PhredEM: a phred-score-informed genotype-calling approach for next-generation sequencing studies

• Type of Material: Article
• Authors: Peizhou Liao, Emory University; Glen A. Satten, Centers for Disease Control and Prevention; Yijuan Hu, Emory University
• Language: English
• Date: 2017-07-01
• Publisher: Wiley
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 WILEY PERIODICALS, INC
• Final Published Version (URL): http://dx.doi.org/10.1002/gepi.22048
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 41
• Issue: 5
• Start Page: 375
• End Page: 387
• Grant/Funding Information: Funding for UK10K was provided by the Wellcome Trust under award WT091310.; This research was supported in part by the University Research Committee (URC) Award at Emory and the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R01GM116065-01A1.
• Keywords: DISCOVERY; Science & Technology; Mathematical & Computational Biology; common variant; Life Sciences & Biomedicine; MAXIMUM-LIKELIHOOD; QUALITY SCORES; ACCURACY; FRAMEWORK; EM algorithm; ASSOCIATION; rare variant; Genetics & Heredity; read data
• Research Categories: Biology, Bioinformatics; Biology, Biostatistics

Abstract

A fundamental challenge in analyzing next-generation sequencing (NGS) data is to determine an individual's genotype accurately, as the accuracy of the inferred genotype is essential to downstream analyses. Correctly estimating the base-calling error rate is critical to accurate genotype calls. Phred scores that accompany each call can be used to decide which calls are reliable. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too high threshold may lose data, while a too low threshold may introduce errors. We propose a new likelihood-based genotype-calling approach that exploits all reads and estimates the per-base error rates by incorporating phred scores through a logistic regression model. The approach, which we call PhredEM, uses the expectation–maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. It also includes a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be nonmonomorphic require application of the EM algorithm. Like GATK, PhredEM can be used together with a linkage-disequilibrium-based method such as Beagle, which can further improve genotype calling as a refinement step. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project and the 1000 Genomes project. The results demonstrate that PhredEM performs better than either GATK or SeqEM, and that PhredEM is an improved, robust, and widely applicable genotype-calling approach for NGS studies. The relevant software is freely available.