{"database":"EGA","file_versions":[],"scores":null,"additional":{"omics_type":["Genomics"],"contact_person":["Darioush Yarand"],"full_dataset_link":["https://ega-archive.org/dacs/EGAC00001000274"],"host":["EGA"],"description":["EGA DAC EGAC00001000274"],"repository":["EGA"],"email":["Darioush.yarand@kcl.ac.uk"],"pubmed_abstract":["Non-additive interaction between genetic variants, or epistasis, is a possible explanation for the gap between heritability of complex traits and the variation explained by identified genetic loci. Interactions give rise to genotype dependent variance, and therefore the identification of variance quantitative trait loci can be an intermediate step to discover both epistasis and gene by environment effects (GxE). Using RNA-sequence data from lymphoblastoid cell lines (LCLs) from the TwinsUK cohort, we identify a candidate set of 508 variance associated SNPs. Exploiting the twin design we show that GxE plays a role in ∼70% of these associations. Further investigation of these loci reveals 57 epistatic interactions that replicated in a smaller dataset, explaining on average 4.3% of phenotypic variance. In 24 cases, more variance is explained by the interaction than their additive contributions. Using molecular phenotypes in this way may provide a route to uncovering genetic interactions underlying more complex traits.DOI: http://dx.doi.org/10.7554/eLife.01381.001.","Small RNAs are functional molecules that modulate mRNA transcripts and have been implicated in the aetiology of several common diseases. However, little is known about the extent of their variability within the human population. Here, we characterise the extent, causes, and effects of naturally occurring variation in expression and sequence of small RNAs from adipose tissue in relation to genotype, gene expression, and metabolic traits in the MuTHER reference cohort. We profiled the expression of 15 to 30 base pair RNA molecules in subcutaneous adipose tissue from 131 individuals using high-throughput sequencing, and quantified levels of 591 microRNAs and small nucleolar RNAs. We identified three genetic variants and three RNA editing events. Highly expressed small RNAs are more conserved within mammals than average, as are those with highly variable expression. We identified 14 genetic loci significantly associated with nearby small RNA expression levels, seven of which also regulate an mRNA transcript level in the same region. In addition, these loci are enriched for variants significant in genome-wide association studies for body mass index. Contrary to expectation, we found no evidence for negative correlation between expression level of a microRNA and its target mRNAs. Trunk fat mass, body mass index, and fasting insulin were associated with more than twenty small RNA expression levels each, while fasting glucose had no significant associations. This study highlights the similar genetic complexity and shared genetic control of small RNA and mRNA transcripts, and gives a quantitative picture of small RNA expression variation in the human population.","Understanding the genetic architecture of gene expression is an intermediate step in understanding the genetic architecture of complex diseases. RNA sequencing technologies have improved the quantification of gene expression and allow measurement of allele-specific expression (ASE). ASE is hypothesized to result from the direct effect of cis regulatory variants, but a proper estimation of the causes of ASE has not been performed thus far. In this study, we take advantage of a sample of twins to measure the relative contributions of genetic and environmental effects to ASE, and we find substantial effects from gene × gene (G×G) and gene × environment (G×E) interactions. We propose a model where ASE requires genetic variability in cis, a difference in the sequence of both alleles, but where the magnitude of the ASE effect depends on trans genetic and environmental factors that interact with the cis genetic variants."],"pubmed_title":["Genetic interactions affecting human gene expression identified by variance association mapping.","Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins.","Extent, causes, and consequences of small RNA expression variation in human adipose tissue."],"pubmed_authors":["Buil Alfonso A, Brown Andrew Anand AA, Lappalainen Tuuli T, Viñuela Ana A, Davies Matthew N MN, Zheng Hou-Feng HF, Richards J Brent JB, Glass Daniel D, Small Kerrin S KS, Durbin Richard R, Spector Timothy D TD, Dermitzakis Emmanouil T ET","Parts Leopold L, Hedman Åsa K ÅK, Keildson Sarah S, Knights Andrew J AJ, Abreu-Goodger Cei C, van de Bunt Martijn M, Guerra-Assunção José Afonso JA, Bartonicek Nenad N, van Dongen Stijn S, Mägi Reedik R, Nisbet James J, Barrett Amy A, Rantalainen Mattias M, Nica Alexandra C AC, Quail Michael A MA, Small Kerrin S KS, Glass Daniel D, Enright Anton J AJ, Winn John J, Deloukas Panos P, Dermitzakis Emmanouil T ET, McCarthy Mark I MI, Spector Timothy D TD, Durbin Richard R, Lindgren Cecilia M CM","Brown Andrew Anand AA, Buil Alfonso A, Viñuela Ana A, Lappalainen Tuuli T, Zheng Hou-Feng HF, Richards J Brent JB, Small Kerrin S KS, Spector Timothy D TD, Dermitzakis Emmanouil T ET, Durbin Richard R"],"additional_accession":[]},"is_claimable":false,"name":"TwinsUK Resource Executive Committee (TREC)","description":"Data Access Committee EGAC00001000274","dates":{"output":"2025-1-9"},"accession":"EGAC00001000274","cross_references":{"TAXONOMY":["9606"],"pubmed":["25436857","24771767","22589741"],"EGA":["EGAS00001000805","EGAS00001002210","EGAS00001001700","EGAS00001001194","EGAS00001000212","EGAS00001001910","EGAD00001001383","EGAD00010000983","EGAD00001000105","EGAD00001007028","EGAD00001001086","EGAD00001001087","EGAD00001001088","EGAD00001001382","EGAD00001005055","EGAD00001001089"]}}