Hi-reComb: constructing recombination maps from bulk gamete Hi-C sequencing

Abstract

Recombination is central to genetics and to evolution of sexually reproducing organisms. However, obtaining accurate estimates of recombination rates, and of how they vary along chromosomes, continues to be challenging. To advance our ability to estimate recombination rates, we present Hi-reComb, a new method and software for estimation of recombination maps from bulk gamete chromosome conformation capture sequencing (Hi-C). Simulations show that Hi-reComb produces robust, accurate recombination landscapes. With empirical data from sperm of five fish species we show the advantages of this approach, including joint assessment of recombination maps and large structural variants, map comparisons using bootstrap, and workflows with trio phasing vs. Hi-C phasing. With off-the-shelf library construction and a straightforward rapid workflow, our approach will facilitate routine recombination landscape estimation for a broad range of studies and model organisms in genetics and evolutionary biology. Hi-reComb is open-source and freely available at https://github.com/millanek/Hi-reComb.

Keywords: Hi-C; cichlids; gametes; genetics; recombination map; software; sperm; stickleback.

Fig. 1.

The principle of detecting crossovers with Hi-reComb. The donor genome is shown with heterozygous sites separating the two haplotypes indicated. For each sperm Hi-C read pair, if both fragments match the same donor haplotype, this indicates an absence of crossover (no-crossover). If the fragments match different donor haplotypes, this indicates a crossover between them. Finally, if both fragments do not cover haterozygous sites in the donor genome, the read pair is not informative with regards to recombination.

Fig. 2.

Hi-reComb recombination map inference from simulated data. a) A comparison of a reference map against 10 maps reconstructed by Hi-reComb from simulated data at 3,000 × effective coverage and 1% error rate. b) The dependence of accuracy of recombination map reconstruction on the effective coverage. c) The dependence of accuracy of recombination map reconstruction on the error rate. d) The accuracy of the estimates of error rate, i.e. the correction factor f, by Hi-reComb.

Fig. 3.

An overview of Hi-C inferred maps. a) The error rates estimated by Hi-reComb from empirical data. b) Recombination map lengths in Morgans (M) compared against three previously published pedigree-based maps (for cichlids, left: (Albertson et al. 2014); right: (Quin et al. 2013); for stickleback, left: (Roesti et al. 2013); right: (Kivioja and Rastas 2024). c) The negative relationship between the chromosome size and the mean recombination rate in cichlids. The two large chromosomes that were excluded in A. calliptera as outliers are highlighted with arrows.

Fig. 4.

Bootstrap facilitates comparisons between recombination landscapes. a) Chromosome 4 of A. stuartgranti with recombination landscapes based on the mean of 50 bootstrap replicates (thick line) and 95% confidence intervals (shaded areas). b) Examples of comparisons between the maps of A. stuartgranti and A. nubila, highlighting areas of significant recombination rate differences between the maps (the $\mathrm{\Delta }(r)$ regions).

Fig. 5.

Hapcut2 vs. trio phasing of stickleback data. a) The error rates estimated by Hi-reComb for the hapcut2 and trio-phased G. aculeatus datasets. b) Correlations between gamete-based Hi-reComb maps and an LD-based map from the same stickleback population. c) The average density of SNPs phased with the hapcut2 approach and the trio approach. Each datapoint corresponds to one chromosome.