A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

Sha, Qiuying; Wang, Zhenchuan; Zhang, Xiao; Zhang, Shuanglin

doi:10.1093/bioinformatics/bty810

Cited by 19 publications

(84 citation statements)

References 58 publications

(40 reference statements)

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“…One of the clusters contains phenotypes FEV1, Emph, and GasTrap, while the other four clusters each contain only a single phenotype. All of these 14 SNPs were previously reported to be associated with COPD (Brehm et al, 2011;Cho et al, 2010;Cho et al, 2014;Cui, Ge, & Ma, 2014;Du, Xue, & Xiao, 2016;Hancock et al, 2010;Li et al, 2011;Liang, et al, 2018;Lutz et al, 2015;Pillai et al, 2009;Sha et al, 2018;Wilk et al, 2009;Wilk et al, 2012;Young et al, 2010;Zhang, Summah, Zhu, & Qu, 2011;Zhu et al, 2014). Table 2 summarizes the 14 significant SNPs that have been identified by at least one of the six methods.…”

Section: Real Data Analysis Resultsmentioning

confidence: 99%

“…if the k th phenotype belongs to the l th cluster or otherwise b = 0 kl , and Σ is the variance-covariance matrix of T. Sha et al (2018) showed that Σ can be estimated by the sample correlation matrix of the K phenotypes. Under the null hypothesis that none of phenotypes are associated with the genetic variant, T CLC L follows a χ 2 distribution with degrees of freedom equal to L. Since for a given data set, the number of clusters of the phenotypes is unknown, in the last step of the CLC method, we use…”

Section: Methodsmentioning

confidence: 99%

“…The clustering linear combination (CLC) method is a recently developed approach, which combines individual test statistics for joint analysis of multiple phenotypes in association analyses (Sha, Wang, Zhang, & Zhang, 2018). The CLC method works particularly well with phenotypes that have natural groupings.…”

Section: Introductionmentioning

confidence: 99%

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Joint analysis of multiple phenotypes using a clustering linear combination method based on hierarchical clustering

Zhang

Sha

2019

Genetic Epidemiology

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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.

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Section: Real Data Analysis Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Joint analysis of multiple phenotypes using a clustering linear combination method based on hierarchical clustering

Zhang

Sha

2019

Genetic Epidemiology

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“…Besides detecting colocalization of possibly causal SNPs for a GWAS trait and a molecular trait like gene expression, it is also of interest for colocalization analysis of multiple complex traits or diseases, which for example may be helpful for understanding the shared biological pathways for multiple diseases and thus for drug repurposing and new therapeutic development. Studies have found that some diseases seem to have commonly associated genetic variants [14,15], which by themselves cannot determine colocalization either; when a variant is associated with multiple traits, it could be due to distinct causal SNPs that are in linkage disequilibrium (LD) [16], which however can be distinguished through joint/conditional modeling of multiple SNPs as in fine mapping [17,18,19]. This critical difference between marginal and conditional associations also highlights the difference between the existing pleiotropy testing [20,21] and the proposed colocalization testing on multiple traits.…”

Section: Introductionmentioning

confidence: 99%

A powerful and versatile colocalization test

Deng

Pan

2020

PLoS Comput Biol

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Transcriptome-wide association studies (TWAS and PrediXcan) have been increasingly applied to detect associations between genetically predicted gene expressions and GWAS traits, which may suggest, however do not completely determine, causal genes for GWAS traits, due to the likely violation of their imposed strong assumptions for causal inference. Testing colocalization moves it closer to establishing causal relationships: if a GWAS trait and a gene's expression share the same associated SNP, it may suggest a regulatory (and thus putative causal) role of the SNP mediated through the gene on the GWAS trait. Accordingly, it is of interest to develop and apply various colocalization testing approaches. The existing approaches may each have some severe limitations. For instance, some methods test the null hypothesis that there is colocalization, which is not ideal because often the null hypothesis cannot be rejected simply due to limited statistical power (with too small sample sizes). Some other methods arbitrarily restrict the maximum number of causal SNPs in a locus, which may lead to loss of power in the presence of widespread allelic heterogeneity. Importantly, most methods cannot be applied to either GWAS/eQTL summary statistics or cases with more than two possibly correlated traits. Here we present a simple and general approach based on conditional analysis of a locus on multiple traits, overcoming the above and other shortcomings of the existing methods. We demonstrate that, compared with other methods, our new method can be applied to a wider range of scenarios and often perform better. We showcase its applications to both simulated and real data, including a large-scale Alzheimer's disease GWAS summary dataset and a gene expression dataset, and a largescale blood lipid GWAS summary association dataset. An R package "jointsum" implementing the proposed method is publicly available at github.

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“…Existing methods for joint analysis on genetic data are mostly built on summary statistics (e.g., McGeachie et al, 2014;Giambartolomei et al, 2014;Kang et al, 2014;Zhu et al, 2015;Bulik-Sullivan et al, 2015;Nieuwboer et al, 2016;Hu et al, 2017;Wen et al, 2017;Liu et al, 2017;Sha et al, 2018;Guo and Wu, 2018) More recently, Turley et al (2018) introduced multi-trait analysis of GWAS (MTAG) that can perform joint analysis using the summary statistics calculated from cohorts with overlapping samples. Zeng et al (2018) proposed a regularized Gaussian mixture model called iMAP to infer the association between SNPs to correlated phenotypes.…”

Section: Introductionmentioning

confidence: 99%

Coupled Mixed Model for Joint Genetic Analysis of Complex Disorders with Two Independently Collected Data Sets

Wang

Pei

Vanyukov

et al. 2018

Preprint

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In the last decade, Genome-wide Association studies (GWASs) have contributed to decoding the human genome by uncovering many genetic variations associated with various diseases. Many followup investigations involve joint analysis of multiple independently generated GWAS data sets. While most of the computational approaches developed for joint analysis are based on summary statistics, the joint analysis based on individual-level data with consideration of confounding factors remains to be a challenge. In this study, we propose a method, called Coupled Mixed Model (CMM), that enables a joint GWAS analysis on two independently collected sets of GWAS data with different phenotypes. The CMM method does not require the data sets to have the same phenotypes as it aims to infer the unknown phenotypes using a set of multivariate sparse mixed models. Moreover, CMM addresses the confounding variables due to population stratication, family structures, and cryptic relatedness, as well as those arising during data collection such as batch effects that frequently appear in joint genetic studies. We evaluate the performance of CMM using simulation experiments. In real data analysis, we illustrate the utility of CMM by an application to evaluating common genetic associations for Alzheimers disease and substance use disorder using datasets independently collected for the two complex human disorders. Comparison of the results with those from previous experiments and analyses supports the utility of our method and provides new insights into the diseases. The software is available at https://github.com/HaohanWang/CMM

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A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 19 publications

References 58 publications

Joint analysis of multiple phenotypes using a clustering linear combination method based on hierarchical clustering

Joint analysis of multiple phenotypes using a clustering linear combination method based on hierarchical clustering

A powerful and versatile colocalization test

Coupled Mixed Model for Joint Genetic Analysis of Complex Disorders with Two Independently Collected Data Sets

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