Robust Linear Trend Test for Low-Coverage Next-Generation Sequence Data Controlling for Covariates
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 unc...
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doaj-0564f4a42adc4c1ea894e6ee32f9694c2020-11-25T02:33:37ZengMDPI AGMathematics2227-73902020-02-018221710.3390/math8020217math8020217Robust Linear Trend Test for Low-Coverage Next-Generation Sequence Data Controlling for CovariatesJung Yeon Lee0Myeong-Kyu Kim1Wonkuk Kim2Department of Psychiatry, New York University School of Medicine, New York, NY 10016, USADepartment of Neurology, Chonnam National University Medical School, Gwangju 61469, KoreaDepartment of Applied Statistics, Chung-Ang University, Seoul 06974, KoreaLow-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.https://www.mdpi.com/2227-7390/8/2/217allele read countslow-coveragemixture modelnext-generation sequencingsandwich variance estimator |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jung Yeon Lee Myeong-Kyu Kim Wonkuk Kim |
spellingShingle |
Jung Yeon Lee Myeong-Kyu Kim Wonkuk Kim Robust Linear Trend Test for Low-Coverage Next-Generation Sequence Data Controlling for Covariates Mathematics allele read counts low-coverage mixture model next-generation sequencing sandwich variance estimator |
author_facet |
Jung Yeon Lee Myeong-Kyu Kim Wonkuk Kim |
author_sort |
Jung Yeon Lee |
title |
Robust Linear Trend Test for Low-Coverage Next-Generation Sequence Data Controlling for Covariates |
title_short |
Robust Linear Trend Test for Low-Coverage Next-Generation Sequence Data Controlling for Covariates |
title_full |
Robust Linear Trend Test for Low-Coverage Next-Generation Sequence Data Controlling for Covariates |
title_fullStr |
Robust Linear Trend Test for Low-Coverage Next-Generation Sequence Data Controlling for Covariates |
title_full_unstemmed |
Robust Linear Trend Test for Low-Coverage Next-Generation Sequence Data Controlling for Covariates |
title_sort |
robust linear trend test for low-coverage next-generation sequence data controlling for covariates |
publisher |
MDPI AG |
series |
Mathematics |
issn |
2227-7390 |
publishDate |
2020-02-01 |
description |
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. |
topic |
allele read counts low-coverage mixture model next-generation sequencing sandwich variance estimator |
url |
https://www.mdpi.com/2227-7390/8/2/217 |
work_keys_str_mv |
AT jungyeonlee robustlineartrendtestforlowcoveragenextgenerationsequencedatacontrollingforcovariates AT myeongkyukim robustlineartrendtestforlowcoveragenextgenerationsequencedatacontrollingforcovariates AT wonkukkim robustlineartrendtestforlowcoveragenextgenerationsequencedatacontrollingforcovariates |
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1724812722690850816 |