Biology subjects

Ana Viñuela

Publications and source records attributed to Ana Viñuela.

3 recordsLinked to original sources

bioRxiv2016

Quantifying the degree of sharing of genetic and non-genetic causes of gene expression variability across four tissues.

Gene expression can provide biological mechanisms which underlie genetic associations with complex traits and diseases, but often the most relevant tissue for the trait is inaccessible and a proxy is the only alternative. Here, we investigate shared and tissue specific patterns of variability in expression in multiple tissues, to quantify the degree of sharing of causes (genetic or non-genetic) of variability in gene expression among tissues. Using gene expression in ~800 female twins from the TwinsUK cohort in skin, fat, whole blood and lymphoblastoid cell lines (LCLs), we identified 9166 significant cis-eQTLs in fat, 9551 in LCLs, 8731 in skin and 5313 in blood (1% FDR). We observed up to 80% of cis-eQTLs are shared in pairs of tissues. In addition, the cis genetic correlation between tissues is > 90% for 35% of the genes, indicating for these genes a largely tissue-shared component of cis regulation. However, variance components show that cis genetic signals explain only a small fraction of the variation in expression, with from 67-87% of the variance explained by environmental factors, and 53% of the genetic effects occurring in trans. We observe a trans genetic correlation of 0 for all genes except a few which show correlation between fat and skin expression. The environmental effects are also observed to be entirely tissue specific, despite related tissues largely sharing exposures. These results demonstrate that patterns of gene expression are largely tissue specific, strongly supporting the need to study higher order regulatory interactions in the appropriate tissue context with large samples sizes and diversity of environmental contexts.

Genomics↗

bioRxiv2015

Pathway based factor analysis of gene expression data produces highly heritable phenotypes that associate with age

Statistical factor analysis methods have previously been used to remove noise components from high dimensional data prior to genetic association mapping, and in a guided fashion to summarise biologically relevant sources of variation. Here we show how the derived factors summarising pathway expression can be used to analyse the relationships between expression, heritability and ageing. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarise patterns of gene expression, both to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 \"pathway phe-notypes\" which summarised patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bon-ferroni threshold (P < 5.38 x 10-5). These phenotypes are more heritable (h2 = 0.32) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolising sugars and fatty acids, others with insulin signalling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors.

Genomics↗

bioRxiv2014

Transcriptome Sequencing Reveals Widespread Gene-Gene and Gene-Environment Interactions

Understanding the genetic architecture of gene expression is an intermediate step to understand the genetic architecture of complex diseases. RNA-seq technologies have improved the quantification of gene expression and allow to measure allelic specific expression (ASE)1-3. 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 to date. In this study we take advantage of a sample of twins to measure the relative contribution of genetic and environmental effects on ASE and we found substantial effects of gene x gene (GxG) and gene x environment (GxE) interactions. We propose a model where ASE requires genetic variability in cis, a difference in the sequence of both alleles, but the magnitude of the ASE effect depends on trans genetic and environmental factors that interact with the cis genetic variants. We uncover large GxG and GxE effects on gene expression and likely complex phenotypes that currently remain elusive.

Genomics↗