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Related Experiment Video

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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

Xueling Li¹, Shuanglin Zhang¹, Qiuying Sha¹

¹Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan.

Genetic Epidemiology

|September 22, 2019

Summary

This study introduces Hierarchical Clustering CLC (HCLC), a computationally efficient method for analyzing multiple phenotypes in complex diseases. HCLC improves upon existing methods by controlling type I error rates and offering high statistical power in genetic association studies.

Keywords:

association studies clustering linear combination hierarchical clustering joint analysis multiple phenotypes

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Area of Science:

Genetics
Statistical Genetics
Computational Biology

Background:

Genetic variants can influence multiple phenotypes, particularly in complex human diseases.
Joint analysis of multiple phenotypes offers novel insights into disease etiology.
Existing methods like Clustering Linear Combination (CLC) are computationally intensive due to the need for simulations to determine the number of clusters.

Purpose of the Study:

To develop a computationally efficient method for joint analysis of multiple phenotypes.
To address the limitations of existing methods, specifically the computational demand of the CLC method.
To introduce a novel approach for determining the number of clusters in CLC, enhancing its applicability in genome-wide association studies.

Main Methods:

Introduced a stopping criterion to determine the number of clusters within the CLC framework.
Developed the Hierarchical Clustering CLC (HCLC) method, incorporating an asymptotic distribution for computational efficiency.
Validated HCLC through extensive simulations and analysis of the COPDGene dataset.

Main Results:

HCLC effectively controls type I error rates across various realistic simulation settings.
The proposed HCLC method demonstrates superior or comparable statistical power to existing methods.
HCLC proved computationally efficient, making it suitable for large-scale genome-wide association studies.

Conclusions:

HCLC provides a computationally efficient and statistically powerful approach for joint analysis of multiple phenotypes.
The method successfully addresses the computational limitations of previous CLC approaches.
HCLC is a promising tool for genetic association studies, offering robust control of error rates and high power.