Dataset Information

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

ABSTRACT: BACKGROUND: Accurate 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. RESULTS: We 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. CONCLUSIONS: BAYSIC 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.

SUBMITTER: Cantarel BL

PROVIDER: S-EPMC3999887 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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

Cantarel Brandi L BL Weaver Daniel D McNeill Nathan N Zhang Jianhua J Mackey Aaron J AJ Reese Justin J

BMC bioinformatics 20140412

<h4>Background</h4>Accurate 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, ...[more]

PMID: 24725768

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Project description:ObjectiveTo identify quantitative measurement variables that characterize mobility in older adults, meet reliability and validity criteria, distinguish fall risk, and predict future falls.DesignObservational study with 1-year weekly falls follow-up.SettingMobility laboratory.ParticipantsCommunity-dwelling volunteers (N=74; age, 65-94y) categorized at entry as 27 nonfallers or 47 fallers by using Medicare criteria (1 injury fall or >1 noninjury fall in the previous year).InterventionsNone.Main outcome measuresTest-retest and within-subject reliability, criterion and concurrent validity; predictive ability indicated by observed sensitivity and specificity to entry fall-risk group (falls status), Tinetti Performance Oriented Mobility Assessment (POMA), computerized dynamic posturography Sensory Organization Test (SOT), and subsequent falls reported weekly.ResultsMeasurement variables were selected that met reliability (intraclass coefficient of correlation >.6) and/or discrimination (P<.01) criteria (clinical variables: turn steps and time, gait velocity, step-in-tub time, downstairs time; forceplate variables: quiet standing Romberg ratio sway area, maximal lean anterior-posterior excursion, sit-to-stand medial-lateral excursion, sway area). Sets were created (3 clinical, 2 forceplate) using combinations of variables appropriate for older adults with different functional activity levels, and composite scores were calculated. Scores identified entry falls status and concurred with POMA and SOT scores. The full clinical set (5 measurement variables) produced sensitivity of 80% and specificity of 74% to falls status. Composite scores were more sensitive and specific overall in predicting subsequent injury falls and multiple falls compared with falls status and POMA or SOT score.ConclusionsSets of quantitative measurement variables obtained with this mobility battery provided sensitive prediction of future injury falls and screening for multiple subsequent falls by using tasks that should be appropriate to diverse participants.

Project description:BackgroundGenomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual's genome that provide a strong explanation of the phenotype observed in this individual.ResultsWe applied the IBI algorithm to the data from the Framingham Heart Study to explore the genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, IBI discovered more individualized and diverse variants that explain hypertension patients better than GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that GWAS missed in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than GWAS, according to the area under the ROC curve.ConclusionsThe results support IBI as a promising approach for complementing GWAS, especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine.

Dataset Information

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

Publications

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

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