Project description:A dissection of Genotype x Environment interactions in grapevine berry using a multi-layered -omics approach Grapevine, the most widely-cultivated perennial fruit crop, is also considered a very environmentally sensitive crop. It is characterized by remarkable phenotypic plasticity which in turn is believed to effectively buffer environmental extremes especially through transcriptomic and epigenomic reprogramming. Thus, the final phenotype (P) of a given vine is the result of the close interaction between its genetic composition (G) and the environment (E). Here we analyzed Genotype x Environment (GxE) interactions in two grapevine varieties by characterizing their transcriptome plasticity when cultivated in different environments. Specifically, two genotypes (Sangiovese and Cabernet Sauvignon) were cultivated in three different locations in Italy (Bolgheri -littoral Tuscany-, Montalcino – Appennine Tuscany- and Romagna -foothill area-), trained in an almost identical manner, and sampled at four developmental stages over two grapevine growing seasons, 2011 and 2012, for a total of 144 samples that were analyzed by hybridization to a whole-genome microarray and by Reduced Representation Bisulfite Sequencing (RRBS-seq). In order to study the relationships among differential gene expression profiles and environmental cues, we have developed a new statistical data mining tool based on data reduction approaches which allowed a dissection of the transcriptomic data into stage-specific, cultivar-related and GxE important clusters of gene expression. This deep inspection of inner relationships between the different dataset variables allowed the identification of several candidate genes that could represent putative markers of berry quality traits in grapevine GxE interactions. Moreover, the methods used to establish our model provide a framework for the analysis of transcriptome plasticity in other crops as they respond to diverse environments.
Project description:this dataset encompasses the transcriptomic changes elicited in liver tissues of 4-months-old mice with either the artificially-knock-in rs6190 genotype, or the control non-mutant (WT) genotype.
Project description:Background: Cases where genotype-phenotype relationships depend on environmental factors have been quantified for many complex diseases. Such genotype-environment interactions (GEI or GxE) may also affect expression Quantitative Trait Loci (eQTL) present in tissues critical for the manifestation of disease. To assess this hypothesis, we performed an analysis of eQTL-GEI resulting from an individual's smoking environment in the lung small airway epithelium (SAE). While the SAE is challenging to sample, this is the cell population that shows the first signs of smoking related stress and gene expression in the SAE appears to play a role in mediating smoking effects on lung disease. Results: We used expression microarrays to assay the SAE transcriptome for a small sample of African-American individuals and we analyzed SNPs genotyped genome-wide to identify GEI affecting eQTL. While a genome-wide trans- analysis identified few instances of GEI after a multiple test correction, an analysis of cis-genotypes identified a small but significant number of GEI affecting lung SAE gene expression. We determined that significant cases of eQTL-GEI were not driven by outliers and we were also able to find corroborative evidence for a few of these eQTL-GEI in a small, independent sample of individuals of European ancestry. Conclusion: Given that the power of GEI tests is low compared to tests of genotype association and that the total sample size of our study was small, including only 61 African American individuals in our focal population, the identification of significant GEI in our study implies that there may be considerable genotype-specific effects on eQTL due to smoking environment. We discuss individual cases of GEI of interest for lung disease, such as SDC1 and ZAK, as well as the broader implications of our results for the analysis of eQTL and for genome-wide association analysis of complex diseases.