Sara Mostafavi | bioRxiv Science

bioRxiv2015

Allele-specific expression reveals interactions between genetic variation and environment

Introduction Introduction Main text Methods References The impact of environment on human health is dramatic, with major risk factors including substance use1, diet2 and exercise3. However, identifying interactions between the environment and an individuals genetic background (GxE) has been hampered by statistical and computational challenges4,5. By combining RNA sequencing of whole blood and extensive environmental annotations collected from 922 individuals6, we have evaluated GxE interactions at a cellular level. We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on association between environment and allele-specific expression (ASE). EAGLE inc ...

Genomics↗

bioRxiv2015

Impact of the X chromosome and sex on regulatory variation

The X chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. We have conducted an analysis of the impact of both sex and the X chromosome on patterns of gene expression identified through transcriptome sequencing of whole blood from 922 individuals. We identified that genes on the X chromosome are more likely to have sex-specific expression compared to the autosomal genes. Furthermore, we identified a depletion of regulatory variants on the X chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X chromosome. To resolve the molecular mechanisms underlying such effects, we generated and connected sex-specific chromatin accessibility to sex-specific expression and regulatory variation. As sex-specific regulatory variants can inform sex differences in genetic disease prevalence, we have integrated our data with genome-wide association study data for multiple immune traits and to identify traits with significant sex biases. Together, our study provides genome-wide insight into how the X chromosome and sex shape human gene regulation and disease.

Genomics↗

bioRxiv2014

Sharing and specificity of co-expression networks across 35 human tissues

To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue-specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue-specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.\n\nAuthor SummaryCells in different tissues perform very different functions with the same DNA. This requires tissue-specific gene expression and regulation; understanding this tissue-specificity is often instrumental to understanding complex diseases. Here, we use tissue-specific gene expression data to learn tissue-specific gene regulatory networks for 35 human tissues, where two genes are linked if their expression levels are correlated. Learning such networks accurately is difficult because of the large number of possible links between genes and small number of samples. We propose a novel algorithm that combats this problem by sharing data between similar tissues and show that this increases the accuracy with which networks are learned. We provide a web tool for exploring these networks, enabling users to pose diverse queries in a gene-or tissue-centric manner, and facilitating explorations into gene function and regulation.

Bioinformatics↗