Detection and correction of artefacts in estimation of rare copy number variants and analysis of rare deletions in type 1 diabetes.

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Cooper, Nicholas J 
Shtir, Corina J 
Smyth, Deborah J 
Guo, Hui 
Swafford, Austin D 

Copy number variants (CNVs) have been proposed as a possible source of 'missing heritability' in complex human diseases. Two studies of type 1 diabetes (T1D) found null associations with common copy number polymorphisms, but CNVs of low frequency and high penetrance could still play a role. We used the Log-R-ratio intensity data from a dense single nucleotide polymorphism (SNP) array, ImmunoChip, to detect rare CNV deletions (rDELs) and duplications (rDUPs) in 6808 T1D cases, 9954 controls and 2206 families with T1D-affected offspring. Initial analyses detected CNV associations. However, these were shown to be false-positive findings, failing replication with polymerase chain reaction. We developed a pipeline of quality control (QC) tests that were calibrated using systematic testing of sensitivity and specificity. The case-control odds ratios (OR) of CNV burden on T1D risk resulting from this QC pipeline converged on unity, suggesting no global frequency difference in rDELs or rDUPs. There was evidence that deletions could impact T1D risk for a small minority of cases, with enrichment for rDELs longer than 400 kb (OR = 1.57, P = 0.005). There were also 18 de novo rDELs detected in affected offspring but none for unaffected siblings (P = 0.03). No specific CNV regions showed robust evidence for association with T1D, although frequencies were lower than expected (most less than 0.1%), substantially reducing statistical power, which was examined in detail. We present an R-package, plumbCNV, which provides an automated approach for QC and detection of rare CNVs that can facilitate equivalent analyses of large-scale SNP array datasets.

Adolescent, Artifacts, Child, Child, Preschool, DNA Copy Number Variations, Data Interpretation, Statistical, Diabetes Mellitus, Type 1, Genetic Predisposition to Disease, Genotyping Techniques, Humans, Quality Control, Sensitivity and Specificity, Sequence Deletion, Software
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Hum Mol Genet
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Oxford University Press (OUP)
National Institute of Diabetes and Digestive and Kidney Diseases (U01DK062418)
Medical Research Council (MR/L003120/1)
Wellcome Trust (100140/Z/12/Z)
Wellcome Trust (091157/Z/10/B)
British Heart Foundation (None)
This work was funded by the JDRF (9-2011-253), the Wellcome Trust (091157) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. The Cambridge 14 | Human Molecular Genetics Downloaded from at University of Cambridge on January 27, 2015 Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140). This work uses resources provided by the T1DGC, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development, and Juvenile Diabetes Research Foundation International (JDRF) and is supported by U01 DK-062418. T1DGC supplied samples. Funding to pay the Open Access publication charges for this article was provided by the University of Cambridge RCUK block grant for Open Access.