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Background: Gastric cancer (GC) is clinically heterogenous according to location (cardia/non-cardia) and histopathology (diffuse/intestinal). We aimed to characterize the genetic risk architecture of GC according to its subtypes. Another aim was to examine whether cardia GC and oesophageal adenocarcinoma (OAC) and its precursor lesion Barrett's oesophagus (BO), which are all located at the gastro-oesophageal junction (GOJ), share polygenic risk architecture. Methods: We did a meta-analysis of ten European genome-wide association studies (GWAS) of GC and its subtypes. All patients had a histopathologically confirmed diagnosis of gastric adenocarcinoma. For the identification of risk genes among GWAS loci we did a transcriptome-wide association study (TWAS) and expression quantitative trait locus (eQTL) study from gastric corpus and antrum mucosa. To test whether cardia GC and OAC/BO share genetic aetiology we also used a European GWAS sample with OAC/BO. Findings: Our GWAS consisting of 5816 patients and 10,999 controls highlights the genetic heterogeneity of GC according to its subtypes. We newly identified two and replicated five GC risk loci, all of them with subtype-specific association. The gastric transcriptome data consisting of 361 corpus and 342 antrum mucosa samples revealed that an upregulated expression of MUC1, ANKRD50, PTGER4, and PSCA are plausible GC-pathomechanisms at four GWAS loci. At another risk locus, we found that the blood-group 0 exerts protective effects for non-cardia and diffuse GC, while blood-group A increases risk for both GC subtypes. Furthermore, our GWAS on cardia GC and OAC/BO (10,279 patients, 16,527 controls) showed that both cancer entities share genetic aetiology at the polygenic level and identified two new risk loci on the single-marker level. Interpretation: Our findings show that the pathophysiology of GC is genetically heterogenous according to location and histopathology. Moreover, our findings point to common molecular mechanisms underlying cardia GC and OAC/BO. Funding: German Research Foundation (DFG).

Background Gastric and esophageal cancers are malignant diseases with rising importance in Western countries. To improve oncologic outcome after surgery, it is essential to understand the relevance of germline mutations. The aim of the study was to identify and distinguish clinically relevant single-nucleotide polymorphisms (SNPs). Patients and Methods In total, 190 patients with curative oncological resections of gastric and distal esophageal adenocarcinomas at Heidelberg University Hospital were eligible for this study. Outcome differences were determined for each SNP by analysis of clinical variables, survival, and mRNA expression levels. Results Significant survival differences were found on univariate analysis for usual prognostic variables (such as pTNM) and for six SNPs. On multivariate survival analysis, the SNPs rs12268840 (intron variant of MGMT, p = 0.045) and rs9972882 (intron variant of STARD3 and eQTL of PGAP3, p = 0.030) were independent and significant survival predictors along with R status and pT/pN category. Group TT of rs12268840 had the highest rate of second primary carcinoma (30.4%, p = 0.0003), lowest expression of MGMT based on cis-eQTL analysis in normal gastroesophageal tissue (p = 1.99 × 10−17), and worst oncologic outcome. Group AA of rs9972882 had the highest rate of distant metastases pM1 (42.9%, p = 0.0117), highest expression of PGAP3 (p = 1.29 × 10−15), and worst oncologic outcome. Conclusions Two intron variant SNPs of MGMT and STARD3 were identified that were significant survival predictors and may influence tumor biology. The data indicate that DNA methylation (MGMT) and malfunction of GPI anchoring (PGAP3) are distinct mechanisms that are relevant for tumor progression and relapse.

BACKGROUND: Achalasia is a primary oesophageal motility disorder. Although aetiology remains mainly unknown, a genetic risk variant, rs28688207 in HLA-DQB1, showed strong achalasia association suggesting involvement of immune-mediated processes in the pathogenesis. High-resolution manometry recognises three types of achalasia. The aim of our study was to perform the first genotype-phenotype analysis investigating the frequency of rs28688207 across the high-resolution manometry subtypes. METHODS: This was a cross-sectional retrospective study. Achalasia patients from tertiary centres in the Czech Republic (n = 163), Germany (n = 114), Greece (n = 70) and controls were enrolled. All subjects were genotyped for the rs28688207 insertion. The Kruskal–Wallis test was used for the genotype-phenotype analysis. RESULTS: A total of 347 achalasia patients (type I – 89, II – 210, III – 48) were included. The overall frequency of the rs28688207 was 10.3%. The distribution of the insertion was significantly different across the high-resolution manometry subtypes (p = 0.038), being most prevalent in type I (14.6%), followed by type II (9.5%) and III (6.3%). CONCLUSION: The frequency of the HLA-DQB1 insertion differs among high-resolution manometry achalasia subtypes. The insertion is most prevalent in type I, suggesting that immune-mediated mechanisms triggered by the insertion may play a more prominent role in the pathogenesis of this subtype.

