Biology subjects

Amos, C. I.

Publications and source records attributed to Amos, C. I..

14 recordsLinked to original sources

bioRxiv2026

Aquaporin-3 amplifies arsenite genotoxicity as a dose-dependent gene-by-environment interaction in human cells

Chronic exposure to inorganic arsenic affects more than 200 million people, and disease outcome varies widely among individuals with comparable intake. Explanations have centered on arsenic metabolism, which acts on arsenite that is already inside the cell. Uptake sits upstream of metabolism and sets how much arsenite reaches the genome, yet whether it acts as a gene-by-environment modifier of genotoxicity has been asserted far more often than it has been measured. We expressed human aquaporin-3 (AQP3), its point mutants and the paralogues AQP7, AQP9 and AQP10 in three human cell lines, and measured DNA damage as {gamma}H2AX by flow cytometry against a damage threshold re-derived from mock-transfected cells in every experiment. In lung fibroblasts, AQP3 raised the {gamma}H2AX-high fraction 2.36-fold at 5 M arsenite. Because a gene-by-environment interaction widens the genotype gap with dose while an independent genotoxin would not, the combination exceeds what the two factors would produce if they simply added by 6.36 percentage points, which is an interaction. The response required the AQP3 sequence, was suppressed by N-acetylcysteine in proportion to arsenite dose, was absent in RKO cells, and was shared with AQP9 and AQP10, which created an arsenite dose-response in a cell line that had none of its own. Low-input RNA sequencing of 135 ten-cell pools located the effect inside the cell: at an identical 1 M applied dose and within a single batch, AQP3 raised HMOX1 (induced when trivalent arsenic modifies KEAP1 thiols, and so a report of intracellular rather than applied exposure) 3.3-fold, a shift equivalent to 40% of the 1-to-10 M interval on the measured dose-response, while AQP3 without arsenite was indistinguishable from untreated. Duplex sequencing detected no consistent change in somatic mutation frequency from arsenite alone at any dose to 10 M, in an assay that resolved a 26-fold response to N-ethyl-N-nitrosourea; the arsenite-induced mutation frequency at the EGFR locus nonetheless differed 1.61-fold by AQP3 genotype. The interaction is therefore not unique to AQP3, and aquaglyceroporin complement belongs alongside metabolism among the host factors that set arsenic susceptibility.

cell biology↗

bioRxiv2026

Uncovering High-Order Epistatic Interactions in GWAS via a Machine Learning-Based Feature Engineering Framework

BackgroundGenome-wide association studies (GWAS) often fail to identify higher-order epistatic interactions that contribute to complex inheritance patterns of traits and diseases. While machine learning (ML) can capture non-linear relationships, extracting interpretable insights from these models remains a challenge. We propose a novel tree-based feature engineering framework that uses Classification and Regression Trees (CART) to explicitly encode high-order interaction decision paths as dummy variables. We investigate three path-based encoding strategies: (i) all decision paths, (ii) leaf-node paths only, and (iii) internal-node paths only. This approach aims to transform complex decision boundaries into discrete features that capture nonlinear interactions that are not readily captured by traditional association models. ResultsThe framework was evaluated using genetic data for ANCA-associated vasculitis (AAV). To manage the high dimensionality of the engineered feature space, we applied a comprehensive suite of ML methods across three tasks: (1) Ensemble Learning (Random Forest, XGBoost, and Gradient Boosting Machine); (2) Decision Tree Analysis (CART); and (3) Regression and Classification Tasks (Regularized Linear Regression/LASSO, Support Vector Machine, and Logistic Regression). Stepwise feature selection and regularization were employed to isolate the most informative interaction patterns. Results indicate that incorporating CART-derived interaction paths--particularly those from high-impact regions of the tree--significantly improves classification accuracy and model interpretability compared to using the original feature space alone. ConclusionsThe proposed framework provides a robust, scalable methodology for identifying high-order genetic interactions. By bridging the gap between the predictive power of ensemble ML and the necessity for mechanistic insight, this approach offers a clearer mapping of the combinatorial genetic processes underlying complex diseases. While applied here to AAV, the method is highly adaptable for exploring the genetic architecture of diverse populations and complex traits.

