MDR Permutation Testing Module Released

The Dartmouth CGL is happy to announce the release of the permutation testing module for the multifactor dimensionality reduction (MDR) software. Download information can be found here.

Mixed effects models

A new paper by Foulkes et al. ("Mixed modelling to characterize genotype-phenotype associations", Stat Med. 2005 Mar 15;24(5):775-89) [PubMed] explores the use of mixed models to detect gene-gene and gene-environment interactions. Here is the abstract:

We propose using mixed effects models to characterize the association between multiple gene polymorphisms, environmental factors and measures of disease progression. Characterizing high-order gene-gene and gene-environment interactions presents an analytic challenge due to the large number of candidate genes and the complex, undescribed interactions among them. Several approaches have been proposed recently to reduce the number of candidate genes and post hoc approaches to identify gene-gene interactions are described. However, these approaches may be inadequate for identifying high-order interactions in the absence of main effects and generally do not permit us to control for potential confounders. We describe how mixed effects models and related testing procedures overcome these limitations and apply this approach to data from a cohort of subjects at risk for cardiovascular disease. Four (4) genetic polymorphisms in three genes of the same gene family are considered. The proposed modelling approach allows us first to test whether there is a significant genetic contribution to the variability observed in our disease outcome. This contribution may be through main effects of multi-locus genotypes or through an interaction between genotype and environmental factors. This approach also enables us to identify specific multi-locus gentoypes that interact with environmental factors in predicting the outcome. Mixed effects models provide a flexible statistical framework for controlling for potential confounders and identifying interactions among multiple genes and environmental factors that explain the variability in measures of disease progression.

Systems Biology Modeling of Epistasis

A recent paper by Nagasaki et al. (see below) highlights the use of Petri nets for modeling of biological systems. We have used Petri nets as a discrete dynamical systems modeling tool for carrying out thought experiments (see below) about the complexity of biochemical systems that are consistent with a statistical model that defines an epistatic relationship between two or more DNA sequence variations and disease susceptibility. Our review of this work was just published. See:

Moore JH, Boczko EM, Summar ML. Connecting the dots between genes, biochemistry, and disease susceptibility: systems biology modeling in human genetics. Mol Genet Metab. 2005 Feb;84(2):104-11. [PubMed]

Nagasaki M, Doi A, Matsuno H, Miyano S. A versatile Petri net based architecture for modelingand simulation of complex biological processes, Genome Inform. 2004;15(1):180-97. [PubMed] [JSBI]

Interested in thought experiments? Here is a great paper on the topic:

Di Paolo et al. Simulation models as opaque thought experiments. In: Bedau et al. Artificial Life VII, pp 497-506, 2000. [CiteSeer] [pdf]

Epistasis in RNA Viruses

Several recent papers document epistasis in RNA viruses. See, for example:

Sanjuan R, Moya A, Elena SF. The contribution of epistasis to the architecture of fitness in an RNA virus. Proc Natl Acad Sci U S A. 2004 Oct 26;101(43):15376-9. [PubMed]

Bonhoeffer S, Chappey C, Parkin NT, Whitcomb JM, Petropoulos CJ. Evidence for positive epistasis in HIV-1. Science. 2004 Nov 26;306(5701):1547-50. [PubMed]

New Epistasis Papers

The February issue of the journal Genetic Epidemiology has two nice papers on gene-gene interactions:

Kooperberg C, Ruczinski I. Identifying interacting SNPs using Monte Carlo logic regression. Genet Epidemiol. 2005 Feb;28(2):157-70. [PubMed]

Bureau et al. Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol. 2005 Feb;28(2):171-82. [PubMed]

Open-Source MDR Released

The CGL has released an open-source version of the Multifactor Dimensionality Reduction (MDR) software package for the detection and characterization of epistasis in genetic and epidemiologic studies of human disease. The software is programmed entirely in JAVA and is distributed for free under a GNU General Public License. The software and information about the method can be found here.

Recent publications on MDR include:

Moore JH. Computational analysis of gene-gene interactions using multifactor dimensionality reduction. Expert Rev Mol Diagn. 2004 Nov;4(6):795-803. [PubMed]

Williams et al. Multilocus analysis of hypertension: a hierarchical approach. Hum Hered. 2004;57(1):28-38. [PubMed]

Coffey et al. An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: the importance of model validation. BMC Bioinformatics. 2004 Apr 30;5(1):49. [PubMed]

Hahn LW, Moore JH. Ideal discrimination of discrete clinical endpoints using multilocus genotypes. In Silico Biol. 2004;4(2):183-94. [PubMed]

Cho et al. Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus. Diabetologia. 2004 Mar;47(3):549-54. [PubMed]

Tsai et al. Renin-angiotensin system gene polymorphisms and atrial fibrillation. Circulation. 2004 Apr 6;109(13):1640-6. [PubMed]

Recent papers that cite or discuss MDR include:

Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005 Feb;6(2):95-108. [PubMed]

Pharoah et al. Association studies for finding cancer-susceptibility genetic variants. Nat Rev Cancer. 2004 Nov;4(11):850-60. [PubMed]

Khoury et al. The emergence of epidemiology in the genomics age. Int J Epidemiol. 2004 Oct;33(5):936-44. [PubMed]

Thornton-Wells et al. Genetics, statistics and human disease: analytical retooling for complexity. Trends Genet. 2004 Dec;20(12):640-7. [PubMed]

AACR Session on Epistasis

Dr. Moore from the CGL is organizing and chairing a session on "Gene-Gene Interactions in Cancer Etiology" at the 96th annual meeting of the American Association for Cancer Research (AACR) in Anaheim, CA. The session will be held on Saturday, April 16th, from 2:00-4:00 in room 303 of the Anaheim Convention Center. The program is as follows:

