Constructing genomic maps of positive selection in humans: Where do we go from here?

Abstract

Identifying targets of positive selection in humans has, until recently, been frustratingly slow, relying on the analysis of individual candidate genes. Genomics, however, has provided the necessary resources to systematically interrogate the entire genome for signatures of natural selection. To date, 21 genome-wide scans for recent or ongoing positive selection have been performed in humans. A key challenge is to begin synthesizing these newly constructed maps of positive selection into a coherent narrative of human evolutionary history and derive a deeper mechanistic understanding of how natural populations evolve. Here, I chronicle the recent history of the burgeoning field of human population genomics, critically assess genome-wide scans for positive selection in humans, identify important gaps in knowledge, and discuss both short- and long-term strategies for traversing the path from the low-resolution, incomplete, and error-prone maps of selection today to the ultimate goal of a detailed molecular, mechanistic, phenotypic, and population genetics characterization of adaptive alleles.

Identification of regions of positive selection using Shared Genomic Segment analysis

“…Furthermore, it has been suggested that regions identified in multiple analyses are more likely to be true positives. 6,7 Regions of the genome that are part of ongoing selective sweeps may include loci involved in phenotypic outcomes, including both deleterious and protective variants. Thus, haplotypes across regions with evidence of recent selection are of interest to be compared with association signals in genetic mapping.…”

Estimating the migration rate from genetic variation data

“…Suppose a new population emerges, in which A is assumed to be advantageous over B, then, there should be preferential migration of A into this new niche, resulting in an increased effective migration rate. This idea has been frequently used to scan a genome for evidence of selection, and there are many successful demonstrations of selection (for example Akey, 2009). For this kind of largescale polymorphism data analysis, there are many situations where simple summary statistics are very useful and powerful.…”

Positive selection rather than relaxation of functional constraint drives the evolution of vision during chicken domestication

“…This pattern is very common in studies on positive natural selection in humans. For example, only ~14.1% loci were identified in two or more studies with large-scale genome-wide scans of PSGs [13]. In the current study, possibly due to the above stated factors, no significant enrichment for any functional category associated with vision was found among the candidates identified by the F ST test.…”

“…First, the distinct statistics applied to scan the genome for positive selection are based on different signatures of population variation [9]. Another reason might be attributable to a pitfall in the outlier approach, whereby an outlier value in one analysis, falling in the 99th percentile of the empirical distribution, may not be classified as an outlier in another study where it may fall within the 98th percentile of the study's empirical distribution [13]. This pattern is very common in studies on positive natural selection in humans.…”