Genome-wide interaction study of dietary intake of fibre, fruits, and vegetables with risk of colorectal cancer.

BACKGROUND: Consumption of fibre, fruits and vegetables have been linked with lower colorectal cancer (CRC) risk. A genome-wide gene-environment (G × E) analysis was performed to test whether genetic variants modify these associations. METHODS: A pooled sample of 45 studies including up to 69,734 participants (cases: 29,896; controls: 39,838) of European ancestry were included. To identify G × E interactions, we used the traditional 1--degree-of-freedom (DF) G × E test and to improve power a 2-step procedure and a 3DF joint test that investigates the association between a genetic variant and dietary exposure, CRC risk and G × E interaction simultaneously. FINDINGS: The 3-DF joint test revealed two significant loci with p-value <5 × 10-8. Rs4730274 close to the SLC26A3 gene showed an association with fibre (p-value: 2.4 × 10-3) and G × fibre interaction with CRC (OR per quartile of fibre increase = 0.87, 0.80, and 0.75 for CC, TC, and TT genotype, respectively; G × E p-value: 1.8 × 10-7). Rs1620977 in the NEGR1 gene showed an association with fruit intake (p-value: 1.0 × 10-8) and G × fruit interaction with CRC (OR per quartile of fruit increase = 0.75, 0.65, and 0.56 for AA, AG, and GG genotype, respectively; G × E -p-value: 0.029). INTERPRETATION: We identified 2 loci associated with fibre and fruit intake that also modify the association of these dietary factors with CRC risk. Potential mechanisms include chronic inflammatory intestinal disorders, and gut function. However, further studies are needed for mechanistic validation and replication of findings. FUNDING: National Institutes of Health, National Cancer Institute. Full funding details for the individual consortia are provided in acknowledgments.

Two genome-wide interaction loci modify the association of nonsteroidal anti-inflammatory drugs with colorectal cancer.

Regular, long-term aspirin use may act synergistically with genetic variants, particularly those in mechanistically relevant pathways, to confer a protective effect on colorectal cancer (CRC) risk. We leveraged pooled data from 52 clinical trial, cohort, and case-control studies that included 30,806 CRC cases and 41,861 controls of European ancestry to conduct a genome-wide interaction scan between regular aspirin/nonsteroidal anti-inflammatory drug (NSAID) use and imputed genetic variants. After adjusting for multiple comparisons, we identified statistically significant interactions between regular aspirin/NSAID use and variants in 6q24.1 (top hit rs72833769), which has evidence of influencing expression of TBC1D7 (a subunit of the TSC1-TSC2 complex, a key regulator of MTOR activity), and variants in 5p13.1 (top hit rs350047), which is associated with expression of PTGER4 (codes a cell surface receptor directly involved in the mode of action of aspirin). Genetic variants with functional impact may modulate the chemopreventive effect of regular aspirin use, and our study identifies putative previously unidentified targets for additional mechanistic interrogation.

A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies.

Background: Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes. Methods: A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent. Results: In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level. Conclusions: The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

Genetic risk impacts the association of menopausal hormone therapy with colorectal cancer risk.

BACKGROUND: Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. METHODS: We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. RESULTS: The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10-8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%-4.0%) vs 6.1% (5.7%-6.5%) (difference 2.4%, P-value = 1.83 × 10-14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%-1.8%) vs 2.2% (1.9%-2.4%) (difference 0.6%, P-value = 1.01 × 10-3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. CONCLUSIONS: MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.

Genome-Wide Gene-Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk.

