Genetic association studies of copy-number variation: should assignment of copy number states precede testing?
Genetic association studies of copy-number variation: should assignment of copy number states precede testing?
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DOI：
10.1371/journal.pone.0034262
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Fridley BL
中科院分区：
文献类型：
--
作者：
Breheny P;Chalise P;Batzler A;Wang L;Fridley BL
最近，基因组中的结构变异与许多复杂疾病有关。使用全基因组单核苷酸多态性（SNP）阵列，研究人员不仅能够研究SNP变异的影响，而且还能够研究拷贝数变异（CNVs）对表型的影响。最常见的分析方法涉及在个体基因组水平上估计每个位置存在的潜在拷贝数。一旦完成，进行测试以确定拷贝数状态和表型之间的关联。另一种方法是首先在单个标志物水平上进行表型和SNP阵列原始强度之间的关联测试，然后聚集相邻测试结果以鉴定与表型相关的CNV。在这里，我们探讨了这两种方法的优点和缺点，使用模拟和真实的数据从化疗药物吉西他滨的药物基因组学研究。我们的研究结果表明，合并标记水平的测试是能够提供一个显着增加的权力（倍）CNV水平的测试，特别是对小CNVs。然而，CNV水平的测试是上级的CNV是大的和罕见的，了解这些权衡是一个重要的考虑因素，在进行关联研究的结构变异。
Recently, structural variation in the genome has been implicated in many complex diseases. Using genomewide single nucleotide polymorphism (SNP) arrays, researchers are able to investigate the impact not only of SNP variation, but also of copy-number variants (CNVs) on the phenotype. The most common analytic approach involves estimating, at the level of the individual genome, the underlying number of copies present at each location. Once this is completed, tests are performed to determine the association between copy number state and phenotype. An alternative approach is to carry out association testing first, between phenotype and raw intensities from the SNP array at the level of the individual marker, and then aggregate neighboring test results to identify CNVs associated with the phenotype. Here, we explore the strengths and weaknesses of these two approaches using both simulations and real data from a pharmacogenomic study of the chemotherapeutic agent gemcitabine. Our results indicate that pooled marker-level testing is capable of offering a dramatic increase in power (-fold) over CNV-level testing, particularly for small CNVs. However, CNV-level testing is superior when CNVs are large and rare; understanding these tradeoffs is an important consideration in conducting association studies of structural variation.
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影响因子：
9.8
作者：
Fellermann, Klaus;Stange, Daniel E.;Stange, Eduard F.
通讯作者：
Stange, Eduard F.
影响因子：
30.8
作者：
Iafrate, AJ;Feuk, L;Lee, C
通讯作者：
Lee, C
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
14.9
作者：
Diskin SJ;Li M;Hou C;Yang S;Glessner J;Hakonarson H;Bucan M;Maris JM;Wang K
通讯作者：
Wang K
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y