A Bayesian framework for de novo mutation calling in parents-offspring trios
A Bayesian framework for de novo mutation calling in parents-offspring trios
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DOI：
10.1093/bioinformatics/btu839
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发表时间：
2015-05-01
期刊：
影响因子：
5.8
通讯作者：
Li, Bingshan
中科院分区：
文献类型：
--
作者：
Wei, Qiang;Zhan, Xiaowei;Li, Bingshan
动机：自发(从头开始)突变在一系列复杂疾病的病因学中发挥着重要作用。识别散发性病例中的从头突变(DNM)为寻找与疾病遗传学有关的基因或基因组区域提供了一种有效的策略。高通量下一代测序通过对双亲先证者三人组进行测序，实现了基因组或外显子范围的DNMS检测。由于测序错误和比对伪影，在大量的噪声中筛选真正的突变是具有挑战性的。结果：在本研究中，我们开发并实现了一种新的基于Trio的贝叶斯DNM调用框架(TrioDeNovo)，该框架通过将先验突变率从数据的似然评估中分离出来，使灵活的先验先验能够在不同的基因组位置进行事后调整，从而克服了这些限制。通过大量的仿真和对实际数据的应用表明，该方法比现有的方法具有更高的灵敏度和特异度，并提供了一个灵活的框架，通过引入适当的先验知识来进一步提高效率。使用基于序列比对特征的有效滤波进一步提高了准确率。
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.