Using genomic prediction to detect microevolutionary change of a quantitative trait

Abstract

Detecting microevolutionary responses to natural selection by observing temporal changes in individual breeding values is challenging. The collection of suitable datasets can take many years and disentangling the contributions of the environment and genetics to phenotypic change is not trivial. Furthermore, pedigree-based methods of obtaining individual breeding values have known biases. Here, we apply a genomic prediction approach to estimate breeding values of adult weight in a 35-year dataset of Soay sheep (Ovis aries). During the study period adult body weight decreased, but the underlying genetic component of body weight increased, at a rate that is unlikely to be attributable to genetic drift. Thus cryptic microevolution of greater adult body weight has probably occurred. Using genomic prediction to study microevolution in wild populations can remove the requirement for pedigree data, potentially opening up new study systems for similar research.

The utility of genomic prediction models in evolutionary genetics

“…In the breeding literature, the additive genetic value an individual has for a phenotype is called the breeding value [20,21]. Estimating the breeding value of an organism can be especially useful when the phenotype is unobservable in that particular individual, such as the breeding value for milk production for a bull or when unmeasured individuals have undergone viability selection [15,19].…”

“…[17] h Soay sheep ( Ovis aries )weight1168∼36 k0.320.51BayesRjaw length897∼36 k0.590.38BayesRforeleg length1126∼36 k0.530.62BayesRhindleg length1139∼36 k0.500.59BayesRmetacarpal length890∼36 k0.620.65BayesRmale horn length472∼36 k0.420.67BayesRcoat colour4737∼36 kDNS1.00BayesRcoat pattern4737∼36 kDNS0.98BayesRHunter et al . [15] i Soay sheep ( Ovis aries )adult body weight1168∼36 k0.34–0.49 [58]DNSBayesR a Heritability from random additive line effect g in ASReml; DNS, ‘data not shown’. b Broad-sense heritability. c Depending on model, SNPs used and sex. This trait is heavily influenced by epistasis. d Genomic heritability. e These are the highest estimates for males and females given in the text on page 1876 of this publication. f Thirty-one pools from pool seq used as training population and 150 individuals from moderate-coverage whole-genome sequencing as test dataset. g Used only SNPs with most significant p -values in GWAS. h Accuracies given for 50% training population and Bayes R taken from Table S2 in the manuscript.…”

“…Thus, the predicted breeding value is typically defined only for a target set of environmental conditions. One interesting example of how to deal with this complicating factor comes from [15,17] who opted to fit mixed models that incorporated fixed effects, non-genetic random effects and repeated measures. The random effect of individual identity was extracted and incorporated as the phenotype in genomic prediction.…”

“…A related field has sprung up in human genetics and personalized medicine, where genomic prediction (often called polygenic risk scores) is used to identify patient risk for disease or treatment options conditioned on their unique genotype [9]. However, only a small number of pioneering studies have applied genomic prediction methods in wild populations [10][11][12][13][14][15][16][17][18].…”

“…The power of genomic prediction is the ability to predict the breeding value of unobserved individuals or individuals who have not yet expressed the trait of interest. This capability could be used in a variety of applications in evolutionary biology, including understanding if there is an association between viability selection and breeding values, or to understand the microevolution of breeding values through time [15,19]. Further, with some care, formal tests of genetic architecture can be made [12] and the additive genetic value for historical populations can be predicted.…”