User talk:The Biology Group

For Tuesday, November 3rd

Epistasis--what it means, what it doesn't mean...

Cordell Epistasis 2002

Questions to Consider thus Far

1. Two-loci modeling, three-loci, four-loci...How complex do we want to get? We need to keep in mind that all though the epistatic interactions we want to characterize are the antithesis of the one-gene, one-phenotype characterization of Mendelian inheritance, a model for a two-locus characterization for multigenic traits may be an oversimplification...but certainly a good place to start!

2. Do we want to also consider plieotrophy in assessing risk? That is to say, if we find a SNP with multiple disease associations, how do we determine which disease risk is greatest, in conjunction with other SNP information/risk we may have?

3. How do we model compensatory protein effects due to SNPs and their downstream effect on phenotype/risk assessment?

Disease Investigations for Method Validation

Type 2 Diabetes: A late-onset disease that may be of interest, as it is both polygenic and includes behavioral/environment risk. Janssens and van Duijn point out that rather than being predictive, genes contributing to heart disease and diabetes can lead to behavioral changes which try to lower risk of developing the disease 1.

Prior to this, Weedon et al. showed that having multiple allele copies increases risk in accordance with a multiplicative model 2 (this type of statistical information can be used in affirming the effectiveness of our modeling). However, other studies such as here [1] and here [2] found that lifestyle/phenotypic factors and family history were more predictive that genetics in whether someone would actually develop diabetes.

As a side-note, I was slightly amused that a google scholar search for "highly predictive polygenic disease" turns up zero hits. Hopefully this will change in the years to come...

Type I Diabetes: From the interacting chromosomal regions explored by Bergholdt et. al, the WDR1, LMO7, HNRPLL and RPS15A genes are potential T1D candidate genes. These genes are involved in transcriptional regulation, DNA binding, RNA binding, ion channel activity, ATP synthesis, actin binding and natural killer cell mediated cytotoxicity and cell proliferation. Other networks with TNFA (a gene proposed to be essential to the onset of T1D because of its locus near HLA) include genes involved in signal transduction, regulation of transcription, protein biosynthesis and folding, histone activity, ubiquitin-protein ligase activity, as well as response to oxidative stress, also of potential relevance in T1D pathogenesis.

In their analysis, Bergholdt et. al obtained protein interaction data from the databases BIND, MINT, IntAct, KEGG annotated protein-protein interactions (PPrel), KEGG Enzymes involved in neighboring steps (ECrel) and Reactome proteins involved in the same complex, indirect complex or same or indirect reaction. These databases could be useful to us in potentially determining SNP effects on protein-protein interactions, and we may be able to incorporate their method of visualization in our own program.

Here's their paper: Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

Protein network interactions described by Bergholdt et. al in Type I Diabetes

Significant functional modules described by Bergholdt et. al in Type I Diabetes

Other Projects for the Trait-O-Matic Add-on

Looking into Pharmacogenetics

• I found this cool article Pharmacogenomics: Tanslating Functional Genomics into Rational Theraputics| that would really help out with the pharmacogenetics project :D

• If we look at table 1, we find a list of polymorphisms of genes important to drug metabolism, and how they would effect different phenotypes. We could start immediately searching for these polymorphisms in the genomes entered as input and scan for these specific mutations, thus being able to readily point spew out a phenotype.

• Perhaps in order to make our searching method more efficient, we could first look for genes involved in the most number of pathways such as CYP3A4, and look for mutations in those, and then work our way from most common to least common. It is nice that in this picture we can start looking at genes in terms of frequency.

• Another interesting find in this article was that pharmacogenetic polymorphisms differ in frequency among ethnic and racial groups. So now we would know to include these as a primary criteria when we choose to look at external factors.