Expression quantitative trait locus analysis for translational medicine.
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ABSTRACT:
SUBMITTER: Gibson G
PROVIDER: S-EPMC4479075 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Similar Datasets
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.
| S-EPMC1331977 | biostudies-literature
Project description:BackgroundQuantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data.ResultsUsing a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL.ConclusionsThis proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.
| S-EPMC10088184 | biostudies-literature
Project description:This data originates from an expression quantitative trait locus analysis of liver in an advanced intercross of Red Jungefowl and White Leghorn chickens. The aim of the study was to map the genetic basis of growth traits and transcript abundance traits in the liver, and use the latter to search for candidate causative genes for chicken growth.
2018-01-25 | E-MTAB-5572 | biostudies-arrayexpress
Project description:This data originates from an expression quantitative trait locus analysis of cerebrum in an advanced intercross of Red Jungefowl and White Leghorn chickens. The aim of the study was to map the genetic basis of cerebrum and body mass, and idenifiy transcriptional differences within the intercross to assess any candidate genes for cerebrum and body mass.
2020-07-07 | E-MTAB-9313 | biostudies-arrayexpress
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:Expression quantitative trait loci (eQTL) can provide a link between disease susceptibility variants discovered by genetic association studies and biology. To date, eQTL mapping studies have been primarily conducted in healthy individuals from population-based cohorts. Genetic effects have been known to be context-specific and vary with changing environmental stimuli. We conducted a transcriptome- and genome-wide eQTL mapping study in a cohort of patients with idiopathic or heritable pulmonary arterial hypertension (PAH) using RNA sequencing (RNAseq) data from whole blood. We sought confirmation from three published population-based eQTL studies, including the GTEx Project, and followed up potentially novel eQTL not observed in the general population. In total, we identified 2314 eQTL of which 90% were cis-acting and 75% were confirmed by at least one of the published studies. While we observed a higher GWAS trait colocalization rate among confirmed eQTL, colocalisation rate of novel eQTL reported for lung-related phenotypes was twice as high as that of confirmed eQTL. Functional enrichment analysis of genes with novel eQTL in PAH highlighted immune-related processes, a suspected contributor to PAH. These potentially novel eQTL specific to or active in PAH could be useful in understanding genetic risk factors for other diseases that share common mechanisms with PAH.
| S-EPMC7690609 | biostudies-literature
Project description:Susceptibility to thrombosis varies in human populations as well as many in inbred mouse strains. The objective of this study was to characterize the genetic control of thrombotic risk on three chromosomes. Previously, utilizing a tail-bleeding/rebleeding assay as a surrogate of hemostasis and thrombosis function, three mouse chromosome substitution strains (CSS) (B6-Chr5(A/J), Chr11(A/J), Chr17(A/J)) were identified (Hmtb1, Hmtb2, Hmtb3). The tail-bleeding/rebleeding assay is widely used and distinguishes mice with genetic defects in blood clot formation or dissolution. In the present study, quantitative trait locus (QTL) analysis revealed a significant locus for rebleeding (clot stability) time (time between cessation of initial bleeding and start of the second bleeding) on chromosome 5, suggestive loci for bleeding time (time between start of bleeding and cessation of bleeding) also on chromosomes 5, and two suggestive loci for clot stability on chromosome 17 and one on chromosome 11. The three CSS and the parent A/J had elevated clot stability time. There was no interaction of genes on chromosome 11 with genes on chromosome 5 or chromosome 17. On chromosome 17, twenty-three candidate genes were identified in synteny with previously identified loci for thrombotic risk on human chromosome 18. Thus, we have identified new QTLs and candidate genes not previously known to influence thrombotic risk.
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Project description:The transcriptome connects genome to the gene function and ultimate phenome in biology. So far, transcriptomic approach was not used in peanut for performing trait mapping in bi-parental populations. In this research, we sequenced the whole transcriptome in immature seeds in a peanut recombinant inbred line (RIL) population and explored thoroughly the landscape of transcriptomic variations and its genetic basis. The comprehensive analysis identified total 49 691 genes in RIL population, of which 92 genes followed a paramutation-like expression pattern. Expression quantitative trait locus (eQTL) analysis identified 1207 local eQTLs and 15 837 distant eQTLs contributing to the whole-genome transcriptomic variation in peanut. There were 94 eQTL hot spot regions detected across the genome with the dominance of distant eQTL. By integrating transcriptomic profile and annotation analyses, we unveiled a putative candidate gene and developed a linked marker InDel02 underlying a major QTL responsible for purple testa colour in peanut. Our result provided a first understanding of genetic basis of whole-genome transcriptomic variation in peanut and illustrates the potential of the transcriptome-aid approach in dissecting important traits in non-model plants.
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Project description:Breast cancer genome-wide association studies have pinpointed dozens of variants associated with breast cancer pathogenesis. The majority of risk variants, however, are located outside of known protein-coding regions. Therefore, identifying which genes the risk variants are acting through presents an important challenge. Variants that are associated with mRNA transcript levels are referred to as expression quantitative trait loci (eQTLs). Many studies have demonstrated that eQTL-based strategies provide a direct way to connect a trait-associated locus with its candidate target gene. Performing eQTL-based analyses in human samples is complicated because of the heterogeneous nature of human tissue. We addressed this issue by devising a method to computationally infer the fraction of cell types in normal human breast tissues. We then applied this method to 13 known breast cancer risk loci, which we hypothesized were eQTLs. For each risk locus, we took all known transcripts within a 2 Mb interval and performed an eQTL analysis in 100 reduction mammoplasty cases. A total of 18 significant associations were discovered (eight in the epithelial compartment and 10 in the stromal compartment). This study highlights the ability to perform large-scale eQTL studies in heterogeneous tissues.
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Project description:Over the years, substantial gains have been made in locating regions of agricultural genomes associated with characteristics, diseases, and agroeconomic traits. These gains have relied heavily on the ability to statistically estimate the association between DNA markers and regions of a genome (quantitative trait loci or QTL) related to a particular trait. The majority of these advances have focused on diploid species, even though many important agricultural crops are, in fact, polyploid. The purpose of our work is to initiate an algorithmic approach for model selection and QTL detection in polyploid species. This approach involves the construction of all possible chromosomal configurations (models) that may result in a gamete, model reduction based on estimation of marker dosage from progeny data, and lastly model selection. While simplified for initial explanation, our approach has demonstrated itself to be extendible to many breeding schemes and less restricted settings.
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