RT Journal Article
SR Electronic
T1 Overcoming constraints on the detection of recessive selection in human genes from population frequency data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.06.443024
DO 10.1101/2021.05.06.443024
A1 Daniel J. Balick
A1 Daniel M. Jordan
A1 Shamil Sunyaev
A1 Ron Do
YR 2021
UL http://biorxiv.org/content/early/2021/05/07/2021.05.06.443024.abstract
AB The identification of genes that evolve under recessive natural selection is a longstanding goal of population genetics research with important applications to disease gene discovery. We found that commonly used methods to evaluate selective constraint at the gene level are highly sensitive to genes under heterozygous selection but ubiquitously fail to detect recessively evolving genes. Additionally, more sophisticated likelihood-based methods designed to detect recessivity similarly lack power for a human gene of realistic length from current population sample sizes. However, extensive simulations suggested that recessive genes may be detectable in aggregate. Here, we offer a method informed by population genetics simulations designed to detect recessive purifying selection in gene sets. Applying this to empirical gene sets produced significant enrichments for strong recessive selection in genes previously inferred to be under recessive selection in a consanguineous cohort and in genes involved in autosomal recessive monogenic disorders.Competing Interest StatementRD received grants from AstraZeneca, grants and nonfinancial support from Goldfinch Bio, is a scientic co-founder, equity holder and consultant for Pensieve Health, and is a consultant for Variant Bio, all not related to this work.