Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer.
Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer.
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DOI：
10.1038/ejhg.2013.69
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发表时间：
2014-01
期刊：
影响因子：
--
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虽然单位点方法已被广泛应用于识别疾病相关的单核苷酸多态性（SNP），复杂的疾病被认为是多个基因座之间的相互作用的产物。这导致了最近发展的统计方法，用于检测两个基因座之间的统计相互作用。典型相关分析（CCA）以前被提出来检测基因-基因的共关联。然而，这种方法仅限于检测线性关系，并且只能在观察的数量超过基因中SNP的数量时应用。这种限制对于下一代测序尤其重要，因为下一代测序可以在有限数量的受试者中产生大量新的变异。为了克服这些局限性，我们提出了一种方法来检测基因-基因相互作用的基础上的核化版本的CCA（KCCA）。我们的模拟研究表明，KCCA控制I型错误，并且在边际效应可以忽略的疾病模型下比领先的基于基因的方法更强大。为了证明我们的方法的实用性，我们还应用KCCA评估了3869例病例和3276例对照中NF-κB通路中200个基因之间的相互作用与卵巢癌风险的关系。我们确定了13个与卵巢癌风险相关的重要基因对（局部错误发现率<0.05）。最后，我们讨论了KCCA在基因-基因互作分析中的优势及其在遗传关联研究中的应用前景。
Although single-locus approaches have been widely applied to identify disease-associated single-nucleotide polymorphisms (SNPs), complex diseases are thought to be the product of multiple interactions between loci. This has led to the recent development of statistical methods for detecting statistical interactions between two loci. Canonical correlation analysis (CCA) has previously been proposed to detect gene–gene coassociation. However, this approach is limited to detecting linear relations and can only be applied when the number of observations exceeds the number of SNPs in a gene. This limitation is particularly important for next-generation sequencing, which could yield a large number of novel variants on a limited number of subjects. To overcome these limitations, we propose an approach to detect gene–gene interactions on the basis of a kernelized version of CCA (KCCA). Our simulation studies showed that KCCA controls the Type-I error, and is more powerful than leading gene-based approaches under a disease model with negligible marginal effects. To demonstrate the utility of our approach, we also applied KCCA to assess interactions between 200 genes in the NF-κB pathway in relation to ovarian cancer risk in 3869 cases and 3276 controls. We identified 13 significant gene pairs relevant to ovarian cancer risk (local false discovery rate <0.05). Finally, we discuss the advantages of KCCA in gene–gene interaction analysis and its future role in genetic association studies.
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DOI：
10.1158/1055-9965.epi-10-1224
发表时间：
2011-06
期刊：
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
影响因子：
--
作者：
Permuth-Wey J;Chen YA;Tsai YY;Chen Z;Qu X;Lancaster JM;Stockwell H;Dagne G;Iversen E;Risch H;Barnholtz-Sloan J;Cunningham JM;Vierkant RA;Fridley BL;Sutphen R;McLaughlin J;Narod SA;Goode EL;Schildkraut JM;Fenstermacher D;Phelan CM;Sellers TA
通讯作者：
Sellers TA
影响因子：
1.8
作者：
Moore, JH
通讯作者：
Moore, JH
影响因子：
11.2
作者：
Engels, Eric A.;Wu, Xifeng;Spitz, Margaret R.
通讯作者：
Spitz, Margaret R.
影响因子：
2.1
作者：
Li, Yun;Willer, Cristen J.;Ding, Jun;Scheet, Paul;Abecasis, Goncalo R.
通讯作者：
Abecasis, Goncalo R.
影响因子：
4.5
作者：
DIACONIS, P;FREEDMAN, D
通讯作者：
FREEDMAN, D