Dataset Information

Detecting sequence polymorphisms associated with meiotic recombination hotspots in the human genome.

ABSTRACT:

Background

Meiotic recombination events tend to cluster into narrow spans of a few kilobases long, called recombination hotspots. Such hotspots are not conserved between human and chimpanzee and vary between different human ethnic groups. At the same time, recombination hotspots are heritable. Previous studies showed instances where differences in recombination rate could be associated with sequence polymorphisms.

Results

In this work we developed a novel computational approach, LDsplit, to perform a large-scale association study of recombination hotspots with genetic polymorphisms. LDsplit was able to correctly predict the association between the FG11 SNP and the DNA2 hotspot observed by sperm typing. Extensive simulation demonstrated the accuracy of LDsplit under various conditions. Applying LDsplit to human chromosome 6, we found that for a significant fraction of hotspots, there is an association between variations in intensity of historical recombination and sequence polymorphisms. From flanking regions of the SNPs output by LDsplit we identified a conserved 11-mer motif GGNGGNAGGGG, whose complement partially matches 13-mer CCNCCNTNNCCNC, a critical motif for the regulation of recombination hotspots.

Conclusions

Our result suggests that computational approaches based on historical recombination events are likely to be more powerful than previously anticipated. The putative associations we identified may be a promising step toward uncovering the mechanisms of recombination hotspots.

SUBMITTER: Zheng J

PROVIDER: S-EPMC3218659 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Publications

Detecting sequence polymorphisms associated with meiotic recombination hotspots in the human genome.

Zheng Jie J Khil Pavel P PP Camerini-Otero R Daniel RD Przytycka Teresa M TM

Genome biology 20101020 10

<h4>Background</h4>Meiotic recombination events tend to cluster into narrow spans of a few kilobases long, called recombination hotspots. Such hotspots are not conserved between human and chimpanzee and vary between different human ethnic groups. At the same time, recombination hotspots are heritable. Previous studies showed instances where differences in recombination rate could be associated with sequence polymorphisms.<h4>Results</h4>In this work we developed a novel computational approach, L ...[more]

PMID: 20961408

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Project description:BackgroundAs a fundamental genomic element, meiotic recombination hotspot plays important roles in life sciences. Thus uncovering its regulatory mechanisms has broad impact on biomedical research. Despite the recent identification of the zinc finger protein PRDM9 and its 13-mer binding motif as major regulators for meiotic recombination hotspots, other regulators remain to be discovered. Existing methods for finding DNA sequence motifs of recombination hotspots often rely on the enrichment of co-localizations between hotspots and short DNA patterns, which ignore the cross-individual variation of recombination rates and sequence polymorphisms in the population. Our objective in this paper is to capture signals encoded in genetic variations for the discovery of recombination-associated DNA motifs.ResultsRecently, an algorithm called "LDsplit" has been designed to detect the association between single nucleotide polymorphisms (SNPs) and proximal meiotic recombination hotspots. The association is measured by the difference of population recombination rates at a hotspot between two alleles of a candidate SNP. Here we present an open source software tool of LDsplit, with integrative data visualization for recombination hotspots and their proximal SNPs. Applying LDsplit on SNPs inside an established 7-mer motif bound by PRDM9 we observed that SNP alleles preserving the original motif tend to have higher recombination rates than the opposite alleles that disrupt the motif. Running on SNP windows around hotspots each containing an occurrence of the 7-mer motif, LDsplit is able to guide the established motif finding algorithm of MEME to recover the 7-mer motif. In contrast, without LDsplit the 7-mer motif could not be identified.ConclusionsLDsplit is a software tool for the discovery of cis-regulatory DNA sequence motifs stimulating meiotic recombination hotspots by screening and narrowing down to hotspot associated SNPs. It is the first computational method that utilizes the genetic variation of recombination hotspots among individuals, opening a new avenue for motif finding. Tested on an established motif and simulated datasets, LDsplit shows promise to discover novel DNA motifs for meiotic recombination hotspots.

