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

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, Phre...

Source: Genetic Epidemiology - May 1, 2017 Category: Epidemiology Authors: Peizhou Liao, Glen A. Satten, Yi ‐Juan Hu Tags: RESEARCH ARTICLE Source Type: research