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

Li, Xueling; Zhang, Shuanglin; Sha, Qiuying

doi:10.1002/gepi.22263

Cited by 7 publications

(8 citation statements)

References 61 publications

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“…The generalized linear regression to relate the genetic variant and phenotypes can be defined as

{g (E [y}_{il} | x_{i}]) = β_{0 l} + β_{1 l} x_{i} for i = 1, 2, … L .

where

g (\cdot)

is a monotonic link function. Two common link functions (X. Li et al, 2020), identity function and logit function, are under the linear regression model framework for continuous or quantitative traits and under the logistic regression model framework for binary or qualitative traits, respectively. We aim to test whether or not there are associations between the phenotypes and the genetic variant under this model, that means we test

H_{0} : β_{1 l} = 0 vs H_{1} : β_{1 l} \neq 0, for l = 1, 2, \dots, L .

…”

Section: Step 2: Association Tests For Each Merged Phenotype and A Ge...mentioning

confidence: 99%

Joint analysis of multiple phenotypes for extremely unbalanced case‐control association studies

Xie

Cao

Zhang

et al. 2023

Genetic Epidemiology

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In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a

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“…The generalized linear regression to relate the genetic variant and phenotypes can be defined as

{g (E [y}_{il} | x_{i}]) = β_{0 l} + β_{1 l} x_{i} for i = 1, 2, … L .

where

g (\cdot)

H_{0} : β_{1 l} = 0 vs H_{1} : β_{1 l} \neq 0, for l = 1, 2, \dots, L .

…”

Section: Step 2: Association Tests For Each Merged Phenotype and A Ge...mentioning

confidence: 99%

Joint analysis of multiple phenotypes for extremely unbalanced case‐control association studies

Xie

Cao

Zhang

et al. 2023

Genetic Epidemiology

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“…Therefore, the CLC method can be applied to PheWAS. However, due to the unknown number of clusters for a given data, the final test statistic of the CLC method is the minimum p-value among all p-values of the test statistics obtained from each possible number of clusters [ 25 ], and a simulation procedure is used to estimate the p-value of the final test statistic which would be time-consuming, especially in the PheWAS setting.…”

Section: Introductionmentioning

confidence: 99%

HCLC-FC: A novel statistical method for phenome-wide association studies

Liang

Cao²,

Sha³

et al. 2022

PLoS ONE

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The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from the UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC.

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“…Therefore, the CLC method can be applied to PheWAS. However, due to the unknown number of clusters for a given data, the final test statistic of the CLC method is the minimum p-value among all p-values of the test statistics obtained from each possible number of clusters [23] and a simulation procedure is used to estimate the p-value of the final test statistic which would be time-consuming especially in the PheWAS setting.…”

Section: Introductionmentioning

confidence: 99%

HCLC-FC: a novel statistical method for phenome-wide association studies

Liang

Cao

Sha

et al. 2022

Preprint

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View full text Add to dashboard Cite

The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC .

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

Cited by 7 publications

References 61 publications

Joint analysis of multiple phenotypes for extremely unbalanced case‐control association studies

Joint analysis of multiple phenotypes for extremely unbalanced case‐control association studies

HCLC-FC: A novel statistical method for phenome-wide association studies

HCLC-FC: a novel statistical method for phenome-wide association studies

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