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. 2014 Apr 11:15:274.
doi: 10.1186/1471-2164-15-274.

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

Nikolas Pontikos, Deborah J Smyth, Helen Schuilenburg, Joanna M M Howson, Neil M Walker, Oliver S Burren, Hui Guo, Suna Onengut-Gumuscu, Wei-Min Chen, Patrick Concannon, Stephen S Rich, Jyothi Jayaraman, Wei Jiang, James A Traherne, John Trowsdale, John A Todd, Chris Wallace  1
Affiliations

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

Nikolas Pontikos et al. BMC Genomics. .

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.

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Figures

Figure 1
Figure 1
Bivariate copy number calling ofKIR3DL1/3DS1 from qPCRΔCt. On the left, the median normalised ΔCt values for KIR3DS1 and KIR3DL1 are shown with the results of clustering into the eight copy number groups coloured according to the group with the highest posterior probability. The three most common KIR3DS1-KIR3DL1 copy number groups are the ones with a total copy number of two: 0-2 (dark green), 1-1 (pink) and 2-0 (dark blue). The ellipses delimit the 95th percentile. On the right, the counts of the most probable copy number groups are shown for cases and controls.
Figure 2
Figure 2
Overlay of ImmunoChip and qPCR samples forR andθ at SNP rs592645. Samples are coloured by the most likely KIR3DS1-KIR3DL1 copy number group according to the qPCR analysis (see Figure 1). It should be apparent that R is representative of the total copy number whereas θ relates to the ratio of copies of KIR3DL1 to KIR3DS1. The first and second row split the samples on the availability of qPCR data, and the third row is the overlay of the samples from the first and second row. The first and second column split the samples by case-control status and the third column is the overlay of the samples from the first and second column.
Figure 3
Figure 3
Leave-one-out crossvalidation error rate for k-nearest neighbour prediction. Leave-one-out cross validation error rates obtained from k-nearest neighbours (knn) prediction of KIR3DL1/3DS1 copy numbers from the R and θ signals of SNP rs592645. Each point shows the proportion of samples for which the knn predicted copy number did not match the qPCR call, averaged over ten multiply imputed qPCR call datasets (using the posterior probabilities from Figure 1). Error bars show the minimum and maximum error rates over the ten multiply imputed datasets. Knn was run in parallel for cases only, controls only and on all samples together. The minimum error rate is achieved for k=8 when the prediction uses both cases and controls.
Figure 4
Figure 4
Error rate of k-nearest neighbour prediction fromR andθ of rs592645 in random subset of samples. Each panel shows the LOOCV error rates of KIR3DL1/3DS1 copy number prediction from R and θ of rs592645 in the remaining unlabeled samples when using a different size subset of the training data. The percentage of the complete training data set and the size of the subset is given in the title of each panel. Each point represents the LOOCV error rate averaged over ten multiply imputed qPCR call datasets (using the posterior probabilities from Figure 1). Smoothing lines show the average over 25 independent random subsets of training data. The black dashed line represent the observed error rate in the complete sample. As the size of the training dataset increases the error rate becomes less sensitive to the choice of the parameter k. Only 295 samples are required to achieve LOOCV error rates <5% and 590 for error rates <2.5%.
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