Epistasis Blog

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

Wednesday, April 23, 2008

Introduction to Gene-Gene Interactions

I published a short introduction to gene-gene interactions in Current Protocols in Human Genetics in 2004. Interestingly, this chapter just appeared on PubMed. If you see this and are interested in reading it you should know that I updated the chapter in January. The new 2008 version doesn't seem to be published yet. If you would like a copy of the new version feel free to email me.

Moore JH. Analysis of gene-gene interactions. Curr Protoc Hum Genet. 2004 Feb;Chapter 1:Unit1.14. [PubMed]

The goal of this unit is to introduce gene-gene interactions or epistasis as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common; then, it reviews several statistical and computational methods for detecting and characterizing genes whose effects are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits since most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.

Saturday, April 19, 2008

Dr. Massimo Pigliucci

Dr. Massimo Pigliucci from SUNY Stony Brook was here for a visit to Dartmouth this week. I have been a huge fan of his work since reading his book on Phenotypic Evolution: A Reaction Norm Perspective while a graduate student. His views on genetics and evolution and very much in line with ours. In addition to a Ph.D. in genetics and a Ph.D. in botany, Massimo has a Ph.D. in the philosophy of science. His work on the relationship between science and religion is very interesting.

I encourage you to explore his websites and to read some of his published work.

http://www.genotypebyenvironment.org/

http://www.rationallyspeaking.org/

http://rationallyspeaking.blogspot.com/

Friday, April 18, 2008

The Pathway Less Traveled

Our paper with Dr. Russ Wilke on using our knowledge about candidate genes and pathways to help guide genome-wide association studies has been accepted for publication in Current Pharmacogenomics and Personalized Medicine. The abstract and an overview figure from the paper are given below.

Wilke, RA, Mareedu, RK, and Moore, JH. The Pathway Less Traveled: Moving from candidate genes to candidate pathways in the analysis of genome-wide data from large scale pharmacogenetic association studies. Current Pharmacogenomics and Personalized Medicine, in press (2008)

Abstract

The candidate gene approach to pharmacogenetics is hypothesis driven, and anchored in biological plausibility. Whole genome scanning is hypothesis generating, and it may lead to new biology. While both approaches are important, the scientific community is rapidly reallocating resources toward the latter. We propose a step-wise approach to large-scale pharmacogenetic association studies that begins with candidate genes, then uses a pathway-based intermediate step, to inform subsequent analyses of data generated through whole genome scanning. Novel computational strategies are explored in the context of two clinically relevant examples, cholesterol synthesis and lipid signaling.










Figure 6. Step-wise approach to pharmacogenetic association studies.
Illustrated are the relationships between the amount of genotype data collected (blue rectangles), the amount of information generated from statistical and computational analysis (green rectangles) and the amount of knowledge about genetic architecture that is generated from interpreting data analysis results (yellow rectangles) for 1) a gene-centric approach that focuses on one or several candidate genes selected on the basis of their biochemical properties, 2) a pathway-based approach that looks at candidate genes in a particular biochemical pathway and 3) a genome-wide approach that considers a dense map of single-nucleotide polymorphisms (SNPs) that capture most of the variability in the genome. (A) Here, genome-wide association studies carried out independently of gene-centric and pathway-based results are considered agnostic to prior biological and analytical knowledge. In this paradigm, the amount of knowledge gained from a genome-wide association study is very small in proportion to the amount of data and information that are generated. This is due to the high level of noise inherent to data where the number of variables greatly outnumbers the sample size. (B) In this paradigm, knowledge gained from gene-centric studies is used to help pick the pathways and the genes that will be considered in a pathway-based approach. Further, the knowledge gained from pathway-based studies is used to help interpret genome-wide data analysis results. Here, the amount of knowledge gained from the genome-wide association study is improved over that provided by the purely agnostic approach outlined in (A). (C) The genome-wide association study is more expensive and more time consuming than either of the other two approaches. This is especially true with respect to the greatly increased amount of time that it takes to carry out the quality control, data management, data analysis and results interpretation. Candidate gene studies therefore provide greater value, defined as knowledge gained by data generated. We propose that that the analysis and interpretation of a genome-wide association study will be most successful when carried out once the gene-centric and pathway-based approaches have been fully explored. This will ultimately increase the value of the genome-wide association study.

Thursday, April 17, 2008

Computer Programmer Wanted

We will be starting a search soon for a B.S. or M.S. level computer scientist to join my Computational Genetics Laboratory at Dartmouth. If you or anyone you know might be interested please let me know.

I am interested in finding an outstanding C++ programmer to work on our cutting-edge machine learning and data mining methods for detecting epistasis in genetic association studies. Bioinformatics experience is a plus. Experience with evolutionary algorithms or other related machine learning methods is a plus.

More details later!

Wednesday, April 09, 2008

Survey of Epistasis Analysis Methods

The following paper provides a survey of different methods for detecting epistasis in genetic association studies.

Motsinger AA, Ritchie MD, Reif DM. Novel methods for detecting epistasis in pharmacogenomics studies. Pharmacogenomics. 2007 Sep;8(9):1229-41.

The importance of gene-gene and gene-environment interactions in the underlying genetic architecture of common, complex phenotypes is gaining wide recognition in the field of pharmacogenomics. In epidemiological approaches to mapping genetic variants that predict drug response, it is important that researchers investigate potential epistatic interactions. In the current review, we discuss data-mining tools available in genetic epidemiology to detect such interactions and appropriate applications. We survey several classes of novel methods available and present an organized collection of successful applications in the literature. Finally, we provide guidance as to how to incorporate these novel methods into a genetic analysis. The overall goal of this paper is to aid researchers in developing an analysis plan that accounts for gene-gene and gene-environment in their own work.

Sunday, April 06, 2008

MDR Downloads

Our MDR software was downloaded 627 times in March. This is a new download record. We greatly appreciate you interest in MDR. Be sure and let us know if you need help. Also, don't forget to read my five-part MDR 101 tutorial that starts in November of 2006 on this blog and continues into December.

Version 1.2 of MDR is almost ready. I need to update the manual and then we will post it on Sourceforge.net. Feel free to email me for the new version.