genomics↗

bioRxiv2026

Multi-Omics Study of Ancestry in Adults with Intracranial Cancers Glioma (MOSAIC)

BackgroundMost genomic studies of adult-type diffuse gliomas have focused on predominantly European ancestry populations, limiting the generalizability of molecular classifications and precision medicine approaches. We assembled a multi-institutional glioma cohort of diverse patients to investigate how germline ancestry, molecular subtypes, and mutational processes shape tumor biology and clinical outcomes. MethodsWe analyzed 1,102 adults with WHO 2021-classified diffuse gliomas (IDH-mutant, 1p/19q-codeleted oligodendroglioma; IDH-mutant astrocytoma; IDH-wildtype glioma) from seven U.S. institutions. Whole-exome sequencing (WES) of FFPE tumors identified somatic alterations and COSMIC SBS v3.2 mutational signatures. Genetic ancestry was estimated from WES using 1000 Genomes reference populations. Overall survival was assessed using Kaplan-Meier and multivariable models. ResultsThe cohort included 66.9% European (EUR), 21.1% Admixed American/Hispanic (AMR), 10.3% Admixed African (AFR), and 1.6% Asian (AS) ancestry. Survival followed expected molecular hierarchy (median overall survival: oligodendroglioma 15.7 years, astrocytoma 10.6 years, IDH-wildtype glioma 1.9 years). Within oligodendroglioma, AMR patients showed improved survival versus EUR (HR 0.67, 95% CI 0.48-0.94; p=0.011), with similar trends across subtypes. Somatic profiling confirmed canonical subtype-defining alterations and revealed higher ATRX alterations in AFR and AMR IDH-wildtype tumors compared with EUR. ATRX alterations were associated with improved survival only in AFR (p=0.003). Mutational signature analysis identified subtype-specific signatures, including therapy-associated signatures. Chemotherapy-related signatures were more frequent in EUR and AMR than in AFR. ConclusionsThis ancestrally diverse glioma cohort confirms established molecular classifications and identifies ancestry-associated differences in survival, somatic alterations, and mutational processes, indicating the critical need for broad representation to inform precision neuro-oncology. Key PointsO_LIA multi-institutional glioma cohort validates subtype and survival patterns across ancestries. C_LIO_LITherapy-associated mutational signatures differ by ancestry, suggesting distinct treatment-related mutational processes. C_LIO_LIAdmixed American patients show improved survival, particularly in oligodendroglioma. C_LI Importance of the StudyMost genomic studies of adult-type diffuse glioma have focused on populations of predominantly European ancestry which limits the ability to examine variation in tumor biology and clinical outcomes across populations. In this study, we assembled one of the largest ancestrally diverse cohorts of molecularly characterized adult diffuse gliomas, integrating germline ancestry inference with tumor whole-exome sequencing and mutational signature analysis. We confirm that established molecular classifications and survival hierarchies remain robust across ancestry groups. However, we also identified ancestry-associated differences in survival within specific tumor subtypes, higher ATRX alteration frequencies in African American and admixed American patients with IDH-wildtype tumors, and variation in therapy-associated mutational signatures across ancestry groups. These findings highlight the importance of incorporating population differences into genomic studies of glioma and provide a resource for future multi-ancestry investigations of glioma risk, tumor evolution, and treatment response, ultimately supporting more inclusive precision neuro-oncology.

bioinformatics↗

bioRxiv2026

Gene-Specific Analysis of Clonal Hematopoiesis Identifies ASXL1 as a Risk Factor for Lung Cancer

