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Evol Lett. 2019 Jan 25;3(1):69-79. doi: 10.1002/evl3.97. eCollection 2019 Feb.

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

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Department of Biology Stanford University Stanford CA.
Department of Computer Science Stanford University Stanford CA.
Dana Farber Cancer Institute Boston MA.
Department of Medicine University of California San Francisco CA.
Bioengineering and Therapeutic Sciences University of California San Francisco CA.


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.


Complex traits; mutation bias; natural selection; polygenic selection

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