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Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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Related Experiment Video

Updated: May 24, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Pathway analysis for family data using nested random-effects models.

Jeanine J Houwing-Duistermaat1, Hae-Won Uh, Roula Tsonaka

  • 1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. j.j.houwing@lumc.nl.

BMC Proceedings
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

This study evaluated a new method for testing genetic pathway effects in disease. The approach showed poor performance in unrelated individuals but demonstrated strong potential in family data analysis, particularly for quantitative traits.

Related Experiment Videos

Last Updated: May 24, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Pathway analysis is crucial for understanding complex diseases.
  • Existing methods may not fully account for genetic correlations.
  • A novel two-step approach was developed to address these limitations.

Purpose of the Study:

  • To adapt and apply a novel two-step pathway analysis method to family data.
  • To evaluate the method's performance using sequence data from Genetic Analysis Workshop 17.
  • To assess the method's power for binary and quantitative disease outcomes.

Main Methods:

  • Utilized a two-step approach incorporating random effects to model genetic correlations.
  • Included gene-gene and gene-environment interactions.
  • Applied the method to unrelated and family-based datasets for performance evaluation.

Main Results:

  • The novel test showed low power (6% binary, 18% quantitative) for unrelated subjects.
  • For family data, the method achieved higher power (39% binary, 89% quantitative for trait Q1).
  • The approach demonstrated particular effectiveness for quantitative traits in family studies.

Conclusions:

  • The two-step pathway analysis method shows promise for family-based genetic studies.
  • Its performance is dependent on the study design, excelling in family data.
  • Further application in family data analysis for complex diseases is warranted.