No abstract

Next-generation sequencing technology has made it possible to detect rare genetic variants associated with complex human traits. In recent literature, various methods specifically designed for rare variants are proposed. These tests can be broadly classified into burden and nonburden tests. In this paper, we take advantage of the burden and nonburden tests, and consider the common effect and the individual deviations from the common effect. To achieve robustness, we use two methods of combining p-values, Fisher’s method and the minimum-p method. In rare variant association studies, to improve the power of the tests, we explore the advantage of the extreme phenotype sampling. At first, we dichotomize the continuous phenotypes before analysis, and the two extremes are treated as two different groups representing a dichotomous phenotype. We next compare the powers of several methods based on extreme phenotype sampling and random sampling. Extensive simulation studies show that our proposed methods by using extreme phenotype sampling are the most powerful or very close to the most powerful one in various settings of true models when the same sample size is used.

Genome-wide association studies (GWAS) can detect common variants associated with diseases. Next generation sequencing technology has made it possible to detect rare variants. Most of association tests, including burden tests and nonburden tests, mainly target rare variants by upweighting rare variant effects and downweighting common variant effects. But there is increasing evidence that complex diseases are caused by both common and rare variants. In this paper, we extend the ADA method (adaptive combination of P-values; Lin et al., 2014) for rare variants only and propose a RC-ADA method (common and rare variants by adaptive combination of P-values). Our proposed method combines the per-site P-values with the weights based on minor allele frequencies (MAFs). The RC-ADA is robust to directions of effects of causal variants and inclusion of a high proportion of neutral variants. The performance of the RC-ADA method is compared with several other association methods. Extensive simulation studies show that the RC-ADA method is more powerful than other association methods over a wide range of models.

Reproducible Research This article has earned an open data badge "Reproducible Research" for making publicly available the code necessary to reproduce the reported results. The results reported in this article were reproduced partially due to data confidentiality issues and computational complexity.