A Bayesian framework for de novo mutation calling in parents-offspring trios

Qiang Wei; Xiaowei Zhan; Xue Zhong; Yongzhuang Liu; Yujun Han; Wei Chen; Bingshan Li

doi:10.1093/bioinformatics/btu839

A Bayesian framework for de novo mutation calling in parents-offspring trios

Bioinformatics. 2015 May 1;31(9):1375-81. doi: 10.1093/bioinformatics/btu839. Epub 2014 Dec 21.

Authors

Qiang Wei¹, Xiaowei Zhan¹, Xue Zhong¹, Yongzhuang Liu², Yujun Han¹, Wei Chen¹, Bingshan Li²

Affiliations

¹ Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
² Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.

Abstract

Motivation: Spontaneous (de novo) mutations play an important role in the disease etiology of a range of complex diseases. Identifying de novo mutations (DNMs) in sporadic cases provides an effective strategy to find genes or genomic regions implicated in the genetics of disease. High-throughput next-generation sequencing enables genome- or exome-wide detection of DNMs by sequencing parents-proband trios. It is challenging to sift true mutations through massive amount of noise due to sequencing error and alignment artifacts. One of the critical limitations of existing methods is that for all genomic regions the same pre-specified mutation rate is assumed, which has a significant impact on the DNM calling accuracy.

Results: In this study, we developed and implemented a novel Bayesian framework for DNM calling in trios (TrioDeNovo), which overcomes these limitations by disentangling prior mutation rates from evaluation of the likelihood of the data so that flexible priors can be adjusted post-hoc at different genomic sites. Through extensively simulations and application to real data we showed that this new method has improved sensitivity and specificity over existing methods, and provides a flexible framework to further improve the efficiency by incorporating proper priors. The accuracy is further improved using effective filtering based on sequence alignment characteristics.

Availability and implementation: The C++ source code implementing TrioDeNovo is freely available at https://medschool.vanderbilt.edu/cgg.

Contact: [email protected]

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Evaluation Study
Research Support, N.I.H., Extramural

MeSH terms

Algorithms
Bayes Theorem
DNA Mutational Analysis / methods*
Genomics / methods
High-Throughput Nucleotide Sequencing / methods*
Humans
Sequence Alignment

Abstract

Publication types

MeSH terms

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