Allelic variations in gene expression.

Duke University Medical Center, Department of Pathology, Durham, North Carolina 27710, USA.
Current Opinion in Oncology (Impact Factor: 3.76). 02/2004; 16(1):39-43. DOI: 10.1097/00001622-200401000-00008
Source: PubMed

ABSTRACT Genetic variants determine phenotypic variability. Many genetic studies suggest that protein structural variations predispose the population to more than 1000 different hereditary diseases. Unfortunately, despite the study of genetic polymorphisms for many decades, the milder phenotypic variations believed to account for most human physical and behavioral differences and underlying the most common human genetic diseases (including cancers) cannot be accounted for easily by these variations in the protein coding sequences. Thus, it has been hypothesized that the study of natural differential expression presenting within and among populations may enhance understanding of human phenotypic variation.
During the last year, reports identifying variations in gene expression in different organisms and finding subtle changes of gene expression associated with common genetic disease have pointed to variations in gene expression as playing a central role in molecular evolution and human disease. Advances in the functional analysis of gene regulatory networks-in particular, new methods for distinguishing cis-acting components from trans-acting factors-have provided the impetus for these discoveries.
This review represents current knowledge about allelic variation in gene expression and its increasingly important role in understanding the genotype-phenotype relation. Characterization of these allelic variations may open largely uncharted territory in genomics for biomedical researchers and may eventually lead to the discovery of the causative genes of common hereditary diseases and their mechanism of action.

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    ABSTRACT: Although gene-environment interactions are known to significantly influence psychopathology-related disease states, only few animal models cover both the genetic background and environmental manipulations. Therefore, we have taken advantage of the bidirectionally inbred high (HAB) and low (LAB) anxiety-related behavior mouse lines to generate HAB × LAB F1 hybrids that intrinsically carry both lines' genetic characteristics, and subsequently raised them in three different environments-standard, enriched (EE) and chronic mild stress (CMS). Assessing genetic correlates of trait anxiety, we focused on two genes already known to play a role in HAB vs. LAB mice, corticotropin releasing hormone receptor type 1 (Crhr1) and high mobility group nucleosomal binding domain 3 (Hmgn3). While EE F1 mice showed decreased anxiety-related and increased explorative behaviors compared to controls, CMS sparked effects in the opposite direction. However, environmental treatments affected the expression of the two genes in distinct ways. Thus, while expression ratios of Hmgn3 between the HAB- and LAB-specific alleles remained equal, total expression resembled the one observed in HAB vs. LAB mice, i.e., decreased after EE and increased after CMS treatment. On the other hand, while total expression of Crhr1 remained unchanged between the groups, the relative expression of HAB- and LAB-specific alleles showed a clear effect following the environmental modifications. Thus, the environmentally driven bidirectional shift of trait anxiety in this F1 model strongly correlated with Hmgn3 expression, irrespective of allele-specific expression patterns that retained the proportions of basic differential HAB vs. LAB expression, making this gene a match for environment-induced modifications. An involvement of Crhr1 in the bidirectional behavioral shift could, however, rather be due to different effects of the HAB- and LAB-specific alleles described here. Both candidate genes therefore deserve attention in the complex regulation of anxiety-related phenotypes including environment-mediated effects.
    Frontiers in Behavioral Neuroscience 03/2014; 8:87. DOI:10.3389/fnbeh.2014.00087 · 4.16 Impact Factor
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