Variation in recombination rate across the genome: evidence and implications

Recent data from humans and other species provide convincing evidence of variation in recombination rate in different genomic regions. Comparison of physical and genetic maps reveals variation on a scale of megabases, with substantial differences between sexes. Recombination is often suppressed near centromeres and elevated near telomeres, but neither of these observations is true for all chromosomes. In humans, patterns of linkage disequilibrium and experimental measures of recombination from sperm-typing reveal dramatic hotspots of recombination on a scale of kilobases. Genome-wide variation in the amount of crossing-over may be due to variation in the density of hotspots, the intensity of hotspots, or both. Theoretical models of selection and linkage predict that genetic variation will be reduced in regions of low recombination, and this prediction is supported by data from several species. Heterogeneity in rates of crossing-over provides both an opportunity and a challenge for identifying disease genes: as associations occur in blocks, genomic regions containing disease loci may be identified with relatively few markers, yet identifying the causal mutations is unlikely to be achieved through associations alone.     

The breakdown of genomic ancestry blocks in hybrid lineages given a finite number of recombination sites

When a lineage originates from hybridization genomic blocks of contiguous ancestry from different ancestors are fragmented through genetic recombination. The resulting blocks are delineated by so called junctions, which accumulate with every generation that passes. Modeling the accumulation of ancestry block junctions can elucidate processes and timeframes of genomic admixture. Previous models have not addressed ancestry block dynamics for chromosomes that consist of a finite number of recombination sites. However, genomic data typically consist of informative markers that are interspersed with fragments for which no ancestry information is available. Hence, repeated recombination events may occur between markers, effectively removing existing junctions. Here, we present an analytical treatment of the dynamics of the mean number of junctions over time, taking into account the number of recombination sites per chromosome, population size, genetic map length, and the frequency of the ancestral species in the founding hybrid swarm. We describe the expected number of junctions using equidistant molecular markers and estimate the number of junctions using random markers. This extended theory of junctions thus reflects properties of empirical data and can serve to study the genomic patterns following admixture.     

Indica and japonica crosses resulting in linkage block and recombination suppression on rice chromosome 12

Understanding linkage block size and molecular mechanisms of recombination suppression is important for plant breeding. Previously large linkage blocks ranging from 14 megabases to 27 megabases were observed around the rice blast resistance gene Pi-ta in rice cultivars and backcross progeny involving an indica and japonica cross. In the present study, the same linkage block was further examined in 456 random recombinant individuals of rice involving 5 crosses ranging from F(2) to F(10) generation, with and without Pi-ta containing genomic indica regions with both indica and japonica germplasm. Simple sequence repeat markers spanning the entire chromosome 12 were used to detect recombination break points and to delimit physical size of linkage blocks. Large linkage blocks ranging from 4.1 megabases to 10 megabases were predicted from recombinant individuals involving genomic regions of indica and japonica. However, a significantly reduced block from less than 800 kb to 2.1megabases was identified from crosses of indica with indica rice regardless of the existence of Pi-ta. These findings suggest that crosses of indica and japonica rice have significant recombination suppression near the centromere on chromosome 12.     

Evidence of recombination in intrapatient populations of hepatitis C virus

Hepatitis C virus (HCV) is a major cause of liver disease worldwide and a potential cause of substantial morbidity and mortality in the future. HCV is characterized by a high level of genetic heterogeneity. Although homologous recombination has been demonstrated in many members of the family Flaviviridae, to which HCV belongs, there are only a few studies reporting recombination on natural populations of HCV, suggesting that these events are rare in vivo. Furthermore, these few studies have focused on recombination between different HCV genotypes/subtypes but there are no reports on the extent of intra-genotype or intra-subtype recombination between viral strains infecting the same patient. Given the important implications of recombination for RNA virus evolution, our aim in this study has been to assess the existence and eventually the frequency of intragenic recombination on HCV. For this, we retrospectively have analyzed two regions of the HCV genome (NS5A and E1-E2) in samples from two different groups: (i) patients infected only with HCV (either treated with interferon plus ribavirin or treatment naïve), and (ii) HCV-HIV co-infected patients (with and without treatment against HIV). The complete data set comprised 17712 sequences from 136 serum samples derived from 111 patients. Recombination analyses were performed using 6 different methods implemented in the program RDP3. Recombination events were considered when detected by at least 3 of the 6 methods used and were identified in 10.7% of the amplified samples, distributed throughout all the groups described and the two genomic regions studied. The resulting recombination events were further verified by detailed phylogenetic analyses. The complete experimental procedure was applied to an artificial mixture of relatively closely viral populations and the ensuing analyses failed to reveal artifactual recombination. From these results we conclude that recombination should be considered as a potentially relevant mechanism generating genetic variation in HCV and with important implications for the treatment of this infection.     

Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data

We introduce a new statistical model for patterns of linkage disequilibrium (LD) among multiple SNPs in a population sample. The model overcomes limitations of existing approaches to understanding, summarizing, and interpreting LD by (i) relating patterns of LD directly to the underlying recombination process; (ii) considering all loci simultaneously, rather than pairwise; (iii) avoiding the assumption that LD necessarily has a "block-like" structure; and (iv) being computationally tractable for huge genomic regions (up to complete chromosomes). We examine in detail one natural application of the model: estimation of underlying recombination rates from population data. Using simulation, we show that in the case where recombination is assumed constant across the region of interest, recombination rate estimates based on our model are competitive with the very best of current available methods. More importantly, we demonstrate, on real and simulated data, the potential of the model to help identify and quantify fine-scale variation in recombination rate from population data. We also outline how the model could be useful in other contexts, such as in the development of more efficient haplotype-based methods for LD mapping.     

Haplotype block structure is conserved across mammals

Genetic variation in genomes is organized in haplotype blocks, and species-specific block structure is defined by differential contribution of population history effects in combination with mutation and recombination events. Haplotype maps characterize the common patterns of linkage disequilibrium in populations and have important applications in the design and interpretation of genetic experiments. Although evolutionary processes are known to drive the selection of individual polymorphisms, their effect on haplotype block structure dynamics has not been shown. Here, we present a high-resolution haplotype map for a 5-megabase genomic region in the rat and compare it with the orthologous human and mouse segments. Although the size and fine structure of haplotype blocks are species dependent, there is a significant interspecies overlap in structure and a tendency for blocks to encompass complete genes. Extending these findings to the complete human genome using haplotype map phase I data reveals that linkage disequilibrium values are significantly higher for equally spaced positions in genic regions, including promoters, as compared to intergenic regions, indicating that a selective mechanism exists to maintain combinations of alleles within potentially interacting coding and regulatory regions. Although this characteristic may complicate the identification of causal polymorphisms underlying phenotypic traits, conservation of haplotype structure may be employed for the identification and characterization of functionally important genomic regions.     

Consistency of estimators of population scaled parameters using composite likelihood

Composite likelihood methods have become very popular for the analysis of large-scale genomic data sets because of the computational intractability of the basic coalescent process and its generalizations: It is virtually impossible to calculate the likelihood of an observed data set spanning a large chromosomal region without using approximate or heuristic methods. Composite likelihood methods are approximate methods and, in the present article, assume the likelihood is written as a product of likelihoods, one for each of a number of smaller regions that together make up the whole region from which data is collected. A very general framework for neutral coalescent models is presented and discussed. The framework comprises many of the most popular coalescent models that are currently used for analysis of genetic data. Assume data is collected from a series of consecutive regions of equal size. Then it is shown that the observed data forms a stationary, ergodic process. General conditions are given under which the maximum composite estimator of the parameters describing the model (e.g. mutation rates, demographic parameters and the recombination rate) is a consistent estimator as the number of regions tends to infinity.     

Frequent non-reciprocal exchange in microsatellite-containing-DNA-regions of vertebrates

Microsatellites are DNA-fragments containing short repetitive motifs with 2–10 bp. They are highly variable in most species and distributed throughout the whole genome. It is broadly accepted that their high degree of variability is closely associated with mispairing of DNA-strands during the replication phase, termed slippage, although recombination is also observed. The aim of this study is to demonstrate evidence that non-reciprocal recombination processes changing the total genomic structure are common in microsatellites and flanking regions. We sequenced DNA fragments from birds in which microsatellites are located, and analyzed the structure of the microsatellites and their flanking regions. Additionally, other data and those from literature of three microsatellite regions of primates coding for the Ataxin-2, the Huntingtin and the TATA-box binding protein were analyzed. The structures of seven avian and three primate microsatellites support the hypothesis that non-reciprocal recombination is a common process that may also contribute considerably to the variation at microsatellite loci. We conclude that results of population genetic studies that are analyzed statistically with methods based on stepwise mutation models should be interpreted with caution if no detailed information on the allelic variation of microsatellites is available.     

