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

机译：检测和校正伪影以估计1型糖尿病的罕见拷贝数变异和罕见缺失的分析

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摘要

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.
机译：已经提出了拷贝数变异（CNV）作为复杂人类疾病中“缺失遗传力”的可能来源。两项关于1型糖尿病（T1D）的研究发现，与常见的拷贝数多态性没有关联，但是低频和高渗透率的CNV仍然可以发挥作用。我们使用来自密集单核苷酸多态性（SNP）阵列ImmunoChip的Log-R-比率强度数据来检测6808例T1D病例，9954例对照和2206例T1D受影响的家庭中罕见的CNV缺失（rDELs）和重复（rDUPs）后代。初步分析检测到CNV关联。然而，这些被证明是假阳性发现，无法通过聚合酶链反应进行复制。我们开发了一系列质量控制（QC）测试，这些测试使用系统的敏感性和特异性测试进行了校准。该QC管线导致的CNV负担T1D风险的病例对照比值比（OR）趋于一致，表明rDELs或rDUP中没有全局频率差异。有证据表明，删除可能会影响少数病例的T1D风险，而对rDEL的富集会超过400 kb（OR = 1.57，P = 0.005）。在受影响的后代中也检测到18个从头开始的rDEL，而未受影响的兄弟姐妹则没有检测到（P = 0.03）。没有具体的CNV区域显示出与T1D相关的有力证据，尽管频率低于预期（最多小于0.1％），从而大大降低了统计功效，对此进行了详细检查。我们提出了一个R程序包plumbCNV，它为QC和稀有CNV的检测提供了一种自动化方法，可以促进大规模SNP阵列数据集的等效分析。

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