Dataset Information

MRCA and MRCE SNP genotypes

ABSTRACT: Gene expression levels can be an important link DNA between variation and phenotypic manifestations. Our previous map of global gene expression based on approximately 400K SNPs and 50K transcripts in 400 sib pairs from the MRCA family panel has been widely used to interpret the results of GWAS. Here, we more than double the size of our initial dataset with expression data on 550 additional individuals from the MRCE family panel using the Illumina whole genome expression array. We have used new statistical methods for dimension reduction to account for non-genetic effects in estimates of expression levels and we have also included SNPs imputed from the 1000 Genomes Project. Our methods reduced false discovery rates and increased the number of eQTLs mapped either locally or at a distance (i.e. in cis or trans) from 1,534 in the MRCA dataset to 4,452 (with <5% FDR). Imputation of 1000 Genomes SNPs further increased the number of eQTLs to 7,302. Using the same methods and imputed SNPs in the newly acquired MRCE dataset we identified eQTLs for 9000 genes. The combined results identify strong local and distant effects for transcripts from 14,177 genes. Our eQTL database (http://www.hsph.harvard.edu/faculty/liming-liang/software/eqtl/) based on these results is freely available to help define the function of disease-associated variants.

OTHER RELATED OMICS DATASETS IN: E-MTAB-1425

PROVIDER: EGAS00000000137 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Publications

A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines.

Liang Liming L Morar Nilesh N Dixon Anna L AL Lathrop G Mark GM Abecasis Goncalo R GR Moffatt Miriam F MF Cookson William O C WO

Genome research 20130123 4

Gene expression levels can be an important link DNA between variation and phenotypic manifestations. Our previous map of global gene expression, based on ~400K single nucleotide polymorphisms (SNPs) and 50K transcripts in 400 sib pairs from the MRCA family panel, has been widely used to interpret the results of genome-wide association studies (GWASs). Here, we more than double the size of our initial data set with expression data on 550 additional individuals from the MRCE family panel using the ...[more]

PMID: 23345460

Similar Datasets

Project description:Motivation: Identification of eQTL, the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model simultaneously accounts for the effects of genotypes while learning hidden factors, where we make use of the complete likelihood of a unified multivariate regression and factor analysis model to derive a ridge estimator for combined factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, no iterative model selection, it provides p-values, and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of SNPs on a standard 8 core 2.6G desktop. Results: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and outperforms related hidden factor methods for eQTL analysis, where the improved performance is particularly evident in the detection of eQTL with multivariate effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in human lung tissue for a study that included presumptive hidden factors. The analysis identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer. We have developed HEFT, a fast multivariate method that detect eQTLs by analyzing thousands of traits simultaneously in the presence of hidden factors. HEFT employs a combined regression factor analysis approach to analyze the gene expression and genotype data sets, looking for univariate or multivariate eQTLs that regulate gene expression, while simultaneously controlling for both orthogonal or non-orthogonal hidden factors. We show by extensive simulation that HEFT outperforms competing methods, and by applying HEFT to a study that included presumptive hidden factors, we identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis. HEFT analysis results file (includes all results, not just top hits) linked below as supplementary file.

Project description:Genetics of gene expression (eQTLs or expression QTLs) has proved an indispensable tool for understanding biological pathways and pathomechanisms of trait associated SNPs. However, power of most genome-wide eQTL studies is still limited. We performed a large eQTL study in peripheral blood mononuclear cells of 2,112 individuals increasing the power to detect trans-effects genome-wide. Going beyond univariate SNP-transcript associations, we analyse relations of eQTLs to biological pathways, polygenetic effects of expression regulation, trans-clusters, and enrichment of co-localised functional elements. We found eQTLs for about 85% of analysed genes, 18% of genes were trans-regulated. Local eSNPs were enriched up to a distance of 5 MB to the transcript challenging typically implemented ranges of cis-regulations. Pathway enrichment within regulated genes of GWAS-related eSNPs supported functional relevance of identified eQTLs. We demonstrate that nearest genes of GWAS-SNPs might often be misleading functional candidates. We identified novel trans-clusters of potential functional relevance for GWAS-SNPs of several phenotypes including obesity-related traits, HDL-cholesterol levels, and haematological phenotypes. We used chromatin immunoprecipitation data for demonstrating biological effects. We show for strongly heritable transcripts that a considerable gap still exists between total heritability resulting from all trans-chromosomes and explained variance of all identified trans-eSNPs. In contrast, the vast majority of most cis-heritability of these genes is already explained. Dissection of co-localised functional elements indicated a prominent role of SNPs in loci of pseudogenes and non-coding RNAs for the regulation of coding genes. In summary, our study substantially increases the catalogue of human eQTLs and improves our understanding of the complex genetic regulation of gene-expression, pathways and disease related processes. Gene expression from human blood mononuclear cells from individuals of the Leipzig LIFE Heart Study was analyzed applying Illumina HT-12 v4 Expression BeadChips. After preprocessing, 28,295 expression probes for 2,112 individuals remained for analysis. In a population-based analysis, we identified associations between DNA variants and RNA expression levels and characterized these findings.

Dataset Information

MRCA and MRCE SNP genotypes

Publications

A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure