BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity.

Brandi L Cantarel,Justin Reese,Daniel Weaver,Aaron J Mackey,Jianhua Zhang,Nathan Mcneill

doi:10.1186/1471-2105-15-104

Abstract

BackgroundAccurate genomic variant detection is an essential step in gleaning medically useful information from genome data. However, low concordance among variant-calling methods reduces confidence in the clinical validity of whole genome and exome sequence data, and confounds downstream analysis for applications in genome medicine.Here we describe BAYSIC (BAYeSian Integrated Caller), which combines SNP variant calls produced by different methods (e.g. GATK, FreeBayes, Atlas, SamTools, etc.) into a more accurate set of variant calls. BAYSIC differs from majority voting, consensus or other ad hoc intersection-based schemes for combining sets of genome variant calls. Unlike other classification methods, the underlying BAYSIC model does not require training using a “gold standard” of true positives. Rather, with each new dataset, BAYSIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and false negative error rates for each input method. The user specifies a posterior probability threshold according to the user’s tolerance for false positive and false negative errors; lowering the posterior probability threshold allows the user to trade specificity for sensitivity while raising the threshold increases specificity in exchange for sensitivity.ResultsWe assessed the performance of BAYSIC in comparison to other variant detection methods using ten low coverage (~5X) samples from The 1000 Genomes Project, a tumor/normal exome pair (40X), and exome sequences (40X) from positive control samples previously identified to contain clinically relevant SNPs. We demonstrated BAYSIC’s superior variant-calling accuracy, both for somatic mutation detection and germline variant detection.ConclusionsBAYSIC provides a method for combining sets of SNP variant calls produced by different variant calling programs. The integrated set of SNP variant calls produced by BAYSIC improves the sensitivity and specificity of the variant calls used as input. In addition to combining sets of germline variants, BAYSIC can also be used to combine sets of somatic mutations detected in the context of tumor/normal sequencing experiments.

Highlights

Accurate genomic variant detection is an essential step in gleaning medically useful information from genome data
We describe BAYSIC (BAYeSian Integrated Caller), a novel algorithm that uses a Bayesian statistical method based on latent class analysis to combine variant sets produced by different bioinformatic packages (e.g., GATK, FreeBayes, Samtools) into a high-confidence set of genome variants
Overview of BAYSIC algorithm Several programs exist for the detection of genome variants such as SNPs and insertions and deletions [9,17,18]

Summary

Introduction

Accurate genomic variant detection is an essential step in gleaning medically useful information from genome data. We describe BAYSIC (BAYeSian Integrated Caller), which combines SNP variant calls produced by different methods (e.g. GATK, FreeBayes, Atlas, SamTools, etc.) into a more accurate set of variant calls. With each new dataset, BAYSIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and false negative error rates for each input method. In the case of humans and certain other genomes (e.g., dogs, cats and livestock), resequencing projects aim to associate genetic changes to disease risk, medical treatment efficacy or the accurate detection of single nucleotide variants (SNPs) and small insertions or deletions (indels) is not trivial. Each algorithm used in SNP detection creates a different balance of sensitivity and specificity, to either increase the number of true positives at the cost of additional false positives or decrease the number of false positives at the cost of reducing the number of true positives. Some algorithms, e.g. GATK, recommend the user include many samples in order to recalibrate quality scores or classify SNPs with distinctions between PASS and LowQual, and thereby increase variant call accuracy

Methods

Results

Conclusion