Fine-scale Recombination Rate Variation Research Articles

The fine-scale recombination rate variation and associations with genomic features in a butterfly.

Recombination is a key molecular mechanism that has profound implications on both micro- and macroevolutionary processes. However, the determinants of recombination rate variation in holocentric organisms are poorly understood, in particular in Lepidoptera (moths and butterflies). The wood white butterfly (Leptidea sinapis) shows considerable intraspecific variation in chromosome numbers and is a suitable system for studying regional recombination rate variation and its potential molecular underpinnings. Here, we developed a large whole-genome resequencing data set from a population of wood whites to obtain high-resolution recombination maps using linkage disequilibrium information. The analyses revealed that larger chromosomes had a bimodal recombination landscape, potentially caused by interference between simultaneous chiasmata. The recombination rate was significantly lower in subtelomeric regions, with exceptions associated with segregating chromosome rearrangements, showing that fissions and fusions can have considerable effects on the recombination landscape. There was no association between the inferred recombination rate and base composition, supporting a limited influence of GC-biased gene conversion in butterflies. We found significant but variable associations between the recombination rate and the density of different classes of transposable elements, most notably a significant enrichment of short interspersed nucleotide elements in genomic regions with higher recombination rate. Finally, the analyses unveiled significant enrichment of genes involved in farnesyltranstransferase activity in recombination coldspots, potentially indicating that expression of transferases can inhibit formation of chiasmata during meiotic division. Our results provide novel information about recombination rate variation in holocentric organisms and have particular implications for forthcoming research in population genetics, molecular/genome evolution, and speciation.

Recombination Rate Variation and Infrequent Sex Influence Genetic Diversity in Chlamydomonas reinhardtii.

Recombination confers a major evolutionary advantage by breaking up linkage disequilibrium between harmful and beneficial mutations, thereby facilitating selection. However, in species that are only periodically sexual, such as many microbial eukaryotes, the realized rate of recombination is also affected by the frequency of sex, meaning that infrequent sex can increase the effects of selection at linked sites despite high recombination rates. Despite this, the rate of sex of most facultatively sexual species is unknown. Here, we use genomewide patterns of linkage disequilibrium to infer fine-scale recombination rate variation in the genome of the facultatively sexual green alga Chlamydomonas reinhardtii. We observe recombination rate variation of up to two orders of magnitude and find evidence of recombination hotspots across the genome. Recombination rate is highest flanking genes, consistent with trends observed in other nonmammalian organisms, though intergenic recombination rates vary by intergenic tract length. We also find a positive relationship between nucleotide diversity and physical recombination rate, suggesting a widespread influence of selection at linked sites in the genome. Finally, we use estimates of the effective rate of recombination to calculate the rate of sex that occurs in natural populations, estimating a sexual cycle roughly every 840 generations. We argue that the relatively infrequent rate of sex and large effective population size creates a population genetic environment that increases the influence of selection on linked sites across the genome.

Exploration of fine-scale recombination rate variation in the domestic horse.

Total genetic map length and local recombination landscapes typically vary within and across populations. As a first step to understanding the recombination landscape in the domestic horse, we calculated population recombination rates and identified likely recombination hotspots using approximately 1.8 million SNP genotypes for 485 horses from 32 distinct breeds. The resulting breed-averaged recombination map spans 2.36 Gb and accounts for 2939.07 cM. Recombination hotspots occur once per 23.8 Mb on average and account for ∼9% of the physical map length. Regions with elevated recombination rates in the entire cohort were enriched for genes in pathways involving interaction with the environment: immune system processes (specifically, MHC class I and class II genes), responses to stimuli, and serotonin receptor pathways. We found significant correlations between differences in local recombination rates and population differentiation quantified by FST. Analysis of breed-specific maps revealed thousands of hotspot regions unique to particular breeds, as well as unique “coldspots,” regions where a particular breed showed below-average recombination, whereas all other breeds had evidence of a hotspot. Finally, we identified relative enrichment (P = 5.88 × 10−27) for the in silico–predicted recognition motif for equine PR/SET domain 9 (PRDM9) in recombination hotspots. These results indicate that selective pressures and PRDM9 function contribute to variation in recombination rates across the domestic horse genome.

A High-Resolution Genetic Map for the Laboratory Rat.

