Single-cell sequencing of human white adipose tissue identifies new cell states in health and obesity.

White adipose tissue (WAT) is an essential regulator of energy storage and systemic metabolic homeostasis. Regulatory networks consisting of immune and structural cells are necessary to maintain WAT metabolism, which can become impaired during obesity in mammals. Using single-cell transcriptomics and flow cytometry, we unveil a large-scale comprehensive cellular census of the stromal vascular fraction of healthy lean and obese human WAT. We report new subsets and developmental trajectories of adipose-resident innate lymphoid cells, dendritic cells and monocyte-derived macrophage populations that accumulate in obese WAT. Analysis of cell-cell ligand-receptor interactions and obesity-enriched signaling pathways revealed a switch from immunoregulatory mechanisms in lean WAT to inflammatory networks in obese WAT. These results provide a detailed and unbiased cellular landscape of homeostatic and inflammatory circuits in healthy human WAT.

A multi-dimensional integrative scoring framework for predicting functional variants in the human genome.

Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

Genome-wide association study identifies high-impact susceptibility loci for HCC in North America.

BACKGROUND AND AIMS: Despite the substantial impact of environmental factors, individuals with a family history of liver cancer have an increased risk for HCC. However, genetic factors have not been studied systematically by genome-wide approaches in large numbers of individuals from European descent populations (EDP). APPROACH AND RESULTS: We conducted a 2-stage genome-wide association study (GWAS) on HCC not affected by HBV infections. A total of 1872 HCC cases and 2907 controls were included in the discovery stage, and 1200 HCC cases and 1832 controls in the validation. We analyzed the discovery and validation samples separately and then conducted a meta-analysis. All analyses were conducted in the presence and absence of HCV. The liability-scale heritability was 24.4% for overall HCC. Five regions with significant ORs (95% CI) were identified for nonviral HCC: 3p22.1, MOBP , rs9842969, (0.51, [0.40-0.65]); 5p15.33, TERT , rs2242652, (0.70, (0.62-0.79]); 19q13.11, TM6SF2 , rs58542926, (1.49, [1.29-1.72]); 19p13.11 MAU2 , rs58489806, (1.53, (1.33-1.75]); and 22q13.31, PNPLA3 , rs738409, (1.66, [1.51-1.83]). One region was identified for HCV-induced HCC: 6p21.31, human leukocyte antigen DQ beta 1, rs9275224, (0.79, [0.74-0.84]). A combination of homozygous variants of PNPLA3 and TERT showing a 6.5-fold higher risk for nonviral-related HCC compared to individuals lacking these genotypes. This observation suggests that gene-gene interactions may identify individuals at elevated risk for developing HCC. CONCLUSIONS: Our GWAS highlights novel genetic susceptibility of nonviral HCC among European descent populations from North America with substantial heritability. Selected genetic influences were observed for HCV-positive HCC. Our findings indicate the importance of genetic susceptibility to HCC development.

Genome-wide interaction analysis identified low-frequency variants with sex disparity in lung cancer risk.

Differences by sex in lung cancer incidence and mortality have been reported which cannot be fully explained by sex differences in smoking behavior, implying existence of genetic and molecular basis for sex disparity in lung cancer development. However, the information about sex dimorphism in lung cancer risk is quite limited despite the great success in lung cancer association studies. By adopting a stringent two-stage analysis strategy, we performed a genome-wide gene-sex interaction analysis using genotypes from a lung cancer cohort including ~ 47 000 individuals with European ancestry. Three low-frequency variants (minor allele frequency < 0.05), rs17662871 [odds ratio (OR) = 0.71, P = 4.29×10-8); rs79942605 (OR = 2.17, P = 2.81×10-8) and rs208908 (OR = 0.70, P = 4.54×10-8) were identified with different risk effect of lung cancer between men and women. Further expression quantitative trait loci and functional annotation analysis suggested rs208908 affects lung cancer risk through differential regulation of Coxsackie virus and adenovirus receptor gene expression in lung tissues between men and women. Our study is one of the first studies to provide novel insights about the genetic and molecular basis for sex disparity in lung cancer development.

Risk assessment with low-pass whole-genome sequencing of cell-free DNA before CD19 CAR T-cell therapy for large B-cell lymphoma.