IntroductionClonal hematopoiesis of indeterminate potential (CHIP) is a recognized risk factor for hematologic malignancies, but its contribution to different types of solid cancers remains incompletely defined. MethodsHere, we performed a systematic, gene-specific analysis of CHIP across 19 common solid cancer types using two large population-based cohorts, the UK Biobank and All of Us with Cox proportional hazards models and nested case-control logistic models. ResultsWe demonstrate that the relationship between CHIP and solid tumors is highly cancer-type specific, with lung cancer exhibiting the strongest association. In lung cancer, this association is largely driven by ASXL1-mutant clones. Specifically, high variant allele fraction (high-VAF) ASXL1 conferring a significantly increased risk (hazard ratio = 3.2), and the associations remained robust after adjustment for age, sex, body mass index (BMI), smoking status, and genetic ancestry. Notably, ASXL1 CHIP was substantially enriched among smokers, and its association with lung cancer risk was restricted to ever-smokers, highlighting a key interaction between CHIP and environmental exposure. The enrichment of ASXL1 CHIP in lung cancer was further validated in two independent cancer-only cohorts, including MSK-IMPACT and TCGA. In addition, rare germline variant association analysis revealed that germline variation in ASXL1 had the strongest association with lung cancer susceptibility among all solid tumors. ConclusionsCollectively, our findings support a model in which smoking-associated expansion of ASXL1-mutant clones contributes to lung cancer development and suggest that gene-specific CHIP metrics may enhance risk stratification and early detection strategies.

bioinformatics↗

bioRxiv2026

Single-cell full-length transcriptome of human lung reveals genetic effects on isoform regulation beyond gene-level expression

Genetic regulation of splicing uniquely contributes to trait-associated genome-wide association studies (GWAS) signals. However, quantitative trait loci (QTL) analysis using short-read sequencing of bulk tissues fails to capture full-length and cell-type-specific isoforms. Here, we present an isoform-level lung cell atlas from 129 never-smoking Korean women using single-cell long-read RNA-sequencing, identifying abundant unannotated and cell-type-specific isoforms. Isoform-level signatures of 37 lung cell types display a larger difference and therefore improve cell-type classification compared to gene-level expression. Notably, isoform-QTLs (isoQTLs) detect unannotated and/or cell-type-specific isoforms with independent genetic regulation from expression-QTL (eQTL), supported by enriched splicing functional elements. IsoQTLs nominate susceptibility isoforms from previously unexplained lung function and cancer GWAS loci, via eQTL-independent signals. We highlight a potentially functional novel variant of PPIL6 in multiciliated cells underlying lung cancer risk through alternative splicing. This isoform-level resource advances our understanding of cell-type-specific isoform regulation and its contribution to lung traits and diseases.

genetics↗

bioRxiv2026

Tumor-infiltrating natural killer cell profiling for therapeutic stratification in patients with resectable non-small cell lung cancer

ObjectiveNeoadjuvant chemoimmunotherapy has improved outcomes in resectable non-small cell lung cancer (NSCLC), yet its real-world implementation is often challenged by surgical delays, immune-mediated fibrosis, and postoperative complications. Smokers with NSCLC, despite a high risk for surgical morbidity, show enhanced responses to neoadjuvant chemoimmunotherapy. This study aimed to identify smoking-associated immune cell determinants that could guide treatment strategies. MethodsSingle-cell RNA sequencing (scRNAseq) was performed on 61 lung tissues from non-smokers and smokers to identify smoking-related immune compositions. The scRNAseq data from 19 invasive lung adenocarcinomas were used to validate their presence in the tumor-immune microenvironment. Bulk RNA sequencing data from 102 resected NSCLC and 24 NSCLC patients treated with neoadjuvant chemoimmunotherapy followed by surgery were used for in silico cellular deconvolution and outcome analyses. ResultsAmong 135 lung cellular phenotypes, two natural killer (NK) cell subsets were strongly associated with smoking and chronic obstructive pulmonary disease (COPD) severity. "Stress-responsive" NK (NKSR) cells exhibited immature features and cytokine-responsive features, and "Adaptive and immunoregulatory NK" (NKAIR) cells showed mature features and elevated multiple immune checkpoint expression. High intratumoral NKSR cells correlated with improved survival after surgery, particularly in current smokers. Conversely, tumors with low NKSR and high NKAIR cells responded more favorably to neoadjuvant chemoimmunotherapy. ConclusionsIntratumoral NK cell phenotyping may aid in therapeutic stratification in patients with NSCLC. NKSR cell preservation predicts benefit from upfront surgery, while NKAIR cell enrichment indicates improved response to neoadjuvant chemoimmunotherapy. These NK cell profiles may help optimize treatment by balancing therapeutic benefit and risk. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/696292v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@168bc03org.highwire.dtl.DTLVardef@10fa947org.highwire.dtl.DTLVardef@132a92dorg.highwire.dtl.DTLVardef@100e4a4_HPS_FORMAT_FIGEXP M_FIG C_FIG Central Picture O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/696292v1_ufig2.gif" ALT="Figure 2"> View larger version (21K): org.highwire.dtl.DTLVardef@1a2d3b4org.highwire.dtl.DTLVardef@f8f84borg.highwire.dtl.DTLVardef@17510bcorg.highwire.dtl.DTLVardef@9da763_HPS_FORMAT_FIGEXP M_FIG C_FIG NK cell profiling for treatment stratification in resectable NSCLC Central MessageTumor-infiltrating NK cell profiling identifies distinct immune phenotypes that can inform personalized treatment strategies in resectable NSCLC. Perspective statementAbundance of intratumoral stress-responsive NK cells predicts favorable outcomes after upfront surgery, whereas enrichment of adaptive and immunoregulatory NK cells suggests greater benefit from neoadjuvant chemoimmunotherapy. These findings support incorporating NK-cell profiling into preoperative decision-making to optimize therapy selection and balance risks in the high-risk surgical population.