BACKGROUND: High red meat and/or processed meat consumption are established colorectal cancer risk factors. We conducted a genome-wide gene-environment (GxE) interaction analysis to identify genetic variants that may modify these associations. METHODS: A pooled sample of 29,842 colorectal cancer cases and 39,635 controls of European ancestry from 27 studies were included. Quantiles for red meat and processed meat intake were constructed from harmonized questionnaire data. Genotyping arrays were imputed to the Haplotype Reference Consortium. Two-step EDGE and joint tests of GxE interaction were utilized in our genome-wide scan. RESULTS: Meta-analyses confirmed positive associations between increased consumption of red meat and processed meat with colorectal cancer risk [per quartile red meat OR = 1.30; 95% confidence interval (CI) = 1.21-1.41; processed meat OR = 1.40; 95% CI = 1.20-1.63]. Two significant genome-wide GxE interactions for red meat consumption were found. Joint GxE tests revealed the rs4871179 SNP in chromosome 8 (downstream of HAS2); greater than median of consumption ORs = 1.38 (95% CI = 1.29-1.46), 1.20 (95% CI = 1.12-1.27), and 1.07 (95% CI = 0.95-1.19) for CC, CG, and GG, respectively. The two-step EDGE method identified the rs35352860 SNP in chromosome 18 (SMAD7 intron); greater than median of consumption ORs = 1.18 (95% CI = 1.11-1.24), 1.35 (95% CI = 1.26-1.44), and 1.46 (95% CI = 1.26-1.69) for CC, CT, and TT, respectively. CONCLUSIONS: We propose two novel biomarkers that support the role of meat consumption with an increased risk of colorectal cancer. IMPACT: The reported GxE interactions may explain the increased risk of colorectal cancer in certain population subgroups.

OvaPrint-A Cell-free DNA Methylation Liquid Biopsy for the Risk Assessment of High-grade Serous Ovarian Cancer.

PURPOSE: High-grade serous ovarian carcinoma (HGSOC) is the most lethal epithelial ovarian cancer (EOC) and is often diagnosed at late stage. In women with a known pelvic mass, surgery followed by pathologic assessment is the most reliable way to diagnose EOC and there are still no effective screening tools in asymptomatic women. In the current study, we developed a cell-free DNA (cfDNA) methylation liquid biopsy for the risk assessment of early-stage HGSOC. EXPERIMENTAL DESIGN: We performed reduced representation bisulfite sequencing to identify differentially methylated regions (DMR) between HGSOC and normal ovarian and fallopian tube tissue. Next, we performed hybridization probe capture for 1,677 DMRs and constructed a classifier (OvaPrint) on an independent set of cfDNA samples to discriminate HGSOC from benign masses. We also analyzed a series of non-HGSOC EOC, including low-grade and borderline samples to assess the generalizability of OvaPrint. A total of 372 samples (tissue n = 59, plasma n = 313) were analyzed in this study. RESULTS: OvaPrint achieved a positive predictive value of 95% and a negative predictive value of 88% for discriminating HGSOC from benign masses, surpassing other commercial tests. OvaPrint was less sensitive for non-HGSOC EOC, albeit it may have potential utility for identifying low-grade and borderline tumors with higher malignant potential. CONCLUSIONS: OvaPrint is a highly sensitive and specific test that can be used for the risk assessment of HGSOC in symptomatic women. Prospective studies are warranted to validate OvaPrint for HGSOC and further develop it for non-HGSOC EOC histotypes in both symptomatic and asymptomatic women with adnexal masses.

Classification of Angle Closure Severity by Hierarchical Cluster Analysis of Ocular Biometrics in the Dark and Light.

Purpose: The purpose of this study was to investigate the classification of angle closure eyes based on hierarchical cluster analysis of ocular biometrics measured in the dark and light using anterior segment optical coherence tomography (AS-OCT). Methods: Participants of the Chinese American Eye Study received complete eye examinations to identify primary angle closure suspects (PACS) and primary angle closure without/with glaucoma (PAC/G). AS-OCT was performed in the dark and light. Biometric parameters describing the angle, iris, lens, and anterior chamber were analyzed. Hierarchical clustering was performed using Ward's method. Post hoc logistic regression models were developed to identify biometric predictors of angle closure staging. Results: Analysis of 159 eyes with PACS (N = 120) or PAC/G (N = 39) produced 2 clusters in the dark and light. In both analyses, cluster 1 (N = 132 in the dark and N = 126 in the light) was characterized by smaller angle opening distance (AOD)750 and trabecular iris space area (TISA)750, greater iris curvature (IC), and greater lens vault (LV; P < 0.001) than cluster 2. The proportion of PAC/PACG to PACS eyes was significantly higher in cluster 1 than 2 in the light (36:90 and 3:30, respectively; P = 0.02), but not the dark (36:96 and 3:24, respectively; P = 0.08). On multivariable regression analyses, smaller TISA750 (odds ratio [OR] = 0.84 per 0.01 mm2) and AOD750 (OR = 0.93 per 0.01 mm) in the light and smaller TISA750 (OR = 0.86 per 0.01 mm2) in the dark conferred higher risk of PAC/G (P ≤ 0.02). Conclusions: Unsupervised cluster analysis of ocular biometrics can classify angle closure eyes by severity. Static biometrics measured in the light and dark are both predictive of PAC/G. Translational Relevance: Clustering of biometrics measured in the light could provide an alternative source of information to risk-stratify angle closure eyes for more severe disease.

