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


ABSTRACT:

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.

SUBMITTER: Lopez de Maturana E 

PROVIDER: S-EPMC7849104 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

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

López de Maturana Evangelina E   Rodríguez Juan Antonio JA   Alonso Lola L   Lao Oscar O   Molina-Montes Esther E   Martín-Antoniano Isabel Adoración IA   Gómez-Rubio Paulina P   Lawlor Rita R   Carrato Alfredo A   Hidalgo Manuel M   Iglesias Mar M   Molero Xavier X   Löhr Matthias M   Michalski Christopher C   Perea José J   O'Rorke Michael M   Barberà Victor Manuel VM   Tardón Adonina A   Farré Antoni A   Muñoz-Bellvís Luís L   Crnogorac-Jurcevic Tanja T   Domínguez-Muñoz Enrique E   Gress Thomas T   Greenhalf William W   Sharp Linda L   Arnes Luís L   Cecchini Lluís L   Balsells Joaquim J   Costello Eithne E   Ilzarbe Lucas L   Kleeff Jörg J   Kong Bo B   Márquez Mirari M   Mora Josefina J   O'Driscoll Damian D   Scarpa Aldo A   Ye Weimin W   Yu Jingru J   García-Closas Montserrat M   Kogevinas Manolis M   Rothman Nathaniel N   Silverman Debra T DT   Albanes Demetrius D   Arslan Alan A AA   Beane-Freeman Laura L   Bracci Paige M PM   Brennan Paul P   Bueno-de-Mesquita Bas B   Buring Julie J   Canzian Federico F   Du Margaret M   Gallinger Steve S   Gaziano J Michael JM   Goodman Phyllis J PJ   Gunter Marc M   LeMarchand Loic L   Li Donghui D   Neale Rachael E RE   Peters Ulrika U   Petersen Gloria M GM   Risch Harvey A HA   Sánchez Maria José MJ   Shu Xiao-Ou XO   Thornquist Mark D MD   Visvanathan Kala K   Zheng Wei W   Chanock Stephen J SJ   Easton Douglas D   Wolpin Brian M BM   Stolzenberg-Solomon Rachael Z RZ   Klein Alison P AP   Amundadottir Laufey T LT   Marti-Renom Marc A MA   Real Francisco X FX   Malats Núria N  

Genome medicine 20210201 1


<h4>Background</h4>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.<h4>Methods</h4>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  ...[more]

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