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

Computational detection of recombination hotspots from population polymorphism data is important both for understanding the nature of recombination and for applications such as association studies. We propose a new method for this task based on a multiple-hotspot model and an (approximate) log-likelihood ratio test. A truncated, weighted pairwise log-likelihood is introduced and applied to the calculation of the log-likelihood ratio, and a forward-selection procedure is adopted to search for the optimal hotspot predictions. The method shows a relatively high power with a low false-positive rate in detecting multiple hotspots in simulation data and has a performance comparable to the best results of leading computational methods in experimental data for which recombination hotspots have been characterized by sperm-typing experiments. The method can be applied to both phased and unphased data directly, with a very fast computational speed. We applied the method to the 10 500-kb regions of the HapMap ENCODE data and found 172 hotspots among the three populations, with average hotspot width of 2.4 kb. By comparisons with the simulation data, we found some evidence that hotspots are not all identical across populations. The correlations between detected hotspots and several genomic characteristics were examined. In particular, we observed that DNaseI-hypersensitive sites are enriched in hotspots, suggesting the existence of human beta hotspots similar to those found in yeast.     

A neutral explanation for the correlation of diversity with recombination rates in humans

One of the most striking findings to emerge from the study of genomic patterns of variation is that regions with lower recombination rates tend to have lower levels of intraspecific diversity but not of interspecies divergence. This uncoupling of variation within and between species has been widely interpreted as evidence that natural selection shapes patterns of genetic variability genomewide. We revisited the relationship between diversity, divergence, and recombination in humans, using data from closely related species and better estimates of recombination rates than previously available. We show that regions that experience less recombination have reduced divergence to chimpanzee and to baboon, as well as lower levels of diversity. This observation suggests that mutation and recombination are associated processes in humans, so that the positive correlation between diversity and recombination may have a purely neutral explanation. Consistent with this hypothesis, diversity levels no longer increase significantly with recombination rates after correction for divergence to chimpanzee.     

Human recombination rates are increased around accelerated conserved regions--evidence for continued selection?

MOTIVATION: We hypothesized that recombination rates might be increased at genetic loci that are subject to more intense selection. Here, we test this hypothesis by using a recently published set of accelerated conserved regions and fine-scale recombination rate estimates provided by the HapMap project. RESULTS: We observed that fine-scale recombination rates are increased around conserved noncoding regions that show accelerated evolution in human or chimp, as compared to noncoding regions showing accelerated evolution in mouse and those being conserved between human and fugu. Recombination rates around hominid accelerated conserved regions (ACRs) are furthermore increased as compared to exonic regions. On the other hand, GC-content is reduced around ACRs, excluding a major confounding influence of GC-content on the observed variation in recombination rate. Conclusion: Our observations indicate that selection intensity could be an important determinant of local recombination rate variation and that continued positive selection might act at many ACR loci. Alternatively, a confounding factor needs to be found that causes a congruent signal in recombination rate estimates based on human polymorphism data and in the comparative genomic data. Researchers who consider the explanation involving selection as more likely may expect more common functional sequence variants at ACRs in genetic association studies.     

Cloning and sequencing of a 16S/23S ribosomal spacer from Haemophilus parainfluenzae reveals an invariant, mosaic-like organisation of sequence blocks

A 16S/23S ribosomal spacer from a Haemophilus parainfluenzae rrn locus was cloned and sequenced. Analysis of PCR-amplified genomic fragments showed that this region is strongly conserved among unrelated isolates; computer analysis of database homologies showed that the spacer consists of sequence blocks, arranged in a mosaic-like structure, with strong homologies with analogous blocks present in the spacer regions of Haemophilus influenzae, Haemophilus ducreyi and Actinobacillus spp. It also contains a tRNA^Glu gene, which is highly homologous to tRNA^Glu genes found in spacers of other species. These data strongly support the hypothesis that recombination events are involved in the organisation of the sequence of the spacer, as a result of horizontal gene transfer.     

