A new method for detecting human recombination hotspots and its applications to the HapMap ENCODE data

Inferring recombination patterns in African populations

Although several high-resolution recombination maps exist for European-descent populations, the recombination landscape of African populations remains relatively understudied. Given that there is high genetic divergence among groups in Africa, it is possible that recombination hotspots also diverge significantly. Both limitations and opportunities exist for developing recombination maps for these populations. In this review, we discuss various recombination inference methods, and the strengths and weaknesses of these methods in analyzing recombination in African-descent populations. Furthermore, we provide a decision tree and recommendations for which inference method to use in various research contexts. Establishing an appropriate methodology for recombination rate inference in a particular study will improve the accuracy of various downstream analyses including but not limited to local ancestry inference, haplotype phasing, fine-mapping of GWAS loci and genome assemblies.

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables. These challenges are traditionally tackled by likelihood-free methods that use scientific simulators to generate datasets and reduce them to hand-designed, permutation-invariant summary statistics, often leading to inaccurate inference. In this work, we develop an exchangeable neural network that performs summary statistic-free, likelihood-free inference. Our framework can be applied in a black-box fashion across a variety of simulation-based tasks, both within and outside biology. We demonstrate the power of our approach on the recombination hotspot testing problem, outperforming the state-of-the-art.

So similar yet so different: The two ends of a double strand break

Homologous recombination (HR) is essential for ensuring proper segregation of chromosomes in the first round of meiotic division. HR is also crucial for preserving genomic integrity of somatic cells due to its ability to rescue collapsed replication forks and eliminate deleterious DNA lesions, such as double-strand breaks (DSBs), interstrand crosslinks, and single-strand DNA gaps. Here, we review the early steps of HR (homology search and strand exchange), focusing on the roles of the two ends of a DSB. A detailed overview of the basic HR machinery and its mechanism for template selection and capture of duplex DNA via strand exchange is provided. Roles of proteins involved in these steps are discussed in both mitotic and meiotic HR. Central to this review is the hypothesis, which suggests that in meiosis, HR begins with a symmetrical DSB, but the symmetry is quickly lost with the two ends assuming different roles; it argues that this disparity of the two ends is essential for regulation of HR in meiosis and successful production of haploid gametes. We also propose a possible evolutionary reason for the asymmetry of the ends in HR.

Detecting Recombination Hotspots from Patterns of Linkage Disequilibrium

With recent advances in DNA sequencing technologies, it has become increasingly easy to use whole-genome sequencing of unrelated individuals to assay patterns of linkage disequilibrium (LD) across the genome. One type of analysis that is commonly performed is to estimate local recombination rates and identify recombination hotspots from patterns of LD. One method for detecting recombination hotspots, LDhot, has been used in a handful of species to further our understanding of the basic biology of recombination. For the most part, the effectiveness of this method (e.g., power and false positive rate) is unknown. In this study, we run extensive simulations to compare the effectiveness of three different implementations of LDhot. We find large differences in the power and false positive rates of these different approaches, as well as a strong sensitivity to the window size used (with smaller window sizes leading to more accurate estimation of hotspot locations). We also compared our LDhot simulation results with comparable simulation results obtained from a Bayesian maximum-likelihood approach for identifying hotspots. Surprisingly, we found that the latter computationally intensive approach had substantially lower power over the parameter values considered in our simulations.

Recombination hotspots: Models and tools for detection

Recombination hotspots are the regions within the genome where the rate, and the frequency of recombination are optimum with a size varying from 1 to 2kb. The recombination event is mediated by the double-stranded break formation, guided by the combined enzymatic action of DNA topoisomerase and Spo 11 endonuclease. These regions are distributed non-uniformly throughout the human genome and cause distortions in the genetic map. Numerous lines of evidence suggest that the number of hotspots known in humans has increased manifold in recent years. A few facts about the hotspot evolutions were also put forward, indicating the differences in the hotspot position between chimpanzees and humans. In mice, recombination hot spots were found to be clustered within the major histocompatibility complex (MHC) region. Several models, that help explain meiotic recombination has been proposed. Moreover, scientists also developed some computational tools to locate the hotspot position and estimate their recombination rate in humans is of great interest to population and medical geneticists. Here we reviewed the molecular mechanisms, models and in silico prediction techniques of hot spot residues.

