# Multiple phenotype association tests based on sliced inverse regression

## TL;DR

This paper introduces a new statistical method for analyzing multiple traits and genetic variants, which is more efficient and accurate than existing approaches.

## Contribution

A novel sliced inverse regression-based association test is proposed for joint analysis of multiple phenotypes and genetic variants.

## Key findings

- The proposed SIR-based method outperforms existing methods in both low- and high-dimensional settings.
- The method achieves higher efficiency and maintains correct type I error rates in genetic association studies.

## Abstract

Joint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in dimensionality has become a very challenging problem in the widespread use of joint analysis. To handle the excessiveness of variables, we consider the sliced inverse regression (SIR) method. Specifically, we propose a novel SIR-based association test that is robust and powerful in testing the association between multiple predictors and multiple outcomes.

We conduct simulation studies in both low- and high-dimensional settings with various numbers of Single-Nucleotide Polymorphisms and consider the correlation structure of traits. Simulation results show that the proposed method outperforms the existing methods. We also successfully apply our method to the genetic association study of ADNI dataset. Both the simulation studies and real data analysis show that the SIR-based association test is valid and achieves a higher efficiency compared with its competitors.

Several scenarios with low- and high-dimensional responses and genotypes are considered in this paper. Our SIR-based method controls the estimated type I error at the pre-specified level \documentclass[12pt]{minimal}
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