MRLR: unraveling high-resolution meiotic recombination by linked reads

Polymorphic transposable elements contribute to variation in recombination landscapes

Meiotic recombination is a prominent force shaping genome evolution, and understanding why recombination rates vary within and between species has remained a central, though challenging, question. Variation in recombination is widely thought to influence the efficacy of selection in purging transposable elements (TEs), prevalent selfish genetic elements, leading to widely observed negative correlations between TE abundance and recombination rates across taxa. However, accumulating evidence suggests that TEs could instead be the cause rather than the consequence of this relationship. To test this prediction, we formally investigated the influence of polymorphic, putatively active TEs on recombination rates. We developed and benchmarked an approach that uses PacBio long-read sequencing to efficiently, accurately, and cost-effectively identify crossovers (COs), a key recombination product, among large numbers of pooled recombinant individuals. By applying this approach to Drosophila strains with distinct TE insertion profiles, we found that polymorphic TEs, especially RNA-based TEs and TEs with local enrichment of repressive marks, reduce the occurrence of COs. Such an effect leads to different CO frequencies between homologous sequences with and without TEs, contributing to varying CO maps between individuals. The suppressive effect of TEs on CO is further supported by two orthogonal approaches-analyzing the distributions of COs in panels of recombinant inbred lines in relation to TE polymorphism and applying marker-assisted estimations of CO frequencies to isogenic strains with and without transgenically inserted TEs. Our investigations reveal how the constantly changing TE landscape can actively modify recombination, shaping genome evolution within and between species.

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

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.

Intergeneric Introgression Enhances the Adaptive Potential of Nine-Spined Stickleback (Pungitius pungitius)

Over the past decades, number of evidences has accumulated that demonstrates the importance of genomic introgression between relatively distant eukaryote species, including the introgression of teleost fish species; the three-spined stickleback (Gasterosteus aculeatus) and the nine-spined stickleback (Pungitius pungitius). The whole-genome datasets of both teleost species give reasons for suggesting that the marine population of nine-spined stickleback increases its adaptive potential to the marine environment through introgression with the anadromous three-spined stickleback. These findings demand a reinterpreting of the mechanisms of evolution towards a process in which organisms acquire new traits not only through longterm accumulation and selection of spontaneous mutations, but also via introgression from other species and ecological forms.

Varying recombination landscapes between individuals are driven by polymorphic transposable elements

Meiotic recombination is a prominent force shaping genome evolution, and understanding the causes for varying recombination landscapes within and between species has remained a central, though challenging, question. Recombination rates are widely observed to negatively associate with the abundance of transposable elements (TEs), selfish genetic elements that move between genomic locations. While such associations are usually interpreted as recombination influencing the efficacy of selection at removing TEs, accumulating findings suggest that TEs could instead be the cause rather than the consequence. To test this prediction, we formally investigated the influence of polymorphic, putatively active TEs on recombination rates. We developed and benchmarked a novel approach that uses PacBio long-read sequencing to efficiently, accurately, and cost-effectively identify crossovers (COs), a key recombination product, among large numbers of pooled recombinant individuals. By applying this approach to Drosophila strains with distinct TE insertion profiles, we found that polymorphic TEs, especially RNA-based TEs and TEs with local enrichment of repressive marks, reduce the occurrence of COs. Such an effect leads to different CO frequencies between homologous sequences with and without TEs, contributing to varying CO maps between individuals. The suppressive effect of TEs on CO is further supported by two orthogonal approaches-analyzing the distributions of COs in panels of recombinant inbred lines in relation to TE polymorphism and applying marker-assisted estimations of CO frequencies to isogenic strains with and without transgenically inserted TEs. Our investigations reveal how the constantly changing mobilome can actively modify recombination landscapes, shaping genome evolution within and between species.

NanoCross: A pipeline that detecting recombinant crossover using ONT sequencing data

Meiotic recombination is crucial for eukaryotes but varies among taxonomic scales (between individuals, groups, species, etc.) and genome resolutions. Studying how and why recombination rates change can help us understand the molecular basis and mechanisms of genetics and evolution. We introduce a genome-wide identification script called NanoCross, which uses ONT sequences to detect pooled gamete DNA cross recombination events. NanoCross first reduced sequencing errors and then constructed individual haplotypes based on homopolymer-filtered ONT sequences. Then, each molecule read is used to estimate cross recombination. In the case of moderate heterozygous variation density and sequencing depth, simulations revealed that our technique offers a good level of sensitivity and specificity. We constructed a high-resolution recombination map of wild boar genomes using NanoCross and compared it to recombination maps of male breeding pig populations. NanoCross provides us with a method and scripts for constructing a high-resolution individual genome recombination map utilizing long-read sequencing, as well as a novel approach for examining the variation in individual recombination rate. The source code and data mechanism are hosted on GitHub (https://github.com/zuoquanchen/NanoCross).

Gene flow biases population genetic inference of recombination rate

Accurate estimates of the rate of recombination are key to understanding a host of evolutionary processes as well as the evolution of the recombination rate itself. Model-based population genetic methods that infer recombination rates from patterns of linkage disequilibrium in the genome have become a popular method to estimate rates of recombination. However, these linkage disequilibrium-based methods make a variety of simplifying assumptions about the populations of interest that are often not met in natural populations. One such assumption is the absence of gene flow from other populations. Here, we use forward-time population genetic simulations of isolation-with-migration scenarios to explore how gene flow affects the accuracy of linkage disequilibrium-based estimators of recombination rate. We find that moderate levels of gene flow can result in either the overestimation or underestimation of recombination rates by up to 20–50% depending on the timing of divergence. We also find that these biases can affect the detection of interpopulation differences in recombination rate, causing both false positives and false negatives depending on the scenario. We discuss future possibilities for mitigating these biases and recommend that investigators exercise caution and confirm that their study populations meet assumptions before deploying these methods.