A High Resolution Genome-Wide Scan for Significant Selective Sweeps: An Application to Pooled Sequence Data in Laying Chickens

Abstract

In most studies aimed at localizing footprints of past selection, outliers at tails of the empirical distribution of a given test statistic are assumed to reflect locus-specific selective forces. Significance cutoffs are subjectively determined, rather than being related to a clear set of hypotheses. Here, we define an empirical p-value for the summary statistic by means of a permutation method that uses the observed SNP structure in the real data. To illustrate the methodology, we applied our approach to a panel of 2.9 million autosomal SNPs identified from re-sequencing a pool of 15 individuals from a brown egg layer line. We scanned the genome for local reductions in heterozygosity, suggestive of selective sweeps. We also employed a modified sliding window approach that accounts for gaps in the sequence and increases scanning resolution by moving the overlapping windows by steps of one SNP only, and suggest to call this a “creeping window” strategy. The approach confirmed selective sweeps in the region of previously described candidate genes, i.e. TSHR, PRL, PRLHR, INSR, LEPR, IGF1, and NRAMP1 when used as positive controls. The genome scan revealed 82 distinct regions with strong evidence of selection (genome-wide p-value<0.001), including genes known to be associated with eggshell structure and immune system such as CALB1 and GAL cluster, respectively. A substantial proportion of signals was found in poor gene content regions including the most extreme signal on chromosome 1. The observation of multiple signals in a highly selected layer line of chicken is consistent with the hypothesis that egg production is a complex trait controlled by many genes.

A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: Testing colocalization among outlier markers, candidate genes, and quantitative trait loci for production traits

“…In pigs, selection signatures have been identified in genomic regions associated with selected traits such as coat color, ear morphology, reproductive characteristics, and fat deposition (Wilkinson et al., 2013; adaptation to low temperatures (Ai et al., 2015) and also immune traits (Yang, Li, Li, Fan, & Tang, 2014)). In chickens, researchers have identified 82 selection signatures associated with traits under artificial selection such as eggshell hardness but have also detected those associated with immune system characteristics (Qanbari et al., 2012). …”

“…Genome scans support the existence of long haplotype blocks in domestic chickens where regions of low heterozygosity containing previously described candidate genes for laying traits that can span several Megabases (Mb) suggesting recent hard sweeps (Qanbari et al., 2012). Soft sweeps characterized by narrow divergent haplotype blocks were more common than hard sweeps characterized by wide divergent haplotype blocks in a population of maize that had undergone strong positive directional selection for ear number for 30 generations (Beissinger et al., 2014).…”

Population genomics of the honey bee reveals strong signatures of positive selection on worker traits

“…Analyses were also performed with 7-and 10-kb windows and results remained consistent across the different window sizes. To avoid estimating F ST across sequence gaps, windows with SNPs spaced greater than 5-kb apart were skipped (14). For the creeping-window approach, outlier windows were statistically identified using simulation as follows: (i) we rescanned the genome 10 million times and randomly sampled new F ST values for every SNP in a given window (14); (ii) windows were deemed outliers if observed average F ST in a window was above the 95th percentile of the empirical distribution of expected F ST , following stringent FDR correction (q < 0.025) (75).…”

“…A recent Bayesian implementation of this classic test uses genome-wide estimates of polymorphism and divergence to improve statistical power (11). Outlier tests of selection are also less sensitive to population demography, which affect all loci within a genome; loci under selection thereby appear as outliers in the empirical distribution of genome-wide data (12)(13)(14). In spatially structured populations, outlier tests of genetic differentiation are especially useful in identifying loci underlying local adaptation (10,15).…”

A Genome-Wide Scan for Evidence of Selection in a Maize Population Under Long-Term Artificial Selection for Ear Number

“…In animals, for instance, Johansson et al (2010) worked with a population of chickens divergently selected for body size and found that the majority of changes can be attributed to selection on standing genetic variation vs. new mutations. Another study using chickens identified 82 putatively selected regions with reduced levels of heterozygosity (Qanbari et al 2012). Similarly in cattle, Flori et al (2009) found 13 regions that were under selection in recent history, a subset of which included genes previously known to affect milk production.…”