A common CTRB misfolding variant associated with pancreatic cancer risk causes ER stress and inflammation in mice.

Objective: Genome wide association studies have identified an exon 6 deletion variant that associates with increased risk of pancreatic cancer. To acquire evidence on its causal role, we developed a new mouse strain carrying an equivalent variant in , the mouse orthologue of .

Design: We used CRISPR/Cas9 to introduce a 707bp deletion in encompassing exon 6 ( ).

Review: Diving into the cow hologenome to reduce methane emissions and increase sustainability.

Interest on methane emissions from livestock has increased in later years as it is an anthropogenic greenhouse gas with an important warming potential. The rumen microbiota has a large influence on the production of enteric methane. Animals harbour a second genome consisting of microbes, collectively referred to as the "microbiome".

Pervasiveness of HLA allele-specific expression loss across tumor types.

Background: Efficient presentation of mutant peptide fragments by the human leukocyte antigen class I (HLA-I) genes is necessary for immune-mediated killing of cancer cells. According to recent reports, patient HLA-I genotypes can impact the efficacy of cancer immunotherapy, and the somatic loss of HLA-I heterozygosity has been established as a factor in immune evasion. While global deregulated expression of HLA-I has also been reported in different tumor types, the role of HLA-I allele-specific expression loss - that is, the preferential RNA expression loss of specific HLA-I alleles - has not been fully characterized in cancer.

Tumor-Infiltrating B- and T-Cell Repertoire in Pancreatic Cancer Associated With Host and Tumor Features.

Background: Infiltrating B and T cells have been observed in several tumor tissues, including pancreatic ductal adenocarcinoma (PDAC). The majority known PDAC risk factors point to a chronic inflammatory process leading to different forms of immunological infiltration. Understanding pancreatic tumor infiltration may lead to improved knowledge of this devastating disease.

Association of Genetic Variants Affecting microRNAs and Pancreatic Cancer Risk.

Genetic factors play an important role in the susceptibility to pancreatic cancer (PC). However, established loci explain a small proportion of genetic heritability for PC; therefore, more progress is needed to find the missing ones. We aimed at identifying single nucleotide polymorphisms (SNPs) affecting PC risk through effects on micro-RNA (miRNA) function.

Associations between pancreatic expression quantitative traits and risk of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal cancers. Its poor prognosis is predominantly due to the fact that most patients remain asymptomatic until the disease reaches an advanced stage, alongside the lack of early markers and screening strategies. A better understanding of PDAC risk factors is essential for the identification of groups at high risk in the population.

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

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.

Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study.

The advent of metagenomics in animal breeding poses the challenge of statistically modelling the relationship between the microbiome, the host genetics and relevant complex traits. A set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed here to jointly analyse the host-metagenome-phenotype relationship. A non-recursive bivariate model was set as benchmark to compare the recursive model.

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.

Bladder Cancer Genetic Susceptibility. A Systematic Review.

Background: The variant/gene candidate approach to explore bladder cancer (BC) genetic susceptibility has been applied in many studies with significant findings reported. However, results are not always conclusive due to the lack of replication by subsequent studies.

Objectives: To identify all epidemiological investigations on the genetic associations with BC risk, to quantify the likely magnitude of the associations by applying metaanalysis methodology and to assess whether there is a potential for publication/reporting bias.

DoriTool: A Bioinformatics Integrative Tool for Post-Association Functional Annotation.

The emergence of high-throughput data in biology has increased the need for functional in silico analysis and prompted the development of integrative bioinformatics tools to facilitate the obtainment of biologically meaningful data. In this paper, we present DoriTool, a comprehensive, easy, and friendly pipeline integrating biological data from different functional tools. The tool was designed with the aim to maximize reproducibility and reduce the working time of the researchers, especially of those with limited bioinformatics skills, and to help them with the interpretation of the results.

Toward the integration of Omics data in epidemiological studies: still a "long and winding road".

Primary and secondary prevention can highly benefit a personalized medicine approach through the accurate discrimination of individuals at high risk of developing a specific disease from those at moderate and low risk. To this end precise risk prediction models need to be built. This endeavor requires a precise characterization of the individual exposome, genome, and phenome.

Inflammatory-Related Genetic Variants in Non-Muscle-Invasive Bladder Cancer Prognosis: A Multimarker Bayesian Assessment.

Background: Increasing evidence points to the role of tumor immunologic environment on urothelial bladder cancer prognosis. This effect might be partly dependent on the host genetic context. We evaluated the association of SNPs in inflammation-related genes with non-muscle-invasive bladder cancer (NMIBC) risk-of-recurrence and risk-of-progression.

Next generation modeling in GWAS: comparing different genetic architectures.

The continuous advancement in genotyping technology has not been accompanied by the application of innovative statistical methods, such as multi-marker methods (MMM), to unravel genetic associations with complex traits. Although the performance of MMM has been widely explored in a prediction context, little is known on their behavior in the quantitative trait loci (QTL) detection under complex genetic architectures. We shed light on this still open question by applying Bayes A (BA) and Bayesian LASSO (BL) to simulated and real data.

Whole genome prediction of bladder cancer risk with the Bayesian LASSO.

To build a predictive model for urothelial carcinoma of the bladder (UCB) risk combining both genomic and nongenomic data, 1,127 cases and 1,090 controls from the Spanish Bladder Cancer/EPICURO study were genotyped using the HumanHap 1M SNP array. After quality control filters, genotypes from 475,290 variants were available. Nongenomic information comprised age, gender, region, and smoking status.

Application of multi-SNP approaches Bayesian LASSO and AUC-RF to detect main effects of inflammatory-gene variants associated with bladder cancer risk.

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing.

Modeling relationships between calving traits: a comparison between standard and recursive mixed models.

Background: The use of structural equation models for the analysis of recursive and simultaneous relationships between phenotypes has become more popular recently. The aim of this paper is to illustrate how these models can be applied in animal breeding to achieve parameterizations of different levels of complexity and, more specifically, to model phenotypic recursion between three calving traits: gestation length (GL), calving difficulty (CD) and stillbirth (SB). All recursive models considered here postulate heterogeneous recursive relationships between GL and liabilities to CD and SB, and between liability to CD and liability to SB, depending on categories of GL phenotype.

Exploring biological relationships between calving traits in primiparous cattle with a Bayesian recursive model.

Structural equation models (SEMs) of a recursive type with heterogeneous structural coefficients were used to explore biological relationships between gestation length (GL), calving difficulty (CD), and perinatal mortality, also known as stillbirth (SB), in cattle, with the last two traits having categorical expression. An acyclic model was assumed, where recursive effects existed from the GL phenotype to the liabilities (latent variables) to CD and SB and from the liability to CD to that of SB considering four periods regarding GL. The data contained GL, CD, and SB records from 90,393 primiparous cows, sired by 1122 bulls, distributed over 935 herd-calving year classes.