Epistasis Blog

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

Thursday, February 28, 2008

MDR Used for Teaching?

Are you using our MDR software for teaching a course or workshop? Email me if you are. I would love to know if the open-source MDR software package is being used for teaching purposes.

Tuesday, February 19, 2008

MDR is #40 out of 1278

Our open-source MDR software package is ranked #40 out 1278 Bioinformatics software packages on Sourceforge.net. This ranking is determined by the total number of downloads. MDR has been downloaded 13,209 times since Feb. of 2005 when we released the first version. We will have a new version with interaction graphs available soon!

Wednesday, February 06, 2008

Hidden Order by John Holland

Our research starts with the idea that human health is a complex adaptive system. Chapter 1 of the book Hidden Order by John Holland has what I think is the best description of what a complex system is. I buy a copy of this book for all my students and have them read it. If you have a description or a definition that you like pelase let me know.

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Monday, February 04, 2008

A Filtering Approach to MDR Analysis

We have previously developed and evaluated a Tuned ReliefF (TuRF) method for filtering or reducing the number of SNPs in a genome-wide association study that are exhaustively analyzed using MDR. My previous post on TuRF can be found here. A pdf of the paper can be found here or can obtained by emailing me. Note that TuRF is available as part of the open-source MDR software package available from http://www.epistasis.org/.

TuRF was originally explored as an approach that could be applied to genome-wide association study (GWAS) data. However, we have more recently shown that TuRF can be equally useful for small candidate gene studies where perhaps only 20 or 30 SNPs have been measured. We have shown that reducing 20 or 30 SNPs to perhaps 5 with TuRF improved MDR analysis results. TuRF seems to be reducing the false-discovery rate by removing the noisy SNPs from the dataset prior to MDR analysis. This pre-processing step yields less chance for overfitting by MDR. We have observed in real datasets that running TuRF first reduces the training accuracy of the best model while increasing the testing accuracy. This suggests that MDR models of TuRF-selected SNPs are a better fit to the data. The following applied paper illustrating this point was just accepted for publication in Arthritis Research and Care.

Lorenzo Beretta, Francesca Cappiello, Jason H. Moore, Morena Barili, Casey S. Greene, Raffaella Scorza. Epistatic interactions of cytokine single nucleotide polymorphisms predict susceptibility to disease subsets in systemic sclerosis patients. Arthritis Research and Care, in press (2008).

Objective. Gene-gene interaction or epistasis is considered a ubiquitous component of complex human diseases such as systemic sclerosis (SSc). Epistasis is difficult to model by traditional statistical approaches, hence nonparametric computational algorithms, such as multifactor dimensionality reduction (MDR), have been developed. Methods. Two-hundred-forty-two consecutive unrelated Italian SSc patients and an equal number of well-matched healthy controls, were genotyped for 22 cytokine SNPs (13 cytokine genes). The distribution of the SNPs between controls and SSc patients and between controls and lcSSc or dcSSc patients was tested by the MDR constructive induction algorithm and by the logistic regression-based approach “focused interaction testing framework (FITF)”. Results. None of the studied SNPs had main independent effects on SSc or disease subset susceptibility and hence no epistatic interaction was detectable by FITF. The MDR analysis showed a significant epistatic interaction among the IL-2 G-330T, the IL-6 C-174G and the IFNg AUTR5644T SNPs and the IL-1R Cpst1970T, the IL-6 Ant565G and the IL-10 C-819T SNPs in lcSSc or dcSSc susceptibility, respectively. The relevance of the single multilocus attributes constructed by the MDR inductive algorithm was then confirmed by the parametric approach (p<0.001 for both control vs lcSSc and control vs dcSSc). Conclusion. We provide evidence for gene-gene interaction among cytokine SNPs in the context of SSc. The interaction among cytokine SNPs with a pro-fibrotic or a regulatory function on pro-fibrotic interleukins is relevant to the susceptibility to SSc subset and it appears to be more important than the contribution of any single cytokine SNP.

Saturday, February 02, 2008

Epistasis, Pleiotropy, Canalization and Relationship QTLs

A great new paper from Jim Cheverud's group has just been published in the journal Evolution. Science at its best!

Pavlicev M, Kenney-Hunt JP, Norgard EA, Roseman CC, Wolf JB, Cheverud JM. Genetic variation in pleiotropy: differential epistasis as a source of variation in the allometric relationship between long bone lengths and body weight. Evolution. 2008 Jan;62(1):199-213. [PubMed]

Pleiotropy is an aspect of genetic architecture underlying the phenotypic covariance structure. The presence of genetic variation in pleiotropy is necessary for natural selection to shape patterns of covariation between traits. We examined the contribution of differential epistasis to variation in the intertrait relationship and the nature of this variation. Genetic variation in pleiotropy was revealed by mapping quantitative trait loci (QTLs) affecting the allometry of mouse limb and tail length relative to body weight in the mouse-inbred strain LG/J by SM/J intercross. These relationship QTLs (rQTLs) modify relationships between the traits affected by a common pleiotropic locus. We detected 11 rQTLs, mostly affecting allometry of multiple bones. We further identified epistatic interactions responsible for the observed allometric variation. Forty loci that interact epistatically with the detected rQTLs were identified. We demonstrate how these epistatic interactions differentially affect the body size variance and the covariance of traits with body size. We conclude that epistasis, by differentially affecting both the canalization and mean values of the traits of a pleiotropic domain, causes variation in the covariance structure. Variation in pleiotropy maintains evolvability of the genetic architecture, in particular the evolvability of its modular organization.