Abstract
The reshuffling of existing genetic variation during meiosis is important both during evolution and in breeding. The reassortment of genetic variants relies on the formation of crossovers (COs) between homologous chromosomes. The pattern of genome-wide CO distributions can be rapidly and precisely established by the short-read sequencing of individuals from F2 populations, which in turn are useful for quantitative trait locus (QTL) mapping. Although sequencing costs have decreased precipitously in recent years, the costs of library preparation for hundreds of individuals have remained high. To enable rapid and inexpensive CO detection and QTL mapping using low-coverage whole-genome sequencing of large mapping populations, we have developed a new method for library preparation along with Trained Individual GenomE Reconstruction, a probabilistic method for genotype and CO predictions for recombinant individuals. In an example case with hundreds of F2 individuals from two Arabidopsis thaliana accessions, we resolved most CO breakpoints to within 2 kb and reduced a major flowering time QTL to a 9-kb interval. In addition, an extended region of unusually low recombination revealed a 1.8-Mb inversion polymorphism on the long arm of chromosome 4. We observed no significant differences in the frequency and distribution of COs between F2 individuals with and without a functional copy of the DNA helicase gene RECQ4A. In summary, we present a new, cost-efficient method for large-scale, high-precision genotyping-by-sequencing.
Highlights
Because the CO landscape is important for the genetic mapping of quantitative trait loci (QTL) (Xu et al 2005), we examined the fine-scale locations of COs and performed quantitative trait locus (QTL) mapping of flowering time in wild-type and recq4a mutant F2 populations
To determine the potential causal genes underlying the major QTL on chromosome 5, we identified the boundaries of the recombination blocks containing the single-nucleotide polymorphisms (SNPs) marker with the highest association with flowering time for the combined population, since including more individuals resulted in more statistical power
Unlike the previously published methods using an hidden Markov modeling (HMM) approach (Xie et al 2010; Andolfatto et al 2011), we used an HMM that was trained on each sample individually, estimating the error rate on the sample data instead of providing general parameters, and handling variation in error rates
Summary
Flowering time phenotypes were scored among the wt and recq4a parents for both the Col-0 and Ws-2 genetic backgrounds. ÃÃÃ indicates statistical significance at the level of 0.01 or less. For yeast (Rockmill et al 2003; Jessop et al 2006); this was not the case (Figure 5). The only significant effect of the recq4a mutation we observed was an acceleration of flowering time. Because this did not result from an interaction between the recq4a mutation and the flowering time QTL, MAF2-5, it is likely that the mild genotoxic stress in recq4a mutants promoted earlier flowering
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