The 8p23 inversion polymorphism determines local recombination heterogeneity across human populations

For decades, chromosomal inversions have been regarded as fascinating evolutionary elements as they are expected to suppress recombination between chromosomes with opposite orientations, leading to the accumulation of genetic differences between the two configurations over time. Here, making use of publicly available population genotype data for the largest polymorphic inversion in the human genome (8p23-inv), we assessed whether this inhibitory effect of inversion rearrangements led to significant differences in the recombination landscape of two homologous DNA segments, with opposite orientation. Our analysis revealed that the accumulation of genetic differentiation is positively correlated with the variation in recombination profiles. The observed recombination dissimilarity between inversion types is consistent across all populations analyzed and surpasses the effects of geographic structure, suggesting that both structures (orientations) have been evolving independently over an extended period of time, despite being subjected to the very same demographic history. Aside this mainly independent evolution, we also identified a short segment (350 kb, <10% of the whole inversion) in the central region of the inversion where the genetic divergence between the two structural haplotypes is diminished. Although it is difficult to demonstrate it, this could be due to gene flow (possibly via double-crossing over events), which is consistent with the higher recombination rates surrounding this segment. This study demonstrates for the first time that chromosomal inversions influence the recombination landscape at a fine-scale and highlights the role of these rearrangements as drivers of genome evolution.

Epigenetic functions enriched in transcription factors binding to mouse recombination hotspots

The regulatory mechanism of recombination is a fundamental problem in genomics, with wide applications in genome-wide association studies, birth-defect diseases, molecular evolution, cancer research, etc. In mammalian genomes, recombination events cluster into short genomic regions called "recombination hotspots". Recently, a 13-mer motif enriched in hotspots is identified as a candidate cis-regulatory element of human recombination hotspots; moreover, a zinc finger protein, PRDM9, binds to this motif and is associated with variation of recombination phenotype in human and mouse genomes, thus is a trans-acting regulator of recombination hotspots. However, this pair of cis and trans-regulators covers only a fraction of hotspots, thus other regulators of recombination hotspots remain to be discovered. In this paper, we propose an approach to predicting additional trans-regulators from DNA-binding proteins by comparing their enrichment of binding sites in hotspots. Applying this approach on newly mapped mouse hotspots genome-wide, we confirmed that PRDM9 is a major trans-regulator of hotspots. In addition, a list of top candidate trans-regulators of mouse hotspots is reported. Using GO analysis we observed that the top genes are enriched with function of histone modification, highlighting the epigenetic regulatory mechanisms of recombination hotspots.

Contrasting methods of quantifying fine structure of human recombination

There has been considerable excitement over the ability to construct linkage maps based only on genome-wide genotype data for single nucleotide polymorphic sites (SNPs) in a population sample. These maps, which are derived from estimates of linkage disequilibrium (LD), rely on population genetics theory to relate the decay of LD to the local rate of recombination, but other population processes also come into play. Here we contrast these LD maps to the classically derived, pedigree-based human recombination maps. The LD maps have a level of resolution greatly exceeding that of the pedigree maps, and at this fine scale, sperm typing allows a means of validation. While at a gross level both the pedigree maps and the sperm typing methods generally agree with LD maps, there are significant local differences between them, and the fact that these maps measure different genetic features should be remembered when using them for other genetic inferences.