immunology↗

bioRxiv2025

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

bioinformatics↗

bioRxiv2025

Gene signatures characterizing driver mutations in lung squamous carcinoma are predictive of the progression of pre-cancer lesions

Patients with lung squamous cell carcinoma (LUSC) are often diagnosed at advanced stages, limiting opportunities for early intervention. LUSC develops through a multistep progression from low-grade lesions to high-grade lesions, including carcinoma in situ (CIS), of which approximately half progress to invasive cancer while the other half regress. Although frequent mutations and copy number alterations have been documented in LUSC and observed in precursor lesions, their prognostic significance in precancerous stages remains largely unexplored. In this study, we leveraged gene expression data from LUSC tumors in The Cancer Genome Atlas (TCGA) to derive transcriptional signatures corresponding to 34 key driver genomic aberrations, including mutations, amplifications, and deletions. These tumor-derived gene signatures were then applied to precancerous lesion datasets to assess their ability to characterize developmental stages and predict progression risk. We found that many of these signatures increased progressively across lesion stages, reflecting their roles in early tumorigenesis. In particular, several signatures accurately predicted which CIS lesions would progress to invasive cancer. Furthermore, these signature scores were more strongly associated with patient prognosis in LUSC than the presence of genomic aberrations alone. We also examined the relationship between driver-associated signatures and the tumor immune microenvironment. Signature scores were significantly correlated with immune features such as immune cell infiltration and immune checkpoint gene expression, including CD274 (PD-L1). Interestingly, these associations varied across lesion stages, indicating dynamic immune interactions during cancer evolution. Together, our findings demonstrate that tumor-derived driver gene expression signatures provide valuable insight into the biology and progression risk of precancerous lesions, offering potential utility for early detection and intervention strategies in LUSC. Novelty and ImpactIn this study, we developed gene expression signatures using lung squamous cell carcinoma (LUSC) transcriptomic and genomic data and apply them to characterize precancerous lesions. These signatures showed high performance in predicting the progression risk of carcinoma in situ (CIS), offering a valuable tool for personalized prevention and early intervention in LUSC. These findings have the potential to improve early lung cancer detection and provide new insights into the tumor microenvironment in the context of precancer evolution of LUSC.

bioinformatics↗

bioRxiv2021

BMI-CNV: A Bayesian framework for multiple genotyping platforms detection of copy number variation