Genome-wide interaction analysis of folate for colorectal cancer risk.

BACKGROUND: Epidemiological and experimental evidence suggests that higher folate intake is associated with decreased colorectal cancer (CRC) risk; however, the mechanisms underlying this relationship are not fully understood. Genetic variation that may have a direct or indirect impact on folate metabolism can provide insights into folate's role in CRC. OBJECTIVES: Our aim was to perform a genome-wide interaction analysis to identify genetic variants that may modify the association of folate on CRC risk. METHODS: We applied traditional case-control logistic regression, joint 3-degree of freedom, and a 2-step weighted hypothesis approach to test the interactions of common variants (allele frequency >1%) across the genome and dietary folate, folic acid supplement use, and total folate in relation to risk of CRC in 30,550 cases and 42,336 controls from 51 studies from 3 genetic consortia (CCFR, CORECT, GECCO). RESULTS: Inverse associations of dietary, total folate, and folic acid supplement with CRC were found (odds ratio [OR]: 0.93; 95% confidence interval [CI]: 0.90, 0.96; and 0.91; 95% CI: 0.89, 0.94 per quartile higher intake, and 0.82 (95% CI: 0.78, 0.88) for users compared with nonusers, respectively). Interactions (P-interaction < 5×10-8) of folic acid supplement and variants in the 3p25.2 locus (in the region of Synapsin II [SYN2]/tissue inhibitor of metalloproteinase 4 [TIMP4]) were found using traditional interaction analysis, with variant rs150924902 (located upstream to SYN2) showing the strongest interaction. In stratified analyses by rs150924902 genotypes, folate supplementation was associated with decreased CRC risk among those carrying the TT genotype (OR: 0.82; 95% CI: 0.79, 0.86) but increased CRC risk among those carrying the TA genotype (OR: 1.63; 95% CI: 1.29, 2.05), suggesting a qualitative interaction (P-interaction = 1.4×10-8). No interactions were observed for dietary and total folate. CONCLUSIONS: Variation in 3p25.2 locus may modify the association of folate supplement with CRC risk. Experimental studies and studies incorporating other relevant omics data are warranted to validate this finding.

A note on p-value multiple-testing adjustment for two-step genome-wide gene-environment interactions scans.

Two-step testing is the state-of-the art approach for performing genome-wide interaction scans (GWIS). It is computationally efficient and yields higher power than standard single-step-based GWIS for virtually all biologically plausible scenarios. However, while two-step tests control the genome-wide type I error rate at the desired level, the lack of associated valid p-values can make it difficult for users to compare with single step-results. We show how multiple-testing adjusted p-values can be defined for two-step test based on standard multiple-testing theory, and how they can be in turn scaled to make valid comparisons with single-step tests possible.

A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk.

Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer. SIGNIFICANCE: This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.

PEACOCK: a machine learning approach to assess the validity of cell type-specific enhancer-gene regulatory relationships.

The vast majority of disease-associated variants identified in genome-wide association studies map to enhancers, powerful regulatory elements which orchestrate the recruitment of transcriptional complexes to their target genes' promoters to upregulate transcription in a cell type- and timing-dependent manner. These variants have implicated thousands of enhancers in many common genetic diseases, including nearly all cancers. However, the etiology of most of these diseases remains unknown because the regulatory target genes of the vast majority of enhancers are unknown. Thus, identifying the target genes of as many enhancers as possible is crucial for learning how enhancer regulatory activities function and contribute to disease. Based on experimental results curated from scientific publications coupled with machine learning methods, we developed a cell type-specific score predictive of an enhancer targeting a gene. We computed the score genome-wide for every possible cis enhancer-gene pair and validated its predictive ability in four widely used cell lines. Using a pooled final model trained across multiple cell types, all possible gene-enhancer regulatory links in cis (~17 M) were scored and added to the publicly available PEREGRINE database ( www.peregrineproj.org ). These scores provide a quantitative framework for the enhancer-gene regulatory prediction that can be incorporated into downstream statistical analyses.