Detection and localization of a single binary trait locus in experimental populations

The advancements made in molecular technology coupled with statistical methodology have led to the successful detection and location of genomic regions (quantitative trait loci; QTL) associated with quantitative traits. Binary traits (e.g. susceptibility/resistance), while not quantitative in nature, are equally important for the purpose of detecting and locating significant associations with genomic regions. Existing interval regression methods used in binary trait analysis are adapted from quantitative trait analysis and the tests for regression coefficients are tests of effect, not detection. Additionally, estimates of recombination that fail to take into account varying penetrance perform poorly when penetrance is incomplete. In this work a complete probability model for binary trait data is developed allowing for unbiased estimation of both penetrance and recombination between a genetic marker locus and a binary trait locus for backcross and F2 experimental designs. The regression model is reparameterized allowing for tests of detection. Extensive simulations were conducted to assess the performance of estimation and testing in the proposed parameterization. The proposed parameterization was compared with interval regression via simulation. The results indicate that our parameterization shows equivalent estimation capabilities, requires less computational effort and works well with only a single marker.     

Recombination rate variation and speciation: theoretical predictions and empirical results from rabbits and mice

Recently diverged taxa may continue to exchange genes. A number of models of speciation with gene flow propose that the frequency of gene exchange will be lower in genomic regions of low recombination and that these regions will therefore be more differentiated. However, several population-genetic models that focus on selection at linked sites also predict greater differentiation in regions of low recombination simply as a result of faster sorting of ancestral alleles even in the absence of gene flow. Moreover, identifying the actual amount of gene flow from patterns of genetic variation is tricky, because both ancestral polymorphism and migration lead to shared variation between recently diverged taxa. New analytic methods have been developed to help distinguish ancestral polymorphism from migration. Along with a growing number of datasets of multi-locus DNA sequence variation, these methods have spawned a renewed interest in speciation models with gene flow. Here, we review both speciation and population-genetic models that make explicit predictions about how the rate of recombination influences patterns of genetic variation within and between species. We then compare those predictions with empirical data of DNA sequence variation in rabbits and mice. We find strong support for the prediction that genomic regions experiencing low levels of recombination are more differentiated. In most cases, reduced gene flow appears to contribute to the pattern, although disentangling the relative contribution of reduced gene flow and selection at linked sites remains a challenge. We suggest fruitful areas of research that might help distinguish between different models.     

Bacterial genomes in epidemiology—present and future

Sequence data are well established in the reconstruction of the phylogenetic and demographic scenarios that have given rise to outbreaks of viral pathogens. The application of similar methods to bacteria has been hindered in the main by the lack of high-resolution nucleotide sequence data from quality samples. Developing and already available genomic methods have greatly increased the amount of data that can be used to characterize an isolate and its relationship to others. However, differences in sequencing platforms and data analysis mean that these enhanced data come with a cost in terms of portability: results from one laboratory may not be directly comparable with those from another. Moreover, genomic data for many bacteria bear the mark of a history including extensive recombination, which has the potential to greatly confound phylogenetic and coalescent analyses. Here, we discuss the exacting requirements of genomic epidemiology, and means by which the distorting signal of recombination can be minimized to permit the leverage of growing datasets of genomic data from bacterial pathogens.     

The Pattern of Neutral Molecular Variation under the Background Selection Model

Stochastic simulations of the infinite sites model were used to study the behavior of genetic diversity at a neutral locus in a genomic region without recombination, but subject to selection against deleterious alleles maintained by recurrent mutation (background selection). In large populations, the effect of background selection on the number of segregating sites approaches the effct on nucleotide site diversity, i.e., the reduction in genetic variability caused by background selection resembles that caused by a simple reduction in effective population size. We examined, by coalescence-based methods, the power of several tests for the departure from neutral expectation of the frequency spectra of alleles in samples from randomly mating populations (TAJIMA's, FU and LI's, and WATTERSON's tests). All of the tests have low power unless the selection against mutant alleles is extremely weak. In Drosophila, significant TAJIMA's tests are usually not obtained with empirical data sets from loci in genomic regions with restricted recombination frequencies and that exhibit low genetic diversity. This is consistent with the operation of background selection as opposed to selective sweeps. It remains to be decided whether background selection is sufficient to explain the observed extent of reduction in diversity in regions of restricted recombination.     

