Bayesian population genomic inference of crossing over and gene conversion

Estimating Gene Conversion Tract Length and Rate From PacBio HiFi Data

Gene conversions are broadly defined as the transfer of genetic material from a "donor" to an "acceptor" sequence and can happen both in meiosis and mitosis. They are a subset of noncrossover (NCO) events and, like crossover (CO) events, gene conversion can generate new combinations of alleles and counteract mutation load by reverting germline mutations through GC-biased gene conversion. Estimating gene conversion rate and the distribution of gene conversion tract lengths remains challenging. We present a new method for estimating tract length, rate, and detection probability of NCO events directly in HiFi PacBio long read data. The method can be used to make inference from sequencing of gametes from a single individual. The method is unbiased even under low single nucleotide variant (SNV) densities and does not necessitate any demographic or evolutionary assumptions. We test the accuracy and robustness of our method using simulated datasets where we vary length of tracts, number of tracts, the genomic SNV density, and levels of correlation between SNV density and NCO event position. Our simulations show that under low SNV densities, like those found in humans, only a minute fraction (∼2%) of NCO events are expected to become visible as gene conversions by moving at least 1 SNV. We finally illustrate our method by applying it to PacBio sequencing data from human sperm.

Hypothesis testing of meiotic recombination rates from population genetic data

Background

Meiotic recombination, one of the central biological processes studied in population genetics, comes in two known forms: crossovers and gene conversions. A number of previous studies have shown that when one of these two events is nonexistent in the genealogical model, the point estimation of the corresponding recombination rate by population genetic methods tends to be inflated. Therefore, it has become necessary to obtain statistical evidence from population genetic data about whether one of the two recombination events is absent.

Results

In this paper, we formulate this problem in a hypothesis testing framework and devise a testing procedure based on the likelihood ratio test (LRT). However, because the null value (i.e., zero) lies on the boundary of the parameter space, the regularity conditions for the large‐sample approximation to the distribution of the LRT statistic do not apply. In turn, the standard chi‐squared approximation is inaccurate. To address this critical issue, we propose a parametric bootstrap procedure to obtain an approximate p‐value for the observed test statistic. Coalescent simulations are conducted to show that our approach yields accurate null p‐values that closely follow the theoretical prediction while the estimated alternative p‐values tend to concentrate closer to zero. Finally, the method is demonstrated on a real biological data set from the telomere of the X chromosome of African Drosophila melanogaster.

Conclusions

Our methodology provides a necessary complement to the existing procedures of estimating meiotic recombination rates from population genetic data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-014-0122-7) contains supplementary material, which is available to authorized users.

Meiotic gene-conversion rate and tract length variation in the human genome

Meiotic recombination occurs in the form of two different mechanisms called crossing-over and gene-conversion and both processes have an important role in shaping genetic variation in populations. Although variation in crossing-over rates has been studied extensively using sperm-typing experiments, pedigree studies and population genetic approaches, our knowledge of variation in gene-conversion parameters (ie, rates and mean tract lengths) remains far from complete. To explore variability in population gene-conversion rates and its relationship to crossing-over rate variation patterns, we have developed and validated using coalescent simulations a comprehensive Bayesian full-likelihood method that can jointly infer crossing-over and gene-conversion rates as well as tract lengths from population genomic data under general variable rate models with recombination hotspots. Here, we apply this new method to SNP data from multiple human populations and attempt to characterize for the first time the fine-scale variation in gene-conversion parameters along the human genome. We find that the estimated ratio of gene-conversion to crossing-over rates varies considerably across genomic regions as well as between populations. However, there is a great degree of uncertainty associated with such estimates. We also find substantial evidence for variation in the mean conversion tract length. The estimated tract lengths did not show any negative relationship with the local heterozygosity levels in our analysis.European Journal of Human Genetics advance online publication, 27 February 2013; doi:10.1038/ejhg.2013.30.

The many landscapes of recombination in Drosophila melanogaster

Recombination is a fundamental biological process with profound evolutionary implications. Theory predicts that recombination increases the effectiveness of selection in natural populations. Yet, direct tests of this prediction have been restricted to qualitative trends due to the lack of detailed characterization of recombination rate variation across genomes and within species. The use of imprecise recombination rates can also skew population genetic analyses designed to assess the presence and mode of selection across genomes. Here we report the first integrated high-resolution description of genomic and population variation in recombination, which also distinguishes between the two outcomes of meiotic recombination: crossing over (CO) and gene conversion (GC). We characterized the products of 5,860 female meioses in Drosophila melanogaster by genotyping a total of 139 million informative SNPs and mapped 106,964 recombination events at a resolution down to 2 kilobases. This approach allowed us to generate whole-genome CO and GC maps as well as a detailed description of variation in recombination among individuals of this species. We describe many levels of variation in recombination rates. At a large-scale (100 kb), CO rates exhibit extreme and highly punctuated variation along chromosomes, with hot and coldspots. We also show extensive intra-specific variation in CO landscapes that is associated with hotspots at low frequency in our sample. GC rates are more uniformly distributed across the genome than CO rates and detectable in regions with reduced or absent CO. At a local scale, recombination events are associated with numerous sequence motifs and tend to occur within transcript regions, thus suggesting that chromatin accessibility favors double-strand breaks. All these non-independent layers of variation in recombination across genomes and among individuals need to be taken into account in order to obtain relevant estimates of recombination rates, and should be included in a new generation of population genetic models of the interaction between selection and linkage.