Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Saturday, January 31, 2015

Epistasis: Methods and Protocols

Our new edited volume on epistasis.

This volume presents a valuable and readily reproducible collection of established and emerging techniques on modern genetic analyses. Chapters focus on statistical or data mining analyses, genetic architecture, the burden of multiple testing, genetic variance, measuring epistasis, multifactor dimensionality reduction, and ReliefF. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls.

Saturday, January 03, 2015

Heuristic identification of biological architectures for simulating complex hierarchical genetic interactions

Moore JH, Amos R, Kiralis J, Andrews PC. Heuristic identification of biological architectures for simulating complex hierarchical genetic interactions. Genet Epidemiol. 2015 Jan;39(1):25-34. [PubMed]


Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype-phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene-gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects.