TY - JOUR

T1 - A multilayered post-GWAS assessment on genetic susceptibility to pancreatic cancer

AU - López de Maturana, Evangelina

AU - Rodríguez, Juan Antonio

AU - Alonso, Lola

AU - Lao, Oscar

AU - Molina-Montes, Esther

AU - Martín-Antoniano, Isabel Adoración

AU - Gómez-Rubio, Paulina

AU - Lawlor, Rita

AU - Carrato, Alfredo

AU - Hidalgo, Manuel

AU - Iglesias, Mar

AU - Molero, Xavier

AU - Löhr, Matthias

AU - Michalski, Christopher

AU - Perea, José

AU - O’Rorke, Michael

AU - Barberà, Victor Manuel

AU - Tardón, Adonina

AU - Farré, Antoni

AU - Muñoz-Bellvís, Luís

AU - Crnogorac-Jurcevic, Tanja

AU - Domínguez-Muñoz, Enrique

AU - Gress, Thomas

AU - Greenhalf, William

AU - Sharp, Linda

AU - Arnes, Luís

AU - Cecchini, Lluís

AU - Balsells, Joaquim

AU - Costello, Eithne

AU - Ilzarbe, Lucas

AU - Kleeff, Jörg

AU - Kong, Bo

AU - Márquez, Mirari

AU - Mora, Josefina

AU - O’Driscoll, Damian

AU - Scarpa, Aldo

AU - Ye, Weimin

AU - Yu, Jingru

AU - García-Closas, Montserrat

AU - Kogevinas, Manolis

AU - Rothman, Nathaniel

AU - Silverman, Debra T.

AU - Albanes, Demetrius

AU - Arslan, Alan A.

AU - Beane-Freeman, Laura

AU - Bracci, Paige M.

AU - Brennan, Paul

AU - Bueno-de-Mesquita, Bas

AU - Buring, Julie

AU - Canzian, Federico

AU - PanGenEU Investigators

AU - SBC/EPICURO Investigators

N1 - Publisher Copyright: © 2021, The Author(s).

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - 3D genomic structure

KW - Genetic susceptibility

KW - Genome-wide association analysis

KW - Local indices of genome spatial autocorrelation

KW - Pancreatic cancer risk

U2 - 10.1186/s13073-020-00816-4

DO - 10.1186/s13073-020-00816-4

M3 - Journal article

C2 - 33517887

AN - SCOPUS:85100156945

VL - 13

JO - Genome Medicine

JF - Genome Medicine

SN - 1756-994X

IS - 1

M1 - 15

ER -