Detecting recent positive selection in the human genome from haplotype structure

Abstract

The ability to detect recent natural selection in the human population would have profound implications for the study of human history and for medicine. Here, we introduce a framework for detecting the genetic imprint of recent positive selection by analysing long-range haplotypes in human populations. We first identify haplotypes at a locus of interest (core haplotypes). We then assess the age of each core haplotype by the decay of its association to alleles at various distances from the locus, as measured by extended haplotype homozygosity (EHH). Core haplotypes that have unusually high EHH and a high population frequency indicate the presence of a mutation that rose to prominence in the human gene pool faster than expected under neutral evolution. We applied this approach to investigate selection at two genes carrying common variants implicated in resistance to malaria: G6PD and CD40 ligand. At both loci, the core haplotypes carrying the proposed protective mutation stand out and show significant evidence of selection. More generally, the method could be used to scan the entire genome for evidence of recent positive selection.

Extreme population differences across Neuregulin 1 gene, with implications for association studies

“…[60][61][62][63] The signatures of local positive selection observed in each of these genes has been in each case explained by known differential environmental influences according to geographical region, such as worldwide variation in the consumption of milk products in the case of the Lactase gene and incidence of malaria in the case of G6PD. It is tempting to speculate on the source of such a selective force acting 'locally' on the NRG1 gene; however, given the extensive role of NRG1 signalling in the neuromuscular system, this may prove to be a difficult task.…”

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

“…FDIST2 typically detects more outliers than does BayeScan in empirical datasets of a few thousand SNPs (Tsumura et al., 2014). Other methods for detecting signatures of selection such as the long‐range haplotype (LRH) test (Sabeti et al., 2002) require higher resolution coverage of the genome for traits of unknown location than was possible with our 3980 SNP dataset.…”

“…Firstly, because of the low number of generations, samples from putative wild founder populations that that have not experienced large changes in allele frequencies from genetic bottlenecks are more likely to be available. Secondly, 5–15 generations is insufficient time for recombination to have reduced the size of long haplotype blocks resulting from hard sweeps (Sabeti et al., 2002) making outlier loci easier to detect with a low density of markers. Finally, realistic simulations of a single locus trait that is under moderately strong selection (s > 0.25) in the aquaculture population but not in the wild founder population for fewer than 10 generations show that the statistical power to detect the divergence in allele frequencies is high (ß > 0.8) (Karlsson & Moen, 2010).…”

Understanding and monitoring the consequences of human impacts on intraspecific variation

“…The former loci can be used to infer population genetic parameters such as those described at the beginning of this section, whereas the latter loci can be used to monitor functional markers. When a reference genome is available, one set of statistical approaches uses the linkage disequilibrium to infer selective sweeps on the genome (e.g., Sabeti et al., 2002). Another set of statistical approaches uses the distribution across loci as measures of population differentiation (e.g., F ST ) to infer candidate loci under selection in a Bayesian framework (Beaumont & Balding, 2004; Foll & Gaggiotti, 2008).…”