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CNVrd a Read-Depth Algorithm for Assigning Copy-Number at the FCGR Locus: Population-Specific Tagging of Copy Number Variation at FCGR3B

机译：CNVrd一种用于在FCGR场所分配拷贝数的深度读取算法：在FCGR3B上针对拷贝数变异的群体特定标记

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The extent of contribution from common gene copy number (CN) variants in human disease is currently unresolved. Part of the reason for this is the technical difficulty in directly measuring CN variation (CNV) using molecular methods, and the lack of single nucleotide polymorphisms (SNPs) that can tag complex CNV that has arisen multiple times on different SNP haplotypes. One CNV locus implicated in human disease is FCGR. Here we aimed to use next-generation sequencing (NGS) data from the 1000 Genomes Project to assign CN at FCGR3A and FCGR3B and to comprehensively assess the ability of SNPs to tag specific CN variants. A read-depth algorithm was developed (CNVrd) and validated on a subset of HapMap samples using CN assignments that had previously been determined using molecular and microarray methods. At 7 out of 9 other complex loci there was >90% concordance with microarray data. However, given that some prior knowledge of CN is required, the generalizability of CNVrd is limited and should be applied to other complex CNV loci with caution. Subsequently, CN was assigned et FCGR3B using CNVrd in a total of 952 samples from the 1000 Genomes Project, using three classes and SNPs that correlated with duplication were identified. The best tag SNP was observed in the Mexican-American sample set for duplication at FCGR3B. This SNP (rs117435514, r2 = 0.79) also tagged similar duplication in Chinese and Japanese (r2 = 0.35–0.60), but not in Caucasian or African. No tag SNP for duplication at FCGR3A or deletion at FCGR3B was identified in any population. We conclude that it is possible to tag CNV at the FCGR locus, but CN and SNPs have to be characterized and correlated on a population-specific basis.
机译：目前尚不清楚人类疾病中常见基因拷贝数（CN）变异的贡献程度。造成这种情况的部分原因是使用分子方法直接测量CN变异（CNV）的技术难度，并且缺乏可以标记复杂CNV的单核苷酸多态性（SNP），该CNV在不同的SNP单倍型上已经多次出现。涉及人类疾病的一种CNV基因座是FCGR。在这里，我们旨在使用来自1000个基因组计划的下一代测序（NGS）数据将CN分配给FCGR3A和FCGR3B，并全面评估SNP标记特定CN变体的能力。开发了一种读取深度算法（CNVrd），并使用以前使用分子和微阵列方法确定的CN分配对HapMap样本的子集进行了验证。在其他9个复杂基因座中，有7个与微阵列数据的一致性> 90％。但是，由于需要CN的一些先验知识，因此CNVrd的可推广性受到限制，应谨慎应用于其他复杂的CNV基因座。随后，在1000个基因组计划的952个样本中，使用CNVrd将CN分配给了FCGR3B，并使用了三类，并确定了与重复相关的SNP。在墨西哥裔美国人样品集中观察到最佳标签SNP，可在FCGR3B处重复。该SNP（rs117435514，r 2 = 0.79）在中文和日语中也标记了相似的重复（r 2 = 0.35-0.60），但在白种人或非洲人中没有。在任何人群中均未发现在FCGR3A处重复或在FCGR3B处缺失的标签SNP。我们得出结论，有可能在FCGR位点标记CNV，但必须在特定人群的基础上对CN和SNP进行表征和关联。

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