FVC as an adaptive and accurate method for filtering variants from popular NGS analysis pipelines

Yongyong Ren; Yan Kong; Xiaocheng Zhou; Georgi Z Genchev; Chao Zhou; Hongyu Zhao; Hui Lu

doi:10.1038/s42003-022-03397-7

FVC as an adaptive and accurate method for filtering variants from popular NGS analysis pipelines

Commun Biol. 2022 Sep 16;5(1):975. doi: 10.1038/s42003-022-03397-7.

Authors

Yongyong Ren^#^{1

2}, Yan Kong^#^{1

2}, Xiaocheng Zhou¹, Georgi Z Genchev^{3

4

5}, Chao Zhou^{1

2}, Hongyu Zhao⁶, Hui Lu^{7

8

9}

Affiliations

¹ State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
² SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
³ Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
⁴ Department of Biostatistics, Yale University, New Haven, CT, USA.
⁵ Center for Biomedical Informatics, Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai Children's Hospital, Shanghai, China.
⁶ Department of Biostatistics, Yale University, New Haven, CT, USA. hongyu.zhao@yale.edu.
⁷ State Key Laboratory of Microbial metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. huilu@sjtu.edu.cn.
⁸ SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China. huilu@sjtu.edu.cn.
⁹ Center for Biomedical Informatics, Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai Children's Hospital, Shanghai, China. huilu@sjtu.edu.cn.

^# Contributed equally.

Abstract

The quality control of variants from whole-genome sequencing data is vital in clinical diagnosis and human genetics research. However, current filtering methods (Frequency, Hard-Filter, VQSR, GARFIELD, and VEF) were developed to be utilized on particular variant callers and have certain limitations. Especially, the number of eliminated true variants far exceeds the number of removed false variants using these methods. Here, we present an adaptive method for quality control on genetic variants from different analysis pipelines, and validate it on the variants generated from four popular variant callers (GATK HaplotypeCaller, Mutect2, Varscan2, and DeepVariant). FVC consistently exhibited the best performance. It removed far more false variants than the current state-of-the-art filtering methods and recalled ~51-99% true variants filtered out by the other methods. Once trained, FVC can be conveniently integrated into a user-specific variant calling pipeline.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Exome*
High-Throughput Nucleotide Sequencing* / methods
Humans
Polymorphism, Single Nucleotide
Software
Whole Genome Sequencing

Associated data

Dryad/10.5061/dryad.hdr7sqvkm

Grants and funding

UL1 TR001863/TR/NCATS NIH HHS/United States