A multivariate regression approach to association analysis of a quantitative trait network.
A multivariate regression approach to association analysis of a quantitative trait network.
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DOI：
10.1093/bioinformatics/btp218
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发表时间：
2009-06-15
期刊：
影响因子：
--
通讯作者：
Xing EP
中科院分区：
文献类型：
--
作者：
Kim S;Sohn KA;Xing EP
动机：许多复杂的疾病综合征，如哮喘，包括大量的高度相关的，而不是独立的，临床表型，提出了一个新的技术挑战，在确定同时与相关性状相关的遗传变异。虽然因果遗传变异可能会影响一组高度相关的性状联合，以前的关联分析认为每个表型单独，或从一组单表型分析的组合结果。结果：我们提出了一个新的统计框架，称为图形引导融合套索，以原则性的方式解决这个问题。我们的方法将数量性状之间的依赖结构明确表示为网络，并利用该性状网络在基因型和性状的多元回归模型中编码结构化正则化，从而可以以高灵敏度和特异性检测共同影响高度相关性状亚组的遗传标记。虽然大多数传统方法独立地检查每个表型，但我们的方法在单个统计方法中共同分析所有性状，以发现共同干扰相关triats子集而不是单个性状的遗传标记。使用基于HapMap联盟数据和哮喘数据集的模拟数据集，我们将我们的方法与单标记分析和其他不使用任何结构信息的稀疏回归方法的性能进行了比较。我们的研究结果表明，有一个显着的优势，在检测真正的因果单核苷酸多态性时，我们将相关模式的性状，使用我们提出的方法。可用性：GFlasso软件可在http://www.sailing.cs.cmu.edu/gflasso.html获得联系人：sssykim@cs.cmu.edu; ksohn@cs.cmu.edu;
Motivation: Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. Although a causal genetic variation may influence a group of highly correlated traits jointly, most of the previous association analyses considered each phenotype separately, or combined results from a set of single-phenotype analyses. Results: We propose a new statistical framework called graph-guided fused lasso to address this issue in a principled way. Our approach represents the dependency structure among the quantitative traits explicitly as a network, and leverages this trait network to encode structured regularizations in a multivariate regression model over the genotypes and traits, so that the genetic markers that jointly influence subgroups of highly correlated traits can be detected with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently, our approach analyzes all of the traits jointly in a single statistical method to discover the genetic markers that perturb a subset of correlated triats jointly rather than a single trait. Using simulated datasets based on the HapMap consortium data and an asthma dataset, we compare the performance of our method with the single-marker analysis, and other sparse regression methods that do not use any structural information in the traits. Our results show that there is a significant advantage in detecting the true causal single nucleotide polymorphisms when we incorporate the correlation pattern in traits using our proposed methods. Availability: Software for GFlasso is available at http://www.sailing.cs.cmu.edu/gflasso.html Contact: sssykim@cs.cmu.edu; ksohn@cs.cmu.edu;
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发表时间：
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影响因子：
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