Whole-exome sequencing (WES) enables detection of Copy number variations (CNVs) with high resolution in protein-coding regions. However, variations in the intergenic or intragenic regions are excluded from studies. Fortunately, samples have been previously sequenced by other genotyping platforms, such as SNP array. Moreover, conventional single sample-based methods suffer from high false discovery rate due to prominent data noise. Therefore, methods for integrating multiple genotyping platforms and samples are highly demanded for improved CNV detection. We developed BMI-CNV, a Bayesian Multi-sample and Integrative CNV (BMI-CNV) profiling method with data sequenced by both WES and microarray. For the multi-sample integration, we identify the shared CNVs regions across samples using a Bayesian probit stick-breaking process model coupled with a Gaussian Mixture model estimation. With extensive simulations, BMI-CNV outperformed existing methods with remarkably improved accuracy. By applying to the matched 1000 genomes project and HapMap project data, we showed that BMI-CNV accurately detected common variants. We further applied it to The Research of International Cancer of Lung (TRICL) consortium with matched WES and OncoArray data and identified lung cancer risk associated genes in 17q11.2, 1p36.12, 8q23.1 and 5q22.2 regions, which may provide new insights into the etiology of lung cancer.

genetics↗

bioRxiv2021

Genetic variants associated mRNA stability in lung

Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs). Specifically, we computationally inferred mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3,942 genes and 186,132 eQTLs for 4,751 genes from 15,122,700 genetic variants for 13,476 genes, respectively. Interesting, our results indicated that stQTLs were enriched in the CDS and 3UTR regions, while eQTLs are enriched in the CDS, 3UTR, 5UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels. Author SummaryIn the past decade, many studies have identified genetic variants associated with gene expression level (eQTLs) in different phenotypes, including tissues and diseases. Gene expression is the result of cooperation between transcriptional regulation, such as transcriptional activity, and post-transcriptional regulation, such as mRNA stability. Here, we present a computational framework that take advantage of recently developed methods to estimate mRNA stability from RNA-Seq, which is widely used to estimate gene expression, and then to identify genetic variants associated with mRNA stability (stQTLs) in lung tissue. Compared to eQTLs, we found that genetic variants that affects mRNA stability are more significantly located in the CDS and 3UTR regions, which are known to interact with RNA-binding proteins (RBPs) or microRNAs to regulate stability. In addition, stQTLs are significantly more likely to overlap the binding sites of RBPs. We show that the six RBPs that most significantly bind to stQTLs are all known to regulate mRNA stability. This pipeline of simultaneously identifying eQTLs and stQTLs using only RNA-Seq data can provide higher resolution than traditional eQTLs study to better understand the molecular mechanisms of genetic variants on the regulation of gene expression.

genetics↗

bioRxiv2020

Dynamic Analysis of Alternative Polyadenylation from Single-Cell RNA-Seq(scDaPars) Reveals Cell Subpopulations Invisible to Gene Expression Analysis

Alternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation in various cellular processes including cell proliferation and differentiation, but the APA heterogeneity among single cells remains largely unknown. Single-cell RNA sequencing (scRNA-seq) has been extensively used to define cell subpopulations at the transcription level. Yet, most scRNA-seq data have not been analyzed in an "APA-aware" manner. Here, we introduce scDaPars, a bioinformatics algorithm to accurately quantify APA events at both single-cell and single-gene resolution using standard scRNA-seq data. Validations in both real and simulated data indicate that scDaPars can robustly recover missing APA events caused by the low amounts of mRNA sequenced in single cells. When applied to cancer and human endoderm differentiation data, scDaPars not only revealed cell-type-specific APA regulation but also identified cell subpopulations that are otherwise invisible to conventional gene expression analysis. Thus, scDaPars will enable us to understand cellular heterogeneity at the post-transcriptional APA level.

