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Related Experiment Video

Updated: May 13, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

A novel method for analyzing genetic association with longitudinal phenotypes.

Douglas Londono¹, Kuo-mei Chen, Anthony Musolf

¹Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Road, Piscataway, NJ 08854, USA.

Statistical Applications in Genetics and Molecular Biology

|March 19, 2013

Summary

This study introduces a new method for gene mapping to predict disease progression using genetic data. The approach accurately identifies genes linked to longitudinal health patterns, aiding personalized medicine.

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Last Updated: May 13, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genetics and Genomics
Biostatistics
Computational Biology

Background:

Understanding genetic influences on longitudinal health patterns is crucial for predicting disease progression.
Existing methods may lack the power or accuracy to effectively map genes associated with dynamic disease trajectories.
Genome-wide association studies (GWAS) are powerful tools, but applying them to longitudinal data requires specialized approaches.

Purpose of the Study:

To develop and validate a systematic procedure for testing the association between single nucleotide polymorphism (SNP) genotypes and longitudinal phenotypes.
To evaluate the false positive rates and statistical power of the proposed method for gene localization in disease progression.
To establish a method for disease progression gene mapping with potential clinical significance in predicting patient outcomes.

Main Methods:

Utilized genome-wide SNP data from the Framingham Heart Study.
Estimated three trajectory curves from longitudinal data from two independent real-world studies.
Generated simulated longitudinal data based on null and alternative hypotheses, assigning individuals to trajectory groups.
Estimated individual Bayesian posterior probabilities (BPPs) for trajectory group membership.
Tested BPPs as quantitative traits for genome-wide association using the Wald test.

Main Results:

The developed method maintained expected false positive rates across all simulation models.
The method demonstrated high empirical statistical power in most simulation scenarios.
The approach successfully localized genes associated with longitudinal disease progression patterns.

Conclusions:

The proposed systematic procedure provides a robust method for disease progression gene mapping.
This approach is statistically sound, maintaining accurate false positive rates and achieving significant power.
The method holds potential clinical significance, enabling prediction of disease progression based on genotype for tailored treatments.