Improved Detection of Rare Genetic Variants for Diseases

PLoS One

Jian Li¹  Lei Zhang¹  Yu-Fang Pei²  Christopher J. Papasian²  Hong-Wen Deng³ 

[1] Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, People's Republic of China;Key Laboratory of Biomedical Information Engineering, School of Life Science and Technology, Ministry of Education and Institute of Molecular Genetics, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China;School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, United States of America

关键词: Algorithms;    Variant genotypes;    Gene pool;    Heredity;    Nucleotide sequencing;    Amino acid analysis;    Amino acid substitution;    Sequence analysis;   

DOI  :  10.1371/journal.pone.0013857

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

Technology advances have promoted gene-based sequencing studies with the aim of identifying rare mutations responsible for complex diseases. A complication in these types of association studies is that the vast majority of non-synonymous mutations are believed to be neutral to phenotypes. It is thus critical to distinguish potential causative variants from neutral variation before performing association tests. In this study, we used existing predicting algorithms to predict functional amino acid substitutions, and incorporated that information into association tests. Using simulations, we comprehensively studied the effects of several influential factors, including the sensitivity and specificity of functional variant predictions, number of variants, and proportion of causative variants, on the performance of association tests. Our results showed that incorporating information regarding functional variants obtained from existing prediction algorithms improves statistical power under certain conditions, particularly when the proportion of causative variants is moderate. The application of the proposed tests to a real sequencing study confirms our conclusions. Our work may help investigators who are planning to pursue gene-based sequencing studies.

【 授权许可】

CC BY