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Open Access Highly Accessed Research article

A hybrid qPCR/SNP array approach allows cost efficient assessment of KIR gene copy numbers in large samples

Nikolas Pontikos1, Deborah J Smyth1, Helen Schuilenburg1, Joanna MM Howson12, Neil M Walker1, Oliver S Burren1, Hui Guo1, Suna Onengut-Gumuscu3, Wei-Min Chen3, Patrick Concannon4, Stephen S Rich3, Jyothi Jayaraman56, Wei Jiang56, James A Traherne56, John Trowsdale56, John A Todd1 and Chris Wallace17*

Author Affiliations

1 JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, CB2 0XY, Cambridge, UK

2 Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, CB1 8RN, Cambridge, UK

3 Center for Public Health Genomics, University of Virginia, 22908-0717, Charlottesville, Virginia, USA

4 University of Florida Genetics Institute, 32610-3610, Gainesville, Florida, USA

5 Division of Immunology, Department of Pathology, University of Cambridge, Tennis Court Road, CB2 1QP, Cambridge, UK

6 Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, CB2 0XY, Cambridge, UK

7 MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, CB2 0SR, Cambridge, UK

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BMC Genomics 2014, 15:274  doi:10.1186/1471-2164-15-274

Published: 11 April 2014

Abstract

Background

Killer Immunoglobulin-like Receptors (KIRs) are surface receptors of natural killer cells that bind to their corresponding Human Leukocyte Antigen (HLA) class I ligands, making them interesting candidate genes for HLA-associated autoimmune diseases, including type 1 diabetes (T1D). However, allelic and copy number variation in the KIR region effectively mask it from standard genome-wide association studies: single nucleotide polymorphism (SNP) probes targeting the region are often discarded by standard genotype callers since they exhibit variable cluster numbers. Quantitative Polymerase Chain Reaction (qPCR) assays address this issue. However, their cost is prohibitive at the sample sizes required for detecting effects typically observed in complex genetic diseases.

Results

We propose a more powerful and cost-effective alternative, which combines signals from SNPs with more than three clusters found in existing datasets, with qPCR on a subset of samples. First, we showed that noise and batch effects in multiplexed qPCR assays are addressed through normalisation and simultaneous copy number calling of multiple genes. Then, we used supervised classification to impute copy numbers of specific KIR genes from SNP signals. We applied this method to assess copy number variation in two KIR genes, KIR3DL1 and KIR3DS1, which are suitable candidates for T1D susceptibility since they encode the only KIR molecules known to bind with HLA-Bw4 epitopes. We find no association between KIR3DL1/3DS1 copy number and T1D in 6744 cases and 5362 controls; a sample size twenty-fold larger than in any previous KIR association study. Due to our sample size, we can exclude odds ratios larger than 1.1 for the common KIR3DL1/3DS1 copy number groups at the 5% significance level.

Conclusion

We found no evidence of association of KIR3DL1/3DS1 copy number with T1D, either overall or dependent on HLA-Bw4 epitope. Five other KIR genes, KIR2DS4, KIR2DL3, KIR2DL5, KIR2DS5 and KIR2DS1, in high linkage disequilibrium with KIR3DL1 and KIR3DS1, are also unlikely to be significantly associated. Our approach could potentially be applied to other KIR genes to allow cost effective assaying of gene copy number in large samples.

Keywords:
KIR3DL1; KIR3DS1; KIR2DS4; KIR2DL3; KIR2DL5; KIR2DS5; KIR2DS1; HLA-Bw4; CNV; qPCR; ImmunoChip; KIR; Imputation; T1D