Volume 6 Supplement 1
Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites
© Huang et al; licensee BioMed Central Ltd 2005
Published: 30 December 2005
Recombination during meiosis is one of the most important biological processes, and the level of recombination rates for a given individual is under genetic control. In this study, we conducted genome-wide association studies to identify chromosomal regions associated with recombination rates. We analyzed genotype data collected on the pedigrees in the Collaborative Study on the Genetics on Alcoholism data provided by Genetic Analysis Workshop 14. A total of 315 microsatellites and 10,081 single-nucleotide polymorphisms from Affymetrix on 22 autosomal chromosomes were used in our association analysis. Genome-wide gender-specific recombination counts for family founders were inferred first and association analysis was performed using multiple linear regressions. We used the positive false discovery rate (pFDR) to account for multiple comparisons in the two genome-wide scans. Eight regions showed some evidence of association with recombination counts based on the single-nucleotide polymorphism analysis after adjusting for multiple comparisons. However, no region was found to be significant using microsatellites.
Recombination between two homologous chromosomes during meiosis generates novel gene combinations and creates genetic diversity among chromosomes. Furthermore, recombination is critical for proper segregation of homologous chromosomes, and is a major factor shaping linkage disequilibrium (LD) patterns in the genome . Much research has been done recently to establish human genetic maps based on recombination and on estimating local recombination rates to augment LD studies and aid in LD study design and interpretation [1–8]. Kong et al.  found marked regional differences in recombination rates and concluded that DNA changes contributing to evolution may not be completely random, but more concentrated within specific regions. This difference may be driven by sequence features. In addition, recombination rate is under genetic control, as exemplified in the finding by Ji et al.  that maize meiotic mutant desynaptic is a recombination modifier that controls recombination rates. In this study, seeking to identify regions potentially affecting recombination rates, we conducted genome-wide association studies based on microsatellites and single-nucleotide polymorphisms (SNPs) of the Collabroative Study on the Genetics of Alcoholism (COGA) data provided by Genetic Analysis Workshop 14 (GAW14). A total of 315 microsatellites and 10,081 SNPs from Affymetrix on 22 autosomal chromosomes were analyzed. We found eight regions/thirteen SNPs that showed some evidence of association with recombination counts. No region was found to be significant using microsatellites after adjusting for multiple comparisons based on the positive false discovery rate (pFDR) criterion.
The COGA data consist of 143 pedigrees with 1,614 individuals, including 1,109 male and female meioses. Genetic maps for microsatellites and SNPs were both provided by GAW14. Some of the distinct SNPs have the same genetic map position, which made inferring recombination events between these SNPs impossible. Therefore, we added 1.0 × 10-6 at these SNPs' genetic map positions to make them distinguishable. To estimate the number of both maternal and paternal recombination events for each female or male meiosis, we used the Best option in the haplotyping analysis in MERLIN , which outputs the most likely haplotype as well as the most likely sites for recombination throughout a pedigree. The total number of gender-specific recombination counts for each parent was obtained by averaging the numbers of recombination events of all the offspring, which was calculated as the total number of recombination events observed in the 22 autosomal chromosomes. For pedigrees with only two generations, i.e., the nuclear families, the inferred average total number of recombination events from each meiosis of the founders was then treated as a quantitative trait and genome-wide association tests were conducted to identify markers associated with this quantitative trait. For the pedigrees with three or more generations, only recombination information from the founders were extracted and considered in the association tests. We compared the results from the two scans using either microsatellites or SNPs.
Genotyping error detection
Because genotyping error may lead to double recombinations within a short distance, it can significantly affect the overall recombination counts. To minimize this impact, the error-checking algorithm implemented in MERLIN, which identifies unlikely genotypes based on double recombination events, was applied and the erroneous genotypes were excluded before applying haplotyping analysis. We used the default parameter in MERLIN, where the erroneous genotypes with a likelihood ratio p ≤ 0.025 were excluded . The same procedure was applied to both SNPs and microsatellies.
Association analysis to identify markers associated with recombination rates
We used multiple linear regressions to evaluate the relation between recombination counts and markers across 22 autosomal chromosomes with adjustments for age and gender for both SNPs and microsatellites. Analysis was carried out based on Whites only to reduce potential confounding factors related to ethnic differences. To account for the multiple comparison problem in the two whole-genome scans, we used pFDR through q-values , where a cut-off point of 5% is chosen. The q-value is a measure of significance in terms of the pFDR, and it is defined to be the minimum pFDR at which the statistic can be called significant. A pFDR of 5% means that among all of the features that are called significant, 5% of them may correspond to the true null hypotheses on average. To get the q-value for each marker, we used the software QVALUE  on the p-values obtained from the multiple regressions.
Results and Discussion
Mean and median genome-wide gender-specific recombination counts using SNPs and microsatellites.
We noted that our inferred female and male genome-wide recombination counts were slightly lower than that from previous studies . One reason may be that the 10,081 SNPs did not cover the entire 22 autosomes since the updated SNP data from Affymetrix were not included in the analysis. Another possible reason was that some portion of the corrected genotypes was excluded as erroneous genotypes from the genotyping error detection algorithm.
Markers associated with recombination counts
Significant results (q-value < 0.05) for genome-wide association analysis for recombination rates using SNPs.
Position (cM) (Marker)
3.99 × 10-5
3.64 × 10-6
3.74 × 10-5
1.01 × 10-5
2.00 × 10-8
1.21 × 10-6
4.95 × 10-5
8.00 × 10-8
6.00 × 10-8
2.60 × 10-7
1.45 × 10-5
2.40 × 10-7
1.59 × 10-5
In summary, we have identified several candidate SNPs likely associated with recombination events, and further studies on these genes may help us gain valuable knowledge on recombination, better understand LD patterns, and lead to more efficient methods to map disease genes.
Collaborative Study on the Genetics on Alcoholism
Genetic Analysis Workshop 14
Positive false discovery rate
Supported in part by NIH grant R01 GM59507 and NSF grant DMS 0241160.
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