Between The Lines Of Genetic Code

Between the Lines of Genetic Code lays out methodologies and tools for the measurement and evaluation of gene-gene and gene-environment studies and gives perspective on the future of this discipline. The book begins by defining terms for interaction studies, describing methodologies, and critically assessing the viability of current study designs and the possibilities for integrating designs. It then provides recent applications data with case studies in rheumatoid arthritis, multiple sclerosis, myositis and other complex human diseases. Last, it examines current studies and directions for future applications in patient care. Recent multivariate studies show that gene-gene and gene-environment interactions can explain significant variances in inheritance that have previously been undetectable in univariate analysis. These links among genes and between genes and their environments during the development of diseases may serve as important hints for understanding pathogenic mechanisms and for developing new tools for prognosis, diagnosis, and treatment of various diseases. Systematically integrates methods of defining and detecting gene interactions to provide an overview of the field Critically analyzes current methods and tools to aid researchers in integrating gene interaction studies Includes examples of current biomedical applications and presents current research expected to shape clinical research in the near future

Following the success of genome-wide association studies, it is now important to unravel interactions between genes as well as between genes and environment. Here we discuss how to interpret higher order statistical interactions in terms of underlying biological processes purportedly generating such dependencies. In this chapter, we specifically review how biological networks, such as those generated from systems biological approaches, could possibly facilitate the interpretation of observed interactions in the development of complex phenotypes, e.g., complex diseases. We conclude that the statistical challenge is essentially a high-dimensional feature selection problem and there is therefore a need to construct intelligent priors using biological networks as a tool for the discovery and interpretation of interactions.

There is growing enthusiasm in the scientific community about the prospect of mapping and sequencing the human genome, a monumental project that will have far-reaching consequences for medicine, biology, technology, and other fields. But how will such an effort be organized and funded? How will we develop the new technologies that are needed? What new legal, social, and ethical questions will be raised? Mapping and Sequencing the Human Genome is a blueprint for this proposed project. The authors offer a highly readable explanation of the technical aspects of genetic mapping and sequencing, and they recommend specific interim and long-range research goals, organizational strategies, and funding levels. They also outline some of the legal and social questions that might arise and urge their early consideration by policymakers.

Gene–gene interactions are a critical component of the genetic architecture of complex, human traits. Unfortunately, the detection and characterization of interactions is a challenge. This is due to a number of factors including the combinatorial explosion of possible interactions in large-scale genomic data, limited power in small sample sizes, and potential difficulty interpreting or validating the resulting interaction models. A number of data mining methods have been developed to deal with one or more of these challenges. In this chapter, we discuss the importance of looking for gene–gene interactions as well as some of the challenges facing this type of analysis. Additionally, we review the alternative analytic approaches being considered to improve our ability to detect and model gene–gene interactions. Finally, we conclude with the future directions of interaction analysis.

Genome-wide interaction studies is an extremely challenging problem in statistics, in which conventional methods are often inadequate in terms of both power and computational efficiency. An exhaustive search for genome-wide gene–gene interactions becomes feasible with modern cluster computing run on graphics processing units. However, the large number of tests accompanying the search raises a serious multiple testing problem. A way to overcome these limits is to apply a filtering step prior to the combinatorial method and to analyze only interesting single nucleotide polymorphisms selected based on a priori (defined by statistical evidence, genetic impact, or biological plausibility). The advantage of the filter approach is speed, and the disadvantage is that attributes with poor quality scores are disregarded. Genome-wide gene–environment interaction is less problematic computational demand compared with pairwise genome-wide gene–gene interaction. Accounting for gene–gene and gene–environment interactions is important for future strategies of diagnosis, prognosis, and management of human diseases and will bring new data regarding pathogenetic mechanisms for human complex diseases.

Functional analysis of genetic variants in association with human complex diseases remains a challenge for modern research. Discovery of gene–gene interactions may represent an additional tool for identifying specific polymorphisms involved in disease development. It not only identifies new candidate genes for mechanistic studies, but also suggests biological relations in the function of these genes with certain variants in developing disease. We describe a few examples of the studies where a link between statistical evidence of gene–gene interaction was found with the expression or function of genes of interest. Finally, we present future perspectives for integration genetic epidemiology data with biologically relevant mechanisms of disease.

This chapter deals with estimation of interaction through different statistical models. Interaction is classified into two different classes, antagonistic and synergistic effects. An antagonistic effect is said to be present if the combination of two factors decrease the measure of interest as compared to the expected sum of the single effect of each factor. Synergistic effect is present if the combination of two factors increases the measure of interest more than the expected sum based on each factor. Furthermore, this chapter also shows how estimation of multiplicative and additive interaction is carried out focusing on gene–environmental interaction.