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PubMed2026

X chromosome-wide association studies for quantitative trait loci based on the mixture of general pedigrees and additional unrelated individuals.

Genome-wide association studies have successfully identified many genetic variants associated with complex traits. However, most existing methods target autosomes rather than X chromosome, and several existing X chromosome-wide association studies (XWAS) at quantitative trait loci (QTL) largely focus on unrelated individuals, with limited attention to general pedigrees or mixture of general pedigrees and additional unrelated individuals (called the mixed data for brevity). In this study, we propose nine novel methods for XWAS at QTL in the mixed data (${\mathrm{MQX}}_{\mathrm{cat}}$, ${\mathrm{MQZ}}_{\mathrm{max}}$, ${\mathrm{MT}}_{\mathrm{plinkw}}$, ${\mathrm{MT}}_{\mathrm{chenw}}$, $\mathrm{MwM}3\mathrm{VNA}$, ${\mathrm{MQMVX}}_{\mathrm{cat}}$, ${\mathrm{MQMVZ}}_{\mathrm{max}}$, $\mathrm{MpMV}$, and $\mathrm{McMV}$), also applicable to general pedigrees alone. The first four methods test for mean differences across genotypes; the latter four test for differences in both means and variances; $\mathrm{MwM}3\mathrm{VNA}$ tests for variance differences only. All mean-based and mean-variance-based methods incorporate X chromosome inactivation information, and all nine methods consider genetic relatedness in pedigrees. Simulation studies confirm well-controlled type I error rates, and inclusion of pedigrees significantly improves statistical power. Note that there has been no study focusing on X chromosome for the mixed data or general pedigrees from UK Biobank database, so we apply our proposed methods to this dataset, which identify five total cholesterol (TC)-associated and 13 low-density lipoprotein cholesterol (LDL-C)-associated single nucleotide polymorphisms (SNPs). Linkage disequilibrium (LD) analysis reveals that these SNPs fall into three distinct LD blocks. Functional annotation and gene ontology enrichment analysis reveal 16 and 28 enriched pathways for TC-associated and LDL-C-associated genes, respectively. These methods provide robust and powerful tools for XWAS at QTL in both mixed data and general pedigrees.

Quantitative Trait Loci↗
PubMed2025

Efficient storage and regression computation for population-scale genome sequencing studies.

MOTIVATION: The growing availability of large-scale population biobanks has the potential to significantly advance our understanding of human health and disease. However, the massive computational and storage demands of whole genome sequencing (WGS) data pose serious challenges, particularly for underfunded institutions or researchers in developing countries. This disparity in resources can limit equitable access to cutting-edge genetic research. RESULTS: We present novel algorithms and regression methods that dramatically reduce both computation time and storage requirements for WGS studies, with particular attention to rare variant representation. By integrating these approaches into PLINK 2.0, we demonstrate substantial gains in efficiency without compromising analytical accuracy. In an exome-wide association analysis of 19.4 million variants for the body mass index phenotype in 125 077 individuals (AllofUs project data), we reduced runtime from 695.35 min (11.5 h) on a single machine to 1.57 min with 30 GB of memory and 50 threads (or 8.67 min with 4 threads). Additionally, the framework supports multi-phenotype analyses, further enhancing its flexibility. AVAILABILITY AND IMPLEMENTATION: Our optimized methods are fully integrated into PLINK 2.0 and can be accessed at: https://www.cog-genomics.org/plink/2.0/.

Humans↗
PubMed2025

MutBERT: probabilistic genome representation improves genomics foundation models.

MOTIVATION: Understanding the genomic foundation of human diversity and disease requires models that effectively capture sequence variation, such as single nucleotide polymorphisms (SNPs). While recent genomic foundation models have scaled to larger datasets and multi-species inputs, they often fail to account for the sparsity and redundancy inherent in human population data, such as those in the 1000 Genomes Project. SNPs are rare in humans, and current masked language models (MLMs) trained directly on whole-genome sequences may struggle to efficiently learn these variations. Additionally, training on the entire dataset without prioritizing regions of genetic variation results in inefficiencies and negligible gains in performance. RESULTS: We present MutBERT, a probabilistic genome-based masked language model that efficiently utilizes SNP information from population-scale genomic data. By representing the entire genome as a probabilistic distribution over observed allele frequencies, MutBERT focuses on informative genomic variations while maintaining computational efficiency. We evaluated MutBERT against DNABERT-2, various versions of Nucleotide Transformer, and modified versions of MutBERT across multiple downstream prediction tasks. MutBERT consistently ranked as one of the top-performing models, demonstrating that this novel representation strategy enables better utilization of biobank-scale genomic data in building pretrained genomic foundation models. AVAILABILITY AND IMPLEMENTATION: https://github.com/ai4nucleome/mutBERT.

