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Pleiotropic genetic association analysis with multiple phenotypes using multivariate response best-subset selection.

Hongping Guo1, Tong Li2, Zixuan Wang3

  • 1School of Mathematics and Statistics, Hubei Normal University, Huangshi, 435002, People's Republic of China. guohongping@hbnu.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a new method for genetic pleiotropy analysis, improving computational efficiency and statistical power. The multivariate response best-subset selection (MRBSS) model enhances understanding of shared genetic mechanisms in complex traits.

Keywords:
0-1 integer optimizationAssociation analysisBest-subsetMultiple phenotypesPleiotropyResponse variable selection

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genetic pleiotropy, where a single gene influences multiple traits, is common and crucial for understanding complex diseases.
  • Identifying pleiotropic genes aids in deciphering shared genetic underpinnings of various phenotypes.

Purpose of the Study:

  • To propose a novel multivariate response best-subset selection (MRBSS) model for pleiotropic association analysis.
  • To develop an efficient computational method for high-dimensional genetic data.

Main Methods:

  • The MRBSS model treats high-dimensional genotype data as responses and multiple phenotypes as predictors.
  • The selection procedure is reformulated as a 0-1 integer optimization problem.
  • Model parameters are estimated using curve search and a modified Bayesian information criterion.

Main Results:

  • The MRBSS method significantly reduces computational time compared to traditional approaches.
  • It demonstrates higher statistical power across various scenarios.
  • The method effectively controls the type I error rate.

Conclusions:

  • The MRBSS model offers an effective and efficient approach for genetic pleiotropy analysis.
  • Its utility is validated through simulations and applications in maize and pig trait datasets.
  • This method advances the study of complex genetic architectures.