Nucleotide polymorphism of the TNF gene cluster in six Chinese populations

DNA variants in a 31-kb region of the human major histocompatibility complex, encompassing the tumor necrosis factor (TNF) gene cluster, were surveyed by direct sequencing of 283 unrelated individuals from six Chinese populations. A total of 273 polymorphic sites were identified, with nearly half of them novel. We observed an excess of rare variants and negative values of selection tests of the region, implying either that these populations experienced a historical expansion or that the surveyed region was subjected to natural selection. Different characteristics of the sequence variation in the six populations outline the genetic differentiation between Northern and Southern Chinese populations. The distributions of recombination rates are similar among all the populations, with variation in the magnitude and/or in the fine location of hot spots. Tag single-nucleotide polymorphisms (SNPs) selected from HapMap (Phase II) CHB data accounted for an average of 64% of common SNPs from the six Chinese populations. We also observed a limited transferability of tag SNPs between Chinese populations on the 31-kb region with an excess of untaggable SNPs and ragged linkage disequilibrium blocks. It suggested that the design and interpretation of future association studies should be more cautious, and that a resequencing approach may refine tag SNP selection on Chinese-specific disease mapping.

Female-to-male breeding ratio in modern humans-an analysis based on historical recombinations

Was the past genetic contribution of women and men to the current human population equal? Was polygyny (excess of breeding women) present among hominid lineages? We addressed these questions by measuring the ratio of population recombination rates between the X chromosome and the autosomes, rho(X)/rho(A). The X chromosome recombines only in female meiosis, whereas autosomes undergo crossovers in both sexes; thus, rho(X)/rho(A) reflects the female-to-male breeding ratio, beta. We estimated beta from rho(X)/rho(A) inferred from genomic diversity data and calibrated with recombination rates derived from pedigree data. For the HapMap populations, we obtained beta of 1.4 in the Yoruba from West Africa, 1.3 in Europeans, and 1.1 in East Asian samples. These values are consistent with a high prevalence of monogamy and limited polygyny in human populations. More mutations occur during male meiosis as compared to female meiosis at the rate ratio referred to as alpha. We show that at alpha not equal 1, the divergence rates and genetic diversities of the X chromosome relative to the autosomes are complex functions of both alpha and beta, making their independent estimation difficult. Because our estimator of beta does not require any knowledge of the mutation rates, our approach should allow us to dissociate the effects of alpha and beta on the genetic diversity and divergence rate ratios of the sex chromosomes to the autosomes.

Estimation of fine-scale recombination intensity variation in the white-echinus interval of D. melanogaster

Accurate assessment of local recombination rate variation is crucial for understanding the recombination process and for determining the impact of natural selection on linked sites. In Drosophila, local recombination intensity has been estimated primarily by statistical approaches, by estimating the local slope of the relationship between the physical and genetic maps. However, these estimates are limited in resolution and, as a result, the physical scale at which recombination intensity varies in Drosophila is largely unknown. Although there is some evidence suggesting as much as a 40-fold variation in crossover rate at a local scale in D. pseudoobscura, little is known about the fine-scale structure of recombination rate variation in D. melanogaster. Here we experimentally examine the fine-scale distribution of crossover events in a 1.2-Mb region on the D. melanogaster X chromosome using a classic genetic mapping approach. Our results show that crossover frequency is significantly heterogeneous within this region, varying approximately 3.5-fold. Simulations suggest that this degree of heterogeneity is sufficient to affect levels of standing nucleotide diversity, although the magnitude of this effect is small. We recover no statistical association between empirical estimates of nucleotide diversity and recombination intensity, which is likely due to the limited number of loci sampled in our population genetic data set. However, codon bias is significantly negatively correlated with fine-scale recombination intensity estimates, as expected. Our results shed light on the relevant physical scale to consider in evolutionary analyses relating to recombination rate and highlight the motivations to increase the resolution of the recombination map in Drosophila.

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.

FstSNP-HapMap3: a database of SNPs with high population differentiation for HapMap3

The International HapMap Project has recently made available genotypes and frequency data for phase 3 (NCBI build 36, dbSNPb129) of the HapMap providing an enriched genotype dataset for approximately 1.6 million single nucleotide polymorphisms (SNPs) from 1,115 individuals with ancestry from parts of Africa, Asia, Europe, North America and Mexico. In the present study, we aim to facilitate pharmacogenetics studies by providing a database of SNPs with high population differentiation through a genomewide test on allele frequency variation among 11 HapMap3 samples. Common SNPs with minor allele frequency greater than 5¢ from each of 11 HapMap3 samples were included in the present analysis. The population differentiation is measured in terms of fixation index (Fst), and the SNPs with Fst values over 0.5 were defined as highly differentiated SNPs. Our tests were carried out between all pairs of the 11 HapMap3 samples or among subgroups with the same continental ancestries. Altogether we carried out 64 genomewide Fst tests and identified 28,215 highly differentiated SNPs for 49 different combinations of HapMap3 samples in the current database.