An accurate and high-resolution genetic map is critical for mapping complex traits, yet the resolution of the current rat genetic map is far lower than human and mouse, and has not been updated since the original Jensen-Seaman map in 2004. For the first time, we have refined the rat genetic map to sub-centimorgan (cM) resolution (<0.02 cM) by using 95,769 genetic markers and 870 informative meioses from a cohort of 528 heterogeneous stock (HS) rats. Global recombination rates in the revised sex-averaged map (0.66 cM/Mb) did not differ compared to the historical map (0.65 cM/Mb); however, substantial refinement was made to the localization of highly recombinant regions within the revised map. Also for the first time, sex-specific rat genetic maps were generated, which revealed both genomewide and fine-scale variation in recombination rates between male and female rats. Reanalysis of multiple quantitative trait loci (QTL) using the historical and refined rat genetic maps demonstrated marked changes to QTL localization, shape, and effect size. As a resource to the rat research community, we have provided revised centimorgan positions for all physical positions within the rat genome and commonly used genetic markers for trait mapping, including 44,828 SSLP markers and the RATDIV genotyping array. Collectively, this study provides a substantial improvement to the rat genetic map and an unprecedented resource for analysis of complex traits and recombination in the rat.

Whole-genome patterns of linkage disequilibrium across flycatcher populations clarify the causes and consequences of fine-scale recombination rate variation in birds.

Recombination rate is heterogeneous across the genome of various species and so are genetic diversity and differentiation as a consequence of linked selection. However, we still lack a clear picture of the underlying mechanisms for regulating recombination. Here we estimated fine-scale population recombination rate based on the patterns of linkage disequilibrium across the genomes of multiple populations of two closely related flycatcher species (Ficedula albicollis and F. hypoleuca). This revealed an overall conservation of the recombination landscape between these species at the scale of 200kb, but we also identified differences in the local rate of recombination despite their recent divergence (<1 million years). Genetic diversity and differentiation were associated with recombination rate in a lineage-specific manner, indicating differences in the extent of linked selection between species. We detected 400-3,085 recombination hotspots per population. Location of hotspots was conserved between species, but the intensity of hotspot activity varied between species. Recombination hotspots were primarily associated with CpG islands (CGIs), regardless of whether CGIs were at promoter regions or away from genes. Recombination hotspots were also associated with specific transposable elements (TEs), but this association appears indirect due to shared preferences of the transposition machinery and the recombination machinery for accessible open chromatin regions. Our results suggest that CGIs are a major determinant of the localization of recombination hotspots, and we propose that both the distribution of TEs and fine-scale variation in recombination rate may be associated with the evolution of the epigenetic landscape.

Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster

Estimating fine-scale recombination maps of Drosophila from population genomic data is a challenging problem, in particular because of the high background recombination rate. In this paper, a new computational method is developed to address this challenge. Through an extensive simulation study, it is demonstrated that the method allows more accurate inference, and exhibits greater robustness to the effects of natural selection and noise, compared to a well-used previous method developed for studying fine-scale recombination rate variation in the human genome. As an application, a genome-wide analysis of genetic variation data is performed for two Drosophila melanogaster populations, one from North America (Raleigh, USA) and the other from Africa (Gikongoro, Rwanda). It is shown that fine-scale recombination rate variation is widespread throughout the D. melanogaster genome, across all chromosomes and in both populations. At the fine-scale, a conservative, systematic search for evidence of recombination hotspots suggests the existence of a handful of putative hotspots each with at least a tenfold increase in intensity over the background rate. A wavelet analysis is carried out to compare the estimated recombination maps in the two populations and to quantify the extent to which recombination rates are conserved. In general, similarity is observed at very broad scales, but substantial differences are seen at fine scales. The average recombination rate of the X chromosome appears to be higher than that of the autosomes in both populations, and this pattern is much more pronounced in the African population than the North American population. The correlation between various genomic features—including recombination rates, diversity, divergence, GC content, gene content, and sequence quality—is examined using the wavelet analysis, and it is shown that the most notable difference between D. melanogaster and humans is in the correlation between recombination and diversity.

