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Related Concept Videos

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Updated: May 8, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Statistical properties on semiparametric regression for evaluating pathway effects.

Inyoung Kim1, Herbert Pang, Hongyu Zhao

  • 1Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA.

Journal of Statistical Planning and Inference
|September 10, 2013
PubMed
Summary
This summary is machine-generated.

This study analyzes gene pathways in statistical regression for continuous outcomes. A new method offers improved parameter estimation and smaller mean squared error compared to existing approaches, validated by simulation and real-world diabetes data.

Keywords:
Gaussian random processKernel machineMixed modelPathway analysisProfile likelihoodRestricted maximum likelihood

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Last Updated: May 8, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Current statistical methods for microarray data analysis often examine genes individually, potentially missing subtle, coordinated changes within biological pathways.
  • Analyzing gene sets (pathways) derived from prior biological knowledge offers a more comprehensive approach to understanding complex biological functions and clinical outcomes.
  • Limited research exists on regression models that simultaneously assess clinical covariates and pathway gene expression for continuous clinical outcomes.

Purpose of the Study:

  • To investigate the asymptotic properties of parameter estimates in semiparametric regression for pathway analysis.
  • To compare a novel restricted maximum likelihood (REML) approach using profile likelihood with Liu et al.'s existing REML method.
  • To develop and evaluate a profile restricted likelihood ratio test for pathway-related non-standard testing problems.

Main Methods:

  • Semiparametric regression framework incorporating gene pathways.
  • Comparison of two REML (Restricted Maximum Likelihood) estimation methods: Liu et al.'s REML and a profile likelihood-based REML.
  • Theoretical analysis of asymptotic properties (consistency, convergence rate, distribution) of parameter estimators.
  • Simulation studies to validate theoretical findings and compare estimator performance.
  • Application to a type II diabetes dataset.

Main Results:

  • Both Liu et al.'s REML and the proposed profile likelihood REML yield consistent estimators with a [Formula: see text] convergence rate.
  • Estimators from both methods exhibit either asymptotic normality or a mixture of normal distributions.
  • The proposed profile likelihood REML provides estimators with a theoretically smaller mean squared error than Liu et al.'s REML.
  • Simulation results support the theoretical superiority of the profile likelihood REML.
  • A profile restricted likelihood ratio test was successfully developed and applied.

Conclusions:

  • The study establishes the theoretical asymptotic properties for semiparametric pathway analysis.
  • The proposed profile likelihood REML method offers improved statistical efficiency (smaller MSE) over existing REML approaches for pathway analysis.
  • The developed methods are applicable to real-world biological data, as demonstrated by the type II diabetes case study.