Study On Genome - Wide Association Analysis Based On Rare Variants

An individual’s disease risk is determined by the compounded action of both common variants and rare variants. Next generation sequencing technologies generate high-dimensional data that allow a nearly complete evaluation of genetic variation. Next generation sequencing technologies also su?er from remarkable limitations: high error rates、enrichment of rare variants and a large proportion of missing values. To date, a large number of common variants underlying complex diseases have been identi?ed by genome-wide association studies. However, the identi?ed variants account for only a small fraction of disease heritability. One of potential sources of missing heritability is the contribution of rare variants.Currently, most of existing methods for rare variant association studies are essentially testing the e?ect of a weighted combination of variants with di?erent weighting schemes. In this paper, based on the optimal weights, we propose the combined optimal weights and collapsing method. It is applicable to both quantitative and qualitative traits, allows covariates, and is robust to directions of e?ects of causal variants. Extensive simulation studies show that the approach we proposed is reasonable and e?ective.