Humans↗
PubMed2025

Tsbrowse: an interactive browser for ancestral recombination graphs.

SUMMARY: Ancestral recombination graphs (ARGs) represent the interwoven paths of genetic ancestry of a set of recombining sequences. The ability to capture the evolutionary history of samples makes ARGs valuable in a wide range of applications in population and statistical genetics. ARG-based approaches are increasingly becoming a part of genetic data analysis pipelines due to breakthroughs enabling ARG inference at biobank-scale. However, there is a lack of visualization tools, which are crucial for validating inferences and generating hypotheses. We present tsbrowse, an open-source, web-based Python application for the interactive visualization of the fundamental building blocks of ARGs, i.e. nodes, edges and mutations. We demonstrate the application of tsbrowse to various data sources and scenarios, and highlight its key features of browsability along the genome, user interactivity, and scalability to very large sample sizes. AVAILABILITY AND IMPLEMENTATION: Tsbrowse is installed as a Python package from PyPI (https://pypi.org/project/tsbrowse/), while a development version is maintained at https://github.com/tskit-dev/tsbrowse. Documentation is available at https://tskit.dev/tsbrowse/docs/. Source code is archived on Zenodo with DOI, https://doi.org/10.5281/zenodo.15683039.

Software↗
PubMed2025

PLAID: ultrafast single-sample gene set enrichment scoring.

SUMMARY: In recent years, computational methods have emerged that calculate enrichment of gene signatures within individual samples. These signatures offer critical insights into the coordinated activity of functionally related genes, proteins or metabolites, enabling the identification of unique molecular profiles in individual cells and patients. This strategy is pivotal for patient stratification and advancement of personalized medicine. However, the rise of large-scale datasets, including single-cell profiles and population biobanks, has exposed significant computational inefficiencies in existing methods. Current methods often demand excessive runtime and memory resources, becoming impractical for large datasets. Overcoming these limitations is a focus of current efforts by bioinformatics teams in academia and the pharmaceutical industry, as essential to support basic and clinical biomedical research. To address this critical need, we developed PLAID (Pathway Level Average Intensity Detection), an ultrafast and memory optimized single sample gene set enrichment algorithm that utilizes sparse matrix computation. PLAID delivers highly accurate gene set scoring and surpasses the performance of current methods in single-cell and bulk transcriptomics, and proteomics data. PLAID uniquely integrates the most widely used gene set scoring algorithms, enabling researchers to apply multiple methods for cross-validation with outstanding runtime efficiency and minimal memory requirement. AVAILABILITY AND IMPLEMENTATION: PLAID is implemented in the R language for statistical computing. PLAID source code and installation instructions are available with no restrictions at https://github.com/bigomics/plaid.

Algorithms↗
PubMed2026

SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs.

MOTIVATION: Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. RESULTS: We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

Genome-Wide Association Study↗
PubMed2026

ReverseGWAS identifies combined phenotypes associated with a genotype in GWA studies.

MOTIVATION: Traditional genome-wide association studies (GWAS) aim to uncover the genetic variants associated with a single phenotype of interest (typically a disease), and to elucidate its genotypic architecture. However, many of today's GWAS simultaneously measure multiple related phenotypes, leading to the possibility of pursuing the reverse aim of elucidating the "phenotypic architecture" of a single genetic variant. In other words, we may ask what combination of measured phenotypes is associated with a given genotypic variant. ReverseGWAS is an algorithmic platform for answering such questions in the context of large-scale multi-phenotype GWAS. RESULTS: We demonstrate the effectiveness of ReverseGWAS on simulated data, showing its ability to identify logical combinations of phenotypes with a reasonable amount of noise. We then apply it to a selection of combined phenotypes from the UK Biobank, obtaining 719 candidate associations using autoimmune diseases and 205 using common ICD10 codes. We find that the majority of these associations (546/719 and 111/205, respectively) successfully replicate in an independent cohort, FinnGen. AVAILABILITY AND IMPLEMENTATION: The source code of ReverseGWAS is freely available to non-commercial users as an installable R package at https://github.com/Leonardini/rgwas.

