Characterization of the genetic and environmental factors driving gene expression variability

Abstract

Gene expression variation is a quantitative trait that drives phenotypic diversity across populations.On a cellular level, gene expression is an intermediate phenotype between stored genetic information and the functional utilization of this information within the cell. Through Genome Wide Association Studies (GWAS), thousands of genetic polymorphisms associated with numerous diseases have been identified. These have provided many novel insights into the disrupted biological processes that drive the etiology of various health conditions.expression Quantitative Trait Loci (eQTLs) provide an additional layer of biological information about the physiological impact of common genetic variants.Therefore, the study of the genetic regulation of gene expression (eQTL studies) has been useful both in the validation and functional characterisation of GWAS polymorphisms. This has contributed to a better understanding of the precise molecular processes that contribute to the development of disease.Global transcriptomic analyses have provided as greater insight into the level of complexity that drives biological systems. Transcriptomic data are often comprised of gene regulatory and co-expression networks, an emergent property of transcriptomic and other omic data. These networks within each omics fields interact with each other to further add layers of complexity that drive biological systems.Variation contained with gene expression datasets can, therefore, provide detail into the flow of information through these biological systems and how these can be influenced by genetic polymorphisms.Transcriptome variation is highly influenced by genetic and environmental factors. Genetic regulation of gene expression represents, with some exceptions, fixed regulatory points that strictly control the expression of genes. Variance attributed to environmental effects, on the other hand, are often biological responses to specific stimuli. The dissection of the genetic and environmental influences on expression levels will help to form a baseline upon which network models can be built to disseminate the biological flow of information in healthy, latent or disease groups. Booth, "The low EOMES/TBX21 molecular phenotype in multiple sclerosis reflects CD56+ cell dysregulation and is affected by immunomodulatory therapies