Phylogenetic evidence for rapid rates of molecular evolution in the single-stranded DNA begomovirus tomato yellow leaf curl virus

Geminiviruses are devastating viruses of plants that possess single-stranded DNA (ssDNA) DNA genomes. Despite the importance of this class of phytopathogen, there have been no estimates of the rate of nucleotide substitution in the geminiviruses. We report here the evolutionary rate of the tomato yellow leaf curl disease-causing viruses, an intensively studied group of monopartite begomoviruses. Sequences from GenBank, isolated from diseased plants between 1988 and 2006, were analyzed using Bayesian coalescent methods. The mean genomic substitution rate was estimated to be 2.88 x 10(-4) nucleotide substitutions per site per year (subs/site/year), although this rate could be confounded by frequent recombination within Tomato yellow leaf curl virus genomes. A recombinant-free data set comprising the coat protein (V1) gene in isolation yielded a similar mean rate (4.63 x 10(-4) subs/site/year), validating the order of magnitude of genomic substitution rate for protein-coding regions. The intergenic region, which is known to be more variable, was found to evolve even more rapidly, with a mean substitution rate of approximately 1.56 x 10(-3) subs/site/year. Notably, these substitution rates, the first reported for a plant DNA virus, are in line with those estimated previously for mammalian ssDNA viruses and RNA viruses. Our results therefore suggest that the high evolutionary rate of the geminiviruses is not primarily due to frequent recombination and may explain their ability to emerge in novel hosts.     

The Correlation Between Recombination Rate and Dinucleotide Bias in Drosophila melanogaster

Revealing how recombination affects genomic sequence is of great significance to our understanding of genome evolution. The present paper focuses on the correlation between recombination rate and dinucleotide bias in Drosophila melanogaster genome. Our results show that the overall dinucleotide bias is positively correlated with recombination rate for genomic sequences including untranslated regions, introns, intergenic regions, and coding sequences. The correlation patterns of individual dinucleotide biases with recombination rate are presented. Possible mechanisms of interaction between recombination and dinucleotide bias are discussed. Our data indicate that there may be a genome-wide universal mechanism acting between recombination rate and dinucleotide bias, which is likely to be neighbor-dependent biased gene conversion.     

A divide-and-conquer approach to determine the Pareto frontier for optimization of protein engineering experiments

In developing improved protein variants by site-directed mutagenesis or recombination, there are often competing objectives that must be considered in designing an experiment (selecting mutations or breakpoints): stability versus novelty, affinity versus specificity, activity versus immunogenicity, and so forth. Pareto optimal experimental designs make the best trade-offs between competing objectives. Such designs are not "dominated"; that is, no other design is better than a Pareto optimal design for one objective without being worse for another objective. Our goal is to produce all the Pareto optimal designs (the Pareto frontier), to characterize the trade-offs and suggest designs most worth considering, but to avoid explicitly considering the large number of dominated designs. To do so, we develop a divide-and-conquer algorithm, Protein Engineering Pareto FRontier (PEPFR), that hierarchically subdivides the objective space, using appropriate dynamic programming or integer programming methods to optimize designs in different regions. This divide-and-conquer approach is efficient in that the number of divisions (and thus calls to the optimizer) is directly proportional to the number of Pareto optimal designs. We demonstrate PEPFR with three protein engineering case studies: site-directed recombination for stability and diversity via dynamic programming, site-directed mutagenesis of interacting proteins for affinity and specificity via integer programming, and site-directed mutagenesis of a therapeutic protein for activity and immunogenicity via integer programming. We show that PEPFR is able to effectively produce all the Pareto optimal designs, discovering many more designs than previous methods. The characterization of the Pareto frontier provides additional insights into the local stability of design choices as well as global trends leading to trade-offs between competing criteria.     

Role of evolutionary history on haplotype block structure in the human genome: implications for disease mapping

With the completion of the first draft of the human genome sequencing project, a new challenge is to characterize patterns of linkage disequilibrium and haplotype structure across genomic regions to identify mutations associated with complex disease. Recent work shows considerable linkage disequilibrium heterogeneity, where genomic regions of extended haplotype blocks are punctuated by recombination hotspots. In this review we explore some of the current approaches to defining and characterizing 'hapblocks', mechanisms by which hapblocks may be generated, and the implications this block-like structure may have for successfully mapping mutations associated with complex disease.