Association Testing of a Group of Genetic Markers Based on Next-Generation Sequencing Data and Continuous Response Using a Linear Model Framework

Abstract

Association testing has been widely used to study the relationship between phenotypes and genetic variants. Most testing methods are based on genotypes. To avoid genotype calling and directly test on next-generation sequencing (NGS) data, sequencing data-based methods have been proposed and shown advantages over genotype-based testing methods in scenarios where genotype calling is inaccurate. Most sequencing data-based testing methods are based on a single genetic marker. The objective of this paper is to extend the methods to allow testing for the association of a continuous response variable with a group of common variants or a group of rare variants without genotype calling. Our proposed methods are derived based on a standard linear model framework. We derive the joint significant test (JS) for a group of common genetic variables and the variable collapse test (VC) for a group of rare genetic variables. We have conducted extensive simulation studies to evaluate the performance of different estimators. According to our results, we found (1) all methods, including our proposed NGS data-based methods and genotype-based methods, can control the Type I error rate probability well; (2) our proposed NGS data-based methods can achieve better performance in terms of statistical power compared with their corresponding genotype-based methods in the literature; (3) when sequencing depth increases, the performance of all methods increases, and the difference between the performance of NGS data-based methods and corresponding genotype-based methods decreases. In conclusion, we have proposed NGS data-based methods that allow testing for the significance of a group of variants using a linear model framework and have shown the advantage of our NGS data-based methods over genotype-based methods in the literature.