Testing for genetic association: a powerful score test

Abstract

To study the association between a candidate gene and a complex genetic disease, Pearson's chi2 statistic can be applied to an mx2 contingency table, where the m categories correspond to m haplotypes or marker alleles. For m>2, two alternative approaches for Pearson's chi2 can be followed, which are more powerful if one haplotype or marker allele is associated. For the first approach, various 2x2 tables are formed by combining various categories and the maximum of the corresponding chi-square statistics is considered as the final statistic. The second approach takes the average over the possible associated categories by writing down an overall likelihood. For the latter approach, we propose a new score statistic, which gives more weight to haplotypes or marker alleles that are common. Since the disease allele is often not observed, the power of the various statistics depends on both the linkage disequilibrium pattern and the frequencies of the associated haplotype or marker allele in the cases and the controls. We heuristically compare various statistics within the two approaches and present the results of a simulation that compares the performance of all considered statistics. Finally, we apply the statistics to a case-control study on the association between COL2A1 gene and radiographic osteoarthritis. Our conclusion is that overall the new proposed score statistic has good power.