An evolutionary compass for detecting signals of polygenic selection and mutational bias.

TitleAn evolutionary compass for detecting signals of polygenic selection and mutational bias.
Publication TypeJournal Article
Year of Publication2019
AuthorsUricchio, LH, Kitano, HC, Gusev, A, Zaitlen, NA
JournalEvol Lett
Date Published2019 Feb

Selection and mutation shape the genetic variation underlying human traits, but the specific evolutionary mechanisms driving complex trait variation are largely unknown. We developed a statistical method that uses polarized genome-wide association study (GWAS) summary statistics from a single population to detect signals of mutational bias and selection. We found evidence for nonneutral signals on variation underlying several traits (body mass index [BMI], schizophrenia, Crohn's disease, educational attainment, and height). We then used simulations that incorporate simultaneous negative and positive selection to show that these signals are consistent with mutational bias and shifts in the fitness-phenotype relationship, but not stabilizing selection or mutational bias alone. We additionally replicate two of our top three signals (BMI and educational attainment) in an external cohort, and show that population stratification may have confounded GWAS summary statistics for height in the GIANT cohort. Our results provide a flexible and powerful framework for evolutionary analysis of complex phenotypes in humans and other species, and offer insights into the evolutionary mechanisms driving variation in human polygenic traits.

Alternate JournalEvol Lett
PubMed ID30788143
PubMed Central IDPMC6369964
Grant ListR01 HG006399 / HG / NHGRI NIH HHS / United States
R01 HG005855 / HG / NHGRI NIH HHS / United States
U01 HG009080 / HG / NHGRI NIH HHS / United States
K12 GM088033 / GM / NIGMS NIH HHS / United States
K25 HL121295 / HL / NHLBI NIH HHS / United States