Robust Linear Trend Test for Low-Coverage Next-Generation Sequence Data Controlling for Covariates

Jung Yeon Lee, Myeong-Kyu Kim and Wonkuk Kim
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Jung Yeon Lee: Department of Psychiatry, New York University School of Medicine, New York, NY 10016, USA
Myeong-Kyu Kim: Department of Neurology, Chonnam National University Medical School, Gwangju 61469, Korea
Wonkuk Kim: Department of Applied Statistics, Chung-Ang University, Seoul 06974, Korea

Mathematics, 2020, vol. 8, issue 2, 1-14

Abstract: Low-coverage next-generation sequencing experiments assisted by statistical methods are popular in a genetic association study. Next-generation sequencing experiments produce genotype data that include allele read counts and read depths. For low sequencing depths, the genotypes tend to be highly uncertain; therefore, the uncertain genotypes are usually removed or imputed before performing a statistical analysis. It may result in the inflated type I error rate and in a loss of statistical power. In this paper, we propose a mixture-based penalized score association test adjusting for non-genetic covariates. The proposed score test statistic is based on a sandwich variance estimator so that it is robust under the model misspecification between the covariates and the latent genotypes. The proposed method takes advantage of not requiring either external imputation or elimination of uncertain genotypes. The results of our simulation study show that the type I error rates are well controlled and the proposed association test have reasonable statistical power. As an illustration, we apply our statistic to pharmacogenomics data for drug responsiveness among 400 epilepsy patients.

Keywords: allele read counts; low-coverage; mixture model; next-generation sequencing; sandwich variance estimator (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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