Dataset Information

Genome-Wide Association Study of Meiotic Recombination Phenotypes.

ABSTRACT: Meiotic recombination is an essential step in gametogenesis, and is one that also generates genetic diversity. Genome-wide association studies (GWAS) and molecular studies have identified genes that influence of human meiotic recombination. RNF212 is associated with total or average number of recombination events, and PRDM9 is associated with the locations of hotspots, or sequences where crossing over appears to cluster. In addition, a common inversion on chromosome 17 is strongly associated with recombination. Other genes have been identified by GWAS, but those results have not been replicated. In this study, using new datasets, we characterized additional recombination phenotypes to uncover novel candidates and further dissect the role of already known loci. We used three datasets totaling 1562 two-generation families, including 3108 parents with 4304 children. We estimated five different recombination phenotypes including two novel phenotypes (average recombination counts within recombination hotspots and outside of hotspots) using dense SNP array genotype data. We then performed gender-specific and combined-sex genome-wide association studies (GWAS) meta-analyses. We replicated associations for several previously reported recombination genes, including RNF212 and PRDM9 By looking specifically at recombination events outside of hotspots, we showed for the first time that PRDM9 has different effects in males and females. We identified several new candidate loci, particularly for recombination events outside of hotspots. These include regions near the genes SPINK6, EVC2, ARHGAP25, and DLGAP2 This study expands our understanding of human meiotic recombination by characterizing additional features that vary across individuals, and identifying regulatory variants influencing the numbers and locations of recombination events.

SUBMITTER: Begum F

PROVIDER: S-EPMC5144969 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Genome-Wide Association Study of Meiotic Recombination Phenotypes.

Begum Ferdouse F Chowdhury Reshmi R Cheung Vivian G VG Sherman Stephanie L SL Feingold Eleanor E

G3 (Bethesda, Md.) 20161207 12

Meiotic recombination is an essential step in gametogenesis, and is one that also generates genetic diversity. Genome-wide association studies (GWAS) and molecular studies have identified genes that influence of human meiotic recombination. RNF212 is associated with total or average number of recombination events, and PRDM9 is associated with the locations of hotspots, or sequences where crossing over appears to cluster. In addition, a common inversion on chromosome 17 is strongly associated wit ...[more]

PMID: 27733454

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Project description:Genome-wide association studies (GWASs) have the potential to reveal the genetics of microbial phenotypes such as antibiotic resistance and virulence. Capitalizing on the growing wealth of bacterial sequence data, microbial GWAS methods aim to identify causal genetic variants while ignoring spurious associations. Bacteria reproduce clonally, leading to strong population structure and genome-wide linkage, making it challenging to separate true 'hits' (i.e. mutations that cause a phenotype) from non-causal linked mutations. GWAS methods attempt to correct for population structure in different ways, but their performance has not yet been systematically and comprehensively evaluated under a range of evolutionary scenarios. Here, we developed a bacterial GWAS simulator (BacGWASim) to generate bacterial genomes with varying rates of mutation, recombination and other evolutionary parameters, along with a subset of causal mutations underlying a phenotype of interest. We assessed the performance (recall and precision) of three widely used single-locus GWAS approaches (cluster-based, dimensionality-reduction and linear mixed models, implemented in plink, pyseer and gemma) and one relatively new multi-locus model implemented in pyseer, across a range of simulated sample sizes, recombination rates and causal mutation effect sizes. As expected, all methods performed better with larger sample sizes and effect sizes. The performance of clustering and dimensionality reduction approaches to correct for population structure were considerably variable according to the choice of parameters. Notably, the multi-locus elastic net (lasso) approach was consistently amongst the highest-performing methods, and had the highest power in detecting causal variants with both low and high effect sizes. Most methods reached the level of good performance (recall >0.75) for identifying causal mutations of strong effect size [log odds ratio (OR) ?2] with a sample size of 2000 genomes. However, only elastic nets reached the level of reasonable performance (recall=0.35) for detecting markers with weaker effects (log OR ~1) in smaller samples. Elastic nets also showed superior precision and recall in controlling for genome-wide linkage, relative to single-locus models. However, all methods performed relatively poorly on highly clonal (low-recombining) genomes, suggesting room for improvement in method development. These findings show the potential for multi-locus models to improve bacterial GWAS performance. BacGWASim code and simulated data are publicly available to enable further comparisons and benchmarking of new methods.

Dataset Information

Genome-Wide Association Study of Meiotic Recombination Phenotypes.

Publications

Genome-Wide Association Study of Meiotic Recombination Phenotypes.

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