Phenotype↗
PubMed2026

Bridging ancestry gaps in genomic risk prediction with tabular foundation models.

MOTIVATION: Models deployed for genomic prediction of diseases perform unevenly across populations, limiting clinical utility. Two factors drive this limitation: large imbalances in sample availability across ancestry groups and non-stationarity of genotype-phenotype effect sizes across the ancestry continuum. While tabular foundation models with in-context learning (ICL) have shown strong sample efficiency in other domains, their effectiveness for genotype-to-phenotype prediction and their robustness to ancestry-driven effect heterogeneity remain unclear. RESULTS: Using large, ancestrally diverse biobank data, we show that ICL-capable tabular foundation models reduce performance degradation in under-sampled ancestry groups compared to conventional supervised approaches. However, we find that prevailing models trained on existing synthetic tabular tasks fail when allele effect sizes vary across ancestry space. Treating genetic ancestry as a continuous variable, we introduce an instruction-tuning framework that exposes models to synthetic tasks with ancestry-dependent non-stationary effects. Instruction-tuned models achieve improved and more stable predictive performance across the genetic ancestry continuum, including for individuals distant from in-context exemplars in ancestry space. AVAILABILITY AND IMPLEMENTATION: All code for instruction-tuning models, synthetic task generation, data wrangling, and model evaluation, is publicly available at https://github.com/ai4pm/Bridging-Ancestry-Gaps-in-Genomic-Risk-Prediction-with-Tabular-Foundation-Models. The final instruction-tuned model (ICL-NS-G2P-proto) is also released in this repository. Detailed documentation is provided, including environment setup instructions and guidelines for running various parts. The instruction-tuning task datasets are available at https://zenodo.org/records/18309187.

Humans↗
PubMed2026

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

MOTIVATION: The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. RESULTS: Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of ∼0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts. AVAILABILITY AND IMPLEMENTATION: The source code of ProMeta is available at GitHub (https://github.com/lihan97/ProMeta).

Proteomics↗
PubMed2026

Genomics-informed approach identifies which cell types regulate the metabolome.

MOTIVATION: Metabolism occurs in a cell type-specific manner, but which cells regulate metabolite levels remains unclear. RESULTS: Here, we integrate some of the largest metabolite quantitative trait loci datasets, TOPMed and UK Biobank, with one of the most extensive single-cell RNA sequencing resources, Tabula Sapiens. This integration allows us to identify cell types that regulate metabolites body-wide. We find hepatocytes are the primary regulatory cell type for most metabolites, associating with 385/410 (94%) metabolites for whom an association is found. Additionally, our multi-gene approach reveals more metabolite associations with beta cells compared to those identified using a single-gene approach. For example, we identify novel metabolite-cell type associations, such as the association between phenylpropanoic acid and beta cells, this metabolite that was previously thought to be regulated by the microbiome. AVAILABILITY: Code used in this work is available via Github at https://github.com/haimkru/Metabolite-Cell-Type-Associations.

Metabolome↗
PubMed2026

Modelling time-varying genetic effects on binary disease risk via functional Mendelian randomization.

MOTIVATION: Genome-wide association studies have identified thousands of genetic variants associated with complex traits, establishing Mendelian randomization (MR) as a powerful framework for causal inference using variants as natural experiments. However, existing MR methods treat causal effects as static, relying on cross-sectional exposure measurements and ignoring how genetic predispositions to disease operate dynamically across the life course. Recovering age-specific causal effect functions from longitudinal data requires combining functional data representations of exposure trajectories with instrumental variable estimation strategies suitable for binary disease endpoints, a methodological gap that has remained unaddressed. RESULTS: We develop a functional MR framework for binary outcomes that integrates functional principal component analysis with two-stage residual inclusion (2SRI), ensuring consistent estimation under the nonlinear logistic link function that renders standard instrumental variable estimators inconsistent. Simulations across different causal effect trajectory shapes, varying measurement densities, and varying instrument strengths demonstrate accurate recovery of time-varying genetically predicted effects with minimal bias. Applied to UK Biobank data, the framework identifies an age-specific causal effect of genetically predicted body mass index on type 2 diabetes risk concentrated in early mid-adulthood and progressively attenuating thereafter. Concordance between the proposed 2SRI estimator applied to type 2 diabetes and the established continuous-outcome functional MR estimator applied to the paired glycated haemoglobin marker in the same cohort provides indirect empirical support for the validity of the proposed approach. AVAILABILITY AND IMPLEMENTATION: The method is implemented in the R package mvfmr, with a full tutorial vignette.

