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Abstract:
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Genome-wide and sequencing association studies yield multiple regions harboring interesting association signals. Given that each region encompasses numerous variants in high linkage disequilibrium, it is not clear which are i) truly causal or ii) just reasonably close to the causal ones. Researchers proposed many methods to predict, albeit not test, the causal SNPs in a region, a process commonly denoted as fine-mapping. Unfortunately, all existing fine-mapping methods output posterior causality probabilities assuming that causal SNPs are among those already measured in the study, or have been cataloged elsewhere. However, due to technological and computational obstacles in calling many types of genetic variants, such an assumption is not realistic. We propose a novel method/software, denoted as Quasi-CAausality Test (QCAT), for testing (not just predicting) the causality of any cataloged genetic variant. QCAT i) makes no assumption that causal variants are among cataloged variants, and ii) makes use of easily available summary statistics from genetic studies, e.g. variant association Z-scores, to make statistical inferences.
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