Abstract
Accurate estimates of recombination rates are of great importance for understanding evolution. In an experimental genetic cross, recombination breaks apart and rejoins genetic material, such that the genomes of the resulting isolates are comprised of distinct blocks of differing parental origin. We here describe a method exploiting this fact to infer genome-wide recombination profiles from sequenced isolates from an advanced intercross line (AIL). We verified the accuracy of the method against simulated data. Next, we sequenced 192 isolates from a twelve-generation cross between West African and North American yeast Saccharomyces cerevisiae strains and inferred the underlying recombination landscape at a fine genomic resolution (mean segregating site distance 0.22 kb). Comparison was made with landscapes inferred for a similar cross between four yeast strains, and with a previous single-generation, intra-strain cross (Mancera et al., Nature 2008). Moderate congruence was identified between landscapes (correlation 0.58–0.77 at 5 kb resolution), albeit with variance between mean genome-wide recombination rates. The multiple generations of mating undergone in the AILs gave more precise inference of recombination rates than could be achieved from a single-generation cross, in particular in identifying recombination cold-spots. The recombination landscapes we describe have particular utility; both AILs are part of a resource to study complex yeast traits (see e.g. Parts et al., Genome Res 2011). Our results will enable future applications of this resource to take better account of local linkage structure heterogeneities. Our method has general applicability to other crossing experiments, including a variety of experimental designs.
Highlights
Accurate estimates of bare rates of evolutionary processes such as mutation and recombination are important building blocks in our understanding of evolution
An alternative approach to learning recombination landscapes, which can lead to estimates of unscaled recombination rates, is that of genetic crosses of model organisms carried out at a large scale [12,13,14,15,16]
Inferences of recombination were made for simulated data of an L-locus system (L~100) with uniform recombination rate (Figure 2a)
Summary
Accurate estimates of bare rates of evolutionary processes such as mutation and recombination are important building blocks in our understanding of evolution. As reviewed in depth elsewhere [2,3], a range of methods have been employed to derive recombination rates from genome sequencing, including the use of pedigree information [4,5], sperm typing [6,7], and the application of methods from coalescent theory [8] to haplotype data [9,10,11] Under this latter approach, probabilities are calculated of observing specific haplotypes under some mutation and recombination rate, these rates subsequently being estimated using, for example, maximum likelihood methods.
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