bioinformatics↗

bioRxiv2018

Cross-cancer pleiotropic associations with lung cancer risk in African Americans

BackgroundIdentifying genetic variants with pleiotropic associations across multiple cancers can reveal shared biologic pathways. Prior pleiotropic studies have primarily focused on European descent individuals. Yet population-specific genetic variation can occur and potential pleiotropic associations among diverse racial/ethnic populations could be missed. We examined cross-cancer pleiotropic associations with lung cancer risk in African Americans.\n\nMethodsWe conducted a pleiotropic analysis among 1,410 African American lung cancer cases and 2,843 controls. We examined 36,958 variants previously associated (or in linkage disequilibrium) with cancer in prior genome-wide association studies. Logistic regression analyses were conducted, adjusting for age, sex, global ancestry, study site, and smoking status.\n\nResultsWe identified three novel genomic regions significantly associated (FDR-corrected p-value < 0.10) with lung cancer risk (rs336958 on 5q14.3, rs7186207 on 16q22.2, and rs11658063 on 17q12). On chromosome16q22.2, rs7186207 was significantly associated with increased risk (OR=1.24, 95% CI: 1.12-1.38) and functional annotation using GTEx showed rs7186207 modifies DHODH gene expression. The risk allele at rs336958 on 5q14.3 was associated with reduced lung cancer risk (OR=0.68, 95% CI: 0.56-0.82), while the risk allele at rs11658063 on 17q12 was associated with increased risk (OR=1.24, 95% CI: 1.11-1.39).\n\nConclusionWe identified novel associations on chromosomes 5q14.3, 16q22.2, and 17q12, which contain HNF1B, DHODH, and HAPLN1 genes, respectively. SNPs within these regions have been previously associated with multiple cancers. This is the first study to examine cross-cancer pleiotropic associations for lung cancer in African Americans.\n\nImpactOur findings demonstrate novel cross-cancer pleiotropic associations with lung cancer risk in African Americans.

genetics↗

bioRxiv2017

Sex-specific genome-wide association study in glioma identifies new risk locus at 3p21.31 in females, and finds sex-differences in risk at 8q24.21

Incidence of glioma is approximately 50% higher in males. Previous analyses have examined exposures related to sex hormones in women as potential protective factors for these tumors, with inconsistent results. Previous glioma genome-wide association studies (GWAS) have not stratified by sex. Potential sex-specific genetic effects were assessed in autosomal SNPs and sex chromosome variants for all glioma, GBM and non-GBM patients using data from four previous glioma GWAS. Datasets were analyzed using sex-stratified logistic regression models and combined using meta-analysis. There were 4,831 male cases, 5,216 male controls, 3,206 female cases and 5,470 female controls. A significant association was detected at rs11979158 (7p11.2) in males only. Association at rs55705857 (8q24.21) was stronger in females than in males. A large region on 3p21.31 was identified with significant association in females only. The identified differences in effect of risk variants do not fully explain the observed incidence difference in glioma by sex.

epidemiology↗

bioRxiv2017

Sex-specific gene and pathway modeling of inherited glioma risk

BackgroundGenome-wide association studies (GWAS) have identified 25 risk variants for glioma, which explain ~30% of heritable risk. Most glioma histologies occur with significantly higher incidence in males. A sex-stratified analysis ide7ntified sex-specific glioma risk variants, and further analyses using gene- and pathway-based approaches may further elucidate risk variation by sex.\n\nMethodsResults from the Glioma International Case-Control Study were used as a testing set, and results from three GWAS were combined via meta-analysis and used as a validation set. Using summary statistics for autosomal SNPs found to be nominally significant (p<0.01) in a previous meta-analysis and X chromosome SNPs with nominally significant association (p<0.01), three algorithms (Pascal, BimBam, and GATES) were used to generate gene-scores, and Pascal was used to generate pathway scores. Results were considered significant when p<3.3x10-6 in [2/3] algorithms.\n\nResults25 genes within five regions and 19 genes within six regions reached the set significance threshold in at least 2/3 algorithms in males and females, respectively. EGFR and RTEL1-TNFRSF6B were significantly associated with all glioma and glioblastoma in males only, and a female-specific association in TERT, all of which remained nominally significant after conditioning on known risk loci. There were nominal associations with the Telomeres, Telomerase, Cellular Aging, and Immortality pathway in both males and females.\n\nConclusionsThese results suggest that there may be biologically relevant significant differences by sex in genetic risk for glioma. Additional gene- and pathway-based analyses may further elucidate the biological processes through which this risk is conferred.

epidemiology↗