首页> 美国卫生研究院文献>Proceedings of the National Academy of Sciences of the United States of America >Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants
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Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants

机译:全基因组测序比全外显子测序功能强大可检测外显子组变异

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摘要

We compared whole-exome sequencing (WES) and whole-genome sequencing (WGS) in six unrelated individuals. In the regions targeted by WES capture (81.5% of the consensus coding genome), the mean numbers of single-nucleotide variants (SNVs) and small insertions/deletions (indels) detected per sample were 84,192 and 13,325, respectively, for WES, and 84,968 and 12,702, respectively, for WGS. For both SNVs and indels, the distributions of coverage depth, genotype quality, and minor read ratio were more uniform for WGS than for WES. After filtering, a mean of 74,398 (95.3%) high-quality (HQ) SNVs and 9,033 (70.6%) HQ indels were called by both platforms. A mean of 105 coding HQ SNVs and 32 indels was identified exclusively by WES whereas 692 HQ SNVs and 105 indels were identified exclusively by WGS. We Sanger-sequenced a random selection of these exclusive variants. For SNVs, the proportion of false-positive variants was higher for WES (78%) than for WGS (17%). The estimated mean number of real coding SNVs (656 variants, ∼3% of all coding HQ SNVs) identified by WGS and missed by WES was greater than the number of SNVs identified by WES and missed by WGS (26 variants). For indels, the proportions of false-positive variants were similar for WES (44%) and WGS (46%). Finally, WES was not reliable for the detection of copy-number variations, almost all of which extended beyond the targeted regions. Although currently more expensive, WGS is more powerful than WES for detecting potential disease-causing mutations within WES regions, particularly those due to SNVs.
机译:我们比较了六个无关个体的全基因组测序(WES)和全基因组测序(WGS)。在WES捕获的目标区域(共有编码基因组的81.5%)中,每个样品的WES单核苷酸变体(SNV)和小插入/缺失(indel)的平均数分别为84192和13325。 WGS分别为84,968和12,702。对于SNV和插入缺失,WGS的覆盖深度,基因型质量和次要阅读率的分布比WES更均匀。过滤后,两个平台均调用了平均74,398(95.3%)个高质量(HQ)SNV和9,033(70.6%)个HQ indel。仅通过WES识别出105个编码HQ SNV和32个插入缺失,而仅通过WGS识别692个HQ SNV和105个Indel。我们对这些独家变体进行随机排序。对于SNV,WES(78%)的假阳性变异比例要高于WGS(17%)。 WGS识别并被WES遗漏的实际编码SNV(656个变体,占所有编码HQ SNV的3%)的估计平均数大于WES识别并被WGS遗漏的SNV的数目(26个变体)。对于插入缺失,WES(44%)和WGS(46%)的假阳性变异比例相似。最后,WES对于检测拷贝数变异并不可靠,几乎所有拷贝都超出了目标区域。尽管目前更昂贵,但是WGS在检测WES地区内潜在的致病突变(尤其是由SNV引起的突变)方面比WES更为强大。

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