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

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Identifying hypothetical genetic influences on complex disease phenotypes.

Benjamin J Keller¹, Richard C McEachin

¹Eastern Michigan University, Computer Science Department, Ypsilanti, MI 48197, USA. bkeller@emich.edu

BMC Bioinformatics

|February 12, 2009

Summary

This study introduces a novel algorithm to uncover gene relationships in complex diseases by analyzing common keywords in gene descriptions. This method helps identify potential biological connections and their roles in disease phenotypes.

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Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Complex genetic diseases often involve interactions between multiple gene loci.
Understanding these interactions is crucial for elucidating disease mechanisms and phenotypes.
Identifying relationships among genes in associated regions can explain statistical interactions.

Purpose of the Study:

To develop a computational method for discovering gene relationships within disease-associated loci.
To explain the observed statistical interactions and the role of genes in complex disease phenotypes.

Main Methods:

A heuristic algorithm, Prioritizing Disease Genes by Analysis of Common Elements (PDG-ACE), was developed.
PDG-ACE mines biomedical keywords from gene descriptions to identify relationships.
Common and over-represented keywords between gene pairs suggest biological connections and disease roles.

Main Results:

The PDG-ACE algorithm successfully identifies previously established gene relationships.
The approach demonstrates robustness across different keyword vocabularies.
The method can generate preliminary hypotheses about gene functions in disease.

Conclusions:

The PDG-ACE algorithm provides a reliable method for generating hypotheses on gene relationships in complex diseases.
The approach is validated by its ability to find known relationships and its robustness.
Case studies, such as one for Type 2 Diabetes, demonstrate the practical application of PDG-ACE in identifying potential genetic hypotheses.