Allele Frequency Matching Between SNPs Reveals an Excess of Linkage Disequilibrium in Genic Regions of the Human Genome

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Abstract
Significant interest has emerged in mapping genetic susceptibility for complex traits through whole-genome association studies. These studies rely on the extent of association, i.e., linkage disequilibrium (LD), between single nucleotide polymorphisms (SNPs) across the human genome. LD describes the nonrandom association between SNP pairs and can be used as a metric when designing maximally informative panels of SNPs for association studies in human populations. Using data from the 1.58 million SNPs genotyped by Perlegen, we explored the allele frequency dependence of the LD statistic r2 both empirically and theoretically. We show that average r2 values between SNPs unmatched for allele frequency are always limited to much less than 1 (theoretical approximately 0.46 to 0.57 for this dataset). Frequency matching of SNP pairs provides a more sensitive measure for assessing the average decay of LD and generates average r2 values across nearly the entire informative range (from 0 to 0.89 through 0.95). Additionally, we analyzed the extent of perfect LD (r2 = 1.0) using frequency-matched SNPs and found significant differences in the extent of LD in genic regions versus intergenic regions. The SNP pairs exhibiting perfect LD showed a significant bias for derived, nonancestral alleles, providing evidence for positive natural selection in the human genome. One of the primary goals for geneticists is isolating regions of the genome that convey increased risk of disease through the association of genetic polymorphisms with phenotypic traits. The recent availability of genome-wide polymorphism data (i.e., single nucleotide polymorphisms [SNPs]) has made association studies possible on an unprecedented scale, and the characterization and selection of these polymorphisms for these studies has been a topic of major interest. One method for choosing informative SNPs has been to compare the correlation between SNPs (a term called linkage disequilibrium), but this can create confounding problems when comparing SNPs of different frequencies. In this study, the authors show that if SNPs are compared to other SNPs of equal or near equal frequency, the correlation between them more accurately represents the true correlation. This also produces a more sensitive method for determining linkage disequilibrium. Using this method, SNPs were compared both within and outside of gene regions to examine the overall correlation between SNPs in each region. Matching SNPs according to their frequency greatly increased the maximum possible correlation and showed significantly higher correlations between SNPs within genes (intragenic) versus between genes (intergenic). Using the recently completed chimpanzee sequence, a larger fraction of high frequency human specific SNPs was found within the perfectly correlated SNP pairs in genic regions compared to intergenic regions. These observations suggest that regions of the genome around genes have been under selective pressure, leading to a greater correlation between SNPs. Genes found in regions with the highest correlations between SNPs will be of particular interest for future genotype-phenotype association studies.