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.

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.

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.

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.

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.

Using Bayes model averaging to leverage both gene main effects and G × E interactions to identify genomic regions in genome-wide association studies.

Genome-wide association studies typically search for marginal associations between a single-nucleotide polymorphism (SNP) and a disease trait while gene-environment (G × E) interactions remain generally unexplored. More powerful methods beyond the simple case-control (CC) approach leverage either marginal effects or CC ascertainment to increase power. However, these potential gains depend on assumptions whose aptness is often unclear a priori. Here, we review G × E methods and use simulations to highlight performance as a function of main and interaction effects and the association of the two factors in the source population. Substantial variation in performance between methods leads to uncertainty as to which approach is most appropriate for any given analysis. We present a framework that (a) balances the robustness of a CC approach with the power of the case-only (CO) approach; (b) incorporates main SNP effects; (c) allows for incorporation of prior information; and (d) allows the data to determine the most appropriate model. Our framework is based on Bayes model averaging, which provides a principled statistical method for incorporating model uncertainty. We average over inclusion of parameters corresponding to the main and G × E interaction effects and the G-E association in controls. The resulting method exploits the joint evidence for main and interaction effects while gaining power from a CO equivalent analysis. Through simulations, we demonstrate that our approach detects SNPs within a wide range of scenarios with increased power over current methods. We illustrate the approach on a gene-environment scan in the USC Children's Health Study.

A Unified Model for the Analysis of Gene-Environment Interaction.

Gene-environment (G × E) interaction is important for many complex traits. In a case-control study of a disease trait, logistic regression is the standard approach used to model disease as a function of a gene (G), an environmental factor (E), G × E interaction, and adjustment covariates. We propose an alternative model with G as the outcome and show how it provides a unified framework for obtaining results from all of the common G × E tests. These include the 1-degree-of-freedom (df) test of G × E interaction, the 2-df joint test of G and G × E, the case-only and empirical Bayes tests, and several 2-step tests. In the context of this unified model, we propose a novel 3-df test and demonstrate that it provides robust power across a wide range of underlying G × E interaction models. We demonstrate the 3-df test in a genome-wide scan of G × sex interaction for childhood asthma using data from the Children's Health Study (Southern California, 1993-2001). This scan identified a strong G × sex interaction at the phosphodiesterase gene 4D locus (PDE4D), a known asthma-related locus, with a strong effect in males (per-allele odds ratio = 1.70; P = 3.8 × 10-8) and virtually no effect in females. We describe a software program, G×EScan (University of Southern California, Los Angeles, California), which can be used to fit standard and unified models for genome-wide G × E studies.

Preoperative hemoglobin A1c levels and increased risk of adverse limb events in diabetic patients undergoing infrainguinal lower extremity bypass surgery in the Vascular Quality Initiative.

BACKGROUND: The impact of preoperative glycemic control on the risk of adverse perioperative outcomes in diabetic patients undergoing lower extremity bypass (LEB) surgery is not well-understood. We determined whether higher preoperative hemoglobin A1c (HbA1c) levels are associated with an increased risk of major adverse limb events, major adverse cardiovascular events, and mortality in diabetic patients undergoing infrainguinal LEB. METHODS: A retrospective review of all infrainguinal LEB surgeries in the Vascular Quality Initiative registry from January 2012 to February 2017 was performed. Only surgeries performed on diabetic patients with complete demographic and clinical data, including HbA1c value at the time of LEB, were included for analysis (n = 7727). Entries were stratified according to the following HbA1c levels: 6 or less (n = 1087), greater than 6 to 7 or less (n = 2137), greater than 7 to 8 or less (n = 1657), and greater than 8 (n = 2846). Multivariate logistic regression was used to determine the association of preoperative HbA1c levels on the risk of in-hospital major adverse limb events (above ankle amputation, loss of primary graft patency), major adverse cardiovascular events (myocardial infarction, stroke, congestive heart failure, cardiac arrhythmia), and mortality. RESULTS: The number of surgeries complicated by adverse limb and cardiovascular events were 356 (4.6%) and 1314 (17.0%), respectively. There were 72 in-hospital deaths (0.9%). After adjustment for clinical and demographic variables, patients with high HbA1c values (≥8%) were at an increased risk of adverse limb events (odds ratio [OR], 1.37; 95% confidence interval [CI], 1.01-1.86) compared with those with a normal HbA1c (>6% to ≤7%). High HbA1c values were not associated with an increased risk of cardiovascular events (OR, 1.07; 95% CI, 0.81-1.43) or mortality (OR, 1.57; 95% CI, 0.83-3.03). Patients with low HbA1c values (≤6%) did not experience a significantly higher risk for any of the three outcomes. In a stratified analysis, the association of high HbA1c values with adverse limb events was only present in those presenting without critical limb ischemia (OR 1.82; 95% CI, 1.05-3.16). CONCLUSIONS: Poor preoperative glycemic control in diabetic individuals undergoing infrainguinal LEB, particularly in those without critical limb ischemia, is associated with an increased risk of in-hospital limb events. Further study should evaluate whether improved efforts to identify individuals with poorly controlled diabetes and subsequent interventions to better optimize glycemic control during the preoperative period improve limb outcomes after LEB.

