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The expression quantitative trait (eQTL) landscape of type 2 diabetes in 404 human islet samples

par Piron, Anthony ;Alonso, Lorena;Morán, Ignasi;Defrance, Matthieu ;Guindo-Martínez, Marta;Bonàs-Guarch, Sílvia;Ferrer, Jorge;Gloyn, Anna A.L.;Esguerra, Jonathan L S;Marselli, Lorella;Marchetti, Piero;Eizirik, Decio L. ;Torrents, David;Cnop, Miriam ;Mercader, Josep Maria
Référence EASD Annual Meeting of the European Association for the Study of Diabetes(56th: 21-09-2020: Virtual meeting), Diabetologia, Vol. 63, page (1–485)
Publication Publié, 2020-09-21

Abstract de conférence

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Résumé :

Background and aims: Type 2 diabetes (T2D) results from progressive pancreatic beta cell failure, caused by genetic and environmental factors. How genetic variants lead to beta cell failure remains poorly understood. Here, we performed a cis-expression quantitative trait loci (eQTL) analysis of human islets to establish the link between genetic variants and gene expression. We leveraged existing and novel genome wide association studies (GWAS) to guide the selection of eQTLs implicated in T2D.Materials and methods: eQTL analysis was performed on 404 human islet transcriptomes, genomes and metadata, brought together in the Translational human pancreatic Islet Genotype tissue-Expression Resource (TIGER, created in the H2020 project T2DSystems). The genomic data was imputed with four panels (1000 Genomes, GoNL, HRC and UK10K), and the results were integrated to increase the number of high quality imputed variants to be analyzed, improving the coverage of low-frequency variants and indels. RNA-sequencing data were analyzed per cohort with RSEM for quantification and normalization, PEER for hidden confounding factors and fastQTL for the eQTL analysis. The by-cohort eQTL results were meta-analyzed, limiting batch effects while increasing statistical power. Co-localization analyses with the DIAMANTE T2D GWAS meta-analysis was done with the coloc R package.Results: Thousands of cis-acting eQTLs were mapped, including novel low minor allele frequency (MAF) variants. Notably, the large sample size and quality of imputation enabled us to identify for the first time an eQTL for the low frequency variant (MAF 0.02) nearby CCND2 that is associated with 50% reduced risk for T2D. The intersection of the eQTL data with GWAS results showed significant eQTLs in human islets for more than 80 of the previously described T2D lead variants. Among these, at least 39 were confirmed by co-localization. Of particular interest, we found co-localization for an eQTL and GWAS locus near IGF2BP2. This T2D risk allele is associated with lower IGF2BP2 expression in human islets; the association seems islet-specific as, according to GTEx, it is absent in other tissues except thyroid. The summarized transcriptomes, genetic variants and eQTL results are available on the open access TIGER portal (http://tiger.bsc.es).Conclusion: We present the largest regulatory variation study in human islet that results in the identification of 39 cis-acting eQTLs, including novel variants, co-localizing with T2D GWAS results. These genetic variants and associated dysfunctional genes expressed in human islets are an invaluable asset to understand the genetics of T2D.