Jason H. Moore (Dartmouth CGL)
"Introduction"

Jim Gauderman (USC)
"Gene-gene interaction in candidate gene studies"

Marylyn D. Ritchie (Vanderbilt)
"Computational approaches to detecting interactions"

Duncan C. Thomas (USC)
"Bayesian modeling of complex metabolic pathways"

Jason H. Moore (Dartmouth CGL)
"Interpreting gene-gene interaction models"

Systems-Level Genetics

A recent paper by Segre et al. ("Modular epistasis in yeast metabolism", Nature Genetics 2005 Jan;37(1):77-83) gives us a taste of what the future holds for systems-level characterization of epistasis. Here is the abstract:

Abstract

Epistatic interactions, manifested in the effects of mutations on the phenotypes caused by other mutations, may help uncover the functional organization of complex biological networks. Here, we studied system-level epistatic interactions by computing growth phenotypes of all single and double knockouts of 890 metabolic genes in Saccharomyces cerevisiae, using the framework of flux balance analysis. A new scale for epistasis identified a distinctive trimodal distribution of these epistatic effects, allowing gene pairs to be classified as buffering, aggravating or noninteracting. We found that the ensuing epistatic interaction network could be organized hierarchically into function-enriched modules that interact with each other 'monochromatically' (i.e., with purely aggravating or purely buffering epistatic links). This property extends the concept of epistasis from single genes to functional units and provides a new definition of biological modularity, which emphasizes interactions between, rather than within, functional modules. Our approach can be used to infer functional gene modules from purely phenotypic epistasis measurements.

We have commented on the implications of this study for detecting, characterizing, and interpreting epistasis in genetic and epidemiologic studies of common human diseases (see Moore, "A global view of epistasis", Nature Genetics 2005 Jan;37(1):13-4).

Conferences

Here are some of the conferences that one or more members of the CGL will be attending this year:

ASM Biodefense, March 20-23, Baltimore
EuroGP, March 30-April 1, Lausanne
AACR, April 16-20, Anaheim, CA
BioGEC, June 25-26, Washington D.C.
GECCO, June 25-29, Washington D.C.
IEEE CEC, September 2-5, Edinburgh, UK
ECAL, September 5-9, Canterbury, UK
IGES, October 23-24, Park City, Utah
ASHG, October 25-29, Salt Lake City, Utah

Epistasis, complex traits, and mapping genes.

Some of you may have missed this great paper by Dr. Michael Wade. It is worth reading. His lab web page at Indiana University can be found here. See also his book on Epistasis and the Evolutionary Process from Oxford Press (2000). It is a classic.

Wade MJ. Epistasis, complex traits, and mapping genes. Genetica. 2001;112-113:59-69.

Abstract

Using a three-locus model wherein two loci regulate a third, candidate locus, I examine physiological epistasis from the 'gene's eye view' of the regulated locus. I show that, depending upon genetic background at the regulatory loci, an allele at the candidate locus can be dominant, additive, recessive, neutral, over-dominant, or under-dominant in its effects on fitness. This kind of variation in allelic effect caused by variation in genetic background from population to population, from time to time in the same population, or sample to sample makes finding and mapping the genes underlying a complex phenotype difficult. The rate of evolution of such genes can also be slowed, especially in genetically subdivided metapopulations with migration. Nevertheless, understanding how variation in genetic background causes variation in allelic effects permits the genetic architecture of such complex traits to be dissected into the interacting component genes. While some backgrounds diminish allelic effects and make finding and mapping genes difficult, other backgrounds enhance allelic effects and facilitate gene mapping.

Genome-Wide Association

Two new papers address some of the important issues surrounding the detection of susceptibility loci using genome-wide association studies. Both briefly discuss epistasis or gene-gene interactions. See:

Hirschhorn and Daly (2005) Genome-wide association studies for common diseases and complex traits. Nature Reviews Genetics 6:95-108.

Wang et al. (2005) Genome-wide association studies: theoretical and practical concerns. Nature Reviews Genetics 6:109-118.

We published a paper last year that specifically addresses some of the challenges with detecting, characterizing, and interpreting epistasis in genome-wide association studies. See:

Moore and Ritchie (2004) The challenges of whole-genome approaches to common diseases. JAMA 291(13):1642-3.

Biological and Statistical Epistasis

Our paper on "Traversing the conceptual divide between biological and statistical epistasis: Systems biology and a more modern synthesis" by Moore and Williams has been accepted for publication in the June issue of BioEssays. Here is the abstract:

Epistasis plays an important role in the genetic architecture of common human diseases and can be viewed from two perspectives, biological and statistical, each derived from and leading to different assumptions and research strategies. Biological epistasis is the result of physical interactions among biomolecules within gene regulatory networks and biochemical pathways in an individual such that the effect of a gene on a phenotype is dependent on one or more other genes. In contrast, statistical epistasis is defined as deviation from additivity in a mathematical model summarizing the relationship between multilocus genotypes and phenotypic variation in a population. The goal of this essay is to review definitions and examples of biological and statistical epistasis and to explore the relationship between the two. Specifically, we present and discuss the following two questions in the context of human health and disease. First, when does statistical evidence of epistasis in human populations imply underlying biomolecular interactions in the etiology of disease? Second, when do biomolecular interactions produce patterns of statistical epistasis in human populations? Answers to these two reciprocal questions will provide an important framework for using genetic information to improve our ability to diagnose, prevent, and treat common human diseases. We propose that systems biology will provide the necessary information for addressing these questions and that model systems such as bacteria, yeast, and digital organisms will be a useful place to start.