Mendelian Randomization Analysis↗
PubMed2025

Semiparametric efficient estimation of small genetic effects in large-scale population cohorts.

Population genetics seeks to quantify DNA variant associations with traits or diseases, as well as interactions among variants and with environmental factors. Computing millions of estimates in large cohorts in which small effect sizes and tight confidence intervals are expected, necessitates minimizing model-misspecification bias to increase power and control false discoveries. We present TarGene, a unified statistical workflow for the semi-parametric efficient and double robust estimation of genetic effects including $ k $-point interactions among categorical variables in the presence of confounding and weak population dependence. $ k $-point interactions, or Average Interaction Effects (AIEs), are a direct generalization of the usual average treatment effect (ATE). We estimate genetic effects with cross-validated and/or weighted versions of Targeted Minimum Loss-based Estimators (TMLE) and One-Step Estimators (OSE). The effect of dependence among data units on variance estimates is corrected by using sieve plateau variance estimators based on genetic relatedness across the units. We present extensive realistic simulations to demonstrate power, coverage, and control of type I error. Our motivating application is the targeted estimation of genetic effects on trait, including two-point and higher-order gene-gene and gene-environment interactions, in large-scale genomic databases such as UK Biobank and All of Us. All cross-validated and/or weighted TMLE and OSE for the AIE $ k $-point interaction, as well as ATEs, conditional ATEs and functions thereof, are implemented in the general purpose Julia package TMLE.jl. For high-throughput applications in population genomics, we provide the open-source Nextflow pipeline and software TarGene which integrates seamlessly with modern high-performance and cloud computing platforms.

Humans↗
PubMed2026

A doubly robust framework for addressing outcome-dependent selection bias in multi-cohort EHR studies.

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics/centers with varying selection mechanisms that may depend on the disease/outcome of interest. Traditional inverse-probability-weighted (IPW) methods, based on constructed parametric selection models, often struggle with misspecifications when selection mechanisms vary across cohorts. This paper introduces a new Joint Augmented Inverse Probability Weighted (JAIPW) method, which integrates individual-level data from multiple cohorts collected under potentially outcome-dependent selection mechanisms, with data from an external probability sample. JAIPW offers double robustness by incorporating a flexible auxiliary score model to address potential misspecifications in the selection models. We outline the asymptotic properties of the JAIPW estimator, and our simulations reveal that JAIPW achieves up to 6 times lower relative bias and 5 times lower root mean square error (RMSE) compared to the best performing joint IPW methods under scenarios with misspecified selection models. Applying JAIPW to the Michigan Genomics Initiative (MGI), a multi-clinic EHR-linked biobank, combined with external national probability samples, resulted in cancer-sex association estimates closely aligned with national benchmark estimates. We also analyzed the association between cancer and polygenic risk scores (PRS) in MGI to illustrate a situation where the exposure variable is not measured in the external probability sample.

Selection Bias↗
PubMed2026

Prediction of incident heart failure in established atherosclerotic cardiovascular disease: the SMART2-HF model.

BACKGROUND AND AIMS: Patients with established atherosclerotic cardiovascular disease (ASCVD) are at high risk of developing heart failure (HF). However, incident HF is not part of the risk assessment of current guideline-recommended models. The aim of this study was to develop and externally validate the SMART2-HF model for prediction of incident HF in patients with ASCVD. METHODS: SMART2-HF was developed in 7698 individuals with established ASCVD (coronary, cerebrovascular, or peripheral artery disease, or abdominal aortic aneurysm) but without prior HF from the UCC-SMART cohort. Cox proportional hazards models including sex-predictor interactions and with age as the time scale were derived to estimate the 10-year and lifetime risk of incident HF (hospitalization for HF or HF-related death), accounting for competing non-HF mortality. Predictors, limited to routinely available clinical characteristics, were aligned with the SMART2 risk model for recurrent cardiovascular (CV) risk in the same population. External validation was performed in 240 741 patients with ASCVD from six data sources: the Clinical Practice Research Datalink, the HUNT3 study, the SWEDEHEART Registry, the ASCVD-Particles cohort, the Estonian Biobank and the international REACH Registry. RESULTS: During a median follow-up of 11.2 years (interquartile range 6.1-16.4 years), 1031 incident HF events (13%) occurred in the UCC-SMART cohort. In the external validation data sources, a total of 24 885 incident HF events (10%) occurred. The pooled C-statistic was .696 (95% confidence interval .674-.717), with consistent performance in subgroups by sex and type of ASCVD. Predicted risks matched observed incidence in external validation. CONCLUSIONS: The SMART2-HF model enables the prediction of incident HF in patients with ASCVD. Aligned with the guideline-recommended SMART2 model for recurrent CV risk, SMART2-HF can be used as a complementary tool in this population.

