Search

There is a growing recognition that gene-environment interaction (G × E) plays a pivotal role in the development and progression of complex diseases. Despite a wealth of genetic data on various complex diseases/traits generated from association and sequencing studies, detecting G × E via genome-wide analysis remains challenging due to power issues. In genome-wide G × E studies, a common strategy to improve power is to first conduct a filtering test and retain only the genetic variants that pass the filtering step for subsequent G × E analyses. Two-stage, multistage, and unified tests have been proposed to jointly consider the filtering statistics in G × E tests. However, such G × E tests based on data from a single study may still be underpowered. Meanwhile, large-scale consortia have been formed to borrow strength across studies and populations. In this work, motivated by existing single-study G × E tests with filtering and the needs for meta-analysis G × E approaches based on consortia data, we propose a meta-analysis framework for detecting gene-based G × E effects, and introduce meta-analysis-based filtering statistics in the gene-level G × E tests. Simulations demonstrate the advantages of the proposed method-the ofGEM test. We apply the proposed tests to existing data from two breast cancer consortia to identify the genes harboring genetic variants with age-dependent penetrance (i.e., gene-age interaction effects). We develop an R software package ofGEM for the proposed meta-analysis tests., (© 2018 WILEY PERIODICALS, INC.)

Telomeres cap chromosome ends, protecting them from degradation, double-strand breaks, and end-to-end fusions. Telomeres are maintained by telomerase, a reverse transcriptase encoded by TERT, and an RNA template encoded by TERC. Loci in the TERT and adjoining CLPTM1L region are associated with risk of multiple cancers. We therefore investigated associations between variants in 22 telomere structure and maintenance gene regions and colorectal, breast, prostate, ovarian, and lung cancer risk. We performed subset-based meta-analyses of 204,993 directly-measured and imputed SNPs among 61,851 cancer cases and 74,457 controls of European descent. Independent associations for SNP minor alleles were identified using sequential conditional analysis (with gene-level p value cutoffs ≤3.08 × 10 -5 ). Of the thirteen independent SNPs observed to be associated with cancer risk, novel findings were observed for seven loci. Across the DCLRE1B region, rs974494 and rs12144215 were inversely associated with prostate and lung cancers, and colorectal, breast, and prostate cancers, respectively. Across the TERC region, rs75316749 was positively associated with colorectal, breast, ovarian, and lung cancers. Across the DCLRE1B region, rs974404 and rs12144215 were inversely associated with prostate and lung cancers, and colorectal, breast, and prostate cancers, respectively. Near POT1, rs116895242 was inversely associated with colorectal, ovarian, and lung cancers, and RTEL1 rs34978822 was inversely associated with prostate and lung cancers. The complex association patterns in telomere-related genes across cancer types may provide insight into mechanisms through which telomere dysfunction in different tissues influences cancer risk., Competing Interests: Author’s disclosures of potential conflicts of interest: non for all authors., (© 2016 UICC.)

Genome-wide association (GWA) studies have identified several pancreatic cancer (PanCa) susceptibility loci. Methods for assessment of polygenic susceptibility can be employed to detect the collective effect of additional association signals for PanCa. Using data on 492,651 autosomal single nucleotide polymorphisms (SNPs) from the PanScan GWA study (2,857 cases, 2,967 controls), we employed polygenic risk score (PRS) cross-validation (CV) methods to (a) confirm the existence of unidentified association signals, (b) assess the predictive value of PRSs, and (c) assess evidence for polygenic effects in specific genomic locations (genic vs. intergenic). After excluding SNPs in known PanCa susceptibility regions, we constructed PRS models using a training GWA dataset and then tested the model in an independent testing dataset using fourfold CV. We also employed a "power-replication" approach, where power to detect SNP associations was calculated using a training dataset, and power was tested for association with "replication status" in a testing dataset. PRS scores constructed using ≥ 10% of genome-wide SNPs showed significant association with PanCa (P< 0.05) across the majority of CV analyses. Associations were stronger for PRSs restricted to genic SNPs compared to intergenic PRSs. The power-replications approach produced weaker associations that were not significant when restricting to SNPs with low pairwise linkage disequilibrium, whereas PRS results were robust to such restrictions. Although the PRS approach will not dramatically improve PanCa prediction, it provides strong evidence for unidentified association signals for PanCa. Our results suggest that focusing association studies on genic regions and conducting larger GWA studies can reveal additional PanCa susceptibility loci., (© 2012 Wiley Periodicals, Inc.)

