GWAS now commonly survey SNPs at a genomic density similar to this study. Consequently, our observation that most of the genome could be organized into multi-SNP haplotypes indicates that available resources are sufficient to conduct haplotype-based mapping on the genomic scale.
Those regions that were significantly associated with RA in both individual-SNP tests and haplotype-based tests represent promising candidates for further study. For example, the HLA region on chromosome 6 remained significant for all three genome-wide association tests, strongly suggesting that genes in this region contribute to disease risk.
Although some associations were observed consistently across methods, some associations were only detected using haplotype-based tests. Several factors might explain these differences. Haplotype-based methods required approximately 65% fewer tests than the individual-SNP approach. As a result, the multiple testing correction was less severe for haplotype-based methods. Haplotype-based methods can also detect cis-
interactions among several causal variants [16
]. Furthermore, because the power to detect associations is maximized when marker and causal variant frequencies are similar, analyses using haplotypes could find associations with rare alleles that analyses using individual SNPs may miss. We also discovered associations using individual SNPs that were not seen in haplotype tests. Perhaps these represented cases in which only a single SNP exhibited strong LD with a causal variant, so that forming haplotypes with several adjacent SNPs diluted the strength of association. Regardless of the explanation for observed differences among methods, our results indicate that the application of both individual-SNP and haplotype-based approaches to GWAS will maximize the potential for finding biologically important associations.
Although some regions show consistent significant associations in different block partitions (GAB and GAM), in most regions, haplotype-based association tests are really sensitive to changes in block partitions. This result suggests that the effects of other block partitioning algorithms on GWAS should be compared. For example, haplotype-based association testing using a sliding window of fixed physical or genetic size would be an alternative approach. Although this strategy is easily implemented, it ignores information about haplotype block structure. The variation in block structure across the genome suggests that methods that use this structure (such as those applied in this paper) should be more powerful for GWAS, but this issue needs to be examined.
Our study also suggests several avenues for future research. Additional measurements of the effects of different haplotype partitioning algorithms on the power of downstream association tests - in both simulations and empirical data - would be useful. For example, the error inherent in haplotype block estimation needs to be incorporated in association analysis. Furthermore, the likelihood ratio tests used here ignored the evolutionary relationships among haplotypes. An improved analysis that uses this information (e.g., a cladistic analysis [17
]) would be worthwhile.