Taxonomizing, sizing, and overcoming the incidentalome

• Published in: Genetics in medicine Vol. 14; no. 4; pp. 399 - 404
• Main Authors: Kohane, Isaac S., Hsing, Michael, Kong, Sek Won
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.04.2012; Elsevier Limited
• Subjects: 631/208/514; 692/700/228/2050/1512; Biomedical and Life Sciences; Biomedicine; False Positive Reactions; Genetics, Medical - methods; Genetics, Medical - standards; Genetics, Medical - statistics & numerical data; Genome, Human - genetics; Genomes; Genomics - methods; Genomics - standards; Human Genetics; Humans; Hypothesis testing; Incidental Findings; Laboratory Medicine; Population studies; Reproducibility of Results; Research Subjects; Sequence Analysis, DNA - methods; Sequence Analysis, DNA - standards; special-article; Truth Disclosure - ethics; clinical interpretation; incidental findings; false-positives; whole-genome sequencing
• ISSN: 1098-3600; 1530-0366; 1530-0366
• DOI: 10.1038/gim.2011.68

Purpose: 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. Methods: 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. Results: 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. Conclusion: 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. Genet Med 2012:14(4):399–404