Dataset Information

Gene-environment interaction in yeast gene expression

ABSTRACT: This study is a quantitative trait linkage mapping study targeted at gene-environment interaction. A family of yeast derived from BY4716 and RM11-1a was grown in two carbon sources (glucose and ethanol) and expression profiled. The analysis revealed how the influence of genetic factors changes in different conditions. Abstract: The effects of genetic variants on phenotypic traits often depend on environmental and physiological conditions, but such gene–environment interactions are poorly understood. Recently developed approaches that treat transcript abundances of thousands of genes as quantitative traits offer the opportunity to broadly characterize the architecture of gene–environment interactions. We examined the genetic and molecular basis of variation in gene expression between two yeast strains (BY and RM) grown in two different conditions (glucose and ethanol carbon sources). We observed that most transcripts vary by strain and condition, with 2,996, 3,448, and 2,037 transcripts showing significant strain, condition, and strain–condition interaction effects, respectively. We expression profiled over 100 segregants derived from a cross between RM and BY in both growth conditions, and identified 1,555 linkages for 1,382 transcripts that show significant gene–environment interaction. At the locus level, local linkages, which usually correspond to polymorphisms in cis-regulatory elements, tend to be more stable across conditions, such that they are more likely to show the same effect or the same direction of effect across conditions. Distant linkages, which usually correspond to polymorphisms influencing trans-acting factors, are more condition-dependent, and often show effects in different directions in the two conditions. We characterized a locus that influences expression of many growth- related transcripts, and showed that the majority of the variation is explained by polymorphism in the gene IRA2. The RM allele of IRA2 appears to inhibit Ras/PKA signaling more strongly than the BY allele, and has undergone a change in selective pressure. Our results provide a broad overview of the genetic architecture of gene–environment interactions, as well as a detailed molecular example, and lead to key insights into how the effects of different classes of regulatory variants are modulated by the environment. These observations will guide the design of studies aimed at understanding the genetic basis of complex traits. Keywords: QTL

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE9376 | GEO | 2008/04/15

SECONDARY ACCESSION(S): PRJNA103083

REPOSITORIES: GEO

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Project description:This study is a quantitative trait linkage mapping study targeted at gene-environment interaction. A family of yeast derived from BY4716 and RM11-1a was grown in two carbon sources (glucose and ethanol) and expression profiled. The analysis revealed how the influence of genetic factors changes in different conditions. Abstract: The effects of genetic variants on phenotypic traits often depend on environmental and physiological conditions, but such gene–environment interactions are poorly understood. Recently developed approaches that treat transcript abundances of thousands of genes as quantitative traits offer the opportunity to broadly characterize the architecture of gene–environment interactions. We examined the genetic and molecular basis of variation in gene expression between two yeast strains (BY and RM) grown in two different conditions (glucose and ethanol carbon sources). We observed that most transcripts vary by strain and condition, with 2,996, 3,448, and 2,037 transcripts showing significant strain, condition, and strain–condition interaction effects, respectively. We expression profiled over 100 segregants derived from a cross between RM and BY in both growth conditions, and identified 1,555 linkages for 1,382 transcripts that show significant gene–environment interaction. At the locus level, local linkages, which usually correspond to polymorphisms in cis-regulatory elements, tend to be more stable across conditions, such that they are more likely to show the same effect or the same direction of effect across conditions. Distant linkages, which usually correspond to polymorphisms influencing trans-acting factors, are more condition-dependent, and often show effects in different directions in the two conditions. We characterized a locus that influences expression of many growth- related transcripts, and showed that the majority of the variation is explained by polymorphism in the gene IRA2. The RM allele of IRA2 appears to inhibit Ras/PKA signaling more strongly than the BY allele, and has undergone a change in selective pressure. Our results provide a broad overview of the genetic architecture of gene–environment interactions, as well as a detailed molecular example, and lead to key insights into how the effects of different classes of regulatory variants are modulated by the environment. These observations will guide the design of studies aimed at understanding the genetic basis of complex traits. Keywords: QTL This data set includes 24 parental arrays (6 of each parent in each condition, 218 segregant arrays (109 in both conditions), and 4 allele replacement arrays (IRA2 replacements in both backgrounds in both conditions). All arrays are from an unique biological sample, and many are randomized for dye (no dye-swaps on the same RNA sample). For the 24 parental arrays, there are three of each strain*condition*dye combination. The segregant arrays were randomized for dye. The replacement arrays are all of the Cy5 = experimental, Cy3 = reference type. All arrays are hybridized to a common reference consisting of an equal portions of RNA samples. from BY and RM grown in glucose and ethanol. Analysis was performed using default settings in Agilent Feature Extractor software versions 8.0-9.5. Spots were excluded if their intensity was below 350 or if they were flagged as non-uniformity outliers. Transcripts were considered good if they were present in all parental arrays (for the parental analysis) or 80% of all arrays in each condition for the segregant analysis.

