Epistasis Blog

From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)

Friday, May 24, 2013

Probabilistic multifactor causation - what do we mean?

This four-part blog post on genetic causation is well worth the long read. I highly recommend it. It is written by Dr. Anne Buchanan and Ken Weiss from Penn State. It is part of The Mermaid's Tale blog.

Who, me? I don't believe in single-gene causation! (or do I?). Part I. What does it mean?

Who, me? I don't believe in single-gene causation! (or do I?). Part II. Probabilistic causation--what do we mean? 

 Who, me? I don't believe in single-gene causation! (or do I?). Part III. Probabilistic multifactor causation--what do we mean?

Who, me? I don't believe in single-gene causation! (or do I?). Part IV. Do we need the probabilistic hypothesis?

Be sure and read their awesome book titled "The Mermaid's Tale"

Friday, May 17, 2013

Journal Impact Factors - Updated

I updated my August, 2012 blog post about journal impact factors and their value as a measure of success. This post has been updated with the recent DORA report and several editorials about it.

Monday, May 06, 2013

A robustness study of parametric and non-parametric tests in model-based multifactor dimensionality reduction for epistasis detection

Kristel van Steen and her colleagues have produced a number of great extensions to our MDR method for detecting gene-gene interactions. Here is their latest paper.

Mahachie John JM, Van Lishout F, Gusareva ES, Van Steen K. A robustness study of parametric and non-parametric tests in model-based multifactor dimensionality reduction for epistasis detection. BioData Min. 2013 Apr 25;6(1):9. [PubMed]

Abstract

BACKGROUND: Applying a statistical method implies identifying underlying (model) assumptions and checking their validity in the particular context. One of these contexts is association modeling for epistasis detection. Here, depending on the technique used, violation of model assumptions may result in increased type I error, power loss, or biased parameter estimates. Remedial measures for violated underlying conditions or assumptions include data transformation or selecting a more relaxed modeling or testing strategy. Model-Based Multifactor Dimensionality Reduction (MB-MDR) for epistasis detection relies on association testing between a trait and a factor consisting of multilocus genotype information. For quantitative traits, the framework is essentially Analysis of Variance (ANOVA) that decomposes the variability in the trait amongst the different factors. In this study, we assess through simulations, the cumulative effect of deviations from normality and homoscedasticity on the overall performance of quantitative Model-Based Multifactor Dimensionality Reduction (MB-MDR) to detect 2-locus epistasis signals in the absence of main effects.

METHODOLOGY: Our simulation study focuses on pure epistasis models with varying degrees of genetic influence on a quantitative trait. Conditional on a multilocus genotype, we consider quantitative trait distributions that are normal, chi-square or Student's t with constant or non-constant phenotypic variances. All data are analyzed with MB-MDR using the built-in Student's t-test for association, as well as a novel MB-MDR implementation based on Welch's t-test. Traits are either left untransformed or are transformed into new traits via logarithmic, standardization or rank-based transformations, prior to MB-MDR modeling.

RESULTS: Our simulation results show that MB-MDR controls type I error and false positive rates irrespective of the association test considered. Empirically-based MB-MDR power estimates for MB-MDR with Welch's t-tests are generally lower than those for MB-MDR with Student's t-tests. Trait transformations involving ranks tend to lead to increased power compared to the other considered data transformations.

CONCLUSIONS: When performing MB-MDR screening for gene-gene interactions with quantitative traits, we recommend to first rank-transform traits to normality and then to apply MB-MDR modeling with Student's t-tests as internal tests for association.