AN OVERVIEW OF MULTIVARIATE ASSOCIATION MEASURES WITH AN APPLICATION IN MULTI-TRAIT ANALYSIS FOR GENOME-WIDE ASSOCIATION STUDIES
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Date
2023-05
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The Ohio State University
Abstract
Correlation is used to discover relationship between two quantitative variables and has been widely applied not only in statistics but also in almost all scientific fields to understand associations. Whereas most existing measures can only detect pairwise associations between two dimensions, the developments of information technology and big data analytics increase the importance of detecting associations in multi-dimensional spaces. For instance, genome-wide association studies (GWAS) involve scanning markers across the genomes of many people to find genetic variations associated with a particular trait of interest. In the study of complex traits, several correlated phenotypes may be measured to study a trait. For example, a person's cognitive ability is usually measured by tests in domains including memory, intelligence, language, executive function, and visual spatial function. Although, it is common to perform GWAS for each individual trait separately, multi-trait GWAS that combine association evidence across traits have some advantages over the standard one-trait analysis. First, joint analysis of traits tends to be statistically more powerful than individual trait analysis, especially when many of the traits are correlated and each trait has only modest association with genotypes. Second, joint analysis may shed light onto shared genetic mechanisms and pleiotropic relationships (occurring when a genetic locus truly affects two or more seemingly unrelated traits), thus improves our biological understanding.
The purpose of this study is to give a comprehensive overview of the multivariate (linear and non-linear) association methods, and to utilize them as a GWAS tool to investigate associations among multiple traits and genomic variants. Real and simulated data sets are used to compare performances of several multivariate measures in detecting associated variants with multiple traits.
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Keywords
Distance Covariance, GWAS, Multivariate Association