used multivariate WGR models and reported estimates of genetic correlations between psychiatric disorders, and Furlotte and Eskin (2015) presented a methodology that incorporates genetic marker information for the analysis of multiple traits that, according to the authors, "provide fundamental insights into the nature of co-expressed genes."…”

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

“…By evaluating power performance, it is shown that it can be advantageous to perform the proposed pleiotropy analysis instead of individual trait analysis. [1][2][3][4][5][6][7][8][9][10]27,44 Among other merits, the MFLM can handle missing genotype data naturally.…”

“…[1][2][3][4][5][6][7][8][9][10] To our knowledge, metaanalysis and pleiotropy analysis have been performed separately so far, and there are no gene-based meta-analysis methods for combining multiple studies together and for carrying out a unified pleiotropy analysis. Here, multivariate functional linear models (MFLM) are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates in a meta-analysis context.…”