Genomic selection on breeding time in a wild bird population.

Phillip Gienapp,Marcel E Visser,Veronika N Laine,Mario P L Calus

doi:10.1002/evl3.103

Abstract

Artificial selection experiments are a powerful tool in evolutionary biology. Selecting individuals based on multimarker genotypes (genomic selection) has several advantages over phenotype‐based selection but has, so far, seen very limited use outside animal and plant breeding. Genomic selection depends on the markers tagging the causal loci that underlie the selected trait. Because the number of necessary markers depends, among other factors, on effective population size, genomic selection may be in practice not feasible in wild populations as most wild populations have much higher effective population sizes than domesticated populations. However, the current possibilities of cost‐effective high‐throughput genotyping could overcome this limitation and thereby make it possible to apply genomic selection also in wild populations. Using a unique dataset of about 2000 wild great tits (Parus major), a small passerine bird, genotyped on a 650 k SNP chip we calculated genomic breeding values for egg‐laying date using the so‐called GBLUP approach. In this approach, the pedigree‐based relatedness matrix of an “animal model,” a special form of the mixed model, is replaced by a marker‐based relatedness matrix. Using the marker‐based relatedness matrix, the model seemed better able to disentangle genetic and permanent environmental effects. We calculated the accuracy of genomic breeding values by correlating them to the phenotypes of individuals whose phenotypes were excluded from the analysis when estimating the genomic breeding values. The obtained accuracy was about 0.20, with very little effect of the used genomic relatedness estimator but a strong effect of the number of SNPs. The obtained accuracy is lower than typically seen in domesticated species but considerable for a trait with low heritability (∼0.2) as avian breeding time. Our results show that genomic selection is possible also in wild populations with potentially many applications, which we discuss here.

Highlights

Selecting individuals based on their genotypes instead of their phenotypes is already widely applied in animal and plant breeding
Egg-laying date in this population was moderate but lower than typical for animal and plant breeding. Despite this reduced accuracy, caused by the high effective population size of our large great tit study population, our results show that genomic selection can be possible in natural populations and we discuss a number of potential applications next to selection experiments
Depending on the used relatedness estimator and whether estimates were scaled according to the pedigree the estimated accuracy of genomic breeding values (GEBVs) for egg-laying date varied between 0.197 and 0.210

Summary

Introduction

Selecting individuals based on their genotypes instead of their phenotypes is already widely applied in animal and plant breeding. GENOMIC SELECTION IN THE WILD egg-laying date in this population was moderate but lower than typical for animal and plant breeding Despite this reduced accuracy, caused by the high effective population size of our large great tit study population, our results show that genomic selection can be possible in natural populations and we discuss a number of potential applications next to selection experiments. Quantitative genetics assumes that a trait is determined by many loci of small effects This assumption, the infinitesimal model (Barton et al 2017), allows to model the traits’ genetic (co)variances and its evolutionary change, the response to selection, based on phenotypic resemblance among individuals of known relatedness without any molecular genetic information. Despite its unrealistic assumptions that, for example, totally ignore gene-by-gene interactions, for which quantitative genetics and the infinitesimal model have been criticized (Nelson et al 2013), this framework has been highly successful in animal and plant breeding (Hill 2012) and in natural populations (Charmantier et al 2014)

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