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

@article{citeulike:3211075, 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.}, address = {Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.}, author = {Barnes, Chris and Plagnol, Vincent and Fitzgerald, Tomas and Redon, Richard and Marchini, Jonathan and Clayton, David and Hurles, Matthew E E. }, citeulike-article-id = {3211075}, doi = {http://dx.doi.org/10.1038/ng.206}, issn = {1546-1718}, journal = {Nature genetics}, keywords = {affymetrix, algorithm, associationstudy, cnv, em, gmm, ld, r, snp, software}, month = {September}, posted-at = {2008-10-08 15:01:08}, priority = {2}, title = {A robust statistical method for case-control association testing with copy number variation.}, url = {http://dx.doi.org/10.1038/ng.206}, year = {2008} }

TY - JOUR ID - citeulike:3211075 L3 - citeulike-article-id:3211075 N2 - 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. JF - Nature genetics SN - 1546-1718 AD - Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. TI - A robust statistical method for case-control association testing with copy number variation. KW - affymetrix KW - algorithm KW - associationstudy KW - cnv KW - em KW - gmm KW - ld KW - r KW - snp KW - software AU - Barnes, Chris AU - Plagnol, Vincent AU - Fitzgerald, Tomas AU - Redon, Richard AU - Marchini, Jonathan AU - Clayton, David AU - Hurles, Matthew E PY - 2008/09/07/ UR - http://dx.doi.org/10.1038/ng.206 ER -