Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Saturday, December 22, 2007

Epistasis, Genetic Heterogeneity and Alzheimer Disease

Our paper on detecting epistasis in the presence of genetic heterogeneity is about to appear in Genetic Epidemiology. This paper is a nice example of how cluster analysis can be used to sort subjects into more genetically homogeneous groups prior to assicuation analysis. Dr. Tricia Thornton-Wells is the lead author and completed this work as part of her dissertation work with Dr. Jonathan Haines at Vanderbilt. She is now a postdoc at Vanderbilt.

Thornton-Wells TA, Moore JH, Martin ER, Pericak-Vance MA, Haines JL. Confronting complexity in late-onset Alzheimer disease: application of two-stage analysis approach addressing heterogeneity and epistasis. Genet Epidemiol. 2007 Dec 12; [Epub ahead of print] [PubMed]

Common diseases with a genetic basis are likely to have a very complex etiology, in which the mapping between genotype and phenotype is far from straightforward. A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype has been proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis and gene-gene interactions in the second stage. We applied this two-stage analysis strategy to late-onset Alzheimer disease (LOAD) data, which included functional and positional candidate genes and markers in a region of interest on chromosome 10. Bayesian classification found statistically significant clusterings for independent family-based and case-control datasets, which used the same five markers in leucine-rich repeat transmembrane neuronal 3 (LRRTM3) as the most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene-gene interactions, markers in three genes-urokinase-type plasminogen activator (PLAU), angiotensin 1 converting enzyme (ACE) and cell division cycle 2 (CDC2)-were found to be associated with LOAD in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes.


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