Standard techniques from genetic epidemiology are ill-suited to formally assess the significance of variants identified from a single case. We developed a statistical inference framework for identifying unusual functional variation from a single exome or genome, what we refer to as the 'n-of-one' problem. Using this approach we assessed our ability to identify the causal genotypes in over 5 million simulated cases of Mendelian disease, identifying 39% of disease genotypes as the most damaging unit in a typical exome background. We applied our approach to 129 n-of-one families from the Undiagnosed Diseases Program, nominating 60% of 30 disease genes determined to be diagnostic by a standard clinical workup. Our method can currently produce well-calibrated P values when applied to single genomes, can facilitate integration of multiple data types for n-of-one analyses, and, with further work, could become a widely used epidemiological method like linkage analysis or genome-wide association analysis.
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We thank D. Wilson and M. Stephens for helpful comments, N. Huang for useful discussions and for providing updated versions of some annotations used in this work, K. Vigh-Conrad for assistance in preparing the figures, D. MacArthur, M. Lek and the members of the ExAC Consortium for generous prepublication sharing of their data, and M. Hoffmann and WU Kidney Translational Research Core (KTRC) for patient enrolment and Genome Technology Access Center (GTAC) for exome sequencing of CAKUT patients. Our work was supported by US National Institutes of Health grant R01MH101810 (to D.F.C.), March of Dimes Foundation grant #6-FY14-430 (to S.J.).
D.F.C. is funded by a research contract with PierianDx to develop novel methods for clinical exome analysis.
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Wilfert, A., Chao, K., Kaushal, M. et al. Genome-wide significance testing of variation from single case exomes. Nat Genet 48, 1455–1461 (2016). https://doi.org/10.1038/ng.3697
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