Case-control genome-wide association (GWA) studies have facilitated the identification of susceptibility loci for many complex diseases; however, these studies are often not adequately powered to detect gene-environment (G x E) and gene-gene (G x G) interactions. Case-only studies are more efficient than case-control studies for detecting interactions and require no data on control subjects. In this article, we discuss the concept and utility of the case-only genome-wide interaction (COGWI) study, in which common genetic variants, measured genome-wide, are screened for association with environmental exposures or genetic variants of interest. An observed G-E (or G-G) association, as measured by the case-only odds ratio (OR), suggests interaction, but only if the interacting factors are unassociated in the population from which the cases were drawn. The case-only OR is equivalent to the interaction risk ratio. In addition to risk-related interactions, we discuss how the COGWI design can be used to efficiently detect G x G, G x E and pharmacogenetic interactions related to disease outcomes in the context of observational clinical studies or randomized clinical trials. Such studies can be conducted using only data on individuals experiencing an outcome of interest or individuals not experiencing the outcome of interest. Sharing data among GWA and COGWI studies of disease risk and outcome can further enhance efficiency. Sample size requirements for COGWI studies, as compared to case-control GWA studies, are provided. In the current era of genome-wide analyses, the COGWI design is an efficient and straightforward method for detecting G x G, G x E and pharmacogenetic interactions related to disease risk, prognosis and treatment response.

Background: Epidemiological evidence suggests that diabetes mellitus (DM) is associated with a decrease in risk for prostate cancer (PCa). The objective of this study was to examine the association between PCa risk and several characteristics of DM (duration, age at diagnosis, treatment) in data from two population-based, case-control studies of PCa., Methods: PCa cases (n = 1,752), and controls (n = 1,644) were residents of King County, Washington identified using the Surveillance, Epidemiology, and End Results Seattle-Puget Sound cancer registry and random digit dialing, respectively. The majority of PCa patients had localized stage disease (78%). Using self-reported on DM, logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (95% CI) for associations between DM characteristics and PCa risk., Results: After adjustment for confounding factors, DM status was not significantly associated with PCa risk (OR = 0.98; 95% CI = 0.76-1.27). However, early-onset DM (diagnosed before age 30) showed a significant inverse association with PCa risk (OR = 0.27; 95% CI = 0.07-0.97). Time since DM diagnosis (median = 6 years) and DM treatment types (i.e., diet changes, oral medications, and insulin) did not show significant associations with PCa risk., Conclusions: These data suggest that DM is not associated with PCa risk; however, early-onset DM, possibly reflecting insulin dependent DM (type 1), may be an important component of the previously reported inverse association between DM on PCa risk, but these findings are based on a small number of observations and require further investigation., ((c) 2008 Wiley-Liss, Inc.)

Background: Prostate cancer is a genetically heterogeneous disease. Using the occurrence of other cancers in hereditary prostate cancer (HPC) families is a promising strategy for developing genetically homogeneous data sets that can enhance the ability to identify susceptibility loci using linkage analysis., Methods: Twelve HPC families with the co-occurrence of adenocarcinoma of the pancreas were selected from the Prostate Cancer Genetic Research Study (PROGRESS). Non-parametric linkage analysis for a prostate/pancreas cancer susceptibility phenotype was performed using 441 genome-wide microsatellite markers., Results: No statistically significant linkage signal was detected in this analysis. The strongest linkage signals, as measured by Kong and Cox LOD score (KC LOD), were observed on chromosomes 2q37.2-q37.3 (KC LOD = 1.01; P = 0.02) and 16q23.2 (KC LOC = 1.05; P = 0.01)., Conclusions: Despite the lack of statistically significant findings, four chromosomal regions, three of which (2q, 16q, 17q) were previously noted as harboring potential susceptibility loci, showed suggestive linkage results in this scan.