Fine-scale recombination rate differences between sexes, populations and individuals

Abstract

Meiotic recombinations contribute to genetic diversity by yielding new combinations of alleles. Recently, high-resolution recombination maps were inferred from high-density single-nucleotide polymorphism (SNP) data using linkage disequilibrium (LD) patterns that capture historical recombination events. The use of these maps has been demonstrated by the identification of recombination hotspots and associated motifs, and the discovery that the PRDM9 gene affects the proportion of recombinations occurring at hotspots. However, these maps provide no information about individual or sex differences. Moreover, locus-specific demographic factors like natural selection can bias LD-based estimates of recombination rate. Existing genetic maps based on family data avoid these shortcomings, but their resolution is limited by relatively few meioses and a low density of markers. Here we used genome-wide SNP data from 15,257 parent-offspring pairs to construct the first recombination maps based on directly observed recombinations with a resolution that is effective down to 10 kilobases (kb). Comparing male and female maps reveals that about 15% of hotspots in one sex are specific to that sex. Although male recombinations result in more shuffling of exons within genes, female recombinations generate more new combinations of nearby genes. We discover novel associations between recombination characteristics of individuals and variants in the PRDM9 gene and we identify new recombination hotspots. Comparisons of our maps with two LD-based maps inferred from data of HapMap populations of Utah residents with ancestry from northern and western Europe (CEU) and Yoruba in Ibadan, Nigeria (YRI) reveal population differences previously masked by noise and map differences at regions previously described as targets of natural selection.

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

“…In real genomes this may correspond to quite a large physical distance: for instance if we assume a selection coefficient of 0.05 (i.e., selection as strong as in our simulations) and a crossover rate of 2 cM/Mb (similar to estimates in Drosophila; e.g., Comeron et al 2012) this corresponds to a physical distance of 1.25 Mb. If instead we assume a crossover rate of 1 cM/Mb (similar to estimates from humans; e.g., Kong et al 2010), this corresponds to a physical distance of 2.5 Mb. While we have assumed a fairly high value of s that may not be representative of all selective sweeps (e.g., Li and Stephan 2006;Jensen et al 2008), known sweeps in humans may occasionally have selection coefficients fairly close to 0.05 (Peter et al 2012).…”

“…For all simulations, we used parameters relevant to human populations: a recombination rate of 1.0 3 10 28 , (approximately equal to the sex-averaged rate from Kong et al 2010) and a mutation rate of 1.2 3 10 28 (from Kong et al 2012). Simulations were performed with our coalescent simulator, discoal (https://github.com/kern-lab/discoal; Kern and Schrider 2016), and example command lines with the appropriate population mutation/recombination rates and population size changes for each demographic scenario are shown in Supplemental Material, Table S1.…”

Improved sensitivity to detect recombination using qPCR for Dyskeratosis Congenita PGD

“…This case illustrates the particular problem of high rates of recombination near the telomeres of human chromosomes [14,7] and the impact it can have when performing PGD with linked informative STR markers too far from the mutation. The exact recombination rates approaching the telomeric ends may not be available or reliable from published studies, and in this case the rates of the full region surrounding the gene were not.…”

A common variant at 8q24.21 is associated with renal cell cancer

“…Long-range phasing of all chipgenotyped individuals was performed with methods described previously 8,[21][22][23][24] . In brief, phasing is achieved using an iterative algorithm, which phases a single proband at a time given the available phasing information about everyone else that shares a long haplotype identically by state with the proband.…”

“…where N is the number of haplotypes in k 2 H n fhg, which for autosomal chromosomes is 2(2,230 À 1) here, and r i ¼ 4N e r i , where r i is the genetic distance between markers i À 1 and i according to the most recent version of the deCODE genetic map 24 and N e was originally meant to be an estimate of the effective number of haplotypes in the population that our sample comes from, we used N e ¼ 7,000. These definitions fully specify the probability distribution Pðz i j all markersÞ.…”