Technical Report abstract


Nature Genetics 40, 1245 - 1252 (2008)
Published online: 7 September 2008 | doi:10.1038/ng.206

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

Chris Barnes1, Vincent Plagnol2, Tomas Fitzgerald1, Richard Redon1, Jonathan Marchini3, David Clayton2 & Matthew E Hurles1


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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  1. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.
  2. Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK.
  3. Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.

Correspondence to: Matthew E Hurles1 e-mail: meh@sanger.ac.uk



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