Improvements to the Escalation with Overdose Control design and a comparison with the restricted Continual Reassessment Method.

The Escalation with Overdose Control (EWOC) design for cancer dose finding clinical trials is a variation of the Continual Reassessment Method (CRM) that was proposed to overcome the limitation of the original CRM of exposing patients to high toxic doses. The properties of EWOC have been studied to some extent, but some aspects of the design are not well studied, and its performance is not fully understood. Comparisons of the EWOC design to the most commonly used modified CRM designs have not yet been performed, and the advantages of EWOC over the modified CRM designs are unclear. In this paper, we assess the properties of the EWOC design and of the restricted CRM and some variations of these designs. We show that EWOC has several weaknesses that CRM does not have that make it impractical to use in its original formulation. We propose modified EWOC designs that address some of the weaknesses and that have some desirable statistical properties compared with the original EWOC design, the restricted CRM design, and the 3 + 3 design. However, their statistical properties are sensitive to correct specification of the prior distribution of their parameters and hence nevertheless will need to be used with some caution. The restricted CRM design is shown to have more stable performance across a wider family of dose-toxicity curves than EWOC and therefore may be a preferable general choice in cancer clinical research.

Data integration by multi-tuning parameter elastic net regression.

BACKGROUND: To integrate molecular features from multiple high-throughput platforms in prediction, a regression model that penalizes features from all platforms equally is commonly used. However, data from different platforms are likely to differ in effect sizes, the proportion of predictive features, and correlations structures. Subtle but important features may be missed by shrinking all features equally. RESULTS: We propose an Elastic net (EN) model with separate tuning parameter penalties for each platform that is fit using standard software. In a comprehensive simulation study, we evaluated the performance of EN logistic regression with multiple tuning penalties. We found that when the number of informative features differs among the platforms, and when there is no notable correlation between the features from different platforms, the multi-tuning parameter EN yields more predictive models. Moreover, the multi-tuning parameter EN is robust, in the sense that there is no loss of predictivity relative to a single tuning parameter EN when features across all platforms have similar effects. We also investigated the performance of multi-tuning parameter EN using real cancer datasets. CONCLUSION: The proposed multi-tuning parameter EN model, fit using standard penalized regression software, can achieve better prediction in sample classification when integrating multiple genomic platforms, compared to the traditional method where a single penalty parameter is used for all features in different platforms.

Comparison of devices used in carotid artery stenting: A vascular quality initiative analysis of commonly used carotid stents and embolic protection devices.

BACKGROUND: Data regarding efficacy of various stent and embolic protection device (EPD) combinations to prevent stroke during carotid artery stenting (CAS) is limited. We compared post-procedure inpatient neurologic outcomes across various carotid stent-EPD platforms recorded in the Vascular Quality Initiative (VQI) registry. METHODS: We analyzed 13,786 consecutive CAS procedures in the VQI registry performed between January 1, 2005 and December 31, 2015. The most commonly used stent-EPD combinations (n = 5407) were included in the analysis. Post-procedure inpatient neurologic outcomes included (1) ipsilateral stroke/transient ischemic attack (TIA) and (2) any stroke/TIA. Multivariate generalized estimating equation regression analysis was performed, adjusting for age, sex, tobacco use, coronary artery disease, congestive heart failure, prior stroke/TIA, hypertension, history of carotid revascularization, and presence of a second ipsilateral stenosis >70%, to determine whether risk of outcomes differed according to device. RESULTS: Of 13,786 CAS procedures, Xact-Emboshield (n = 2,438, 17.6%), Precise-Angioguard (n = 1,480, 10.7%), Acculink-Accunet (n = 829, 6.01%), and Acculink-Emboshield (n = 660, 4.8%) were the most commonly used combinations, accounting for a total of 5,407 procedures. Inpatient event rates for ipsilateral stroke/TIA and any stroke/TIA were 1.9 and 2.7% in the Accunet-Acculink, 3.0 and 3.2% in Acculink-Emboshield, 3.2 and 4.1% in Precise-Angioguard and 2.2 and 3.0% in Xact-Emboshield. There was no evidence of difference in risk of ipsilateral stroke/TIA or any stroke/TIA across device combinations (P = 0.15 and P = 0.16, respectively). CONCLUSION: CAS with current carotid stent-EPD combinations is associated with low rates of inpatient stroke/TIA. There is no statistically significant difference in rates of inpatient stroke/TIA across device combinations.

