BSU Virtual Seminar: 'Genetic effects on gene expression across tissues, cell types, and biological contexts'
Duration: 1 hour 3 mins
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About this item
| Description: |
Speaker: Kaur Alasoo, Wellcome Sanger Institute
Title: 'Genetic effects on gene expression across tissues, cell types, and biological contexts' |
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| Created: | 2021-01-13 09:46 |
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| Collection: | BSU Virtual Seminars 2020 |
| Publisher: | University of Cambridge |
| Copyright: | A.S. Quenault |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Keywords: | Genetics; biostatistics; seminar; |
| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | Yes |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | An increasing number of gene expression quantitative trait locus (eQTL) studies have made summary statistics publicly available, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and colocalisation. However, differences between these datasets, in their variants tested, allele codings, and in the transcriptional features quantified, are a barrier to their widespread use. Consequently, target genes for most GWAS signals have still not been identified. Here, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl/), a resource which contains quality controlled, uniformly re-computed QTLs from 21 eQTL studies. We find that for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most cis-eQTLs were shared between most bulk tissues, the analysis of purified cell types identified a greater diversity of cell-type-specific eQTLs, a subset of which also manifested as novel disease colocalisations. Our summary statistics can be downloaded by FTP, accessed via a REST API, and visualised on the Ensembl genome browser. New datasets will continuously be added to the eQTL Catalogue, enabling the systematic interpretation of human GWAS associations across a large number of cell types and tissues.
However, to understand disease biology, we need to go beyond cis-eQTL colocalisations and uncover the cellular processes that contribute to disease development and progression. Although trans-acting expression quantitative trait loci (trans-eQTLs) can directly reveal cellular processes modulated by disease variants, detecting trans-eQTLs remains challenging due to their small effect sizes. Here, we analysed gene expression and genotype data from six blood cell types from 226 to 710 individuals. We used co-expression modules inferred from gene expression data with five methods as traits in trans-eQTL analysis to limit multiple testing and improve interpretability. In addition to replicating three established associations, we discovered a novel trans-eQTL near SLC39A8 regulating a module of metallothionein genes in LPS-stimulated monocytes. Interestingly, this effect was mediated by a transient cis-eQTL present only in early LPS response and lost before the trans effect appeared. Our analyses highlight how co-expression combined with functional enrichment analysis improves the identification and prioritisation of trans-eQTLs when applied to emerging cell-type-specific datasets. |
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