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Network regression analysis for binary and ordinal categorical phenotypes in transcriptome-wide association studies.

Liye Zhang1,2, Tao Ju1,2, Xiuyuan Jin1,2

  • 1Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China.

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This study introduces a new network regression method for transcriptome-wide association studies (TWAS) to analyze complex diseases involving multiple genes. The method effectively identifies disease-related gene networks and improves association detection for categorical phenotypes.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Transcriptome-wide association studies (TWAS) integrate genetic and gene expression data to uncover disease mechanisms.
  • Current TWAS methods often analyze genes individually, overlooking the complex network interactions crucial for multifactorial diseases.
  • Many biomedical phenotypes are categorical (binary or ordinal), posing challenges for existing TWAS approaches.

Purpose of the Study:

  • To develop a novel network regression method for TWAS that can analyze associations between biological networks and categorical phenotypes.
  • To enable the simultaneous identification of disease-associated network nodes and edges.
  • To improve upon existing TWAS methods by considering gene-gene interactions within a network context.

Main Methods:

  • Developed the Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study (POLONE-TWAS).
  • Employed a two-stage framework: 1) Dirichlet process regression for SNP effect estimation on genes, and 2) Pointwise Mutual Information for network relationship modeling.
  • Applied proportional odds logistic regression for association analysis of network nodes and edges with categorical phenotypes.

Main Results:

  • POLONE-TWAS demonstrated calibrated Type I error control in simulations across various between-node correlation patterns.
  • The method achieved higher statistical power compared to existing TWAS approaches.
  • Simulations confirmed the ability of POLONE-TWAS to simultaneously identify relevant network components.

Conclusions:

  • POLONE-TWAS offers a robust framework for network-based TWAS, particularly for categorical traits.
  • The method effectively detects associations within biological networks, advancing the understanding of complex disease genetics.
  • Application to UK Biobank data for bipolar disorder, depression, and blood pressure highlights its real-world utility.