A multilayered post-GWAS assessment on genetic susceptibility to pancreatic cancer

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  • Evangelina López de Maturana
  • Rodríguez, Juan Antonio
  • Lola Alonso
  • Oscar Lao
  • Esther Molina-Montes
  • Isabel Adoración Martín-Antoniano
  • Paulina Gómez-Rubio
  • Rita Lawlor
  • Alfredo Carrato
  • Manuel Hidalgo
  • Mar Iglesias
  • Xavier Molero
  • Matthias Löhr
  • Christopher Michalski
  • José Perea
  • Michael O’Rorke
  • Victor Manuel Barberà
  • Adonina Tardón
  • Antoni Farré
  • Luís Muñoz-Bellvís
  • Tanja Crnogorac-Jurcevic
  • Enrique Domínguez-Muñoz
  • Thomas Gress
  • William Greenhalf
  • Linda Sharp
  • Arnes Perez, Luis
  • Lluís Cecchini
  • Joaquim Balsells
  • Eithne Costello
  • Lucas Ilzarbe
  • Jörg Kleeff
  • Bo Kong
  • Mirari Márquez
  • Josefina Mora
  • Damian O’Driscoll
  • Aldo Scarpa
  • Weimin Ye
  • Jingru Yu
  • Montserrat García-Closas
  • Manolis Kogevinas
  • Nathaniel Rothman
  • Debra T. Silverman
  • Demetrius Albanes
  • Alan A. Arslan
  • Laura Beane-Freeman
  • Paige M. Bracci
  • Paul Brennan
  • Bas Bueno-de-Mesquita
  • Julie Buring
  • Federico Canzian
  • PanGenEU Investigators
  • SBC/EPICURO Investigators

Background: Pancreatic cancer (PC) is a complex disease in which both non-genetic and genetic factors interplay. To date, 40 GWAS hits have been associated with PC risk in individuals of European descent, explaining 4.1% of the phenotypic variance. Methods: We complemented a new conventional PC GWAS (1D) with genome spatial autocorrelation analysis (2D) permitting to prioritize low frequency variants not detected by GWAS. These were further expanded via Hi-C map (3D) interactions to gain additional insight into the inherited basis of PC. In silico functional analysis of public genomic information allowed prioritization of potentially relevant candidate variants. Results: We identified several new variants located in genes for which there is experimental evidence of their implication in the biology and function of pancreatic acinar cells. Among them is a novel independent variant in NR5A2 (rs3790840) with a meta-analysis p value = 5.91E−06 in 1D approach and a Local Moran’s Index (LMI) = 7.76 in 2D approach. We also identified a multi-hit region in CASC8—a lncRNA associated with pancreatic carcinogenesis—with a lowest p value = 6.91E−05. Importantly, two new PC loci were identified both by 2D and 3D approaches: SIAH3 (LMI = 18.24), CTRB2/BCAR1 (LMI = 6.03), in addition to a chromatin interacting region in XBP1—a major regulator of the ER stress and unfolded protein responses in acinar cells—identified by 3D; all of them with a strong in silico functional support. Conclusions: This multi-step strategy, combined with an in-depth in silico functional analysis, offers a comprehensive approach to advance the study of PC genetic susceptibility and could be applied to other diseases.

Original languageEnglish
Article number15
JournalGenome Medicine
Issue number1
Number of pages18
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

    Research areas

  • 3D genomic structure, Genetic susceptibility, Genome-wide association analysis, Local indices of genome spatial autocorrelation, Pancreatic cancer risk

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