The Impact of Genetic Architecture on Genome-Wide Evaluation Methods

Abstract

The rapid increase in high-throughput single-nucleotide polymorphism data has led to a great interest in applying genome-wide evaluation methods to identify an individual's genetic merit. Genome-wide evaluation combines statistical methods with genomic data to predict genetic values for complex traits. Considerable uncertainty currently exists in determining which genome-wide evaluation method is the most appropriate. We hypothesize that genome-wide methods deal differently with the genetic architecture of quantitative traits and genomes. A genomic linear method (GBLUP), and a genomic nonlinear Bayesian variable selection method (BayesB) are compared using stochastic simulation across three effective population sizes and a wide range of numbers of quantitative trait loci (N QTL ). GBLUP had a constant accuracy, for a given heritability and sample size, regardless of N QTL . BayesB had a higher accuracy than GBLUP when N QTL was low, but this advantage diminished as N QTL increased and when N QTL became large, GBLUP slightly outperformed BayesB. In addition, deterministic equations are extended to predict the accuracy of both methods and to estimate the number of independent chromosome segments (M e ) and N QTL . The predictions of accuracy and estimates of M e and N QTL were generally in good agreement with results from simulated data. We conclude that the relative accuracy of GBLUP and BayesB for a given number of records and heritability are highly dependent on M e, which is a property of the target genome, as well as the architecture of the trait (N QTL ).

Integrating genomic selection into dairy cattle breeding programmes: a review

“…Fixing GEBV accuracies to currently achieved values consequently depicts a rather pessimistic scenario, which is safe when genomic schemes are compared with conventional schemes. However, accuracy of GEBVs was shown to be sensitive to many intermingled parameters comprising the genetic architecture of traits (Daetwyler et al, 2010), the LD existing between markers and QTL (Goddard, 2009;Goddard et al, 2011), the statistical model of genomic evaluation (Daetwyler et al, 2010;Bastiaansen et al, 2012), the frequency of updating prediction equations (Muir, 2007) and the composition of the training population and its relationship with selection candidates (Lillehammer et al, 2011;Pszczola et al, 2012). As a consequence, different designs of genomic schemes can lead to different evolutions of GEBV accuracy (Lillehammer et al, 2011).…”

Integration of genomic information into sport horse breeding programs for optimization of accuracy of selection

“…However, when referring to other species, for example, dairy cattle, accuracies of GEBVs are substantially higher for production traits compared with fertility, somatic cell score or longevity (VanRaden et al, 2009). Nevertheless, on the basis of results from simulation studies or deterministic predictions (Calus et al, 2008;Daetwyler et al, 2010), a correlation of r mg 5 0.5 should be feasible also for GS for functional traits in horses. Such a crucial value doubles r TI at this very early point of selection (Figure 2) compared with the accuracy of the conventional index.…”

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