Evolutionary perspectives on polygenic selection, missing heritability, and GWAS

  • Lawrence H. UricchioEmail author
Original Investigation
Part of the following topical collections:
  1. Genetic epidemiology of complex diseases


Genome-wide association studies (GWAS) have successfully identified many trait-associated variants, but there is still much we do not know about the genetic basis of complex traits. Here, we review recent theoretical and empirical literature regarding selection on complex traits to argue that “missing heritability” is as much an evolutionary problem as it is a statistical problem. We discuss empirical findings that suggest a role for selection in shaping the effect sizes and allele frequencies of causal variation underlying complex traits, and the limitations of these studies. We then use simulations of selection, realistic genome structure, and complex human demography to illustrate the results of recent theoretical work on polygenic selection, and show that statistical inference of causal loci is sharply affected by evolutionary processes. In particular, when selection acts on causal alleles, it hampers the ability to detect causal loci and constrains the transferability of GWAS results across populations. Last, we discuss the implications of these findings for future association studies, and suggest that future statistical methods to infer causal loci for genetic traits will benefit from explicit modeling of the joint distribution of effect sizes and allele frequencies under plausible evolutionary models.



Many thanks to Noah Rosenberg, Doc Edge, Noah Zaitlen, Ryan Hernandez, and my anonymous reviewers, whose detailed comments substantially  improved the manuscript. Conversations with the aforementioned individuals as well as Chris Gignoux, Arbel Harpak, Jaehee Kim, and Aaron Stern helped motivate this research, and I am grateful for the opportunity to speak with each of them about polygenic selection and GWAS over the past several years. LHU received support from NIGMS grant K12GM088033 and the Stanford/SJSU IRACDA program. Additional support was provided by NIH R01 HG005855 and NSF DBI-1458059 (each to Noah Rosenberg).

Compliance with ethical standards

Conflict of interest

No conflict of interest exists.


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Authors and Affiliations

  1. 1.Department of BiologyStanford UniversityStanfordUSA
  2. 2.Department of Integrative BiologyUniversity of California, BerkeleyBerkeleyUSA

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