Humans↗
PubMed2026

Genetic screening of children for familial hypercholesterolaemia: the VRONI study.

BACKGROUND AND AIMS: The role of genetic testing as part of universal screening programmes for familial hypercholesterolaemia (FH) in children is not well defined. Here, a two-step approach to identify children carrying FH-causing variants was investigated. METHODS: In this study from Southern Germany, paediatricians were invited to offer FH screening to all children aged 4.8-14.9 years at routine paediatric examinations. The FH screening programme began in September 2020 in Bavaria and has involved up to 480 paediatricians. It included biochemical and genetic testing using 0.2 mL of blood taken from a fingertip. In case of low-density lipoprotein cholesterol (LDL-C) serum concentration ≥3.36 mmol/L (≥130 mg/dL), FH-causing variants were determined in the same sample with a focused panel covering most frequent variants (n = 48) and sequencing of relevant genes. RESULTS: Out of 25 431 children screened so far, 1689 children had an LDL-C ≥ 3.36 mmol/L (>130 mg/dL), which defined this concentration as the 93rd percentile. Pathogenic variants were identified by the focused panel in 157 and by next-generation sequencing in 283 children, respectively. While 17% (283/1670) of all genetically analysed children tested positive, the fraction of individuals with FH-causing variants increased across the spectrum of LDL-C serum concentrations from 4.7% (23/492) at 3.36-3.49 mmol/L (130-135 mg/dL) to 78.6% (81/103) above 5.17 mmol/L (200 mg/dL). Overall, the prevalence of FH-causing variants was high (1:90). One reason was a founder variant (n = 63) within the LDLR gene, found 40 times more frequent than European average. The analysis of recruitment data revealed significant ascertainment bias, with lower recruitment rate practices exhibiting higher prevalence. After adjustment for the bias using a generalized linear mixed model, the predicted prevalence was 1 in 163 (0.61%), which is highly consistent with large-scale genomic benchmarks as gnomAD (1:165, n = 622 057) and the UK Biobank (1:176, n = 48 741). CONCLUSIONS: The prevalence of FH determined in this study is significantly higher than previously published estimates (∼1:250), highlighting the importance of this condition for public health and supporting calls for a national paediatric screening programme, given the availability of effective treatment options. For children between 5 and 15 years, biochemical screening is an effective way to select patients for genetic testing, with sequencing of candidate genes being superior to variant screening. In summary, the VRONI study demonstrates the feasibility and efficacy of a combined biochemical and genetic screening for FH in children.

Humans↗
PubMed2026

Circulating tumor human papillomavirus DNA whole genome sequencing enables human papillomavirus-associated oropharynx cancer early detection.

BACKGROUND: Early detection of HPV-associated oropharyngeal squamous cell carcinoma, the most common HPV cancer in the United States, could reduce disease-related morbidity and mortality, yet currently, there are no early detection tests. HPV circulating tumor DNA (ctDNA) is a sensitive and specific biomarker for HPV-associated oropharyngeal squamous cell carcinoma at diagnosis. It is unknown if ctDNA HPV is detectable prior to diagnosis, and thus its potential as an early detection test is also unknown. METHODS: Plasma samples from the Mass General Brigham Biobank collected 1.3-10.8 years prior to diagnosis from HPV-associated oropharyngeal squamous cell carcinoma patients (n = 28) and age- and sex-matched controls (n = 28) were blinded and run on a newly developed and validated multifeature HPV whole genome sequencing liquid biopsy assay and a validated HPV antibody assay. RESULTS: HPV ctDNA results were positive in 22 of 28 prediagnostic samples from HPV-associated oropharyngeal squamous cell carcinoma cases (sensitivity 79%) with a maximum lead time of 7.8 years. HPV ctDNA results were negative in all controls (0 of 28, 100% specificity). Diagnostic accuracy was highest within 4 years of cancer diagnosis and was higher than HPV Ab detection within the same timeframe (P = .004). Application of a machine-learning model trained and tested on an independent cohort of 306 cases and controls increased the sensitivity of detection to 27 of 28 cases (overall sensitivity 96%) and the maximum lead time to 10.3 years. CONCLUSIONS: HPV ctDNA can be detected in the blood years prior to diagnosis with HPV-associated oropharyngeal squamous cell carcinoma, with high specificity, in a case-control cohort of 56 participants. HPV ctDNA detection alone, or in combination with previously identified serological biomarkers, may be a feasible approach to early detection of HPV-associated oropharyngeal squamous cell carcinoma.