Project description:Meiotic recombination is required for the orderly segregation of chromosomes during meiosis and for providing genetic diversity among offspring. Among mammals, as well as yeast and higher plants, recombination preferentially occurs at highly delimited chromosomal sites 1-2 kb long known as hotspots. Although considerable progress has been made in understanding the roles various proteins play in carrying out the molecular events of the recombination process, relatively little is understood about the factors controlling the location and relative activity of mammalian recombination hotspots. To search for trans-acting factors controlling the positioning of recombination events, we compared the locations of crossovers arising in an 8-Mb segment of a 100-Mb region of mouse Chromosome 1 (Chr 1) when the longer region was heterozygous C57BL/6J (B6) x CAST/EiJ (CAST) and the remainder of the genome was either similarly heterozygous or entirely homozygous B6. The lack of CAST alleles in the remainder of the genome resulted in profound changes in hotspot activity in both females and males. Recombination activity was lost at several hotspots; new, previously undetected hotspots appeared; and still other hotspots remained unaffected, indicating the presence of distant trans-acting gene(s) whose CAST allele(s) activate or suppress the activity of specific hotspots. Testing the activity of three activated hotspots in sperm samples from individual male progeny of two genetic crosses, we identified a single trans-acting regulator of hotspot activity, designated Rcr1, that is located in a 5.30-Mb interval (11.74-17.04 Mb) on Chr 17. Using an Escherichia coli cloning assay to characterize the molecular products of recombination at two of these hotspots, we found that Rcr1 controls the appearance of both crossover and noncrossover gene conversion events, indicating that it likely controls the sites of the double-strand DNA breaks that initiate the recombination process.

Project description:BackgroundMicrosatellites are highly abundant in eukaryotic genomes but their function and evolution are not yet well understood. Their elevated mutation rate makes them ideal markers of genetic difference, but high levels of unexplained heterogeneity in mutation rates among microsatellites at different genomic locations need to be elucidated in order to improve the power and accuracy of the many types of study that use them as genetic markers. Recombination could contribute to this heterogeneity, since while replication errors are thought to be the predominant mechanism for microsatellite mutation, meiotic recombination is involved in some mutation events. There is also evidence suggesting that microsatellites could function as recombination signals. The yeast S. cerevisiae is a useful model organism with which to further explore the link between microsatellites and recombination, since it is very amenable to genetic study, and meiotic recombination hotspots have been mapped throughout its entire genome.ResultsWe examined in detail the relationship between microsatellites and hotspots of meiotic double-strand breaks, the precursors of meiotic recombination, throughout the S. cerevisiae genome. We included all tandem repeats with motif length (repeat period) between one and six base pairs. Long, short and two-copy arrays were considered separately. We found that long, mono-, di- and trinucleotide microsatellites are around twice as frequent in hot than non-hot intergenic regions. The associations are weak or absent for repeats with less than six copies, and also for microsatellites with 4-6 base pair motifs, but high-copy arrays with motif length greater than three are relatively very rare throughout the genome. We present evidence that the association between high-copy, short-motif microsatellites and recombination hotspots is not driven by effects on microsatellite distribution of other factors previously linked to both recombination and microsatellites, including transcription, GC-content and transposable elements.ConclusionOur findings suggest that a mutation bias relating to recombination hotspots causing repeats to form and grow, and/or regulation of a subset of hotspots by simple sequences, may be significant processes in yeast. Some previous evidence has cast doubt on both of these possibilities, and as a result they have not been explored on a large scale, but the strength of the association we report suggests that they deserve further experimental testing.

Project description:Deleterious mutations inevitably emerge in any evolutionary process and are speculated to decisively influence the structure of the genome. Meiosis, which is thought to play a major role in handling mutations on the population level, recombines chromosomes via non-randomly distributed hot spots for meiotic recombination. In many genomes, various types of genetic elements are distributed in patterns that are currently not well understood. In particular, important (essential) genes are arranged in clusters, which often cannot be explained by a functional relationship of the involved genes. Here we show by computer simulation that essential gene (EG) clustering provides a fitness benefit in handling deleterious mutations in sexual populations with variable levels of inbreeding and outbreeding. We find that recessive lethal mutations enforce a selective pressure towards clustered genome architectures. Our simulations correctly predict (i) the evolution of non-random distributions of meiotic crossovers, (ii) the genome-wide anti-correlation of meiotic crossovers and EG clustering, (iii) the evolution of EG enrichment in pericentromeric regions and (iv) the associated absence of meiotic crossovers (cold centromeres). Our results furthermore predict optimal crossover rates for yeast chromosomes, which match the experimentally determined rates. Using a Saccharomyces cerevisiae conditional mutator strain, we show that haploid lethal phenotypes result predominantly from mutation of single loci and generally do not impair mating, which leads to an accumulation of mutational load following meiosis and mating. We hypothesize that purging of deleterious mutations in essential genes constitutes an important factor driving meiotic crossover. Therefore, the increased robustness of populations to deleterious mutations, which arises from clustered genome architectures, may provide a significant selective force shaping crossover distribution. Our analysis reveals a new aspect of the evolution of genome architectures that complements insights about molecular constraints, such as the interference of pericentromeric crossovers with chromosome segregation.

Project description:BACKGROUND: The regulatory mechanism of recombination is one of the most fundamental problems in genomics, with wide applications in genome wide association studies (GWAS), birth-defect diseases, molecular evolution, cancer research, etc. Recombination events cluster into short genomic regions called "recombination hotspots". Recently, a zinc finger protein PRDM9 was reported to regulate recombination hotspots in human and mouse genomes. In addition, a 13-mer motif contained in the binding sites of PRDM9 is found to be enriched in human hotspots. However, this 13-mer motif only covers a fraction of hotspots, indicating that PRDM9 is not the only regulator of recombination hotspots. Therefore, the challenge of discovering other regulators of recombination hotspots becomes significant. Furthermore, recombination is a complex process. Hence, multiple proteins acting as machinery, rather than individual proteins, are more likely to carry out this process in a precise and stable manner. Therefore, the extension of the prediction of individual trans-regulators to protein complexes is also highly desired. RESULTS: In this paper, we introduce a pipeline to identify genes and protein complexes associated with recombination hotspots. First, we prioritize proteins associated with hotspots based on their preference of binding to hotspots and coldspots. Second, using the above identified genes as seeds, we apply the Random Walk with Restart algorithm (RWR) to propagate their influences to other proteins in protein-protein interaction (PPI) networks. Hence, many proteins without DNA-binding information will also be assigned a score to implicate their roles in recombination hotspots. Third, we construct sub-PPI networks induced by top genes ranked by RWR for various species (e.g., yeast, human and mouse) and detect protein complexes in those sub-PPI networks. CONCLUSIONS: The GO term analysis show that our prioritizing methods and the RWR algorithm are capable of identifying novel genes associated with recombination hotspots. The trans-regulators predicted by our pipeline are enriched with epigenetic functions (e.g., histone modifications), demonstrating the epigenetic regulatory mechanisms of recombination hotspots. The identified protein complexes also provide us with candidates to further investigate the molecular machineries for recombination hotspots. Moreover, the experimental data and results are available on our web site http://www.ntu.edu.sg/home/zhengjie/data/RecombinationHotspot/NetPipe/.

Project description:In many mammals, including humans and mice, the zinc finger histone methyltransferase PRDM9 performs the first step in meiotic recombination by specifying the locations of hotspots, the sites of genetic recombination. PRDM9 binds to DNA at hotspots through its zinc finger domain and activates recombination by trimethylating histone H3K4 on adjacent nucleosomes through its PR/SET domain. Recently, the isolated PR/SET domain of PRDM9 was shown capable of also trimethylating H3K36 in vitro, raising the question of whether this reaction occurs in vivo during meiosis, and if so, what its function might be. Here, we show that full-length PRDM9 does trimethylate H3K36 in vivo in mouse spermatocytes. Levels of H3K4me3 and H3K36me3 are highly correlated at hotspots, but mutually exclusive elsewhere. In vitro, we find that although PRDM9 trimethylates H3K36 much more slowly than it does H3K4, PRDM9 is capable of placing both marks on the same histone molecules. In accord with these results, we also show that PRDM9 can trimethylate both K4 and K36 on the same nucleosomes in vivo, but the ratio of K4me3/K36me3 is much higher for the pair of nucleosomes adjacent to the PRDM9 binding site compared to the next pair further away. Importantly, H3K4me3/H3K36me3-double-positive nucleosomes occur only in regions of recombination: hotspots and the pseudoautosomal (PAR) region of the sex chromosomes. These double-positive nucleosomes are dramatically reduced when PRDM9 is absent, showing that this signature is PRDM9-dependent at hotspots; the residual double-positive nucleosomes most likely come from the PRDM9-independent PAR. These results, together with the fact that PRDM9 is the only known mammalian histone methyltransferase with both H3K4 and H3K36 trimethylation activity, suggest that trimethylation of H3K36 plays an important role in the recombination process. Given the known requirement of H3K36me3 for double strand break repair by homologous recombination in somatic cells, we suggest that it may play the same role in meiosis.

Dataset Information

Detecting sequence polymorphisms associated with meiotic recombination hotspots in the human genome.

Background

Results

Conclusions

Publications

Detecting sequence polymorphisms associated with meiotic recombination hotspots in the human genome.

Similar Datasets

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