Patients with relapsed or refractory large B-cell lymphomas (rrLBCL) can achieve long-term remission after CD19 chimeric antigen receptor T-cell therapy (CART19). However, more than half of recipients will experience treatment failure. Thus, approaches are needed to identify high-risk patients who may benefit from alternative or consolidative therapy. We evaluated low-pass whole-genome sequencing (lpWGS) of cell-free DNA (cfDNA) before CART19 as a new approach for risk stratification. We performed lpWGS on pretreatment plasma samples from 122 patients at time of leukapheresis who received standard-of-care CART19 for rrLBCL to define DNA copy number alterations (CNAs). In multivariable selection, high focal CNA score (FCS) denoting genomic instability was the most significant pretreatment variable associated with inferior 3-month complete response rates (28% vs 56%, P = .0029), progression-free survival (PFS; P = .0007; hazard ratio, 2.11), and overall survival (OS; P = .0026; hazard ratio, 2.10). We identified 34 unique focal CNAs in 108 (89%) patients; of these, deletion 10q23.3 leading to loss of FAS death receptor was the most highly associated with poor outcomes, leading to inferior PFS (P < .0001; hazard ratio, 3.49) and OS (P = .0027; hazard ratio, 2.68). By combining FCS with traditional markers of increased tumor bulk (elevated lactate dehydrogenase and >1 extranodal site), we built a simple risk model that could reliably risk stratify patients. Thus, lpWGS of cfDNA is a minimally invasive assay that could rapidly identify high-risk patients and may guide patient selection for and targeted therapies to evaluate in future clinical trials.

Variance-components tests for genetic association with multiple interval-censored outcomes.

Massive genetic compendiums such as the UK Biobank have become an invaluable resource for identifying genetic variants that are associated with complex diseases. Due to the difficulties of massive data collection, a common practice of these compendiums is to collect interval-censored data. One challenge in analyzing such data is the lack of methodology available for genetic association studies with interval-censored data. Genetic effects are difficult to detect because of their rare and weak nature, and often the time-to-event outcomes are transformed to binary phenotypes for access to more powerful signal detection approaches. However transforming the data to binary outcomes can result in loss of valuable information. To alleviate such challenges, this work develops methodology to associate genetic variant sets with multiple interval-censored outcomes. Testing sets of variants such as genes or pathways is a common approach in genetic association settings to lower the multiple testing burden, aggregate small effects, and improve interpretations of results. Instead of performing inference with only a single outcome, utilizing multiple outcomes can increase statistical power by aggregating information across multiple correlated phenotypes. Simulations show that the proposed strategy can offer significant power gains over a single outcome approach. We apply the proposed test to the investigation that motivated this study, a search for the genes that perturb risks of bone fractures and falls in the UK Biobank.

Differences in set-based tests for sparse alternatives when testing sets of outcomes compared to sets of explanatory factors in genetic association studies.

Set-based association tests are widely popular in genetic association settings for their ability to aggregate weak signals and reduce multiple testing burdens. In particular, a class of set-based tests including the Higher Criticism, Berk-Jones, and other statistics have recently been popularized for reaching a so-called detection boundary when signals are rare and weak. Such tests have been applied in two subtly different settings: (a) associating a genetic variant set with a single phenotype and (b) associating a single genetic variant with a phenotype set. A significant issue in practice is the choice of test, especially when deciding between innovated and generalized type methods for detection boundary tests. Conflicting guidance is present in the literature. This work describes how correlation structures generate marked differences in relative operating characteristics for settings (a) and (b). The implications for study design are significant. We also develop novel power bounds that facilitate the aforementioned calculations and allow for analysis of individual testing settings. In more concrete terms, our investigation is motivated by translational expression quantitative trait loci (eQTL) studies in lung cancer. These studies involve both testing for groups of variants associated with a single gene expression (multiple explanatory factors) and testing whether a single variant is associated with a group of gene expressions (multiple outcomes). Results are supported by a collection of simulation studies and illustrated through lung cancer eQTL examples.

A phase II study of neoadjuvant atezolizumab and nab-paclitaxel in patients with anthracycline-resistant early-stage triple-negative breast cancer.

