Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free data mining method for detecting, characterizing, and interpreting epistasis in the absence of significant main effects in genetic and epidemiologic studies of complex traits such as disease susceptibility. The goal of MDR is to change the representation of the data using a constructive induction algorithm to make nonadditive interactions easier to detect using any classification method such as naïve Bayes or logistic regression. Traditionally, MDR constructed variables have been evaluated with a naïve Bayes classifier that is combined with 10-fold cross validation to obtain an estimate of predictive accuracy or generalizability of epistasis models. Traditionally, we have used permutation testing to statistically evaluate the significance of models obtained through MDR. The advantage of permutation testing is that it controls for false-positives due to multiple testing. The disadvantage is that permutation testing is computationally expensive. This is in an important issue that arises in the context of detecting epistasis on a genome-wide scale. The goal of the present study was to develop and evaluate several alternatives to large-scale permutation testing for assessing the statistical significance of MDR models. Using data simulated from 70 different epistasis models, we compared the power and type I error rate of MDR using a 1000-fold permutation test with hypothesis testing using an extreme value distribution (EVD). We find that this new hypothesis testing method provides a reasonable alternative to the computationally expensive 1000-fold permutation test and is 50 times faster. We then demonstrate this new method by applying it to a genetic epidemiology study of bladder cancer susceptibility that was previously analyzed using MDR and assessed using a 1000-fold permutation test.

The Val158Met polymorphism of the catechol-O-methyltransferase (COMT) gene may be related to individual differences in cognition, likely via modulation of prefrontal dopamine catabolism. However, the available studies have yielded mixed results, possibly in part because they do not consistently account for other genes that affect cognition. We hypothesized that COMT Met allele homozygosity, which is associated with higher levels of prefrontal dopamine, would predict better executive function as measured using standard neuropsychological testing, and that other candidate genes might interact with COMT to modulate this effect. Participants were 95 healthy, right-handed adults who underwent genotyping and cognitive testing. COMT genotype predicted executive ability as measured by the Trail-Making Test, even after covarying for demographics and APOE, BDNF and ANKK1 genotype. There was a COMT-ANKK1 interaction in which individuals having both the COMT Val allele and the ANKK1 T allele showed the poorest performance. This study suggests the heterogeneity in COMT effects reported in the literature may be due in part to gene-gene interactions that influence central dopaminergic systems.

One of the central goals of human genetics is the identification of loci with alleles or genotypes that confer increased susceptibility. The availability of dense maps of single-nucleotide polymorphisms (SNPs) along with high-throughput genotyping technologies has set the stage for routine genomewide association studies that are expected to significantly improve our ability to identify susceptibility loci. Before this promise can be realized, there are some significant challenges that need to be addressed. We address here the challenge of detecting epistasis or gene-gene interactions in genome-wide association studies. Discovering epistatic interactions in high dimensional datasets remains a challenge due to the computational complexity resulting from the analysis of all possible combinations of SNPs. One potential way to overcome the computational burden of a genome-wide epistasis analysis would be to devise a logical way to prioritize the many SNPs in a dataset so that the data may be analyzed more efficiently and yet still retain important biological information. One of the strongest demonstrations of the functional relationship between genes is protein-protein interaction. Thus, it is plausible that the expert knowledge extracted from protein interaction databases may allow for a more efficient analysis of genome-wide studies as well as facilitate the biological interpretation of the data. In this review we will discuss the challenges of detecting epistasis in genome-wide genetic studies and the means by which we propose to apply expert knowledge extracted from protein interaction databases to facilitate this process. We explore some of the fundamentals of protein interactions and the databases that are publicly available.

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.

Important biochemical constituents of the fibrinolytic system include tissue-type plasminogen activator (t-PA) and plasminogen activator inhibitor-1 (PAI-1). In the current review, we aim to describe the genetic architecture of t-PA and PAI-1. Several genetic polymorphisms in the T-PA and PAI-1 gene have been found to be associated with t-PA and PAI-1 levels in different patient cohorts. However, these genetic variations explain only a minor part of the heritability of t-PA and PAI-1, suggesting that genes in other pathways may influence t-PA and PAI-1 levels, and that epistasis and gene-environment interactions may play an important role in determining plasma levels of t-PA and PAI-1. Several studies reported that interindividual variation in plasma levels of t-PA and PAI-1 are significantly influenced by common polymorphisms in genes from the renin-angiotensin and bradykinin systems. In addition, we and others documented several gene-environment interactions and epistatic effects of genetic polymorphisms in the renin-angiotensin, bradykinin, and fibrinolytic systems on plasma t-PA and PAI-1 levels. In future studies, we need to consider high-order interactions and additional polymorphisms in genes from other (unknown) pathways detected by genome-wide association studies to fully understand the complex genetic architecture of these important intermediate quantitative traits and thereby thrombosis.