Using genotype array data to compare multi- and single-sample variant calls and improve variant call sets from deep coverage whole-genome sequencing data

Suyash S. Shringarpure, Rasika Mathias, Ryan D. Hernandez, Timothy D. O'Connor, Zachary A. Szpiech, Raul Torres, Francisco M. De La Vega, Carlos D. Bustamante, Kathleen C. Barnes, Margaret Anne Taub

Research output: Contribution to journalArticle

Abstract

Motivation: Variant calling from next-generation sequencing (NGS) data is susceptible to false positive calls due to sequencing, mapping and other errors. To better distinguish true from false positive calls, we present a method that uses genotype array data from the sequenced samples, rather than public data such as HapMap or dbSNP, to train an accurate classifier using Random Forests. We demonstrate our method on a set of variant calls obtained from 642 African-ancestry genomes from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA), sequenced to high depth (30X). Results: We have applied our classifier to compare call sets generated with different calling methods, including both single-sample and multi-sample callers. At a False Positive Rate of 5%, our method determines true positive rates of 97.5%, 95% and 99% on variant calls obtained using Illuminas single-sample caller CASAVA, Real Time Genomics multisample variant caller, and the GATK UnifiedGenotyper, respectively. Since NGS sequencing data may be accompanied by genotype data for the same samples, either collected concurrent to sequencing or from a previous study, our method can be trained on each dataset to provide a more accurate computational validation of site calls compared to generic methods. Moreover, our method allows for adjustment based on allele frequency (e.g. a different set of criteria to determine quality for rare versus common variants) and thereby provides insight into sequencing characteristics that indicate call quality for variants of different frequencies.

Original languageEnglish (US)
Pages (from-to)1147-1153
Number of pages7
JournalBioinformatics
Volume33
Issue number8
DOIs
StatePublished - Apr 15 2017

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Fingerprint Dive into the research topics of 'Using genotype array data to compare multi- and single-sample variant calls and improve variant call sets from deep coverage whole-genome sequencing data'. Together they form a unique fingerprint.

  • Cite this

    Shringarpure, S. S., Mathias, R., Hernandez, R. D., O'Connor, T. D., Szpiech, Z. A., Torres, R., De La Vega, F. M., Bustamante, C. D., Barnes, K. C., & Taub, M. A. (2017). Using genotype array data to compare multi- and single-sample variant calls and improve variant call sets from deep coverage whole-genome sequencing data. Bioinformatics, 33(8), 1147-1153. https://doi.org/10.1093/bioinformatics/btw786