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Variation in human recombination rates and its genetic determinants.

Fledel-Alon A, Leffler EM, Guan Y, Stephens M, Coop G, Przeworski M - PLoS ONE (2011)

Bottom Line: We replicated associations of RNF212 with the mean rate in males and in females as well as the association of Inversion 17q21.31 with the female mean rate.In addition, we identified a set of new candidate regions for further validation.These findings suggest that variation at broad and fine scales is largely separable and that, beyond three known loci, there is no evidence for common variation with large effects on recombination phenotypes.

View Article: PubMed Central - PubMed

Affiliation: Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America.

ABSTRACT

Background: Despite the fundamental role of crossing-over in the pairing and segregation of chromosomes during human meiosis, the rates and placements of events vary markedly among individuals. Characterizing this variation and identifying its determinants are essential steps in our understanding of the human recombination process and its evolution.

Study design/results: Using three large sets of European-American pedigrees, we examined variation in five recombination phenotypes that capture distinct aspects of crossing-over patterns. We found that the mean recombination rate in males and females and the historical hotspot usage are significantly heritable and are uncorrelated with one another. We then conducted a genome-wide association study in order to identify loci that influence them. We replicated associations of RNF212 with the mean rate in males and in females as well as the association of Inversion 17q21.31 with the female mean rate. We also replicated the association of PRDM9 with historical hotspot usage, finding that it explains most of the genetic variance in this phenotype. In addition, we identified a set of new candidate regions for further validation.

Significance: These findings suggest that variation at broad and fine scales is largely separable and that, beyond three known loci, there is no evidence for common variation with large effects on recombination phenotypes.

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Related in: MedlinePlus

A close up of the association signal at previously reported and new candidate regions for male mean recombination rate.The figures show the p-values across the candidate regions for the genotyped and imputed SNPs plotted using the LocusZoom software [51] (only SNPs with an rs numbers are shown, but plots using all SNPs were qualitatively similar). The 1000 Genomes Project data [31] was used for the imputation in all LocusZoom figures, with the exception of Figure 4A for which HapMap data were used (as in this region LD patterns in the 1000 Genomes data were inconsistent with those from HapMap and the AGRE and FHS samples). The imputation-based approach uses a different test statistic than we employed in our analysis [32], so p-values can differ slightly from those reported in the main text. The focal SNP (with the lowest p-value) is plotted as a purple diamond; other data points are colored according to their r2 with the focal SNP; SNPs with missing linkage disequilibrium information are shown in grey. A. Association of male mean recombination rate with SNPs in RNF212. B. Top associations for male mean recombination rate.
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pone-0020321-g004: A close up of the association signal at previously reported and new candidate regions for male mean recombination rate.The figures show the p-values across the candidate regions for the genotyped and imputed SNPs plotted using the LocusZoom software [51] (only SNPs with an rs numbers are shown, but plots using all SNPs were qualitatively similar). The 1000 Genomes Project data [31] was used for the imputation in all LocusZoom figures, with the exception of Figure 4A for which HapMap data were used (as in this region LD patterns in the 1000 Genomes data were inconsistent with those from HapMap and the AGRE and FHS samples). The imputation-based approach uses a different test statistic than we employed in our analysis [32], so p-values can differ slightly from those reported in the main text. The focal SNP (with the lowest p-value) is plotted as a purple diamond; other data points are colored according to their r2 with the focal SNP; SNPs with missing linkage disequilibrium information are shown in grey. A. Association of male mean recombination rate with SNPs in RNF212. B. Top associations for male mean recombination rate.

Mentions: Results of the meta-analysis of FHS and AGRE for sex-specific recombination rate are shown in Figure 3A–C. For male mean recombination rate, markers in RNF212 meet the cut-off for genome-wide significance (Figure 4A, p = 10−15 for the strongest association; see Table 1). Although our sample size is much smaller, RNF212 also has a low p-value in the HUTT (p = 4.14×10−3). Thus, the effect of this locus is confirmed in three population samples. In the FHS, it explains 7% of male variance in mean rates, with one allele estimated to add an average of 118 cM to the genetic map. We also replicated the association of RNF212 with female rates, to our knowledge for the first time (p = 2.15×10−4). Of note, the set of SNPs most strongly associated with male rates has no effect on female rates (the lowest p-value among them in females  = 0.162), whereas the set of SNPs associated with female rates show a weaker association in males (p = 1.379×10−9). This pattern suggests that, rather than a single causal SNP with antagonistic effects between sexes, there may be distinct causative SNPs in RNF212. Using the 1000 Genomes and Hapmap panels to impute untyped SNPs does not help to localize the causative allele(s), as the SNPs with the strong associations are seen throughout the gene (Figure 4A).


