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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-environme...
Towards Precision Medicine for Immune-Mediated Disorders: Advances in Using Big Data and Artificial Intelligence to Understand Heterogeneity in Inflammatory Responses
Topic Editor Dr. MacLeod is employed by Janssen. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Between the Lines of Genetic Code
We introduce new quantitative characteristics of the population using an analogy to the system of multi-spin molecules: the disease fields, which may depend on interactions, and the susceptibility to disease as derivative of genetic vector’s (GV’s) frequency of cases with respect to these fields. The genetic vector’s approach (GVA) is applied to statistical analysis of the interaction of two SNP haplotype of HTR2A and shared epitope (SE) alleles in relation to development of rheumatoid arthritis (RA). The analysis is performed for two independent cohorts, EIRA and NARAC, and based on the evaluation of double- and triple genotype–genotype versus SE alleles correlations. The Gibbs-like parametrization of GV frequencies makes analysis transparent and easy interpretable. We find that the main contribution into association to RA comes from GVs containing double SE. GVA may resolve an opposite role in risk/protection from different pairs of genetic variations and reveal an association to RA whereas the univariate analysis does not show significant association.
Between the Lines of Genetic Code
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.
Between the Lines of Genetic Code
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 ...
Between the Lines of Genetic Code
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.