Adaptive Set-Based Methods for Association Testing.

With a typical sample size of a few thousand subjects, a single genome-wide association study (GWAS) using traditional one single nucleotide polymorphism (SNP)-at-a-time methods can only detect genetic variants conferring a sizable effect on disease risk. Set-based methods, which analyze sets of SNPs jointly, can detect variants with smaller effects acting within a gene, a pathway, or other biologically relevant sets. Although self-contained set-based methods (those that test sets of variants without regard to variants not in the set) are generally more powerful than competitive set-based approaches (those that rely on comparison of variants in the set of interest with variants not in the set), there is no consensus as to which self-contained methods are best. In particular, several self-contained set tests have been proposed to directly or indirectly "adapt" to the a priori unknown proportion and distribution of effects of the truly associated SNPs in the set, which is a major determinant of their power. A popular adaptive set-based test is the adaptive rank truncated product (ARTP), which seeks the set of SNPs that yields the best-combined evidence of association. We compared the standard ARTP, several ARTP variations we introduced, and other adaptive methods in a comprehensive simulation study to evaluate their performance. We used permutations to assess significance for all the methods and thus provide a level playing field for comparison. We found the standard ARTP test to have the highest power across our simulations followed closely by the global model of random effects (GMRE) and a least absolute shrinkage and selection operator (LASSO)-based test.

Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association Study.

A genome-wide association study (GWAS) typically is focused on detecting marginal genetic effects. However, many complex traits are likely to be the result of the interplay of genes and environmental factors. These SNPs may have a weak marginal effect and thus unlikely to be detected from a scan of marginal effects, but may be detectable in a gene-environment (G × E) interaction analysis. However, a genome-wide interaction scan (GWIS) using a standard test of G × E interaction is known to have low power, particularly when one corrects for testing multiple SNPs. Two 2-step methods for GWIS have been previously proposed, aimed at improving efficiency by prioritizing SNPs most likely to be involved in a G × E interaction using a screening step. For a quantitative trait, these include a method that screens on marginal effects [Kooperberg and Leblanc, 2008] and a method that screens on variance heterogeneity by genotype [Paré et al., 2010] In this paper, we show that the Paré et al. approach has an inflated false-positive rate in the presence of an environmental marginal effect, and we propose an alternative that remains valid. We also propose a novel 2-step approach that combines the two screening approaches, and provide simulations demonstrating that the new method can outperform other GWIS approaches. Application of this method to a G × Hispanic-ethnicity scan for childhood lung function reveals a SNP near the MARCO locus that was not identified by previous marginal-effect scans.

Update on the State of the Science for Analytical Methods for Gene-Environment Interactions.

The analysis of gene-environment interaction (G×E) may hold the key for further understanding the etiology of many complex traits. The current availability of high-volume genetic data, the wide range in types of environmental data that can be measured, and the formation of consortiums of multiple studies provide new opportunities to identify G×E but also new analytical challenges. In this article, we summarize several statistical approaches that can be used to test for G×E in a genome-wide association study. These include traditional models of G×E in a case-control or quantitative trait study as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing G×E with gene sets and with data in a consortium setting are summarized, as are issues that arise due to the complexity of environmental data. We provide some speculation on why detecting G×E in a genome-wide association study has thus far been difficult. We conclude with a description of software programs that can be used to implement most of the methods described in the paper.

Increased risk of mortality after lower extremity bypass in individuals with acute kidney injury in the Vascular Quality Initiative.

