Pontikos Nikolas, Smyth Deborah J, Schuilenburg Helen, Howson Joanna M M, Walker Neil M, Burren Oliver S, Guo Hui, Onengut-Gumuscu Suna, Chen Wei-Min, Concannon Patrick, Rich Stephen S, Jayaraman Jyothi, Jiang Wei, Traherne James A, Trowsdale John, Todd John A, Wallace Chris
JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, CB2 0XY, Cambridge, UK.
BMC Genomics. 2014 Apr 11;15:274. doi: 10.1186/1471-2164-15-274.
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.
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.
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.
杀伤细胞免疫球蛋白样受体（KIRs）是自然杀伤细胞的表面受体，可与其相应的人类白细胞抗原（HLA）I类配体结合，使其成为包括1型糖尿病（T1D）在内的HLA相关自身免疫性疾病的有趣候选基因。然而，KIR区域的等位基因和拷贝数变异有效地掩盖了它，使其无法进行标准的全基因组关联研究：靶向该区域的单核苷酸多态性（SNP）探针通常会被标准基因型分型软件丢弃，因为它们显示出可变的聚类数。定量聚合酶链反应（qPCR）分析解决了这个问题。然而，在检测复杂遗传疾病中通常观察到的效应所需的样本量下，其成本过高。
我们提出了一种更强大且更具成本效益的替代方法，该方法将现有数据集中发现的具有三个以上聚类的SNP信号与一部分样本上的qPCR相结合。首先，我们表明通过标准化和多个基因的同时拷贝数测定可以解决多重qPCR分析中的噪声和批次效应。然后，我们使用监督分类从SNP信号中推断特定KIR基因的拷贝数。我们应用这种方法评估两个KIR基因KIR3DL1和KIR3DS1的拷贝数变异，它们是T1D易感性的合适候选基因，因为它们编码已知与HLA - Bw4表位结合的唯一KIR分子。我们在6744例病例和5362例对照中未发现KIR3DL1 / 3DS1拷贝数与T1D之间的关联；样本量比以往任何KIR关联研究大20倍。由于我们的样本量，我们可以在5％的显著性水平上排除常见KIR3DL1 / 3DS1拷贝数组的比值比大于1.1的情况。
我们没有发现KIR3DL1 / 3DS1拷贝数与T1D总体或依赖于HLA - Bw4表位相关的证据。与KIR3DL1和KIR3DS1处于高度连锁不平衡状态的其他五个KIR基因，即KIR2DS4、KIR2DL3、KIR2DL5、KIR2DS5和KIR2DS1，也不太可能存在显著关联。我们的方法可能潜在地应用于其他KIR基因，以便在大样本中进行具有成本效益的基因拷贝数测定。
