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Genome-wide significance testing of variation from single case exomes

Nature Genetics volume 48, pages 1455–1461 (2016)Cite this article

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Abstract

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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Figure 1: Approach to the n-of-one problem.
Figure 2: PSAP calibration.
Figure 3: Three primary sources of information contribute to the performance of PSAP values: the use of gene-specific models, modeling gene-specific singleton rates and integration of frequency information from ExAC.
Figure 4: Benchmarking our ability to identify the causal gene in simluated n-of-one cases.
Figure 5: Application of PSAP to real cases of the n-of-1 problem.
Figure 6: PSAP facilitates integrative analysis of rare disease patients.

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Acknowledgements

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.).

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Authors and Affiliations

  1. Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA

    Amy B Wilfert & Donald F Conrad

  2. National Institutes of Health Undiagnosed Diseases Program, US National Institutes of Health, Bethesda, Maryland, USA

    Katherine R Chao & David R Adams

  3. Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA

    Madhurima Kaushal & Sanjay Jain

  4. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA

    Sanjay Jain & Donald F Conrad

  5. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA

    Sebastian Zöllner

Authors
  1. Amy B Wilfert
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  7. Donald F Conrad
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Contributions

D.F.C. designed the study. S.Z. provided helpful conceptual guidance on modeling and interpretation. D.F.C. wrote the simulation code. A.B.W. developed the PSAP pipeline, performed spike-in analyses, and evaluated the impact of population structure on PSAP values. D.F.C., A.B.W., D.R.A. and K.R.C. performed the UDP analyses. M.K. and S.J. contributed CAKUT samples and data. A.B.W. and D.F.C. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Donald F Conrad.

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Competing interests

D.F.C. is funded by a research contract with PierianDx to develop novel methods for clinical exome analysis.

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Supplementary Text and Figures

Supplementary Figures 1–17, Supplementary Tables 1–7 and Supplementary Note. (PDF 2925 kb)

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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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