Classical Genetics Meets Next-Generation Sequencing: Uncovering a Genome-Wide Recombination Map in Drosophila melanogaster

Classical Genetics Meets Next-Generation Sequencing: Uncovering a Genome-Wide Recombination Map in Drosophila melanogaster

Genetic and evolutionary correlates of fine-scale recombination rate variation in Drosophila persimilis

Recombination is fundamental to meiosis in many species and generates variation on which natural selection can act, yet fine-scale linkage maps are cumbersome to construct. We generated a fine-scale map of recombination rates across two major chromosomes in Drosophila persimilis using 181 SNP markers spanning two of five major chromosome arms. Using this map, we report significant fine-scale heterogeneity of local recombination rates. However, we also observed “recombinational neighborhoods,” where adjacent intervals had similar recombination rates after excluding regions near the centromere and telomere. We further found significant positive associations of fine-scale recombination rate with repetitive element abundance and a 13-bp sequence motif known to associate with human recombination rates. We noted strong crossover interference extending 5–7 Mb from the initial crossover event. Further, we observed that fine-scale recombination rates in D. persimilis are strongly correlated with those obtained from a comparable study of its sister species, D. pseudoobscura. We documented a significant relationship between recombination rates and intron nucleotide sequence diversity within species, but no relationship between recombination rate and intron divergence between species. These results are consistent with selection models (hitchhiking and background selection) rather than mutagenic recombination models for explaining the relationship of recombination with nucleotide diversity within species. Finally, we found significant correlations between recombination rate and GC content, supporting both GC-biased gene conversion (BGC) models and selection-driven codon bias models. Overall, this genome-enabled map of fine-scale recombination rates allowed us to confirm findings of broader-scale studies and identify multiple novel features that merit further investigation.

Population genomic inference of recombination rates and hotspots

As more human genomic data become available, fine-scale recombination rate variation can be inferred on a genome-wide scale. Current statistical methods to infer recombination rates that can be applied to moderate, or large, genomic regions are limited to approximated likelihoods. Here, we develop a Bayesian full-likelihood method using Markov Chain Monte Carlo (MCMC) to estimate background recombination rates and hotspots. The probability model is inspired by the observed patterns of recombination at several genomic regions analyzed in sperm-typing studies. Posterior probabilities and Bayes factors of recombination hotspots along chromosomes are inferred. For moderate-size genomic regions (e.g., with <100 SNPs), the full-likelihood method is used. Larger regions are split into subintervals (typically each having between 20 and 50 markers). The likelihood is approximated based on the genealogies for each subinterval. The background recombination rates, hotspots, and parameters are evaluated by using a parallel computing approach and assuming shared parameters across the subintervals. Simulation analyses show that our method can accurately estimate the variation in recombination rates across genomic regions. In particular, clusters of hotspots can be distinguished even though weaker hotspots are present. The method is applied to SNP data from the HLA region, the MS32, and chromosome 19.

Evidence for substantial fine-scale variation in recombination rates across the human genome.

Characterizing fine-scale variation in human recombination rates is important, both to deepen understanding of the recombination process and to aid the design of disease association studies. Current genetic maps show that rates vary on a megabase scale, but studying finer-scale variation using pedigrees is difficult. Sperm-typing experiments have characterized regions where crossovers cluster into 1-2-kb hot spots, but technical difficulties limit the number of studies. An alternative is to use population variation to infer fine-scale characteristics of the recombination process. Several surveys reported 'block-like' patterns of diversity, which may reflect fine-scale recombination rate variation, but limitations of available methods made this impossible to assess. Here, we applied a new statistical method, which overcomes these limitations, to infer patterns of fine-scale recombination rate variation in 74 genes. We found extensive rate variation both within and among genes. In particular, recombination hot spots are a common feature of the human genome: 47% (35 of 74) of genes showed substantive evidence for a hot spot, and many more showed evidence for some rate variation. No primary sequence characteristics are consistently associated with precise hot-spot location, although G+C content and nucleotide diversity are correlated with local recombination rate.

Assessing the Performance of the Haplotype Block Model of Linkage Disequilibrium

Several recent studies have suggested that linkage disequilibrium (LD) in the human genome has a fundamentally "blocklike" structure. However, thus far there has been little formal assessment of how well the haplotype block model captures the underlying structure of LD. Here we propose quantitative criteria for assessing how blocklike LD is and apply these criteria to both real and simulated data. Analyses of several large data sets indicate that real data show a partial fit to the haplotype block model; some regions conform quite well, whereas others do not. Some improvement could be obtained by genotyping higher marker densities but not by increasing the number of samples. Nonetheless, although the real data are only moderately blocklike, our simulations indicate that, under a model of uniform recombination, the structure of LD would actually fit the block model much less well. Simulations of a model in which much of the recombination occurs in narrow hotspots provide a much better fit to the observed patterns of LD, suggesting that there is extensive fine-scale variation in recombination rates across the human genome.