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

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.

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

“…The distribution of singleton variants in large studies such as ours has been only modestly shaped by purifying selection and largely reflects underlying mutation processes 38 . Thus, studying singletons and other rare variants in our sample offers opportunities to dissect the mutation processes that generate extant human variation.…”

Signatures of replication timing, recombination and sex in the spectrum of rare variants on the human X chromosome and autosomes

“…The much higher density of rare variants across the genome can then be used to more robustly investigate associations with genomic features. Using this strategy, a recent study of human autosomal data identified mutation types and contexts significantly associated with a variety of genomic features (34). While the authors suggested putative biochemical sources for three signatures in the germline based on their similarity to patterns that have been reported in tumors, it is unclear to what degree these mechanisms can be directly extrapolated to the germline (36,37).…”

Cytosine methylation affects the mutability of neighbouring nucleotides in human, Arabidopsis, and rice

“…First, as mutation rate strongly varies with local sequence context (Blake et al 1992;Zhao and Boerwinkle 2002;Hwang and Green 2004;Carlson et al 2018), we required the four nucleotides either side (±4bp) of the focal CpG to be the same. Matching the sequence context in this manner also controls for local GC content, previously shown to correlate inversely with CpG mutability (as further discussed below), and sequence complexity, which is an important determinant of indel formation propensity.…”

“…The mutational impact of methylation is also visible over longer evolutionary timescales: CpG to TpG transitions often dominate substitution profiles between species (Ebersberger et al 2002;Hwang and Green 2004) and genomes where CpG methylation is common are depleted of CpGs (Josse et al 1961;Russell et al 1976;Salser 1978;Bird 1980;Simmen 2008). Importantly, higher transition rates at CpGs are also evident in data from parent-child trios (Kong et al 2012;Francioli et al 2015;Rahbari et al 2016;Jónsson et al 2017) somatic mutations in healthy tissues (Hoang et al 2016;Martincorena et al 2018), mutation accumulation lines (Ossowski et al 2010;Lee et al 2012;Weng et al 2019), and when considering rare SNPs (Rahbari et al 2016;Carlson et al 2018), strongly supporting mutational processes as the driving force. Finally, whereas early studies had to rely on CpGs as a (reasonable) proxy for methylation, more recent analyses have tethered elevated rates directly to methylation by integrating base-resolution methylation maps with polymorphism/somatic mutation data and comparing rates of evolution or SNP incidence at methylated and unmethylated CpGs explicitly (Ossowski et al 2010;Mugal and Ellegren 2011;Lee et al 2012;Xia et al 2012;Supek et al 2014;Tomkova et al 2016;Weng et al 2019).…”