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A robust statistical method for case-control association testing with copy number variation

Nature Genetics volume 40, pages 1245–1252 (2008)Cite this article

Abstract

Copy number variation (CNV) is pervasive in the human genome and can play a causal role in genetic diseases. The functional impact of CNV cannot be fully captured through linkage disequilibrium with SNPs. These observations motivate the development of statistical methods for performing direct CNV association studies. We show through simulation that current tests for CNV association are prone to false-positive associations in the presence of differential errors between cases and controls, especially if quantitative CNV measurements are noisy. We present a statistical framework for performing case-control CNV association studies that applies likelihood ratio testing of quantitative CNV measurements in cases and controls. We show that our methods are robust to differential errors and noisy data and can achieve maximal theoretical power. We illustrate the power of these methods for testing for association with binary and quantitative traits, and have made this software available as the R package CNVtools.

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Figure 1: Example of CNV data showing poor clustering quality and differential errors.
Figure 2: Methods for performing CNV-association testing.
Figure 3: Modelling the dependency between copy number and disease.
Figure 4: Sensitivity of 1-d.f. association testing methods to clustering quality and differential errors between cases and controls in simulated data.
Figure 5: Statistical power of the likelihood ratio trend test.
Figure 6: Examples of empirical CNV associations.

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Acknowledgements

C.B., T.F., R.R. and M.E.H. are funded by the Wellcome Trust (WT), J.M. is funded by the WT and the National Institute of General Medical Sciences, V.P. is supported by a Juvenile Diabetes Research Foundation (JDRF) fellowship, and D.C. is supported by a JDRF/WT fellowship. The authors would like to thank the Wellcome Trust Case Control Consortium, D. Conrad, A. Moses, N. Carter, M. Dermitzakis, B. Stranger, J. Armour and E. Hollox for data access and helpful discussions.

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Authors and Affiliations

  1. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK

    Chris Barnes, Tomas Fitzgerald, Richard Redon & Matthew E Hurles

  2. Department of Medical Genetics, Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK

    Vincent Plagnol & David Clayton

  3. Department of Statistics, University of Oxford, Oxford, OX1 3TG, UK

    Jonathan Marchini

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  1. Chris Barnes
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  2. Vincent Plagnol
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Correspondence to Matthew E Hurles.

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Supplementary Figures 1?5, Supplementary Table 1, Supplementary Methods (PDF 334 kb)

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Barnes, C., Plagnol, V., Fitzgerald, T. et al. A robust statistical method for case-control association testing with copy number variation. Nat Genet 40, 1245–1252 (2008). https://doi.org/10.1038/ng.206

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