OBJECTIVE: The development of acute kidney injury (AKI) and its effect on prognosis after lower extremity bypass (LEB) surgery have not been well described. We determined risk factors associated with AKI in patients undergoing infrainguinal LEB surgery and whether individuals with AKI are at increased risk for cardiovascular events and mortality. METHODS: Data for 12,907 operations entered in the Vascular Quality Initiative (VQI) registry from January 2012 through April 2015 were retrospectively reviewed. Procedures performed on patients not on dialysis before the surgery with perioperative assessments of renal function were eligible for the study. AKI was defined as a postoperative increase in serum creatinine ≥0.5 mg/dL or new dialysis requirement. Logistic regression was performed to determine the effect of AKI on the risk of in-hospital cardiovascular events, including myocardial infarction, stroke, congestive heart failure, or arrhythmias, and mortality. Cox proportional hazards regression was performed to determine the risk of long-term mortality (median follow-up of 11.5 months). RESULTS: AKI developed after 507 (4%) of the 12,907 operations performed in 11,859 patients. After adjustment for demographic, clinical, and perioperative variables, AKI was associated with an increased risk of in-hospital cardiovascular events (odds ratio, 2.50; 95% confidence interval [CI], 1.91-3.28) and in-hospital mortality (odds ratio, 6.96; 95% CI, 3.94-12.31). Risk of mortality persisted over the course of follow-up (hazard ratio, 1.98; 95% CI, 1.58-2.47). CONCLUSIONS: AKI after LEB is associated with an increased risk of cardiovascular events and all-cause mortality. Further study should evaluate whether preoperative interventions before LEB can be effectively applied for at-risk patients to reduce the incidence of AKI and its associated morbidity and mortality.

Novel Gene Expression Signature Predictive of Clinical Recurrence After Radical Prostatectomy in Early Stage Prostate Cancer Patients.

BACKGROUND: Current clinical tools have limited accuracy in differentiating patients with localized prostate cancer who are at risk of recurrence from patients with indolent disease. We aimed to identify a gene expression signature that jointly with clinical variables could improve upon the prediction of clinical recurrence after RP for patients with stage T2 PCa. METHODS: The study population includes consented patients who underwent a radical retropubic prostatectomy (RP) and bilateral pelvic lymph node dissection at the University of Southern California in the PSA-era (1988-2008). We used a nested case-control study of 187 organ-confined patients (pT2N0M0): 154 with no recurrence ("controls") and 33 with clinical recurrence ("cases"). RNA was obtained from laser capture microdissected malignant glands representative of the overall Gleason score of each patient. Whole genome gene expression profiles (29,000 transcripts) were obtained using the Whole Genome DASL HT platform (Illumina, Inc). A gene expression signature of PCa clinical recurrence was identified using stability selection with elastic net regularized logistic regression. Three existing datasets generated with the Affymetrix Human Exon 1.0ST array were used for validation: Mayo Clinic (MC, n = 545), Memorial Sloan Kettering Cancer Center (SKCC, n = 150), and Erasmus Medical Center (EMC, n = 48). The areas under the ROC curve (AUCs) were obtained using repeated fivefold cross-validation. RESULTS: A 28-gene expression signature was identified that jointly with key clinical variables (age, Gleason score, pre-operative PSA level, and operation year) was predictive of clinical recurrence (AUC of clinical variables only was 0.67, AUC of clinical variables, and 28-gene signature was 0.99). The AUC of this gene signature fitted in each of the external datasets jointly with clinical variables was 0.75 (0.72-0.77) (MC), 0.90 (0.86-0.94) (MSKCC), and 0.82 (0.74-0.91) (EMC), whereas the AUC for clinical variables only in each dataset was 0.72 (0.70-0.74), 0.86 (0.82-0.91), and 0.76 (0.67-0.85), respectively. CONCLUSIONS: We report a novel gene-expression based classifier identified using agnostic approaches from whole genome expression profiles that can improve upon the accuracy of clinical indicators to stratify early stage localized patients at risk of clinical recurrence after RP. Prostate 76:1239-1256, 2016. © 2016 Wiley Periodicals, Inc.

Testicular germ cell tumor susceptibility associated with the UCK2 locus on chromosome 1q23.

Genome-wide association studies (GWASs) have identified multiple common genetic variants associated with an increased risk of testicular germ cell tumors (TGCTs). A previous GWAS reported a possible TGCT susceptibility locus on chromosome 1q23 in the UCK2 gene, but failed to reach genome-wide significance following replication. We interrogated this region by conducting a meta-analysis of two independent GWASs including a total of 940 TGCT cases and 1559 controls for 122 single-nucleotide polymorphisms (SNPs) on chromosome 1q23 and followed up the most significant SNPs in an additional 2202 TGCT cases and 2386 controls from four case-control studies. We observed genome-wide significant associations for several UCK2 markers, the most significant of which was for rs3790665 (PCombined = 6.0 × 10(-9)). Additional support is provided from an independent familial study of TGCT where a significant over-transmission for rs3790665 with TGCT risk was observed (PFBAT = 2.3 × 10(-3)). Here, we provide substantial evidence for the association between UCK2 genetic variation and TGCT risk.

Radical Prostatectomy or External Beam Radiation Therapy vs No Local Therapy for Survival Benefit in Metastatic Prostate Cancer: A SEER-Medicare Analysis.

