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). Comparisons are made with a traditional pedigree-based approach. 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. Genomic and pedigree-based approaches gave largely consistent results. Thus, 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.

Keywords: cryptic evolution; genomic estimated breeding value; genomic prediction; microevolution; soay sheep.

Figure 1.

Adult weight (corrected for age and sex) has declined over the course of the long-term study. (Online version in colour.)

Figure 2.

(a) GEBVs for adult birth weight have increased over the course of the study. Bold blue line is posterior cohort means. Light blue shading shows the 95% posterior credible interval. Thin grey lines are the 1000 gene-dropped simulations, showing the expected changes in adult weight GEBVs due to genetic drift. (c) The distribution of the difference in slope of GEBV against year for the real data minus the gene-dropped data. The proportion of slope differences less than 0 gives the probability that the observed slope is caused by genetic drift. Panels (b) and (d) show the equivalent results, using pedigreed-derived EBVs rather than genomic ones, and drift simulations using breeding values rather than the individual loci (see Methods). Data are from training and test population individuals from 1990–2015. Panels (e)–(h) show the same plots but for the period 1985–2005. (Online version in colour.)