Prenatal exposure to tailpipe and non-tailpipe tracers of particulate matter pollution and autism spectrum disorders.

BACKGROUND: Traffic-related air pollution exposure is associated with increased risk of autism spectrum disorder (ASD). It is unknown whether carbonaceous material from vehicular tailpipe emissions or redox-active non-tailpipe metals, eg. from tire and brake wear, are responsible. We assessed ASD associations with fine particulate matter (PM2.5) tracers of tailpipe (elemental carbon [EC] and organic carbon [OC]) and non-tailpipe (copper [Cu]; iron [Fe] and manganese [Mn]) sources during pregnancy in a large cohort. METHODS: This retrospective cohort study included 318,750 children born in Kaiser Permanente Southern California (KPSC) hospitals during 2001-2014, followed until age 5. ASD cases were identified by ICD codes. Monthly estimates of PM2.5 and PM2.5 constituents EC, OC, Cu, Fe, and Mn with 4 km spatial resolution were obtained from a source-oriented chemical transport model. These exposures and NO2 were assigned to each maternal address during pregnancy, and associations with ASD were assessed using Cox regression models adjusted for covariates. PM constituent effect estimates were adjusted for PM2.5 and NO2 to assess independent effects. To distinguish ASD risk associated with non-tailpipe from tailpipe sources, the associations with Cu, Fe, and Mn were adjusted for EC and OC, and vice versa. RESULTS: There were 4559 children diagnosed with ASD. In single-pollutant models, increased ASD risk was associated with gestational exposures to tracers of both tailpipe and non-tailpipe emissions. The ASD hazard ratios (HRs) per inter-quartile increment of exposure) for EC, OC, Cu, Fe, and Mn were 1.11 (95% CI: 1.06-1.16), 1.09 (95% CI: 1.04-1.15), 1.09 (95% CI: 1.04-1.13), 1.14 (95% CI: 1.09-1.20), and 1.17 (95% CI: 1.12-1.22), respectively. Estimated effects of Cu, Fe, and Mn (reflecting non-tailpipe sources) were largely unchanged in two-pollutant models adjusting for PM2.5, NO2, EC or OC. In contrast, ASD associations with EC and OC were markedly attenuated by adjustment for non-tailpipe sources. CONCLUSION: Results suggest that non-tailpipe emissions may contribute to ASD. Implications are that reducing tailpipe emissions, especially from vehicles with internal combustion engines, may not eliminate ASD associations with traffic-related air pollution.

Improved two-step testing of genome-wide gene-environment interactions.

Two-step tests for gene-environment ( G × E $G\times E$ ) interactions exploit marginal single-nucleotide polymorphism (SNP) effects to improve the power of a genome-wide interaction scan. They combine a screening step based on marginal effects used to "bin" SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide Type I error. However, the presence of many SNPs with detectable marginal effects on the trait of interest can reduce power by "displacing" true interactions with weaker marginal effects and by adding to the number of tests that need to be corrected for multiple testing. We introduce a new significance-based allocation into bins for Step-2 G × E $G\times E$ testing that overcomes the displacement issue and propose a computationally efficient approach to account for multiple testing within bins. Simulation results demonstrate that these simple improvements can provide substantially greater power than current methods under several scenarios. An application to a multistudy collaboration for understanding colorectal cancer reveals a G × Sex interaction located near the SMAD7 gene.

Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response.

