Leveraging distant relatedness to quantify human mutation and gene conversion rates

Leveraging distant relatedness to quantify human mutation and gene conversion rates

Pier Francesco Palamara, Laurent Francioli, Giulio Genovese, Peter Wilton, Alexander Gusev, Hilary Finucane, Sriram Sankararaman, Shamil Sunyaev, Paul Debakker, John Wakeley, Itsik Pe’er, Alkes L. Price, The Genome of the Netherlands Consortium
doi: http://dx.doi.org/10.1101/020776

The rate at which human genomes mutate is a central biological parameter that has many implications for our ability to understand demographic and evolutionary phenomena. We present a method for inferring mutation and gene conversion rates using the number of sequence differences observed in identical-by-descent (IBD) segments together with a reconstructed model of recent population size history. This approach is robust to, and can quantify, the presence of substantial genotyping error, as validated in coalescent simulations. We applied the method to 498 trio-phased Dutch individuals from the Genome of the Netherlands (GoNL) project, sequenced at an average depth of 13×. We infer a point mutation rate of 1.66 ± 0.04 × 10-8 per base per generation, and a rate of 1.26 ± 0.06 × 10-9 for <20 bp indels. Our estimated average genome-wide mutation rate is higher than most pedigree-based estimates reported thus far, but lower than estimates obtained using substitution rates across primates. By quantifying how estimates vary as a function of allele frequency, we infer the probability that a site is involved in non-crossover gene conversion as 5.99 ± 0.69 × 10-6, consistent with recent reports. We find that recombination does not have observable mutagenic effects after gene conversion is accounted for, and that local gene conversion rates reflect recombination rates. We detect a strong enrichment for recent deleterious variation among mismatching variants found within IBD regions, and observe summary statistics of local IBD sharing to closely match previously proposed metrics of background selection, but find no significant effects of selection on our estimates of mutation rate. We detect no evidence for strong variation of mutation rates in a number of genomic annotations obtained from several recent studies.


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