Taxonomizing, sizing, and overcoming the incidentalome.
Taxonomizing, sizing, and overcoming the incidentalome.
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DOI：
10.1038/gim.2011.68
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发表时间：
2012-04
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随着全基因组测序的出现，临床上可用的偶然发现的数量可能会增加。假阳性的偶然发现特别值得临床关注。我们对这些假阳性结果的大小进行了估计，并将其分为四大类。全基因组序列（WGS）的9个人进行了扫描，与几个全面的公共注释数据库和平均估计的发现数量。然后从各种假阳性注释错误来源的角度对这些估计值进行评估。目前，有四个主要来源的假阳性偶然发现：错误的注释，测序错误，不正确的同源性估计，和多重假设检验。其中，前两个问题可能在近期内得到解决。保守地说，目前的方法为每个人提供了数百个假阳性的偶然发现。全基因组序列解读中的假阳性负担威胁着目前提供临床级全基因组临床解读的能力。新一代的人口研究和临床决策支持方法的重组将需要克服这一威胁。
With the advent of whole-genome sequencing made clinically available, the number of incidental findings is likely to rise. The false-positive incidental findings are of particular clinical concern. We provide estimates on the size of these false-positive findings and classify them into four broad categories. Whole-genome sequences (WGS) of nine individuals were scanned with several comprehensive public annotation databases and average estimates for the number of findings. These estimates were then evaluated in the perspective of various sources of false-positive annotation errors. At present there are four main sources of false-positive incidental findings: erroneous annotations, sequencing error, incorrect penetrance estimates, and multiple hypothesis testing. Of these, the first two are likely to be addressed in the near term. Conservatively, current methods deliver hundreds of false-positive incidental findings per individual. The burden of false-positives in whole-genome sequence interpretation threatens current capabilities to deliver clinical-grade whole-genome clinical interpretation. A new generation of population studies and retooling of the clinical decision-support approach will be required to overcome this threat.