Humans↗
PubMed2026

Identification of immune cell type-specific susceptibility genes in multiple cancers using transcriptome-wide association studies.

BACKGROUND: Transcriptome-wide association studies (TWAS) integrate gene expression and genome-wide association studies (GWAS) to identify disease susceptibility genes. Because gene expression varies substantially across cell types within tissues, cell type-specific prediction models may enhance the power of TWAS. METHODS: We conducted cell type-specific TWAS leveraging single-cell RNA sequencing data from the OneK1K cohort (14 immune cell types, 1.27 million cells) and GWAS summary statistics for 7 cancers (>290 000 cases in total). To improve prediction accuracy, we developed a modeling framework that incorporates shared gene expression effects across cell types. RESULTS: At a false discovery rate of 5%, we identified 106 (Bonferroni 5%: 13) previously unreported loci for breast cancer, 51 (4) loci for prostate cancer, 11 (4) loci for lung cancer, 39 (5) loci for melanoma, 9 (1) loci for ovarian cancer, and 2 (1) loci for diffuse large B-cell lymphoma, with most genes exhibiting cell type specificity. Gene set analyses confirmed joint associations of unreported genes with breast and prostate cancer risk in UK Biobank data. Additional lung tissue single-cell RNA sequencing data with 113 individuals validated 18 of 32 (56.3%) statistically significant genes for lung cancer. Across cancers, 139 statistically significant genes were shared by at least 2 cancer types and were primarily enriched in specific immune cell types. CONCLUSION: Cell type-specific TWAS improve the identification of novel cancer susceptibility loci and provide insights into the immune landscape of cancer etiology.

Humans↗
PubMed2026

Genetic risk stratification of common diseases in breast cancer survivors: a population-based cohort study.

IMPORTANCE: Patients diagnosed with breast cancer (BCa) are at increased risk of multiple common diseases; however, the spectrum of these diseases and the contribution of inherited genetic susceptibility remain incompletely characterized. METHODS: We evaluated 15 common diseases and tested their associations with BCa exposure and disease-specific polygenic risk scores (PRS) in the UK Biobank (UKB; N&#x2009;=&#x2009;254,736). Analyses were performed using cause-specific Cox proportional hazards models within a full-cohort framework, with time-updated BCa status, delayed entry at study recruitment, and age as the underlying time scale. RESULTS: After recruitment, incident BCa was diagnosed in 11,386 women (4.47%), including 2,742 (24.08%) with metastatic BCa. Patients with BCa had an increased risk of nine diseases spanning cardiovascular, metabolic, and neuropsychiatric domains (P<0.003, Bonferroni-corrected). Elevated risks were generally observed among patients with both early staged and advanced BCa. Inherited susceptibility further stratified disease risk, with the highest risks observed among patients with BCa with elevated disease-specific PRS. For example, compared with women without BCa, the hazard ratio (HR; 95% CI) for osteoporosis was 2.33 (2.15-2.52) among women with any BCa, 2.38 (2.18-2.59) among those with non-metastatic BCa, and 2.12 (1.78-2.54) among those with metastatic BCa; the HR was 4.48 (3.99-5.02) among patients with BCa in the highest quartile of osteoporosis-specific PRS (all P<0.001). In contrast, BCa was not significantly associated with risk of coronary artery disease. CONCLUSION: BCa and inherited genetic susceptibility jointly contribute to increased risk of multiple common diseases, supporting the integration of genetic risk stratification into survivorship care.

Complications↗