The rapid development of sequencing technology and simultaneously the availability of large quantities of sequence data provides an unprecedented opportunity for researchers to conduct studies to detect rare variants associated with the disease. However, none of current existing statistical methods has uniform power in all scenarios because they more or less are affected by nonfunctional variants and variants with opposite effect. The present study focuses on identifying rare variant associated with the disease.
we present a robust approach to identify rare variant using weighted entropy theory.
This approach here takes the proportion of the minor allele among all k variants as its probability distribution, which reduces the noise incurred by non-causal variants, and uses a weight to strike a balance between deleterious rare variants and protective rare variants, which makes our method impacted less by variants with opposite effect. Through simulation studies, we investigate the performance of our method for rare variant association analyses as well as for common variant association analyses and compared it with Burden test and the SKAT-O test. Simulation study show that the proposed method is valid and outperform two existing methods. Meanwhile, the proposed method is affected slightly by non-causal variants and opposite effect variants with high and stable power for various paraments set.
We conclude that the proposed method here can be used effectively to detect rare variant associated with the disease.