Epistasis Blog

From the Computational Genetics Laboratory at Dartmouth Medical School (www.epistasis.org)

Saturday, September 29, 2007

The Patient Rule-Induction Method

This is a very interesting paper by Dyson et al. in Genetic Epidemiology that looks at genetic epi data in a different way. It is worth checking out.

Dyson G, Frikke-Schmidt R, Nordestgaard BG, Tybjaerg-Hansen A, Sing CF. An application of the patient rule-induction method for evaluating the contribution of the Apolipoprotein E and Lipoprotein Lipase genes to predicting ischemic heart disease. Genet Epidemiol. 2007 Sep;31(6):515-27. [PubMed]

Different combinations of genetic and environmental risk factors are known to contribute to the complex etiology of ischemic heart disease (IHD) in different subsets of individuals. We employed the Patient Rule-Induction Method (PRIM) to select the combination of risk factors and risk factor values that identified each of 16 mutually exclusive partitions of individuals having significantly different levels of risk of IHD. PRIM balances two competing objectives: (1) finding partitions where the risk of IHD is high and (2) maximizing the number of IHD cases explained by the partitions. A sequential PRIM analysis was applied to data on the incidence of IHD collected over 8 years for a sample of 5,455 unrelated individuals from the Copenhagen City Heart Study (CCHS) to assess the added value of variation in two candidate susceptibility genes beyond the traditional, lipid and body mass index risk factors for IHD. An independent sample of 362 unrelated individuals also from the city of Copenhagen was used to test the model obtained for each of the hypothesized partitions.

Friday, September 28, 2007

DNA Repair Polymorphisms Modify Bladder Cancer Risk: A Multi-factor Analytic Strategy

A new paper in Human Heredity by Andrew et al. from here at Dartmouth reports gene-gene interactions in bladder cancer using MDR and a number of other methods including our entropy-based approaches. This paper is a nice example of how multiple methods can and should be used for detecting and characterizing epistasis in epidemiologic studies.

Andrew AS, Karagas MR, Nelson HH, Guarrera S, Polidoro S, Gamberini S, Sacerdote C, Moore JH, Kelsey KT, Demidenko E, Vineis P, Matullo G. DNA Repair Polymorphisms Modify Bladder Cancer Risk: A Multi-factor Analytic Strategy. Hum Hered. 2007 Sep 26;65(2):105-118 [Epub ahead of print] [PubMed]

Objectives: A number of common non-synonymous single nucleotide polymorphisms (SNPs) in DNA repair genes have been reported to modify bladder cancer risk. These include: APE1-Asn148Gln, XRCC1-Arg399Gln and XRCC1-Arg194Trp in the BER pathway, XPD-Gln751Lys in the NER pathway and XRCC3-Thr241Met in the DSB repair pathway. Methods: To examine the independent and interacting effects of these SNPs in a large study group, we analyzed these genotypes in 1,029 cases and 1,281 controls enrolled in two case-control studies of incident bladder cancer, one conducted in New Hampshire, USA and the other in Turin, Italy. Results: The odds ratio among current smokers with the variant XRCC3-241 (TT) genotype was 1.7 (95% CI 1.0-2.7) compared to wild-type. We evaluated gene-environment and gene-gene interactions using four analytic approaches: logistic regression, Multifactor Dimensionality Reduction (MDR), hierarchical interaction graphs, classification and regression trees (CART), and logic regression analyses. All five methods supported a gene-gene interaction between XRCC1-399/XRCC3-241 (p = 0.001) (adjusted OR for XRCC1-399 GG, XRCC3-241 TT vs. wild-type 2.0 (95% CI 1.4-3.0)). Three methods predicted an interaction between XRCC1-399/XPD-751 (p = 0.008) (adjusted OR for XRCC1-399 GA or AA, XRCC3-241 AA vs. wild-type 1.4 (95% CI 1.1-2.0)). Conclusions: These results support the hypothesis that common polymorphisms in DNA repair genes modify bladder cancer risk and highlight the value of using multiple complementary analytic approaches to identify multi-factor interactions.

Thursday, September 27, 2007

MDR Application List Updated

I have updated the list of published studies that have used MDR for the analysis of real genetic data. If you know of any that aren't on the list please let me know. The list can be found here.

Wednesday, September 26, 2007

MDR Benchmarks

I have taken a bit of a break from blogging this summer and promise to be more vigilant the rest of the year. Hopefully this will the first of many new posts over the coming weeks.

We just received some new desktop computers with the new Intel Quad Core Xeon chips and wanted to compare their performance with MDR to one of our old machines that is three years old now. The task was to run MDR 1.1 from the command line on the MDR sample data using the default settings. We expected the newer machines to run MDR faster than the older machines. It is important to remember that the MDR software automatically detects multiple processors and hyperthreading and will run in parallel to speed up the analysis. Here are the results for the two machines both running Linux.

1) Old machine (circa Sept. 2004)

Specs: 2 x Intel(R) Xeon(TM) CPU 3.00GHz, hyperthreaded
Speed: 9.326 seconds

2) New machine (circa Sept. 2007)

Specs: 4 x Intel(R) Xeon(R) CPU X5365 @ 3.00GHz
Speed: 5.764 seconds

Both machines had MDR running in parallel on four threads but the Quad Core machine was much faster even though the chip speed is the same. I highly recommend running MDR on a multi-processor machine.