Elodie Persyn, Cambridge University, UK

Elodie Persyn, PhD
Cambridge University, UK
 

Invited by :

 

Abstract

Genome-wide association studies (GWAS) have enabled the identification of many genetic variations associated with diseases and traits. To better understand the underlying mechanisms of genetic variations, quantitative trait loci (QTLs) have been identified for molecular traits from transcriptomics, proteomics or metabolomics datasets. In this study, we conducted the analysis of RNA-sequencing data from the blood samples of 4,732 participants from the INTERVAL cohort. We aimed to (1) identify the genetic variants regulating the gene expression and splicing, (2) integrate multiple -omics data from INTERVAL, and (3) study the relationship with diseases and traits.

From the QTL mapping analyses, we identified 17,233 genes with a cis-expression QTL (cis-eQTL) and 6,853 genes with a cis-splicing QTL (cis-sQTL). Colocalization analyses were conducted to better understand the shared genetic etiology with the other molecular traits from the INTERVAL study. We could identify 524 proteins and 719 metabolites with a colocalized association signal with either a cis-eQTL or a cis-sQTL. From samples with multiple omics data being collected, we further explored the causality in colocalization results by testing the mediated effect of genetic variants on downstream molecular traits through transcriptional traits. Genetic effects for a total of 233 molecular traits were completely mediated by expression level or splicing. `We performed colocalization analyses for disease traits including COVID-19 susceptibility and severity and 20 disease phenotypes from the FinnGen project. Across the COVID-19 traits, we found colocalized association signals with 67 cis-eGenes and 42 cis-sGenes, with OAS1 splicing as a notable example that has been previously shown to have an impact on COVID-19 severity.

The findings of this study will help in understanding regulatory biological mechanisms of genetic variations that have been found associated with disease traits. The results will be made publicly available through an online portal. Furthermore, the eQTL results have been integrated in the recent OMICSPRED

Biography

Dr Elodie Persyn studied agricultural engineering in Rennes (France) and obtained a Masters degree in Cellular and Molecular Biology and in Applied Statistics. She did a PhD in Nantes (France) with Richard Redon, Christian Dina and Lise Bellanger on rare variant association studies. She developed research interests in genetic epidemiology and population genetics with the comparison of statistical methods and the application to complex traits. After her PhD, she moved to the UK to join Cathryn Lewis’ team in King’s College London as a post-doctoral researcher. In collaboration with Hugh Markus from the University of Cambridge, she studied the genetics of brain imaging biomarkers of small vessel disease through the analysis of UK Biobank data. Currently part of Mike Inouye’s team in the Cardiovascular Epidemiology Unit (CEU) of the University of Cambridge, she is analysing RNA-sequencing data and investigating the integration of multi-omics data from the INTERVAL study (https://www.intervalstudy.org.uk/).