ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest

• Published in: PLoS computational biology 2019-12, Vol.15 (12), p.e1007556-e1007556
• Main Authors: Li, Jiajin, Jew, Brandon, Zhan, Lingyu, Hwang, Sungoo, Coppola, Giovanni, Freimer, Nelson B, Sul, Jae Hoon
• Format: Article
• Language: English
• Subjects: Algorithms; Biology and Life Sciences; Classification; Computational Biology; Computer and Information Sciences; Control methods; Databases, Genetic - statistics & numerical data; Datasets; Disease; Filtration; Geffen, David; Gene sequencing; Genetic diversity; Genetic variance; Genetic Variation; Genomes; Genotype & phenotype; Genotyping; High-Throughput Nucleotide Sequencing - standards; High-Throughput Nucleotide Sequencing - statistics & numerical data; Humans; Learning algorithms; Machine Learning; Next-generation sequencing; Physical Sciences; Polymorphism, Single Nucleotide; Psychiatry; Quality Control; Research and Analysis Methods; Software; Whole genome sequencing; Whole Genome Sequencing - standards; Whole Genome Sequencing - statistics & numerical data
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007556

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data.