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

Evolution Letters
Lawrence H UricchioNoah A Zaitlen

Abstract

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.

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Citations

May 8, 2019·Nature Ecology & Evolution·Lawrence H UricchioDavid Enard
Jan 31, 2020·ELife·Hakhamanesh MostafaviMolly Przeworski
Mar 22, 2019·ELife·Jeremy J BergGraham Coop
Sep 3, 2020·Annual Review of Genomics and Human Genetics·Andy Dahl, Noah Zaitlen
Jan 14, 2021·American Journal of Human Genetics·Aaron J SternRasmus Nielsen
Jan 26, 2021·PLoS Biology·Arbel Harpak, Molly Przeworski
Mar 11, 2021·American Journal of Human Genetics·Arun Durvasula, Kirk E Lohmueller
Apr 27, 2021·Human Genetics·Mariko IsshikiJun Ohashi
May 28, 2021·Current Opinion in Clinical Nutrition and Metabolic Care·Shuang Yang, Kaixiong Ye
Aug 24, 2021·Frontiers in Genetics·Evan K Irving-PeaseFernando Racimo

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Methods Mentioned

BETA
genotyping

Software Mentioned

UKBB
_ CODE
SFS
GIANT

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