TIGER: The gene expression regulatory variation landscape of human pancreatic islets

Lorena Alonso(Barcelona Supercomputing Center), Anthony Piron(Université Libre de Bruxelles), Ignasi Morán(Barcelona Supercomputing Center), Marta Guindo-Martínez(Barcelona Supercomputing Center), Sílvia Bonàs‐Guarch(Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas), Goutham Atla(Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas), Irene Miguel-Escalada(Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas), Romina Royo(Barcelona Supercomputing Center), Montserrat Puiggròs(Barcelona Supercomputing Center), Xavier Garcia-Hurtado(Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas), Mara Suleiman(University of Pisa), Lorella Marselli(University of Pisa), Jonathan L.S. Esguerra(Lund University), Jean‐Valéry Turatsinze(Université Libre de Bruxelles), Jason Torres(Centre for Human Genetics), Vibe Nylander(University of Oxford), Ji Chen(University of Exeter), Lena Eliasson(Lund University), Matthieu Defrance(Université Libre de Bruxelles), Ramon Amela(Barcelona Supercomputing Center), Hindrik Mulder(Lund University), Anna L. Gloyn(Centre for Human Genetics), Leif Groop(Lund University), Piero Marchetti(University of Pisa), Décio L. Eizirik(Université Libre de Bruxelles), Jorge Ferrer(Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas), Josep M. Mercader(Broad Institute), Miriam Cnop(Université Libre de Bruxelles), David Torrents(Institució Catalana de Recerca i Estudis Avançats)
Cell Reports
October 1, 2021
Cited by 99Open Access
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Abstract

Genome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D.


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