Gene-level quantitative trait mapping in Caenorhabditis elegans.
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ABSTRACT:
SUBMITTER: Noble LM
PROVIDER: S-EPMC8022935 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The gene (odc-1) encoding ornithine decarboxylase, a key enzyme in polyamine biosynthesis, was cloned and characterized. Two introns interrupt the coding sequence of the gene. The deduced protein contains 422 amino acids and is homologous to ornithine decarboxylases of other eukaryotic species. In vitro translation of a transcript of the cDNA yielded an enzymatically active product. The mRNA is 1.5 kb in size and is formed by trans-splicing to SL1, a common 5' RNA segment. odc-1 maps to the middle of LG V, between dpy-11 and unc-42 and near a breakpoint of the nDf32 deficiency strain. Enzymatic activity is low in starved stage 1 (L1) larva and, after feeding, rises progressively as the worms develop. Targeted gene disruption was used to create a null allele. Homozygous mutants are normally viable and show no apparent defects, with the exception of a somewhat reduced brood size. In vitro assays for ornithine decarboxylase activity, however, show no detectable enzymatic activity, suggesting that ornithine decarboxylase is dispensible for nematode growth in the laboratory.
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Project description:Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. The consideration of gene expression correlation over a time or tissue dimension has proved valuable in predicting gene function. Here, we consider correlations over a genetic dimension. In addition to identifying coregulated genes, the genetic dimension also supplies us with information about the genomic locations of putative regulatory loci. We calculated correlations among approximately 45,000 expression traits derived from 60 individuals in an F2 sample segregating for obesity and diabetes. By combining the correlation results with linkage mapping information, we were able to identify regulatory networks, make functional predictions for uncharacterized genes, and characterize novel members of known pathways. We found evidence of coordinate regulation of 174 G protein-coupled receptor protein signaling pathway expression traits. Of the 174 traits, 50 had their major LOD peak within 10 cM of a locus on Chromosome 2, and 81 others had a secondary peak in this region. We also characterized a Riken cDNA clone that showed strong correlation with stearoyl-CoA desaturase 1 expression. Experimental validation confirmed that this clone is involved in the regulation of lipid metabolism. We conclude that trait correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we studied only mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping alone.
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Project description:Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. To map gene regulatory pathways, we used microarray-derived gene expression measurements in 60 individuals of an F2 sample segregating for diabetes. We performed correlation analysis among ~40,000 expression traits. By combining correlation among expression traits and linkage mapping information, we were able to identify regulatory networks, make functional predictions to uncharacterized genes, and characterize novel members of known pathways. Using 36 seed traits, we found evidence of coordinate regulation of 160 G-protein coupled receptor (GPCR) pathway expression traits. Of the 160 traits, 50 had their major LOD peak within 8 cM of a locus on chromosome 2, and 81 others had a secondary peak in this region. A previously uncharacterized Riken cDNA clone, which showed strong correlation with stearoyl CoA desaturase 1 expression, was experimentally validated to be responsive to conditions that regulate lipid metabolism. Using linkage mapping, we identified multiple genes whose expression is under the control of transcription regulatory loci. Trait-correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we only studied mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping only. References: Kendziorski C., M. Chen, M. Yuan, H. Lan, and A.D. Attie. Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping. Biometrics, to appear, 2005. Lan H, Chen M, Flowers JB, Yandell BS, Stapleton DS, et al. (2006) Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping. PLoS Genet 2(1): e6. Keywords: Genetics of gene expression
2005-09-21 | GSE3330 | GEO
Project description:Mendelian loci that control the expression levels of transcripts are called expression quantitative trait loci (eQTL). When mapping eQTL, we often deal with thousands of expression traits simultaneously, which complicates the statistical model and data analysis. Two simple approaches may be taken in eQTL analysis: (1) individual transcript analysis in which a single expression trait is mapped at a time and the entire eQTL mapping involves separate analysis of thousands of traits and (2) individual marker analysis where differentially expressed transcripts are detected on the basis of their association with the segregation pattern of an individual marker and the entire analysis requires scanning markers of the entire genome. Neither approach is optimal because data are not analyzed jointly. We develop a Bayesian clustering method that analyzes all expressed transcripts and markers jointly in a single model. A transcript may be simultaneously associated with multiple markers. Additionally, a marker may simultaneously alter the expression of multiple transcripts. This is a model-based method that combines a Gaussian mixture of expression data with segregation of multiple linked marker loci. Parameter estimation for each variable is obtained via the posterior mean drawn from a Markov chain Monte Carlo sample. The method allows a regular quantitative trait to be included as an expression trait and subject to the same clustering assignment. If an expression trait links to a locus where a quantitative trait also links, the expressed transcript is considered to be associated with the quantitative trait. The method is applied to a microarray experiment with 60 F(2) mice measured for 25 different obesity-related quantitative traits. In the experiment, approximately 40,000 transcripts and 145 codominant markers are investigated for their associations. A program written in SAS/IML is available from the authors on request.
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