Genetic associations between variants on chromosome 5p13 and 8q24 and gastric cancer (GC) have been previously reported in the Asian population. We aimed to replicate these findings and to characterize the associations at the genome and transcriptome level. We performed a fine-mapping association study in 1926 GC patients and 2012 controls of European descent using high dense SNP marker sets on both chromosomal regions. Next, we performed expression quantitative trait locus (eQTL) analyses using gastric transcriptome data from 143 individuals focusing on the GC associated variants. On chromosome 5p13 the strongest association was observed at rs6872282 (P = 2.53 x 10(-04)) and on chromosome 8q24 at rs2585176 (P = 1.09 x 10(-09)). On chromosome 5p13 we found cis-eQTL effects with an up-regulation of PTGER4 expression in GC risk allele carrier (P = 9.27 x 10(-11)). On chromosome 8q24 we observed cis-eQTL effects with an upregulation of PSCA expression in GC risk allele carrier (P = 2.17 x 10(-47)). In addition, we found trans-eQTL effects for the same variants on 8q24 with a downregulation of MBOAT7 expression in GC risk allele carrier (P = 3.11 x 10(-09)). In summary, we confirmed and refined the previously reported GC associations at both chromosomal regions. Our data point to shared etiological factors between Asians and Europeans. Furthermore, our data imply an upregulated expression of PTGER4 and PSCA as well as a downregulated expression of MBOAT7 in gastric tissue as risk-conferring GC pathomechanisms.

Esophageal adenocarcinoma (EA) and its precancerous condition Barrett's esophagus (BE) are multifactorial diseases with rising prevalence rates in Western populations. A recent meta-analysis of genome-wide association studies (GWAS) data identified 14 BE/EA risk loci located in non-coding genomic regions. Knowledge about the impact of non-coding variation on disease pathology is incomplete and needs further investigation. The aim of the present study was (i) to identify candidate genes of functional relevance to BE/EA at known risk loci and (ii) to find novel risk loci among the suggestively associated variants through the integration of expression quantitative trait loci (eQTL) and genetic association data. eQTL data from two BE/EA-relevant tissues (esophageal mucosa and gastroesophageal junction) generated within the context of the GTEx project were cross-referenced with the GWAS meta-analysis data. Variants representing an eQTL in at least one of the two tissues were categorized into genome-wide significant loci (P < 5?0⁻⁸) and novel candidate loci (5?0⁻⁸ ≤ P ≤ 5?0⁻⁵). To follow up these novel candidate loci, a genetic association study was performed in a replication cohort comprising 1,993 cases and 967 controls followed by a combined analysis with the GWAS meta-analysis data. The cross-referencing of eQTL and genetic data yielded 2,180 variants that represented 25 loci. Among the previously reported genome-wide significant loci, 22 eQTLs were identified in esophageal mucosa and/or gastroesophageal junction tissue. The regulated genes, most of which have not been linked to BE/EA etiology so far, included C2orf43/LDAH, ZFP57, and SLC9A3. Among the novel candidate loci, replication was achieved for two variants (rs7754014, Pcₒmbinₑd = 3.16?0⁻⁷ and rs1540, Pcₒmbinₑd = 4.16?0⁻⁶) which represent eQTLs for CFDP1 and SLC22A3, respectively. In summary, the present approach identified candidate genes whose expression was regulated by risk variants in disease-relevant tissues. These findings may facilitate the elucidation of BE/EA pathophysiology. CA extern

Background A usually confronted problem in association studies is the occurrence of population stratification. In this work, we propose a novel framework to consider population matchings in the contexts of genome-wide and sequencing association studies. We employ pairwise and groupwise optimal case-control matchings and present an agglomerative hierarchical clustering, both based on a genetic similarity score matrix. In order to ensure that the resulting matches obtained from the matching algorithm capture correctly the population structure, we propose and discuss two stratum validation methods. We also invent a decisive extension to the Cochran-Armitage Trend test to explicitly take into account the particular population structure. Results We assess our framework by simulations of genotype data under the null hypothesis, to affirm that it correctly controls for the type-1 error rate. By a power study we evaluate that structured association testing using our framework displays reasonable power. We compare our result with those obtained from a logistic regression model with principal component covariates. Using the principal components approaches we also find a possible false-positive association to Alzheimer’s disease, which is neither supported by our new methods, nor by the results of a most recent large meta analysis or by a mixed model approach. Conclusions Matching methods provide an alternative handling of confounding due to population stratification for statistical tests for which covariates are hard to model. As a benchmark, we show that our matching framework performs equally well to state of the art models on common variants. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0521-4) contains supplementary material, which is available to authorized users.