Evidence map›Paper›PMID 28560825›Full record

ArticleGenetic epidemiology2017

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

Peizhou Liao, Glen A Satten, Yi-Juan Hu

Abstract read
In one paragraph

Article in Genetic epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  13. GRHL2-controlled gene expression networks in luminal breast cancer.Cell communication and signaling : CCS · 2023
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Peizhou LiaoDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America.
Glen A SattenCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Yi-Juan HuDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America.

Funding

Association Tests of Rare Variants Using Sequence Reads without Calling GenotypesR01GM116065 · NIGMS · EMORY UNIVERSITY · PI HU, YIJUAN · 2015 to 2019
$1.5M
NIGMS NIH HHS R01 GM116065Wellcome Trust
6 · The paper itself

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.

Indexed as

GenotypeSoftwareAlgorithmsDatabases, GeneticGenomicsHigh-Throughput Nucleotide SequencingHumansModels, GeneticPolymorphism, Single NucleotideSequence Analysis, DNAcommon variantEM algorithmrare variantread data

Identifiers

PMID28560825
PMCPMC5564424

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.