Standing genetic variation as a major contributor to adaptation in the Virginia chicken lines selection experiment

Abstract

BackgroundArtificial selection provides a powerful approach to study the genetics of adaptation. Using selective-sweep mapping, it is possible to identify genomic regions where allele-frequencies have diverged during selection. To avoid false positive signatures of selection, it is necessary to show that a sweep affects a selected trait before it can be considered adaptive. Here, we confirm candidate, genome-wide distributed selective sweeps originating from the standing genetic variation in a long-term selection experiment on high and low body weight of chickens.ResultsUsing an intercross between the two divergent chicken lines, 16 adaptive selective sweeps were confirmed based on their association with the body weight at 56 days of age. Although individual additive effects were small, the fixation for alternative alleles across the loci contributed at least 40 % of the phenotypic difference for the selected trait between these lines. The sweeps contributed about half of the additive genetic variance present within and between the lines after 40 generations of selection, corresponding to a considerable portion of the additive genetic variance of the base population.ConclusionsLong-term, single-trait, bi-directional selection in the Virginia chicken lines has resulted in a gradual response to selection for extreme phenotypes without a drastic reduction in the genetic variation. We find that fixation of several standing genetic variants across a highly polygenic genetic architecture made a considerable contribution to long-term selection response. This provides new fundamental insights into the dynamics of standing genetic variation during long-term selection and adaptation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0785-z) contains supplementary material, which is available to authorized users.

Imputation-Based Fine-Mapping Suggests That Most QTL in an Outbred Chicken Advanced Intercross Body Weight Line Are Due to Multiple, Linked Loci

“…, Siegel 1962a,b; Jacobsson et al 2005; Wahlberg et al 2009; Johansson et al 2010; Besnier et al 2011; Pettersson et al 2011, 2013; Sheng et al 2015). We therefore implemented a forward-selection/backward-elimination procedure with a termination criteria suitable for a polygenic trait in a bootstrap-based framework to correct for population structure in the AIL (Valdar et al 2009; Sheng et al 2015). As all markers with genotypes could not be included in a backward-elimination analysis due to the limited sample size, we first used a forward-selection based single-marker association analysis to identify a smaller set of statistically suggestive independent signals within each QTL region.…”

“…As all markers with genotypes could not be included in a backward-elimination analysis due to the limited sample size, we first used a forward-selection based single-marker association analysis to identify a smaller set of statistically suggestive independent signals within each QTL region. The backward-elimination analysis (Valdar et al 2009; Sheng et al 2015) was then used to identify associations robust to possible influences of genetic dependencies (linkage or LD) between markers within the QTL or population structure in the AIL (Peirce et al 2008; Cheng et al 2010). …”

“…In short, we used a bootstrap-based backward-elimination model selection framework (Sheng et al 2015) across the markers selected by forward-selection in the QTL. An adaptive model selection criterion controlling the False Discovery Rate (Abramovich et al 2006; Gavrilov et al 2009) was used during backward-elimination in a standard linear model framework, starting with a full model including the fixed effects of sex and generation, and the additive effects of all markers (Model 2):$y=\mu +{\beta }_{\text{sex},\text{generation},\text{markers}}X+\epsilon \text{(Model 2)}$where phenotype, sex, and generation were coded as described for Model 1 and where ε again is assumed to be iid and normally distributed around 0 with variance ${\sigma }^2.$ The intercept, $\mu ,$ represents the mean body weight at 56 d of age for female individuals from the F 2 generation.…”

“…Also within species, the genetic basis of variations in body size among strains of mice (Valdar et al 2006), breeds of cattle (Saatchi et al 2014), pigs (Yoo et al 2014), and chickens (Van Goor et al 2015) is often polygenic and due to polymorphisms with modest individual effects. Studies of experimental crosses from artificially selected populations with extreme body sizes in the mouse (Bevova et al 2006; Parker et al 2011) and chicken (Sheng et al 2015) using, for example, chromosome substitution strains (Bevova et al 2006) and advanced intercross lines (AILs) (Darvasi and Soller 1995; Besnier et al 2011; Parker et al 2011) have revealed that the responses to selection in these populations has resulted from selection on highly complex and polygenic genetic architectures.…”

“…Also, a network of epistatic loci has been found to make a significant contribution to long-term selection response through the release of selection-induced additive variation (Carlborg et al 2006; Le Rouzic et al 2007; Le Rouzic and Carlborg 2008). Explorations of the genome-wide footprint of selection by selective-sweep mapping suggests that perhaps >100 loci throughout the genome have contributed to selection response (Johansson et al 2010; Pettersson et al 2013), and many of these contribute to 56 d body weight (Sheng et al 2015). …”