PURPOSE: We assessed survival after radical prostatectomy, intensity modulated radiation therapy or conformal radiation therapy vs no local therapy for metastatic prostate cancer adjusting for patient comorbidity, androgen deprivation therapy and other factors. MATERIALS AND METHODS: We identified men 66 years old or older with metastatic prostate cancer treated with radical prostatectomy, intensity modulated radiation therapy, conformal radiation therapy or no local therapy in the SEER-Medicare linked database from 2004 to 2009. Multivariable Cox proportional hazards models before and after inverse propensity score weighting were used to assess all cause and prostate cancer specific mortality. Competing risk regression analysis was done to assess prostate cancer specific mortality. RESULTS: Of 4,069 men with metastatic prostate cancer radical prostatectomy in 47, intensity modulated radiation therapy in 88 and conformal radiation therapy in 107 were selected as local therapy vs no local therapy in 3,827. Radical prostatectomy was associated with a 52% decrease (HR 0.48, 95% CI 0.27-0.85) in the risk of prostate cancer specific mortality after adjusting for sociodemographics, primary tumor characteristics, comorbidity, androgen deprivation therapy and bone radiation within 6 months of diagnosis. Intensity modulated radiation therapy was associated with a 62% decrease (HR 0.38, 95% CI 0.24-0.61) in the risk of prostate specific cancer specific mortality. Conformal radiation therapy was not associated with improved survival compared to no local therapy. Propensity score weighting yielded comparable results. Competing risk analysis revealed a 42% and 57% decrease (SHR 0.58, 95% CI 0.35-0.95 and SHR 0.43, 95% CI 0.27-0.68, respectively) in the risk of prostate cancer specific mortality for radical prostatectomy and intensity modulated radiation therapy. CONCLUSIONS: Local therapy with radical prostatectomy and intensity modulated radiation therapy but not with conformal radiation therapy was associated with a survival benefit in men with metastatic prostate cancer. This finding warrants prospective evaluation in clinical trials.

Finding novel genes by testing G × E interactions in a genome-wide association study.

In a genome-wide association study (GWAS), investigators typically focus their primary analysis on the direct (marginal) associations of each single nucleotide polymorphism (SNP) with the trait. Some SNPs that are truly associated with the trait may not be identified in this scan if they have a weak marginal effect and thus low power to be detected. However, these SNPs may be quite important in subgroups of the population defined by an environmental or personal factor, and may be detectable if such a factor is carefully considered in a gene-environment (G × E) interaction analysis. We address the question "Using a genome wide interaction scan (GWIS), can we find new genes that were not found in the primary GWAS scan?" We review commonly used approaches for conducting a GWIS in case-control studies, and propose a new two-step screening and testing method (EDG×E) that is optimized to find genes with a weak marginal effect. We simulate several scenarios in which our two-step method provides 70-80% power to detect a disease locus while a marginal scan provides less than 5% power. We also provide simulations demonstrating that the EDG×E method outperforms other GWIS approaches (including case only and previously proposed two-step methods) for finding genes with a weak marginal effect. Application of this method to a G × Sex scan for childhood asthma reveals two potentially interesting SNPs that were not identified in the marginal-association scan. We distribute a new software program (G×Escan, available at http://biostats.usc.edu/software) that implements this new method as well as several other GWIS approaches.

Efficient two-step testing of gene-gene interactions in genome-wide association studies.

Exhaustive testing of all possible SNP pairs in a genome-wide association study (GWAS) generally yields low power to detect gene-gene (G × G) interactions because of small effect sizes and stringent requirements for multiple-testing correction. We introduce a new two-step procedure for testing G × G interactions in case-control GWAS to detect interacting single nucleotide polymorphisms (SNPs) regardless of their marginal effects. In an initial screening step, all SNP pairs are tested for gene-gene association in the combined sample of cases and controls. In the second step, the pairs that pass the screening are followed up with a traditional test for G × G interaction. We show that the two-step method is substantially more powerful to detect G × G interactions than the exhaustive testing approach. For example, with 2,000 cases and 2,000 controls, the two-step method can have more than 90% power to detect an interaction odds ratio of 2.0 compared to less than 50% power for the exhaustive testing approach. Moreover, we show that a hybrid two-step approach that combines our newly proposed two-step test and the two-step test that screens for marginal effects retains the best power properties of both. The two-step procedures we introduce have the potential to uncover genetic signals that have not been previously identified in an initial single-SNP GWAS. We demonstrate the computational feasibility of the two-step G × G procedure by performing a G × G scan in the asthma GWAS of the University of Southern California Children's Health Study.