The evolution of group differences in changing environments

Abstract

The selection pressures that have shaped the evolution of complex traits in humans remain largely unknown, and in some contexts highly contentious, perhaps above all where they concern mean trait differences among groups. To date, the discussion has focused on whether such group differences have any genetic basis, and if so, whether they are without fitness consequences and arose via random genetic drift, or whether they were driven by selection for different trait optima in different environments. Here, we highlight a plausible alternative: that many complex traits evolve under stabilizing selection in the face of shifting environmental effects. Under this scenario, there will be rapid evolution at the loci that contribute to trait variation, even when the trait optimum remains the same. These considerations underscore the strong assumptions about environmental effects that are required in ascribing trait differences among groups to genetic differences.

Interindividual plasticity in metabolic and thermal tolerance traits from populations subjected to recent anthropogenic heating

“…due to antagonistic pleiotropy) or are not selectively important in this TE environment or (ii) that maintenance of variation in these traits is more important than canalization towards an optimum trait value. Additionally, interactions between ecological and evolutionary dynamics in response to temperature variation may result in similar phenotypic optima despite apparently different environments obscuring local adaptation patterns [ 68 , 69 ]. This could include epigenetic effects if variation in a trait can be attributed to epigenetic changes within a generation (e.g.…”

Population differentiation of polygenic score predictions under stabilizing selection

“…Many traits have likely experienced a mixture of stabilizing selection and bursts of directional selection across human history. Even if the populations share the same phenotypic optimum, if an environmental change systematically shifts one population away from this optimum, there would be directional polygenic adaptation to move that population back towards the optimum, resulting in a difference in polygenic scores but no difference in the mean phenotypes between populations (Harpak and Przeworski, 2021). Therefore, under current ascertainment schemes and pervasive stabilizing selection, the difference in mean polygenic scores among populations provides very unreliable information about the potential role of selection in generating phenotypic differences among populations.…”

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries

“…Unless this practice is curbed by the biomedical research community, it will exacerbate already existing disparities in healthcare across diverse communities. There are undoubted benefits from increased sampling in a given ancestry for association mapping using the standard GWA framework, but it is still unknown the extent to which results from larger GWA and fine-mapping studies using European-ancestry genomes will generalize to the entire human population 2, 20 .…”

“…However, many methods that aggregate SNP-level effects, test for effect size heterogeneity, leverage genomic annotations and gene interaction networks offer opportunities to directly test these fundamental questions. Such approaches have yet to be combined with studies of how evolutionary processes play a role in shaping the genetic architecture of complex traits, although the evolution of human complex trait architecture has been the subject of recent scrutiny 20,40,80,81 . While many studies note that differences in LD across ancestries affects transferability of effect size estimates 6,38,[82][83][84] In this perspective, we have not addressed the downstream consequences of using self-identified ancestry to define cohorts in biobank sized GWA studies (but see Urbut et al 35 , Willer et al 85 , Lin et al 86 , Yang et al 87 ).…”

“…ADHD is a trait with heritability estimates as high as 75% which had no known genome-wide significant SNP-level associations at the time; Nakka et al studied genotype data from just 3,319 individuals with cases, 2,455 controls and 2,064 trios 57 . A study by Demontis et al 58 later found a SNP-level association in the ST3GAL3 gene, but was only able to do so with a cohort an order of magnitude larger (20,183 individuals diagnosed with ADHD and 35,191 controls, totaling 55,374 individuals).…”