Extracting disease risk profiles from expression data for linkage analysis: application to prostate cancer

Area of Science:

• Genetics
• Bioinformatics
• Genomic Medicine

Background:

• Understanding genetic factors in complex diseases remains a challenge.
• Gene expression data offers potential for novel phenotype definition in genetic studies.

Purpose of the Study:

• To explore the utility of gene expression data for defining phenotypes in complex disease linkage analysis.
• To develop and test a composite 'risk profile' for mapping disease-associated loci using prostate cancer data.

Main Methods:

• Utilized microarray gene expression data from 26 genes associated with prostate cancer.
• Defined three risk phenotypes (high, neutral, low) based on expression levels.
• Performed linkage analyses using MCLINK and MERLIN for identified phenotypes.

Main Results:

• Suggestive genome-wide linkage evidence was observed on chromosomes 6 and 4.
• Linkage signals were not strongly influenced by the location of the initially selected genes.
• MCLINK and MERLIN analyses showed close agreement.

Conclusions:

• Composite gene expression profiles show potential for identifying additional loci in complex disease processes.
• Expression data can augment current knowledge of gene relationships in complex diseases.
• Caution is advised when extrapolating findings due to cell-type specific expression data limitations.