Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection

Armin P. Schoech (), Daniel M. Jordan, Po-Ru Loh, Steven Gazal, Luke J. O’Connor, Daniel J. Balick, Pier F. Palamara, Hilary K. Finucane, Shamil R. Sunyaev and Alkes L. Price ()
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Armin P. Schoech: Harvard T.H. Chan School of Public Health
Daniel M. Jordan: Icahn School of Medicine at Mount Sinai
Po-Ru Loh: Broad Institute of MIT and Harvard
Steven Gazal: Harvard T.H. Chan School of Public Health
Luke J. O’Connor: Harvard T.H. Chan School of Public Health
Daniel J. Balick: Brigham and Women’s Hospital and Harvard Medical School
Pier F. Palamara: University of Oxford
Hilary K. Finucane: Broad Institute of MIT and Harvard
Shamil R. Sunyaev: Broad Institute of MIT and Harvard
Alkes L. Price: Harvard T.H. Chan School of Public Health

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 − p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of –0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF

Date: 2019
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DOI: 10.1038/s41467-019-08424-6

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