Open AccessA Bayesian framework for efficient and accurate variant prediction
Author(s) -
Dajun Qian,
Shuwei Li,
Yuan Tian,
Jacob Clifford,
Brice A. J. Sarver,
Tina Pesaran,
Chia-Ling Gau,
Aaron Elliott,
HsiaoMei Lu,
Mary Helen Black
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0203553
Subject(s) - in silico , bayesian probability , computational biology , computer science , bayes' theorem , statistics , machine learning , data mining , bioinformatics , artificial intelligence , biology , mathematics , genetics , gene
There is a growing need to develop variant prediction tools capable of assessing a wide spectrum of evidence. We present a Bayesian framework that involves aggregating pathogenicity data across multiple in silico scores on a gene-by-gene basis and multiple evidence statistics in both quantitative and qualitative forms, and performs 5-tiered variant classification based on the resulting probability credible interval. When evaluated in 1,161 missense variants, our gene-specific in silico model-based meta-predictor yielded an area under the curve (AUC) of 96.0% and outperformed all other in silico predictors. Multifactorial model analysis incorporating all available evidence yielded 99.7% AUC, with 22.8% predicted as variants of uncertain significance (VUS). Use of only 3 auto-computed evidence statistics yielded 98.6% AUC with 56.0% predicted as VUS, which represented sufficient accuracy to rapidly assign a significant portion of VUS to clinically meaningful classifications. Collectively, our findings support the use of this framework to conduct large-scale variant prioritization using in silico predictors followed by variant prediction and classification with a high degree of predictive accuracy.