Extremely rare variants reveal patterns of germline mutation rate heterogeneity in humans

Jedidiah Carlson(University of Michigan), Adam E. Locke(James S. McDonnell Foundation), Matthew Flickinger(University of Michigan), Matthew Zawistowski(University of Michigan), Shawn Levy(HudsonAlpha Institute for Biotechnology), R Myers(HudsonAlpha Institute for Biotechnology), Michael Boehnke(University of Michigan), Hyun Min Kang(University of Michigan), Laura J. Scott(University of Michigan), Jun Z. Li(University of Michigan), Sebastian Zöllner(University of Michigan), Devin Absher(HudsonAlpha Institute for Biotechnology), Huda Akil(University of Michigan), Gerome Breen(King's College London), Margit Burmeister(University of Michigan), Sarah Cohen‐Woods(Flinders University), William G. Iacono(University of Minnesota), James A. Knowles(University of Southern California), Lisa N. Legrand(University of Minnesota), Qing Lu(Michigan State University), Matthew McGue(University of Minnesota), Melvin G. McInnis(University of Michigan), Carlos N. Pato(SUNY Downstate Health Sciences University), Michele T. Pato(SUNY Downstate Health Sciences University), Margarita Rivera(King's College London), Janet L. Sobell(University of Southern California), John B. Vincent(Mental Health Research Canada), Stanley J. Watson(University of Michigan)
Nature Communications
September 10, 2018
Cited by 159Open Access
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Abstract

A detailed understanding of the genome-wide variability of single-nucleotide germline mutation rates is essential to studying human genome evolution. Here, we use ~36 million singleton variants from 3560 whole-genome sequences to infer fine-scale patterns of mutation rate heterogeneity. Mutability is jointly affected by adjacent nucleotide context and diverse genomic features of the surrounding region, including histone modifications, replication timing, and recombination rate, sometimes suggesting specific mutagenic mechanisms. Remarkably, GC content, DNase hypersensitivity, CpG islands, and H3K36 trimethylation are associated with both increased and decreased mutation rates depending on nucleotide context. We validate these estimated effects in an independent dataset of ~46,000 de novo mutations, and confirm our estimates are more accurate than previously published results based on ancestrally older variants without considering genomic features. Our results thus provide the most refined portrait to date of the factors contributing to genome-wide variability of the human germline mutation rate.


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