What can be learned by scanning the genome for molecular convergence in wild populations?

Abstract

Convergent evolution, where independent lineages evolve similar phenotypes in response to similar challenges, can provide valuable insight into how selection operates and the limitations it encounters. However, it has only recently become possible to explore how convergent evolution is reflected at the genomic level. The overlapping outlier approach (OOA), where genome scans of multiple independent lineages are used to find outliers that overlap and therefore identify convergently evolving loci, is becoming popular. Here, we present a quantitative analysis of 34 studies that used this approach across many sampling designs, taxa, and sampling intensities. We found that OOA studies with increased biological sampling power within replicates have increased likelihood of finding overlapping, "convergent" signals of adaptation between them. When identifying convergent loci as overlapping outliers, it is tempting to assume that any false-positive outliers derived from individual scans will fail to overlap across replicates, but this cannot be guaranteed. We highlight how population demographics and genomic context can contribute toward both true convergence and false positives in OOA studies. We finish with an exploration of emerging methods that couple genome scans with phenotype and environmental measures, leveraging added information from genome data to more directly test hypotheses of the likelihood of convergent evolution.

Keywords: convergent evolution; genome scans; parallel evolution; population genomics.

Figure 1

Modes of convergent molecular evolution, where independent lineages are adapting to new, similar environments (green lineages). Selection can be on (A) de novo mutation (mutation indicated by a star, where an A is mutated to a T), (B) standing genetic variation, and (C) gene flow (where migration between lineages, indicated by the arrow).

Figure 2

The overlapping outlier approach (OOA) used to detect convergent molecular evolution. (A) The different types of sampling design. (B) Overlapping outliers among replicates using a quantile‐based cutoff (0.95) are detected. (C) Overlapping outliers along a chromosome with an arbitrary threshold of 0.3 are visualized.

Figure 3

Various aspects of sampling and study design affect the amount of enrichment (i.e., detected molecular convergence). (A) Sampling design differed in reported enrichment, cline (C), common ancestor (CA), multiple comparisons (MC), and replicate pairs (RP). (B) A positive relationship was found between the number of SNPs genotyped and enrichment. (C) A positive relationship was found between the number of individuals genotyped and enrichment. In A, boxes denote 0.25, 0.5, and 0.75 quantiles, with whiskers extending to the furthest point within 1.5× the interquartile range (0.25–0.75). In B and C, the large sample of replicates from Stuart et al.34 are represented with ^*, and lines denote model effects with 95% upper and lower confidence intervals.

Figure 4

Types of demographic parameters that will affect the likelihood of convergent evolution, where independent lineages are adapting to a new similar environment (indicated in green). Parameters include founding effective populations size, for example, bottlenecks ((bot)N_E), current effective population size (N_E), migration between similar environments (m1), migration between different environments (m2), and time since divergence of replicates (t1) and within replicates (t2).