Dataset Information

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

ABSTRACT: 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).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.Code is available on Github at: https://github.com/suyashss/variant_validation.suyashs@stanford.edu or mtaub@jhsph.edu.Supplementary data are available at Bioinformatics online.

SUBMITTER: Shringarpure SS

PROVIDER: S-EPMC5408850 | biostudies-other | 2017 Apr

REPOSITORIES: biostudies-other

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

Shringarpure Suyash S SS Mathias Rasika A RA Hernandez Ryan D RD O'Connor Timothy D TD Szpiech Zachary A ZA Torres Raul R De La Vega Francisco M FM Bustamante Carlos D CD Barnes Kathleen C KC Taub Margaret A MA

Bioinformatics (Oxford, England) 20170401 8

<h4>Motivation</h4>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 ...[more]

PMID: 28035032

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Project description:MotivationGenome-wide association studies (GWAS) are used to discover genes underlying complex, heritable disorders for which less powerful study designs have failed in the past. The number of GWAS has skyrocketed recently with findings reported in top journals and the mainstream media. Microarrays are the genotype calling technology of choice in GWAS as they permit exploration of more than a million single nucleotide polymorphisms (SNPs) simultaneously. The starting point for the statistical analyses used by GWAS to determine association between loci and disease is making genotype calls (AA, AB or BB). However, the raw data, microarray probe intensities, are heavily processed before arriving at these calls. Various sophisticated statistical procedures have been proposed for transforming raw data into genotype calls. We find that variability in microarray output quality across different SNPs, different arrays and different sample batches have substantial influence on the accuracy of genotype calls made by existing algorithms. Failure to account for these sources of variability can adversely affect the quality of findings reported by the GWAS.ResultsWe developed a method based on an enhanced version of the multi-level model used by CRLMM version 1. Two key differences are that we now account for variability across batches and improve the call-specific assessment of each call. The new model permits the development of quality metrics for SNPs, samples and batches of samples. Using three independent datasets, we demonstrate that the CRLMM version 2 outperforms CRLMM version 1 and the algorithm provided by Affymetrix, Birdseed. The main advantage of the new approach is that it enables the identification of low-quality SNPs, samples and batches.AvailabilitySoftware implementing of the method described in this article is available as free and open source code in the crlmm R/BioConductor package.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:INTRODUCTION: Angiogenesis is an important process in the development of destructive synovial pannus in rheumatoid arthritis (RA). The ITGAV +gene encodes a cell cycle-associated antigen, integrin alphanubeta 3, which plays a role in RA angiogenesis. Previously, two independent studies identified an association between the major allele of the ITGAV single-nucleotide polymorphism (SNP) rs3738919 and RA. We therefore tested this association in an independent study using New Zealand (NZ) and Oxford (UK) RA case control samples. METHODS: We compared genotype frequencies in 740 NZ Caucasian RA patients and 553 controls genotyped for rs3738919, using a polymerase chain reaction-restriction fragment length polymorphism assay. A TaqMan genotyping SNP assay was used to type 713 Caucasian RA patients and 515 control samples from Oxford for the rs3738919 variant. Association of rs3738919 with RA was tested in these two sample sets using the chi-square goodness-of-fit test. The Mantel-Haenszel test was used to perform a meta-analysis, combining the genetic results from four independent Caucasian case control cohorts, consisting of 3,527 cases and 4,126 controls. Haplotype analysis was also performed using SNPs rs3911238, rs10174098 and rs3738919 in the Wellcome Trust Case Control Consortium, NZ and Oxford case control samples. RESULTS: We found no evidence for association between ITGAV and RA in either the NZ or Oxford sample set (odds ratio [OR] = 0.88, P(allelic) = 0.11 and OR = 1.18, P(allelic) = 0.07, respectively). Inclusion of these data in a meta-analysis (random effects) of four independent cohorts (3,527 cases and 4,126 controls) weakens support for the hypothesis that rs3738919 plays a role in the development of RA (OR(combined) = 0.92, 95% confidence interval 0.80 to 1.07; P = 0.29). No consistent haplotype associations were evident. CONCLUSIONS: Association of ITGAV SNP rs7378919 with RA was not replicated in NZ or Oxford case control sample sets. Meta-analysis of these and previously published data lends limited support for a role for the ITGAV in RA in Caucasians of European ancestry.

Dataset Information

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

Publications

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

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