PURPOSE: Neoadjuvant anti-PD-(L)1 therapy improves the pathological complete response (pCR) rate in unselected triple-negative breast cancer (TNBC). Given the potential for long-term morbidity from immune-related adverse events (irAEs), optimizing the risk-benefit ratio for these agents in the curative neoadjuvant setting is important. Suboptimal clinical response to initial neoadjuvant therapy (NAT) is associated with low rates of pCR (2-5%) and may define a patient selection strategy for neoadjuvant immune checkpoint blockade. We conducted a single-arm phase II study of atezolizumab and nab-paclitaxel as the second phase of NAT in patients with doxorubicin and cyclophosphamide (AC)-resistant TNBC (NCT02530489). METHODS: Patients with stage I-III, AC-resistant TNBC, defined as disease progression or a < 80% reduction in tumor volume after 4 cycles of AC, were eligible. Patients received atezolizumab (1200 mg IV, Q3weeks × 4) and nab-paclitaxel (100 mg/m2 IV,Q1 week × 12) as the second phase of NAT before undergoing surgery followed by adjuvant atezolizumab (1200 mg IV, Q3 weeks, × 4). A two-stage Gehan-type design was employed to detect an improvement in pCR/residual cancer burden class I (RCB-I) rate from 5 to 20%. RESULTS: From 2/15/2016 through 1/29/2021, 37 patients with AC-resistant TNBC were enrolled. The pCR/RCB-I rate was 46%. No new safety signals were observed. Seven patients (19%) discontinued atezolizumab due to irAEs. CONCLUSION: This study met its primary endpoint, demonstrating a promising signal of activity in this high-risk population (pCR/RCB-I = 46% vs 5% in historical controls), suggesting that a response-adapted approach to the utilization of neoadjuvant immunotherapy should be considered for further evaluation in a randomized clinical trial.

Prognostic impact of corticosteroids on efficacy of chimeric antigen receptor T-cell therapy in large B-cell lymphoma.

Corticosteroids are commonly used for the management of severe toxicities associated with chimeric antigen receptor (CAR) T-cell therapy. However, it remains unclear whether their dose, duration, and timing may affect clinical efficacy. Here, we determined the impact of corticosteroids on clinical outcomes in patients with relapsed or refractory large B-cell lymphoma treated with standard of care anti-CD19 CAR T-cell therapy. Among 100 patients evaluated, 60 (60%) received corticosteroids for management of CAR T-cell therapy-associated toxicities. The median cumulative dexamethasone-equivalent dose was 186 mg (range, 8-1803) and the median duration of corticosteroid treatment was 9 days (range, 1-30). Corticosteroid treatment was started between days 0 and 7 in 45 (75%) patients and beyond day 7 in 15 (25%). After a median follow-up of 10 months (95% confidence interval, 8-12 months), use of higher cumulative dose of corticosteroids was associated with significantly shorter progression-free survival. More importantly, higher cumulative dose of corticosteroids, and prolonged and early use after CAR T-cell infusion were associated with significantly shorter overall survival. These results suggest that corticosteroids should be used at the lowest dose and for the shortest duration and their initiation should be delayed whenever clinically feasible while managing CAR T-cell therapy-associated toxicities.

Effect of delayed cell infusion in patients with large B-cell lymphoma treated with chimeric antigen receptor T-cell therapy.

Complications occurring after lymphodepleting chemotherapy (LDC) may delay chimeric antigen receptor (CAR) T-cell infusion. The effect of these delays on clinical outcomes is unclear. We performed a retrospective analysis of 240 patients with relapsed/refractory large B-cell lymphoma treated with standard-of-care axicabtagene ciloleucel (axi-cel) and identified 40 patients (16.7%) who had delay in axi-cel infusion. Of these, 85% had delay due to infection. At time of LDC initiation, patients with delayed infusion had lower absolute neutrophil count (p=0.006), lower platelets (p=0.004), lower hemoglobin (p5 days (4.6 vs. 8.2 months; p=0.036), but not 1 day (5.7 vs. 8.2 months; p=0.238). Following propensity score matching, patients with delayed infusion continued to have shorter median PFS (3.5 vs. 6.0 months; p=0.015). Levels of proinflammatory cytokines on day of infusion were significantly higher in patients with delayed infusion. Together, these findings suggest that delays in CAR T-cell administration after initiation of LDC are associated with inferior outcomes. Further studies are needed to guide strategies to improve efficacy in such patients.

