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Motivation: Testing SNP--SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP--SNP interactions are underdeveloped. Results: We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR, EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP--SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. [ABSTRACT FROM AUTHOR]

Outlier sums were proposed by Tibshirani & Hastie (2007) and Wu (2007) for detecting outlier genes where only a small subset of disease samples shows unusually high gene expression, but they did not develop their distributional properties and formal statistical inference. In this study, a new outlier sum for detection of outlier genes is proposed, its asymptotic distribution theory is developed, and the p-value based on this outlier sum is formulated. Its analytic form is derived on the basis of the large-sample theory. We compare the proposed method with existing outlier sum methods by power comparisons. Our method is applied to DNA microarray data from samples of primary breast tumors examined by Huang et al. (2003). The results show that the proposed method is more efficient in detecting outlier genes. [ABSTRACT FROM PUBLISHER]

Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions to pancreatic ductal adenocarcinoma that are challenging to manage due to limited imaging, cytologic, and molecular markers that accurately classify lesions, grade of dysplasia, or focus of invasion preoperatively. The objective of this pilot study was to determine the frequency and type of DNA mutations in a cohort of surgically resected, pathologically confirmed IPMN, and to determine if concordant mutations are detectable in paired pretreatment plasma samples. Formalin-fixed paraffin-embedded (FFPE) tissue from 46 surgically resected IPMNs (31 low-grade, 15 high-grade) and paired plasma from a subset of 15 IPMN cases (10 low-grade, 5 high-grade) were subjected to targeted mutation analysis using a QIAseq Targeted DNA Custom Panel. Common driver mutations were detected in FFPE from 44 of 46 (95.6%) IPMN cases spanning all grades; the most common DNA mutations included: KRAS (80%), RNF43 (24%), and GNAS (43%). Of note, we observed a significant increase in the frequency of RNF43 mutations from low-grade to high-grade IPMNs associated or concomitant with invasive carcinoma (trend test, P=0.01). Among the subset of cases with paired plasma, driver mutations identified in the IPMNs were not detected in circulation. Overall, our results indicate that mutational burden for IPMNs is a common occurrence, even in low-grade IPMNs. Furthermore, although blood-based biopsies are an attractive, noninvasive method for detecting somatic DNA mutations, the QIAseq panel was not sensitive enough to detect driver mutations that existed in IPMN tissue using paired plasma in the volume we were able to retrieve for this retrospective study. [ABSTRACT FROM AUTHOR]