Differential Regulation of Cryptic Genetic Variation Shapes the Genetic Interactome Underlying Complex Traits
| Autor: | Kaustubh Dhole, Anupama Yadav, Himanshu Sinha |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
cryptic genetic variation Population Quantitative Trait Loci Gene regulatory network Saccharomyces cerevisiae Quantitative trait locus Biology 03 medical and health sciences variance QTL genetic networks Gene Expression Regulation Fungal Genetic variation Genetics Gene Regulatory Networks Gene–environment interaction Allele education gene–environment interaction Ecology Evolution Behavior and Systematics education.field_of_study phenotypic buffering Chromosome Mapping Genetic Variation Epistasis Genetic Genetic architecture 030104 developmental biology Evolutionary biology Gene-Environment Interaction Adaptation Research Article |
| Zdroj: | Genome Biology and Evolution |
| ISSN: | 1759-6653 |
| Popis: | Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets. |
| Databáze: | OpenAIRE |
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