METHODS: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided.
RESULTS: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets.
CONCLUSION: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.
Materials and Methods: All the variants' information was retrieved from the Ensembl genome database, and then different variation prediction analyses were performed. UTRScan was used to predict UTR variants while MaxEntScan was used to predict splice site variants. Meta-analysis by PredictSNP2 was used to predict sSNPs. Parallel prediction analyses by five different software packages including SIFT, PolyPhen-2, Mutation Assessor, I-Mutant2.0 and SNPs&GO were used to predict the effects of nsSNPs. The level of evolutionary conservation of deleterious nsSNPs was further analyzed using ConSurf server. Mutant protein structures of deleterious nsSNPs were modelled and refined using SPARKS-X and ModRefiner for structural comparison.
Results: A total of 56 deleterious variants were identified in this study, including 12 UTR variants, 18 splice site variants, eight sSNPs and 18 nsSNPs. Among these 56 deleterious variants, seven variants were also identified in the Alzheimer's Disease Sequencing Project (ADSP), Alzheimer's Disease Neuroimaging Initiative (ADNI) and Mount Sinai Brain Bank (MSBB) studies.
Discussion: The 56 deleterious variants were predicted to affect the regulation of gene expression, or have functional impacts on these three endocytosis genes and their gene products. The deleterious variants in these genes are expected to affect their cellular function in endocytosis and may be implicated in the pathogenesis of AD as well. The biological consequences of these deleterious variants and their potential impacts on the disease risks could be further validated experimentally and may be useful for gene-disease association study.
METHODS: We conducted a gene-environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I-III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case-control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics.
RESULTS: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 × 10-6) was observed in the meta-analysis (P GxE = 1.2 ×10-6, P Joint = 4.2 ×10-7).
CONCLUSIONS: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans.
IMPACT: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.
METHODS: The genotypes were assessed on 144 histologically confirmed NAFLD patients and 198 controls using a Sequenom MassARRAY platform.
RESULTS: The GCKR rs1260326 and rs780094 allele T were associated with susceptibility to NAFLD (OR 1.49, 95 % CI 1.09-2.05, p = 0.012; and OR 1.51, 95 % CI 1.09-2.09, p = 0.013, respectively), non-alcoholic steatohepatitis (NASH) (OR 1.55, 95 % CI 1.10-2.17, p = 0.013; and OR 1.56, 95 % CI 1.10-2.20, p = 0.012, respectively) and NASH with significant fibrosis (OR 1.50, 95 % CI 1.01-2.21, p = 0.044; and OR 1.52, 95 % CI 1.03-2.26, p = 0.038, respectively). Following stratification by ethnicity, significant association was seen in Indian patients between the two SNPs and susceptibility to NAFLD (OR 2.64, 95 % CI 1.28-5.43, p = 0.009; and OR 4.35, 95 % CI 1.93-9.81, p < 0.0001, respectively). The joint effect of GCKR with adiponutrin rs738409 indicated greatly increased the risk of NAFLD (OR 4.14, 95 % CI 1.41-12.18, p = 0.010). Histological data showed significant association of GCKR rs1260326 with high steatosis grade (OR 1.76, 95 % CI 1.08-2.85, p = 0.04).
CONCLUSION: This study suggests that risk allele T of the GCKR rs780094 and rs1260326 is associated with predisposition to NAFLD and NASH with significant fibrosis. The GCKR and PNPLA3 genes interact to result in increased susceptibility to NAFLD.
METHODS: Consecutive biopsy-proven NAFLD patients (n = 191) and healthy controls (n = 188) were enrolled and genotyped for HLA-DQA1 and HLA-DQB1 alleles using the sequence-specific oligonucleotide-polymerase chain reaction method.
RESULTS: No association was found between the HLA alleles and NAFLD or NASH in a case-control setting. Nevertheless, among NAFLD patients, the frequency of HLA-DQB1*06 allele was significantly the lowest in NASH with significant fibrosis (10.4%) and approximately similar for NASH without significant fibrosis (22.9%) and NAFL (22.5%) (P = 0.004). It is noteworthy that the association remains significant after correction for multiple comparisons (Pc = 0.04). Multivariate analysis revealed that HLA-DQB1*06 allele is also associated with fibrosis score (P = 0.001); the result remains significant after correction for multiple comparisons.
CONCLUSION: These findings suggest that HLA-DQB1*06 is associated with lower fibrosis score in NAFLD patients.
METHODS: We performed a genome-wide survival analysis of cause-specific death in 24,023 prostate cancer patients (3,513 disease-specific deaths) from the PRACTICAL and BPC3 consortia. Top findings were assessed for replication in a Norwegian cohort (CONOR).
RESULTS: We observed no significant association between genetic variants and prostate cancer survival.
CONCLUSIONS: Common genetic variants with large impact on prostate cancer survival were not observed in this study.
IMPACT: Future studies should be designed for identification of rare variants with large effect sizes or common variants with small effect sizes.