Discovery and Statistical Genotyping of Copy-Number Variation from Whole-Exome Sequencing Depth
Discovery and Statistical Genotyping of Copy-Number Variation from Whole-Exome Sequencing Depth
复制标题
DOI：
10.1016/j.ajhg.2012.08.005
复制
发表时间：
2012-10-05
影响因子：
9.8
通讯作者：
Purcell, Shaun M.
中科院分区：
文献类型：
--
作者：
Fromer, Menachem;Moran, Jennifer L.;Purcell, Shaun M.
基因编码区（外显子组）的测序越来越多地用于研究人类疾病，其中拷贝数变异（CNV）是关键的遗传成分。然而，从外显子组测序检测拷贝数是具有挑战性的，因为捕获的外显子的非连续性质。这与读取深度和拷贝数之间的复杂关系有关;这是由靶向基因组杂交的偏倚、序列因素（如GC含量）以及收集和测序期间样品的重复性造成的。我们提出了一种统计工具（外显子组隐马尔可夫模型[XHMM]），它使用主成分分析（PCA）来规范化外显子组读取深度，并使用隐马尔可夫模型（HMM）来发现样本中外显子分辨率CNV和基因型变异。我们评估了90个精神分裂症三人组和1,017个病例对照样本的性能。X-HMM检测两个罕见的中位数（
Sequencing of gene-coding regions (the exome) is increasingly used for studying human disease, for which copy-number variants (CNVs) are a critical genetic component. However, detecting copy number from exome sequencing is challenging because of the noncontiguous nature of the captured exons. This is compounded by the complex relationship between read depth and copy number; this results from biases in targeted genomic hybridization, sequence factors such as GC content, and batching of samples during collection and sequencing. We present a statistical tool (exome hidden Markov model [XHMM]) that uses principal-component analysis (PCA) to normalize exome read depth and a hidden Markov model (HMM) to discover exon-resolution CNV and genotype variation across samples. We evaluate performance on 90 schizophrenia trios and 1,017 case-control samples. X HMM detects a median of two rare (