BACKGROUND: Tobacco smoking is an established risk factor for colorectal cancer. However, genetically defined population subgroups may have increased susceptibility to smoking-related effects on colorectal cancer. METHODS: A genome-wide interaction scan was performed including 33,756 colorectal cancer cases and 44,346 controls from three genetic consortia. RESULTS: Evidence of an interaction was observed between smoking status (ever vs. never smokers) and a locus on 3p12.1 (rs9880919, P = 4.58 × 10-8), with higher associated risk in subjects carrying the GG genotype [OR, 1.25; 95% confidence interval (CI), 1.20-1.30] compared with the other genotypes (OR <1.17 for GA and AA). Among ever smokers, we observed interactions between smoking intensity (increase in 10 cigarettes smoked per day) and two loci on 6p21.33 (rs4151657, P = 1.72 × 10-8) and 8q24.23 (rs7005722, P = 2.88 × 10-8). Subjects carrying the rs4151657 TT genotype showed higher risk (OR, 1.12; 95% CI, 1.09-1.16) compared with the other genotypes (OR <1.06 for TC and CC). Similarly, higher risk was observed among subjects carrying the rs7005722 AA genotype (OR, 1.17; 95% CI, 1.07-1.28) compared with the other genotypes (OR <1.13 for AC and CC). Functional annotation revealed that SNPs in 3p12.1 and 6p21.33 loci were located in regulatory regions, and were associated with expression levels of nearby genes. Genetic models predicting gene expression revealed that smoking parameters were associated with lower colorectal cancer risk with higher expression levels of CADM2 (3p12.1) and ATF6B (6p21.33). CONCLUSIONS: Our study identified novel genetic loci that may modulate the risk for colorectal cancer of smoking status and intensity, linked to tumor suppression and immune response. IMPACT: These findings can guide potential prevention treatments.

Hierarchical Ridge Regression for Incorporating Prior Information in Genomic Studies.

There is a great deal of prior knowledge about gene function and regulation in the form of annotations or prior results that, if directly integrated into individual prognostic or diagnostic studies, could improve predictive performance. For example, in a study to develop a predictive model for cancer survival based on gene expression, effect sizes from previous studies or the grouping of genes based on pathways constitute such prior knowledge. However, this external information is typically only used post-analysis to aid in the interpretation of any findings. We propose a new hierarchical two-level ridge regression model that can integrate external information in the form of "meta features" to predict an outcome. We show that the model can be fit efficiently using cyclic coordinate descent by recasting the problem as a single-level regression model. In a simulation-based evaluation we show that the proposed method outperforms standard ridge regression and competing methods that integrate prior information, in terms of prediction performance when the meta features are informative on the mean of the features, and that there is no loss in performance when the meta features are uninformative. We demonstrate our approach with applications to the prediction of chronological age based on methylation features and breast cancer mortality based on gene expression features.

Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region.

BACKGROUND: Currently known associations between common genetic variants and colorectal cancer explain less than half of its heritability of 25%. As alcohol consumption has a J-shape association with colorectal cancer risk, nondrinking and heavy drinking are both risk factors for colorectal cancer. METHODS: Individual-level data was pooled from the Colon Cancer Family Registry, Colorectal Transdisciplinary Study, and Genetics and Epidemiology of Colorectal Cancer Consortium to compare nondrinkers (≤1 g/day) and heavy drinkers (>28 g/day) with light-to-moderate drinkers (1-28 g/day) in GxE analyses. To improve power, we implemented joint 2df and 3df tests and a novel two-step method that modifies the weighted hypothesis testing framework. We prioritized putative causal variants by predicting allelic effects using support vector machine models. RESULTS: For nondrinking as compared with light-to-moderate drinking, the hybrid two-step approach identified 13 significant SNPs with pairwise r2 > 0.9 in the 10q24.2/COX15 region. When stratified by alcohol intake, the A allele of lead SNP rs2300985 has a dose-response increase in risk of colorectal cancer as compared with the G allele in light-to-moderate drinkers [OR for GA genotype = 1.11; 95% confidence interval (CI), 1.06-1.17; OR for AA genotype = 1.22; 95% CI, 1.14-1.31], but not in nondrinkers or heavy drinkers. Among the correlated candidate SNPs in the 10q24.2/COX15 region, rs1318920 was predicted to disrupt an HNF4 transcription factor binding motif. CONCLUSIONS: Our study suggests that the association with colorectal cancer in 10q24.2/COX15 observed in genome-wide association study is strongest in nondrinkers. We also identified rs1318920 as the putative causal regulatory variant for the region. IMPACT: The study identifies multifaceted evidence of a possible functional effect for rs1318920.

Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint.

