Human genomic regions with exceptionally high levels of population differentiation identified from 911 whole-genome sequences

Abstract

BackgroundPopulation differentiation has proved to be effective for identifying loci under geographically localized positive selection, and has the potential to identify loci subject to balancing selection. We have previously investigated the pattern of genetic differentiation among human populations at 36.8 million genomic variants to identify sites in the genome showing high frequency differences. Here, we extend this dataset to include additional variants, survey sites with low levels of differentiation, and evaluate the extent to which highly differentiated sites are likely to result from selective or other processes.ResultsWe demonstrate that while sites with low differentiation represent sampling effects rather than balancing selection, sites showing extremely high population differentiation are enriched for positive selection events and that one half may be the result of classic selective sweeps. Among these, we rediscover known examples, where we actually identify the established functional SNP, and discover novel examples including the genes ABCA12, CALD1 and ZNF804, which we speculate may be linked to adaptations in skin, calcium metabolism and defense, respectively.ConclusionsWe identify known and many novel candidate regions for geographically restricted positive selection, and suggest several directions for further research.

Complex interplay between neutral and adaptive evolution shaped differential genomic background and disease susceptibility along the Italian peninsula

“…Only when N_ITA and S_ITA clusters were contrasted, four SNPs showed significant ∆DAF (p < 3.19 × 10 −8 , Table 1) considerably close to the threshold (0.25) used to pinpoint highly differentiated loci among intra-continental populations40 (Supplementary Information). However, when correction for background differentiation between populations was applied, obtained p-values considerably increased (p < 2.53 × 10 −04 ), suggesting that demographic processes played an appreciable role in shaping the observed ∆DAFs in addition to putative different selective pressures.…”

Understanding rare and common diseases in the context of human evolution

“…Genes associated with complex disease display signs of less pervasive purifying selection than Mendelian disease genes [32, 173], and are generally enriched in signals of positive selection [23, 28, 32, 37, 110, 122, 169]. There is also increasing evidence to suggest that genetic adaptations can alter complex disease susceptibility, and the population distribution of common susceptibility alleles is unlikely to result from neutral processes alone [12, 91, 177–179]. For example, the difference in susceptibility to hypertension and metabolic disorders between populations is thought to result from past adaptation to different environmental pressures [91, 179, 180].…”

Refining the Use of Linkage Disequilibrium as a Robust Signature of Selective Sweeps

“…Recombination rate is controlled for using either the HapMap genetic map [derived from LD patterns (Frazer et al 2007)] or the deCODE genetic map [derived from inferred recombination events in a large Icelandic cohort (Kong et al (2010)]. We assessed the ability of the various statistics to replicate selection signals previously identified based on the site frequency spectrum and/or population differentiation (Carlson et al 2005;Nielsen et al 2005;Oleksyk et al 2008;Pickrell et al 2009;Chen et al 2010;Ronen et al 2013;Colonna et al 2014;Pagani et al 2016), genetic features that should be relatively independent of local LD patterns and recombination rate variation under neutrality (although see, e.g., figure 3 of FerrerAdmetlla et al 2014 andThornton 2005).…”

“…Often, several outlier regions occurred in succession, such that it is difficult to identify specific genes, variants, or features that might be driving a signal. Different approaches using pairwise LD (Clemente et al 2014), other population genetic patterns [e.g., DIND (Barreiro et al 2009;Pagani et al 2016) and DDAF (1000Genomes Project Consortium et al 2012Colonna et al 2014)], or especially biological information on the impact of variants can simplify this task. Certain novel 200-kb regions contained a single gene or a few genes, such as signals overlapping MKRN3 and ARHGAP11B (Europeans) and ABCA12 (Asians).…”

“…Mutations in MKRN3 can affect the onset of puberty (Abreu et al 2013), and variants in this region have been associated with age at menarche (Perry et al 2014). Finally, ABCA12 has been previously identified as a possible selection candidate based on population differentiation, possibly related to adaptation of the skin in response to ultraviolet light exposure (Colonna et al 2014). We did not formally class this as a replication as our 200-kb region does not include the previously highlighted variant.…”