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Joint analysis of multiple phenotypes using a clustering linear combination method based on hierarchical clustering.
Genetic Epidemiology ( IF 2.1 ) Pub Date : 2019-09-20 , DOI: 10.1002/gepi.22263
Xueling Li 1 , Shuanglin Zhang 1 , Qiuying Sha 1
Affiliation  

Emerging evidence suggests that a genetic variant can affect multiple phenotypes, especially in complex human diseases. Therefore, joint analysis of multiple phenotypes may offer new insights into disease etiology. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes, including the clustering linear combination (CLC) method. Due to the unknown number of clusters for a given data, a simulation procedure must be used to evaluate the p-value of the final test statistic of CLC. This makes the CLC method computationally demanding. In this paper, we use a stopping criterion to determine the number of clusters in the CLC method. We have named our method, hierarchical clustering CLC (HCLC). HCLC has an asymptotic distribution, which is very computationally efficient and makes it applicable for genome-wide association studies. Extensive simulations together with the COPDGene data analysis have been used to assess the type I error rates and power of our proposed method. Our simulation results demonstrate that the type I error rates of HCLC are effectively controlled in different realistic settings. HCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.

中文翻译:

使用基于层次聚类的聚类线性组合方法对多个表型进行联合分析。

新兴证据表明,遗传变异可以影响多种表型,尤其是在复杂的人类疾病中。因此,多种表型的联合分析可能为疾病病因学提供新的见解。最近,已经开发了许多统计方法用于多种表型的联合分析,包括聚类线性组合(CLC)方法。由于给定数据的簇数未知,因此必须使用模拟程序来评估CLC最终测试统计量的p值。这使得CLC方法在计算上要求很高。在本文中,我们使用停止准则来确定CLC方法中的簇数。我们将其命名为分层聚类CLC(HCLC)。HCLC具有渐近分布,它具有很高的计算效率,可用于全基因组关联研究。广泛的模拟与COPDGene数据分析一起已用于评估I型错误率和我们提出的方法的功效。我们的仿真结果表明,在不同的实际设置中,HCLC的I型错误率得到有效控制。HCLC的性能优于所有其他方法,或者具有与已被比较的最强大的方法非常接近的统计能力。
更新日期:2019-11-01
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