A haplotype sharing method for determining the relative age of SNP alleles.

There are two aspects regarding the age of alleles that are relevant as indicators of the timing of mutational events. The first is to know which alleles are species-specific; the second is about the time of origin of species-specific alleles. Both aspects can be analyzed using haplotype-sharing methods, by using the length of shared haplotypes as a measure of the speed of coalescence to common ancestors. The availability of sequence data for closely related species makes it possible to infer the original SNP allele. The allele present in more than one species is the original allele. In general, original alleles are expected to be more frequent, because the cumulative effects of genetic drift determine the maximum frequency a new mutant can reach. The human species is relatively young, and founder effects are still observable as extended linkage disequilibrium. Coalescence to a single founder takes place in human populations over a time frame that is so small that original haplotypes spanning several markers are still observable in current high-density SNP genotyping arrays. We show here that the length of shared haplotypes surrounding alleles is an indicator of the relative ages of alleles, and it is applicable to original and species-specific alleles.

Association testing by haplotype-sharing methods applicable to whole-genome analysis.

We propose two new haplotype-sharing methods for identifying disease loci: the haplotype sharing statistic (HSS), which compares length of shared haplotypes between cases and controls, and the CROSS test, which tests whether a case and a control haplotype show less sharing than two random haplotypes. The significance of the HSS is determined using a variance estimate from the theory of U-statistics, whereas the significance of the CROSS test is estimated from a sequential randomization procedure. Both methods are fast and hence practical, even for whole-genome screens with high marker densities. We analyzed data sets of Problems 2 and 3 of Genetic Analysis Workshop 15 and compared HSS and CROSS to conventional association methods. Problem 2 provided a data set of 2300 single-nucleotide polymorphisms (SNPs) in a 10-Mb region of chromosome 18q, which had shown linkage evidence for rheumatoid arthritis. The CROSS test detected a significant association at approximately position 4407 kb. This was supported by single-marker association and HSS. The CROSS test outperformed them both with respect to significance level and signal-to-noise ratio. A 20-kb candidate region could be identified. Problem 3 provided a simulated 10 k SNP data set covering the whole genome. Three known candidate regions for rheumatoid arthritis were detected. Again, the CROSS test gave the most significant results. Furthermore, both the HSS and the CROSS showed better fine-mapping accuracy than straightforward haplotype association. In conclusion, haplotype sharing methods, particularly the CROSS test, show great promise for identifying disease gene loci.

Summary of contributions to GAW15 Group 13: candidate gene association studies.

Here we summarize the contributions to Group 13 of the Genetic Analysis Workshop 15 held in St. Pete Beach, Florida, on November 12-14, 2006. The focus of this group was to identify candidate genes associated with rheumatoid arthritis or surrogate outcomes. The association methods proposed in this group were diverse, from better known approaches, such as logistic regression for single nucleotide polymorphism (SNP) analysis and haplotype sharing tests to methods less familiar to genetic epidemiologists, such as machine learning and visualization methods. The majority of papers analyzed Genetic Analysis Workshop 15 Problems 2 (rheumatoid arthritis data) and 3 (simulated data). The highlighted points of this group analyses were: (1) haplotype-based statistics can be more powerful than single SNP analysis for risk-locus localization; (2) considering linkage disequilibrium block structure in haplotype analysis may reduce the likelihood of false-positive results; and (3) visual representation of genetic models for continuous covariates may help identify SNPs associated with the underlying quantitative trait loci.