Fraction of informative recombinations: a heuristic approach to analyze recombination rates

In this article we present a new heuristic approach (informative recombinations, InfRec) to analyze recombination density at the sequence level. InfRec is intuitive and easy and combines previously developed methods that (i) resolve genotypes into haplotypes, (ii) estimate the minimum number of recombinations, and (iii) evaluate the fraction of informative recombinations. We tested this approach in its sliding-window version on 117 genes from the SeattleSNPs program, resequenced in 24 African-Americans (AAs) and 23 European-Americans (EAs). We obtained population recombination rate estimates (rho(obs)) of 0.85 and 0.37 kb(-1) in AAs and EAs, respectively. Coalescence simulations indicated that these values account for both the recombinations and the gene conversions in the history of the sample. The intensity of rho(obs) varied considerably along the sequence, revealing the presence of recombination hotspots. Overall, we observed approximately 80% of recombinations in one-third and approximately 50% in only 10% of the sequence. InfRec performance, tested on published simulated and additional experimental data sets, was similar to that of other hotspot detection methods. Fast, intuitive, and visual, InfRec is not constrained by sample size limitations. It facilitates understanding data and provides a simple and flexible tool to analyze recombination intensity along the sequence.

Mammalian meiotic recombination hot spots

Our understanding of the details of mammalian meiotic recombination has recently advanced significantly. Sperm typing technologies, linkage studies, and computational inferences from population genetic data have together provided information in unprecedented detail about the location and activity of the sites of crossing-over in mice and humans. The results show that the vast majority of meiotic recombination events are localized to narrow DNA regions (hot spots) that constitute only a small fraction of the genome. The data also suggest that the molecular basis of hot spot activity is unlikely to be strictly determined by specific DNA sequence motifs in cis. Further molecular studies are needed to understand how hot spots originate, function and evolve.

Recombination rate estimation in the presence of hotspots

Fine-scale estimation of recombination rates remains a challenging problem. Experimental techniques can provide accurate estimates at fine scales but are technically challenging and cannot be applied on a genome-wide scale. An alternative source of information comes from patterns of genetic variation. Several statistical methods have been developed to estimate recombination rates from randomly sampled chromosomes. However, most such methods either make poor assumptions about recombination rate variation, or simply assume that there is no rate variation. Since the discovery of recombination hotspots, it is clear that recombination rates can vary over many orders of magnitude at the fine scale. We present a method for the estimation of recombination rates in the presence of recombination hotspots. We demonstrate that the method is able to detect and accurately quantify recombination rate heterogeneity, and is a substantial improvement over a commonly used method. We then use the method to reanalyze genetic variation data from the HLA and MS32 regions of the human genome and demonstrate that the method is able to provide accurate rate estimates and simultaneously detect hotspots.

SequenceLDhot: detecting recombination hotspots

MOTIVATION

There is much local variation in recombination rates across the human genome--with the majority of recombination occurring in recombination hotspots--short regions of around approximately 2 kb in length that have much higher recombination rates than neighbouring regions. Knowledge of this local variation is important, e.g. in the design and analysis of association studies for disease genes. Population genetic data, such as that generated by the HapMap project, can be used to infer the location of these hotspots. We present a new, efficient and powerful method for detecting recombination hotspots from population data.

RESULTS

We compare our method with four current methods for detecting hotspots. It is orders of magnitude quicker, and has greater power, than two related approaches. It appears to be more powerful than HotspotFisher, though less accurate at inferring the precise positions of the hotspot. It was also more powerful than LDhot in some situations: particularly for weaker hotspots (10-40 times the background rate) when SNP density is lower (< 1/kb).

AVAILABILITY

Program, data sets, and full details of results are available at: http://www.maths.lancs.ac.uk/~fearnhea/Hotspot.