Epistasis Blog

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

Tuesday, January 17, 2012

Lower-order effects adjustment in quantitative traits model-based multifactor

Here is a new MDR paper from Kristel Van Steen.

Mahachie John JM, Cattaert T, Van Lishout F, Gusareva ES, Van Steen K. Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction. PLoS One. 2012;7(1):e29594. [PubMed]


Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic effects. These may hamper the identification of genuine epistasis. If lower-order genetic effects contribute to the genetic variance of a trait, identified statistical interactions may simply be due to a signal boost of these effects. In this study, we restrict attention to quantitative traits and bi-allelic SNPs as genetic markers. Moreover, our interaction study focuses on 2-way SNP-SNP interactions. Via simulations, we assess the performance of different corrective measures for lower-order genetic effects in Model-Based Multifactor Dimensionality Reduction epistasis detection, using additive and co-dominant coding schemes. Performance is evaluated in terms of power and familywise error rate. Our simulations indicate that empirical power estimates are reduced with correction of lower-order effects, likewise familywise error rates. Easy-to-use automatic SNP selection procedures, SNP selection based on "top" findings, or SNP selection based on p-value criterion for interesting main effects result in reduced power but also almost zero false positive rates. Always accounting for main effects in the SNP-SNP pair under investigation during Model-Based Multifactor Dimensionality Reduction analysis adequately controls false positive epistasis findings. This is particularly true when adopting a co-dominant corrective coding scheme. In conclusion, automatic search procedures to identify lower-order effects to correct for during epistasis screening should be avoided. The same is true for procedures that adjust for lower-order effects prior to Model-Based Multifactor Dimensionality Reduction and involve using residuals as the new trait. We advocate using "on-the-fly" lower-order effects adjusting when screening for SNP-SNP interactions using Model-Based Multifactor Dimensionality Reduction analysis.

Saturday, January 14, 2012

Imaging Genetics

I am collaborating with Drs. Andy Saykin and Li Shen at IUPUI to develop and apply novel methods for the genetic analysis of neuroimaging phenotypes. This is a really hot new area. I will be giving a talk on some of our recent results at the 8th International Imaging Genetics Conference, to be held on January 16th and 17th, 2012 at the Beckman Center of the National Academy of Sciences in Irvine, CA. We have applied some of our recent network science methods (see paper below) to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data.

Hu T, Sinnott-Armstrong NA, Kiralis JW, Andrew AS, Karagas MR, Moore JH. Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinformatics. 2011 Sep 12;12:364. [PubMed]

Sunday, January 01, 2012

List of Epistasis Blog Posts from 2011

January, 2011

Yeast genetics is complex. What about humans?

The Meaning of Interaction

Model-based multifactor dimensionality reduction for detecting epistasis

Application of the Explicit Test of Epistasis to Colon Cancer

Real-world comparison of CPU and GPU implementations of SNPrank

NIH/NIGMS Funding by Priority Score

Layers of Epistasis

February, 2011

Gene-Gene Interaction Analysis Using ReliefF and MDR

A genome-wide screen of gene-gene interactions for rheumatoid arthritis susceptibility

Epistatic Interactions in Genetic Regulation of t-PA and PAI-1 Levels in a Ghanaian Population

Dissecting genetic networks underlying complex phenotypes: the theoretical framework

A Comparison of Multifactor Dimensionality Reduction and Penalized Regression

March, 2011

Interactome Networks and Human Disease

Gene–Environment Interactions in Human Disease

Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data

April, 2011

Genetic analysis of complex traits in the emerging collaborative cross

Travelling the world of gene-gene interactions

May, 2011

Detecting genetic interactions for quantitative traits with U-statistics

Transcriptional robustness and protein interactions are associated in yeast

The effects of linkage disequilibrium in large scale SNP datasets for MDR

Computational Intelligence Using Genetic Programming

Microbiome Studies at the 2012 Pacific Symposium on Biocomputing

June, 2011

Pathway of distinction analysis

Molecular mechanisms of epistasis

Two Epistasis Papers in Science

July, 2011

Generating data with complex genotype-phenotype relationships

Powerful SNP-set analysis for case-control genome-wide association studies

August, 2011

People are inherently biased against creative ideas

New Center Grant on Gene-Environment Interactions

September, 2011

Gene-environment interaction in psychiatric research

Characterizing Genetic Interactions in Human Disease Association Studies Using Statistical Epistasis Networks

HyperCube Rule Mining

An R Package Implementation of Multifactor Dimensionality Reduction

The 24/7 Lab - Does Creativity Suffer?

November, 2011

Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions

December, 2011

The Causes of Epistasis