Variation in human recombination rates and its genetic determinants.

Fledel-Alon A, Leffler EM, Guan Y, Stephens M, Coop G, Przeworski M - PLoS ONE (2011)

A close up of the association signal at previously reported and new candidate regions for male mean recombination rate.The figures show the p-values across the candidate regions for the genotyped and imputed SNPs plotted using the LocusZoom software [51] (only SNPs with an rs numbers are shown, but plots using all SNPs were qualitatively similar). The 1000 Genomes Project data [31] was used for the imputation in all LocusZoom figures, with the exception of Figure 4A for which HapMap data were used (as in this region LD patterns in the 1000 Genomes data were inconsistent with those from HapMap and the AGRE and FHS samples). The imputation-based approach uses a different test statistic than we employed in our analysis [32], so p-values can differ slightly from those reported in the main text. The focal SNP (with the lowest p-value) is plotted as a purple diamond; other data points are colored according to their r2 with the focal SNP; SNPs with missing linkage disequilibrium information are shown in grey. A. Association of male mean recombination rate with SNPs in RNF212. B. Top associations for male mean recombination rate.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3117798&req=5

pone-0020321-g004: A close up of the association signal at previously reported and new candidate regions for male mean recombination rate.The figures show the p-values across the candidate regions for the genotyped and imputed SNPs plotted using the LocusZoom software [51] (only SNPs with an rs numbers are shown, but plots using all SNPs were qualitatively similar). The 1000 Genomes Project data [31] was used for the imputation in all LocusZoom figures, with the exception of Figure 4A for which HapMap data were used (as in this region LD patterns in the 1000 Genomes data were inconsistent with those from HapMap and the AGRE and FHS samples). The imputation-based approach uses a different test statistic than we employed in our analysis [32], so p-values can differ slightly from those reported in the main text. The focal SNP (with the lowest p-value) is plotted as a purple diamond; other data points are colored according to their r2 with the focal SNP; SNPs with missing linkage disequilibrium information are shown in grey. A. Association of male mean recombination rate with SNPs in RNF212. B. Top associations for male mean recombination rate.
Mentions: Results of the meta-analysis of FHS and AGRE for sex-specific recombination rate are shown in Figure 3A–C. For male mean recombination rate, markers in RNF212 meet the cut-off for genome-wide significance (Figure 4A, p = 10−15 for the strongest association; see Table 1). Although our sample size is much smaller, RNF212 also has a low p-value in the HUTT (p = 4.14×10−3). Thus, the effect of this locus is confirmed in three population samples. In the FHS, it explains 7% of male variance in mean rates, with one allele estimated to add an average of 118 cM to the genetic map. We also replicated the association of RNF212 with female rates, to our knowledge for the first time (p = 2.15×10−4). Of note, the set of SNPs most strongly associated with male rates has no effect on female rates (the lowest p-value among them in females  = 0.162), whereas the set of SNPs associated with female rates show a weaker association in males (p = 1.379×10−9). This pattern suggests that, rather than a single causal SNP with antagonistic effects between sexes, there may be distinct causative SNPs in RNF212. Using the 1000 Genomes and Hapmap panels to impute untyped SNPs does not help to localize the causative allele(s), as the SNPs with the strong associations are seen throughout the gene (Figure 4A).

Bottom Line: We replicated associations of RNF212 with the mean rate in males and in females as well as the association of Inversion 17q21.31 with the female mean rate.In addition, we identified a set of new candidate regions for further validation.These findings suggest that variation at broad and fine scales is largely separable and that, beyond three known loci, there is no evidence for common variation with large effects on recombination phenotypes.

View Article: PubMed Central - PubMed

Affiliation: Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America.

ABSTRACT

Background: Despite the fundamental role of crossing-over in the pairing and segregation of chromosomes during human meiosis, the rates and placements of events vary markedly among individuals. Characterizing this variation and identifying its determinants are essential steps in our understanding of the human recombination process and its evolution.

Study design/results: Using three large sets of European-American pedigrees, we examined variation in five recombination phenotypes that capture distinct aspects of crossing-over patterns. We found that the mean recombination rate in males and females and the historical hotspot usage are significantly heritable and are uncorrelated with one another. We then conducted a genome-wide association study in order to identify loci that influence them. We replicated associations of RNF212 with the mean rate in males and in females as well as the association of Inversion 17q21.31 with the female mean rate. We also replicated the association of PRDM9 with historical hotspot usage, finding that it explains most of the genetic variance in this phenotype. In addition, we identified a set of new candidate regions for further validation.

Significance: These findings suggest that variation at broad and fine scales is largely separable and that, beyond three known loci, there is no evidence for common variation with large effects on recombination phenotypes.

Show MeSH
Related in: MedlinePlus