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Abstract:
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Genetic association analyses have successfully uncovered thousands of genetic variants associated with complex diseases. However, many discoveries were revealed only after reaching sample sizes in the hundreds of thousands. To improve power to detect novel associations with rare variants, one can exploit additional information available, such as phenotypes of ungenotyped relatives. In this presentation, we propose a novel approach called Familial History Aggregation-based Tests (FHAT) to incorporate family history of disease in rare variant association analyses. Our simulation results show that the type I error of FHAT is well controlled with disease prevalence as low as 10%. We illustrate our novel approaches in a search for Alzheimer's Disease (AD)-associated loci in the UK biobank dataset. Out of eight genes previously implicated in AD susceptibility, seven genes yield improved evidence of association after incorporating parental history of AD using FHAT. Incorporating available disease history of relatives is a cost-effective way to improve the power of genetic association analyses.
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