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Historically, the majority of statistical association methods have beendesigned assuming availability of SNP-level information. However, moderngenetic and sequencing data present new challenges to access and sharing ofgenotype-phenotype datasets, including cost management, difficulties inconsolidation of records across research groups, etc. These issues make methodsbased on SNP-level summary statistics for a joint analysis of variants in agroup particularly appealing. The most common form of combining statistics is asum of SNP-level squared scores, possibly weighted, as in burden tests for rarevariants. The overall significance of the resulting statistic is evaluatedusing its distribution under the null hypothesis. Here, we demonstrate thatthis basic approach can be substantially improved by decorrelating scores priorto their addition, resulting in remarkable power gains in situations that aremost commonly encountered in practice; namely, under heterogeneity of effectsizes and diversity between pairwise LD. In these situations, the power of thetraditional test, based on the added squared scores, quickly reaches a ceiling,as the number of variants increases. Thus, the traditional approach does notbenefit from information potentially contained in any additional SNPs, whileour decorrelation by orthogonal transformation (DOT) method yields steady gainin power. We present theoretical and computational analyses of both approaches,and reveal causes behind sometimes dramatic difference in their respectivepowers. We showcase DOT by analyzing breast cancer data, in which our methodstrengthened levels of previously reported associations and implied thepossibility of multiple new alleles that jointly confer breast cancer risk.

DOT: Gene-set analysis by combining decorrelated association statistics