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Rele, C.

Publications and source records attributed to Rele, C..

2 recordsLinked to original sources

Gene model for the ortholog of Ilp3 in Drosophila eugracilis

Gene model for the ortholog of Insulin-like peptide 3 (Ilp3) in the D. eugracilis Apr. 2013 (BCM-HGSC/Deug_2.0) (DeugGB2) Genome Assembly of D. eugracilis (GenBank Accession: GCA_000236325.2) of Drosophila eugracilis. This ortholog was characterized as part of a dataset to study the evolution of the Insulin/ insulin-like growth factor signaling pathway (IIS) across the genus Drosophila using the Genomics Education Partnership gene annotation protocol for Course-based Undergraduate Research Experiences.

genomics↗

Mapping of genotype-by-environment interaction loci for Metabolic Syndrome-like traits using the multi-parent Drosophila Synthetic Population Resource determines that main genetic effects are distinct from environment dependent plastic loci

Metabolic Syndrome (MetS) risk, driven by genotype-environment interactions like diet, is rising globally. Due to its genetic and environmental complexity, the genetic architecture and interconnected traits underlying MetS is poorly understood. In Drosophila, genotype-by-diet interactions significantly influence MetS-like traits. This study used the Drosophila Synthetic Population Resource to dissect the genetic architecture of both genotypic and genotype-by-diet interaction effects underlying trait variation. The study hypotheses were: 1) Loci responsible for metabolic phenotypic variation should be shared across traits. 2) Genetic loci responsible for plasticity and epistatic interactions for metabolic traits should also be the loci responsible for the main effects. 3) Genes responsible for variation in metabolic traits should share common functions. Using a round-robin crossing scheme and novel analyses, we mapped additive, dominance, and epistatic loci--some diet-specific, others diet-independent. Main-effect and plastic loci were largely distinct, as were epistatic loci from main-effect loci, highlighting that main genetic effects alone will not explain how genetic variants interact with the environment or the genome to influence disease risk. gene-by-diet or gene-by-gene interactions influencing MetS risk. Further, tremendous cryptic genetic variation for metabolic traits is lurking in natural populations. We explored the function of candidate genes from our study empirically and with bioinformatics. While some of the candidate genes might have been expected, most would not have been identified a priori, thus with this study we have identified many new candidate mechanisms contributing to the genetic and genotype-by-diet interaction effects on MetS variance.

genetics↗