Project description:Sexually dimorphic traits are by definition exaggerated in one sex, which may arise from a history of sex-specific selection â in males, females, or both. If this exaggeration comes at a cost, exaggeration is expected to be greater in higher condition individuals (condition-dependent). Although studies using small numbers of morphological traits are generally supportive, this prediction has not been examined at a larger scale. We test this prediction across the trancriptome by determining the condition-dependence of sex-biased (dimorphic) gene expression. We find that high-condition populations are more sexually dimorphic in transcription than low-condition populations. High condition populations have more male-biased genes and more female-biased genes, and a greater degree of sexually dimorphic expression in these genes. Also, condition-dependence in male-biased genes was greater than in a set of unbiased genes. Interestingly, male-biased genes expressed in the testes were not more condition-dependent than those in the soma. By contrast, increased female-biased expression under high condition may be have occurred because of the greater contribution of the ovary-specific transcripts to the entire mRNA pool. We did not find any genomic signatures distinguishing the condition-dependent sex-biased genes. The degree of condition-dependent sexual dimorphism (CDSD) did not differ between the autosomes and the X-chromosome. There was only weak evidence that rates of evolution correlated with CDSD. We suggest that the sensitivity of both female-biased genes and male-biased genes to condition may be akin to the overall heightened sensitivity to condition that life-history and sexually selected traits tend to exhibit. Our results demonstrate that through condition-dependence, early life experience has dramatic effects on sexual dimorphism in the adult transcriptome. There were 8 biologically distinct samples. Each was replicated 6 times for a total of 48 biological samples on 24 arrays. There was no reference or control sample as a loop design was used. Each of the 48 samples are represented separately.

Project description:Numerous genome-wide screens for polymorphisms that influence gene expression have provided key insights into the genetic control of transcription. Despite this work, the relevance of specific polymorphisms to in vivo expression and splicing remains unclear. We carried out the first genome-wide screen, to our knowledge, for SNPs that associate with alternative splicing and gene expression in human primary cells, evaluating 93 autopsy-collected cortical brain tissue samples with no defined neuropsychiatric condition and 80 peripheral blood mononucleated cell samples collected from living healthy donors. We identified 23 high confidence associations with total expression and 80 with alternative splicing as reflected by expression levels of specific exons. Fewer than 50% of the implicated SNPs however show effects in both tissue types, reflecting strong evidence for distinct genetic control of splicing and expression in the two tissue types. The data generated here also suggest the possibility that splicing effects may be responsible for up to 13 out of 84 reported genome-wide significant associations with human traits. These results emphasize the importance of establishing a database of polymorphisms affecting splicing and expression in primary tissue types and suggest that splicing effects may be of more phenotypic significance than overall gene expression changes. We evaluated 93 autopsy-collected cortical brain tissue samples with no defined neuropsychiatric condition and 80 peripheral blood mononucleated cell samples collected from living healthy donors. Affymetrix Human ST 1.0 exon arrays were used to assess exon and transcript expression levels for all samples used in the study. Replicate analyses were not performed. Exon array sample preparation from total RNA was conducted based on standard Affymetrix protocols. Data were normalized separately within PBMC and brain sets Affymetrix PLIER protocol with a sketch-quantile normalization procedure (Affymetrix Expression Console). Normalized files include brain core transcripts (NABRAINSETCOMBplier-gene-core.summary), brain all transcripts (NABRAINSETCOMBplier-gene-full.summary), brain core exons (NABRAINSETCOMBplier-exon-core.summary), brain all exons (NABRAINSETCOMBplier-exon-all.summary), PBMC core transcripts (NAPBMCfullsetplier-gene-core.summary), PBMC all transcripts (NAPBMCfullsetplier-gene-full.summary), PBMC core exons (NAPBMCfullsetplier-exon-core.summary), PBMC all exons (NAPBMCfullsetplier-exon-all.summary). all, full and core definitions are provided by Affymetrix.

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Gene-environment interaction in yeast gene expression

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