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

Funder: Fundación Científica Asociación Española Contra el Cáncer (ES)Funder: Cancer Focus Northern Ireland and Department for Employment and LearningFunder: Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USAAbstract: 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

Estudio descriptivo de la dispensación de analgésicos: su autoconsumo en una población universitaria

La importancia del estudio del dolor en jóvenes radica en que su manifestación a temprana edad puede convertirse en un predictor de dolor crónico en la edad adulta. El dolor es el síntoma más comúnmente percibido por los adultos. En 1998 la Organización Mundial de la Salud (OMS) estimó que la incidencia del dolor afecta al 22% de la población. Entre los dolores que presentan los estudiantes universitarios estarían los originados por el estrés como consecuencia de los exámenes, la falta de sueño para preparar las materias, los osteo articulares por su reducción del ejercicio físico y mala praxis de conducta postural, así como la alteración de las comidas durante los años que dura la carrera. Así, un 20% de los estudiantes universitarios padecen migrañas e intensos dolores de cabeza que llegan a presentar incluso varias veces a la semana siendo, la causa principal de absentismo escolar y llegando incluso en algunas ocasiones a hacerles perder el año académico. El consumo de medicamentos en España se sitúa ligeramente por encima de la media de la Unión Europea, el precio medio de los medicamentos es el más bajo, exceptuando Francia y las ventas medias por farmacia son de las más bajas de Europa después de Grecia. El aumento en la utilización de AINE en los últimos años se ha debido, sobre todo, al incremento del uso de ibuprofeno, que viene a representar el 46% del consumo de AINES en España en 2006 ya que fue el más consumido ese año. La automedicación se ha definido de forma clásica como «el consumo de medicamentos, hierbas y remedios caseros por propia iniciativa o por consejo de otra persona no cualificada, sin consultar al médico para aliviar un síntoma o curar una enfermedad». Es decir, «la utilización de fármacos que no hubiesen sido indicados por un médico», reutilizándose un viejo medicamento recetado o no en el pasado por el médico, y que quedó disponible en casa, recibiendo como único consejo el de un familiar no médico. La automedicación entre los estudiantes universitarios ha sido estudiada por diversos países, mostrando variaciones para cada país, que oscilan desde el 40% hasta incluso el 95%

Challenges in the Integration of Omics and Non-Omics Data

Omics data integration is already a reality. However, few omics-based algorithms show enough predictive ability to be implemented into clinics or public health domains. Clinical/epidemiological data tend to explain most of the variation of health-related traits, and its joint modeling with omics data is crucial to increase the algorithm’s predictive ability. Only a small number of published studies performed a “real„ integration of omics and non-omics (OnO) data, mainly to predict cancer outcomes. Challenges in OnO data integration regard the nature and heterogeneity of non-omics data, the possibility of integrating large-scale non-omics data with high-throughput omics data, the relationship between OnO data (i.e., ascertainment bias), the presence of interactions, the fairness of the models, and the presence of subphenotypes. These challenges demand the development and application of new analysis strategies to integrate OnO data. In this contribution we discuss different attempts of OnO data integration in clinical and epidemiological studies. Most of the reviewed papers considered only one type of omics data set, mainly RNA expression data. All selected papers incorporated non-omics data in a low-dimensionality fashion. The integrative strategies used in the identified papers adopted three modeling methods: Independent, conditional, and joint modeling. This review presents, discusses, and proposes integrative analytical strategies towards OnO data integration

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

Altres ajuts: The work was partially supported by Red Temática de Investigación Cooperativa en Cáncer, Spain (#RD12/0036/0034, #RD12/0036/0050, #RD12/0036/0073); Fundación Científica de la AECC, Spain; European Cooperation in Science and Technology COST action #BM1204Pancreatic 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. 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. 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. 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. The online version contains supplementary material available at 10.1186/s13073-020-00816-4