Clinical and Prognostic Biomarker Value of Blood-Circulating Inflammatory Cytokines in Hepatocellular Carcinoma.

INTRODUCTION: Circulating inflammatory cytokines play critical roles in tumor-associated inflammation and immune responses. Recent data have suggested that several interleukins (ILs) mediate carcinogenesis in hepatocellular carcinoma (HCC). However, the predictive and prognostic value of circulating ILs is yet to be validated. Our study aimed to evaluate the association of the serum ILs with overall survival (OS) and clinicopathologic features in a large cohort of HCC patients. METHODS: We prospectively collected data and serum samples from 767 HCC patients treated at the University of Texas MD Anderson Cancer Center between 2001 and 2014, with a median follow-up of 67.4 months (95% confidence interval [CI]: 52.5, 83.3). Biomarker association with OS was evaluated by the log-rank method. RESULTS: The median OS in this cohort was 14.2 months (95% CI: 12, 16.1 months). Clinicopathologic features were more advanced, and OS was significantly inferior in patients with high circulating levels of IL1-R1, IL-6, IL-8, IL-10, IL-15, IL-16, and IL-18. CONCLUSION: Our study shows that several serum IL levels are valid prognostic biomarker candidates and potential targets for therapy in HCC.

Leveraging a surrogate outcome to improve inference on a partially missing target outcome.

Sample sizes vary substantially across tissues in the Genotype-Tissue Expression (GTEx) project, where considerably fewer samples are available from certain inaccessible tissues, such as the substantia nigra (SSN), than from accessible tissues, such as blood. This severely limits power for identifying tissue-specific expression quantitative trait loci (eQTL) in undersampled tissues. Here we propose Surrogate Phenotype Regression Analysis (Spray) for leveraging information from a correlated surrogate outcome (eg, expression in blood) to improve inference on a partially missing target outcome (eg, expression in SSN). Rather than regarding the surrogate outcome as a proxy for the target outcome, Spray jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. We describe and implement an expectation conditional maximization algorithm for performing estimation in the presence of bilateral outcome missingness. Spray estimates the same association parameter estimated by standard eQTL mapping and controls the type I error even when the target and surrogate outcomes are truly uncorrelated. We demonstrate analytically and empirically, using simulations and GTEx data, that in comparison with marginally modeling the target outcome, jointly modeling the target and surrogate outcomes increases estimation precision and improves power.

Inference for set-based effects in genetic association studies with interval-censored outcomes.

The rapid acceleration of genetic data collection in biomedical settings has recently resulted in the rise of genetic compendiums filled with rich longitudinal disease data. One common feature of these data sets is their plethora of interval-censored outcomes. However, very few tools are available for the analysis of genetic data sets with interval-censored outcomes, and in particular, there is a lack of methodology available for set-based inference. Set-based inference is used to associate a gene, biological pathway, or other genetic construct with outcomes and is one of the most popular strategies in genetics research. This work develops three such tests for interval-censored settings beginning with a variance components test for interval-censored outcomes, the interval-censored sequence kernel association test (ICSKAT). We also provide the interval-censored version of the Burden test, and then we integrate ICSKAT and Burden to construct the interval censored sequence kernel association test-optimal (ICSKATO) combination. These tests unlock set-based analysis of interval-censored data sets with analogs of three highly popular set-based tools commonly applied to continuous and binary outcomes. Simulation studies illustrate the advantages of the developed methods over ad hoc alternatives, including protection of the type I error rate at very low levels and increased power. The proposed approaches are applied to the investigation that motivated this study, an examination of the genes associated with bone mineral density deficiency and fracture risk.

Regression analysis of general mixed recurrent event data.

In modern biomedical datasets, it is common for recurrent outcomes data to be collected in an incomplete manner. More specifically, information on recurrent events is routinely recorded as a mixture of recurrent event data, panel count data, and panel binary data; we refer to this structure as general mixed recurrent event data. Although the aforementioned data types are individually well-studied, there does not appear to exist an established approach for regression analysis of the three component combination. Often, ad-hoc measures such as imputation or discarding of data are used to homogenize records prior to the analysis, but such measures lead to obvious concerns regarding robustness, loss of efficiency, and other issues. This work proposes a maximum likelihood regression estimation procedure for the combination of general mixed recurrent event data and establishes the asymptotic properties of the proposed estimators. In addition, we generalize the approach to allow for the existence of terminal events, a common complicating feature in recurrent event analysis. Numerical studies and application to the Childhood Cancer Survivor Study suggest that the proposed procedures work well in practical situations.

