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Many colleges and universities across the globe now offer bachelors, masters, and doctoral degrees, along with certificate programs in bioinformatics. While there is some consensus surrounding curricula competencies, programs vary greatly in their core foci, with some leaning heavily toward the biological sciences and others toward quantitative areas. This allows prospective students to choose a program that best fits their interests and career goals. In the digital age, most scientific fields are facing an enormous growth of data, and as a consequence, the goals and challenges of bioinformatics are rapidly changing; this requires that bioinformatics education also change. In this workshop, we seek to ascertain current trends in bioinformatics education by asking the question, “What are the core competencies all bioinformaticians should have at the end of their training, and how successful have programs been in placing students in desired careers?”
PMCID: PMC3935419  PMID: 24297567
2.  COMT Val158Met Genotype and Individual Differences in Executive Function in Healthy Adults 
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.
PMCID: PMC3114452  PMID: 21144101
cognition; neuropsychological tests; executive control; catechol-O-methyltransferase; polymorphism; epistasis
3.  Role for protein–protein interaction databases in human genetics 
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.
PMCID: PMC2813729  PMID: 19929610
epistasis; expert knowledge; multifactor dimensionality reduction; protein–protein interaction; single nucleotide polymorphism
4.  A Computationally Efficient Hypothesis Testing Method for Epistasis Analysis using Multifactor Dimensionality Reduction 
Genetic epidemiology  2009;33(1):87-94.
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.
PMCID: PMC2700860  PMID: 18671250
Extreme Value Distribution; Permutation Testing; Power; Type I Error; Bladder Cancer; Data Mining
5.  Exploiting the Proteome to Improve the Genome-Wide Genetic Analysis of Epistasis in Common Human Diseases 
Human genetics  2008;124(1):19-29.
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 genome-wide 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.
PMCID: PMC2780579  PMID: 18551320
protein-protein interaction; expert knowledge; epistasis; MDR; SNP

Results 1-5 (5)