Defined by their genetic profile, individuals may exhibit differential clinical outcomes due to an environmental exposure. Identifying subgroups based on specific exposure-modifying genes can lead to targeted interventions and focused studies. Genome-wide interaction scans (GWIS) can be performed to identify such genes, but these scans typically suffer from low power due to the large multiple testing burden. We provide a novel framework for powerful two-step hypothesis tests for GWIS with a time-to-event endpoint under the Cox proportional hazards model. In the Cox regression setting, we develop an approach that prioritizes genes for Step-2 G×E testing based on a carefully constructed Step-1 screening procedure. Simulation results demonstrate this two-step approach can lead to substantially higher power for identifying gene-environment ( G×E ) interactions compared to the standard GWIS while preserving the family wise error rate over a range of scenarios. In a taxane-anthracycline chemotherapy study for breast cancer patients, the two-step approach identifies several gene expression by treatment interactions that would not be detected using the standard GWIS.

A scalable hierarchical lasso for gene-environment interactions.

We describe a regularized regression model for the selection of gene-environment (G×E) interactions. The model focuses on a single environmental exposure and induces a main-effect-before-interaction hierarchical structure. We propose an efficient fitting algorithm and screening rules that can discard large numbers of irrelevant predictors with high accuracy. We present simulation results showing that the model outperforms existing joint selection methods for (G×E) interactions in terms of selection performance, scalability and speed, and provide a real data application. Our implementation is available in the gesso R package.

Effect of UNOS policy change and exception status request on outcomes in patients bridged to heart transplant with an intra-aortic balloon pump.

BACKGROUND: Intra-aortic balloon pumps (IABP) are used to bridge select end-stage heart disease patients to heart transplant (HT). IABP use and exception requests both increased dramatically after the UNOS policy change (PC). The purpose of this study was to evaluate the effect of PC and exception status requests on waitlist and post-transplant outcomes in patients bridged to HT with IABP support. METHODS: We analyzed adult, first-time, single-organ HT recipients from the UNOS Registry either on IABP at the time of registration for HT or at the time of HT. We compared waitlist and post-HT outcomes between patients from the PRE (October 18, 2016 to May 30, 2018) and POST (October 18, 2018 to May 30, 2020) eras using Kaplan-Meier curves and time-to-event analyses. RESULTS: A total of 1267 patients underwent HT from IABP (261 pre-policy/1006 post-policy). On multivariate analysis, PC was associated with an increase in HT (sub-distribution hazard ratio (sdHR): 2.15, p < .001) and decrease in death/deterioration (sdHR: 0.55, p = .011) on the waitlist with no effect on 1-year post-HT survival (p = .8). The exception status of patients undergoing HT was predominantly seen in the POST era (29%, 293/1006); only four patients in the PRE era. Exception requests in the POST era did not alter patient outcomes. CONCLUSIONS: In patients bridged to heart transplant with an IABP, policy change is associated with decreased rates of death/deterioration and increased rates of heart transplantation on the waitlist without affecting 1-year post-transplant survival. While exception status use has markedly increased post-PC, it is not associated with patient outcomes.

Incorporating prior knowledge into regularized regression.

MOTIVATION: Associated with genomic features like gene expression, methylation and genotypes, used in statistical modeling of health outcomes, there is a rich set of meta-features like functional annotations, pathway information and knowledge from previous studies, that can be used post hoc to facilitate the interpretation of a model. However, using this meta-feature information a priori rather than post hoc can yield improved prediction performance as well as enhanced model interpretation. RESULTS: We propose a new penalized regression approach that allows a priori integration of external meta-features. The method extends LASSO regression by incorporating individualized penalty parameters for each regression coefficient. The penalty parameters are, in turn, modeled as a log-linear function of the meta-features and are estimated from the data using an approximate empirical Bayes approach. Optimization of the marginal likelihood on which the empirical Bayes estimation is performed using a fast and stable majorization-minimization procedure. Through simulations, we show that the proposed regression with individualized penalties can outperform the standard LASSO in terms of both parameters estimation and prediction performance when the external data is informative. We further demonstrate our approach with applications to gene expression studies of bone density and breast cancer. AVAILABILITY AND IMPLEMENTATION: The methods have been implemented in the R package xtune freely available for download from https://cran.r-project.org/web/packages/xtune/index.html.