Factors for poor oral health in long-term childhood cancer survivors.

BACKGROUND: Survivors of childhood cancer are at risk for therapy-related dental diseases. The purpose of the study was to investigate the associations between clinical, socioeconomic, and demographic factors and oral diseases in the St. Jude Lifetime Cohort (SJLIFE) participants. METHODS: We performed a retrospective medical chart review and evaluated longitudinal self-reported dental outcomes in 4856 childhood cancer survivors and 591 community controls participating in the St. Jude Lifetime Cohort (SJLIFE) study. Univariate and multivariable logistic regression models were used to assess the impact of socioeconomic factors, treatment exposures and patient demographics on dental outcomes. RESULTS: Cancer survivors were more likely to report microdontia (odds ratio (OR) = 7.89, 95% confidence interval (CI) [4.64, 14.90]), abnormal root development (OR = 6.19, CI [3.38, 13.00]), hypodontia (OR = 2.75, CI [1.83, 4.33]), enamel hypoplasia (OR = 4.24, CI [2.9, 6.49]), xerostomia (OR = 7.72, CI [3.27, 25.10]), severe gingivitis (OR = 2.04, CI [1.43, 3.03]), and ≥ 6 missing teeth (OR = 3.73, CI [2.46, 6.00]) compared to controls without cancer history. Survivors who received classic alkylating agents (OR = 1.6, CI [1.36, 1.88]), anthracycline antibiotics (OR = 1.22, CI [1.04, 1.42] or radiation therapy potentially exposing the oral cavity (OR = 1.48, CI [1.26, 1.72]) were more likely to report at least one dental health problem after controlling for socioeconomic factors, age at last follow-up and diagnosis, other treatment exposures, and access to dental services. Survivors who had radiation therapy potentially exposing the oral cavity (OR = 1.52, CI [1.25, 1.84]) were also more likely to report at least one soft tissue abnormality after controlling for socioeconomic factors, age at last follow-up and diagnosis, other treatment exposures, and access and utilization of dental services. CONCLUSIONS: Childhood cancer survivors have a higher prevalence of oral-dental abnormalities than the controls without a cancer history. Cancer treatment, socioeconomic factors, and access to oral health care contribute to the prevalence of dental abnormalities.

Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer.

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1ß pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

Are there predictors of flexible ureteroscopic aptitude among novice trainees? objective assessment using simulation-based trainer.

PURPOSE: Non-surgical skills involving hand-eye coordination and bimanual dexterity may have a transferable impact on the acquisition of ureteroscopy skills. In this study, we aim to investigate the predictors of initial flexible ureteroscopy skills among novice trainees. METHODS: This was a prospective study involving students with no prior ureteroscopy exposure. Non-surgical parameters were assessed with a detailed survey, including demographics, video game, and musical history. Musical ability was objectively evaluated with the mini-Profile of Music Perception Skills test. Ureteroscopic performance was evaluated using a Boston Scientific© flexible ureteroscope on a bench model. Each participant completed diagnostic ureteroscopy and stone extraction. Outcomes included both speed and quality of performance, based on an Objective Structured Assessment of Technical Skills rubric. RESULTS: A total of 28 pre-clerkship medical students and 10 urology residents were included. Age and musical background were not associated with ureteroscopic aptitude. Those with video game history tended to perform ureteroscopy tasks faster with a higher OSATS score, although no statistical significance was reached. Male gender was associated with faster task completion with statistically higher OSATS score independent of video game activities (p = 0.011), however, the absolute score difference was small. CONCLUSIONS: Among novice trainees, musical and video game experience was not predictive of ureteroscopy skills. Male gender was associated with slightly faster and higher ureteroscopy technique scores, however, the differences are small and unlikely to represent clinical significance. Nevertheless, the use of ureteroscopy trainer provides useful insights and should be adopted in training programs as a marker of skills progression.

Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic.

A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.