About this Research Topic

Background

Genes initiate and regulate all physiological processes throughout an organism’s lifetime. This genetic regulation is modulated by the environment (genotype by environment interaction, GxE), the age or developmental stage of the organism (genotype by time interaction, GxT), and the combination thereof (GxExT); for example, the impact of light on plant development where different intensities evoke different responses in gene expression (photomorphogenesis). In human genetics, a field known as functional modelling has been introduced to map dynamic genes. For example, the development of infectious diseases often follows a dynamic pattern (e.g. viral load in blood plasma). In livestock research, a similar approach is known as the modelling of longitudinal or dynamic traits. Here, studies often aim at the prediction of future trait expression and genetic selection of superior individuals.
The phenotypic changes occurring during aging or under different environments have long been studied and described through various mathematical functions. With the discovery of DNA and the age of genotyping and whole DNA sequencing, thousands of genes have been identified. However, the study of gene expression or the association of a gene/marker with a specific phenotype is often based on static or averaged phenotypic records, neglecting the potential time- and environment dependent activities of genes.
Several techniques have been developed to uncover genes affecting phenotypes. At the molecular level, gene expression studies measure the protein profile and quantity within cells of a certain tissue or at a specific time-point. To measure gene expression, biopsies of the tissue are required, however, this invasive process damages the donor organism and the laboratory methods are financially and diuturnally expensive. Alternatively, genetic markers can be used as a non-invasive and tissue-independent approach. The more often a specific marker is identified within individuals with a certain trait the more likely it is that the actual candidate gene with the causative variation is close by. Linkage analyses with relatively few markers are performed within families or populations where the relationship status is known and the inheritance of markers can be traced back. High-density genome-wide studies ensure close linkage between marker and causal gene, thus, known family structures become less important. Whilst these genome-wide association studies (GWAS) do not determine the amount of gene product, a strong association between a marker and a phenotype infers that a gene linked to this marker has an effect on the expression of the phenotype. Thus, putative causative genes can be identified.
Understanding GxExT interactions has the potential to solve problems in the food availability chain such as energy deficiency during peak production or imbalanced growth in livestock, which will have a direct impact on the animals’ health, longevity, production costs and level. Plant growth and the application of fertilizer is similarly impacted. There are also implications for related research subjects, such as the ability of an organism to react to environmental changes such as climate, or pathogen burdens. Finally, GxExT interactions could potentially provide targets for the development of gene-specific treatment plans in animals, humans, and plants alike.

Keywords: time-dependent, longitudinal, time series, GWAS, environment-dependent, GxE, GxT, GxExT, dynamic

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