Integrative functional genomics and fine-mapping identify regulatory mechanisms of multivariate obesity GWAS and its cardiometabolic implications.

IntroductionCardiovascular diseases (CVDs), including myocardial infarction (MI), heart failure (HF), atrial fibrillation (AF), and arrhythmia, are major contributors to global mortality and often share overlapping risk factors and pathophysiological mechanisms. While genome-wide association studies (GWAS) have identified many loci for individual CVDs, the shared genetic architecture across related traits—particularly in East Asian populations—remains underexplored.Materials and methodsWe integrated large-scale GWAS summary statistics from East Asian populations to perform genome-wide and local genetic correlation analyses across four CVD phenotypes and five cardiometabolic traits (blood pressure and lipid levels). Using stratified LD score regression, we assessed tissue-specific heritability enrichment. Multi-trait analysis of GWAS (MTAG) was then employed to identify pleiotropic loci associated with multiple traits, with functional annotation and expression quantitative trait loci (eQTL) data used to explore biological relevance.ResultsWe observed extensive genetic correlations among CVDs and between CVDs and cardiometabolic traits, with HF showing the strongest connections to both MI and arrhythmia. Notable genome-wide correlations were found between MI and SBP (rg = 0.35, P = 1.59 × 10−14) and between HF and DBP (rg = 0.54, P = 9.84 × 10−9). Stratified heritability analyses revealed significant enrichment in heart and arterial tissues, highlighting the relevance of cardiovascular-specific regulatory elements. MTAG identified several pleiotropic loci, including established genes such as APOB and MC4R, and novel East Asian-enriched signals such as QSOX2 and GUCY1A1/GUCY1B1. Functional data indicated that QSOX2 variants regulate gene expression in arterial and cardiac tissues, implicating redox regulation in HF and hypertension pathogenesis.ConclusionOur findings provide comprehensive insight into the shared genetic determinants of cardiovascular and metabolic diseases in East Asian populations. The identification of pleiotropic and ancestry-specific loci, along with tissue-specific regulatory patterns, underscores the need for integrative multi-trait and population-informed approaches in cardiovascular genetics and risk prediction.

Transcriptome‐Wide Association Studies (TWASs) have become increasingly popular in identifying genes (or other endophenotypes or exposures) associated with complex traits. In TWAS, one first builds a predictive model for gene expressions using an expression quantitative trait loci (eQTL) data set in stage 1, then tests the association between the predicted gene expression and a trait based on a large, independent genome‐wide association study (GWAS) data set in stage 2. However, since the sample size of the eQTL data set is usually small and the coefficient of multiple determination (i.e., R2) of the model for many genes is also small, a question of interest is to what extent these factors affect the statistical power of TWAS. In addition, in contrast to a standard (univariate) TWAS (UV‐TWAS) considering only a single gene at a time, multivariate TWAS (MV‐TWAS) methods have recently emerged to account for the effects of multiple genes, or a gene's nonlinear effects, simultaneously. With the absence of the power analysis for these MV‐TWAS methods, it would be of interest to investigate whether one can gain or lose power by using the newly proposed MV‐TWAS instead of UV‐TWAS. In this paper, we first outline a general method for sample size/power calculations for two‐sample TWAS, then use real data—the Alzheimer's Disease Neuroimaging Initiative (ADNI) expression quantitative trait loci (eQTL) data and the Genotype‐Tissue Expression (GTEx) eQTL data for stage 1, the International Genomics of Alzheimer's Project Alzheimer's disease (AD) GWAS summary data and UK Biobank (UKB) individual‐level data for stage 2—to empirically address these questions. Our most important conclusions are the following. First, a sample size of a few thousands (~8000) would suffice in stage 1, where the power of TWAS would be more determined by cis‐heritability of gene expression. Second, as in the general case of simple regression versus multiple regression, the power of MV‐TWAS may be higher or lower than that of UV‐TWAS, depending on the specific relationships among the GWAS trait and multiple genes (or linear and nonlinear terms of the same gene's expression levels), such as their correlations and effect sizes. Interestingly, several top genes with large power gains in MV‐TWAS (over that in UV‐TWAS) were known to be (and in our data more significantly) associated with AD. We also reached similar conclusions in an application to the GTEx whole blood gene expression data and UKB GWAS data of high‐density lipoprotein cholesterol. The proposed method and the conclusions are expected to be useful in planning and designing future TWAS and other related studies (e.g., Proteome‐ or Metabolome‐Wide Association Studies) when determining the sample sizes for the two stages.

Difficulty in detecting rare variants is one of the problems in conventional genome-wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set method named "RAINBOW" and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes. We prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. Our proposed method was shown to be superior to these in three aspects: (1) controlling false positives; (2) in detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset; and (3) it showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, primarily when the causal variants were located very close to each other, and the directions of their effects were opposite. By using the SNP-set approach as in this study, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as an R package named "RAINBOWR" and is available from CRAN (https://cran.r-project.org/web/packages/RAINBOWR/index.html) and GitHub (https://github.com/KosukeHamazaki/RAINBOWR).

Difficulty in detecting rare variants is one of the problems in conventional genome-wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set method named “RAINBOW” and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes. We prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. Our proposed method was shown to be superior to these in three aspects: (1) controlling false positives; (2) in detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset; and (3) it showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, primarily when the causal variants were located very close to each other, and the directions of their effects were opposite. By using the SNP-set approach as in this study, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as an R package named “RAINBOWR” and is available from CRAN (https://cran.r-project.org/web/packages/RAINBOWR/index.html) and GitHub (https://github.com/KosukeHamazaki/RAINBOWR).

Aim To identify novel candidate genes and gene sets for diabetes. Methods We performed an integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) data for diabetes. Summary data was driven from a large-scale GWAS of diabetes, totally involving 58,070 individuals. eQTLs dataset included 923,021 cis-eQTL for 14,329 genes and 4,732 trans-eQTL for 2,612 genes. Integrative analysis of GWAS and eQTLs data was conducted by summary data-based Mendelian randomization (SMR). To identify the gene sets associated with diabetes, the SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA). A total of 13,311 annotated gene sets were analyzed in this study. Results SMR analysis identified 6 genes significantly associated with fasting glucose, such as C11ORF10 (p value = 6.04 × 10−8), MRPL33 (p value = 1.24 × 10−7), and FADS1 (p value = 2.39 × 10−7). Gene set analysis identified HUANG_FOXA2_TARGETS_UP (false discovery rate = 0.047) associated with fasting glucose. Conclusion Our study provides novel clues for clarifying the genetic mechanism of diabetes. This study also illustrated the good performance of SMR approach and extended it to gene set association analysis for complex diseases.

Evidence indicates that common variants found in genome-wide association studies increase risk of disease through gene regulation via expression Quantitative Trait Loci. Using multiple genome-wide methods, we examined if Single Nucleotide Polymorphisms increase risk of Amyotrophic Lateral Sclerosis through expression Quantitative Trait Loci, and whether expression Quantitative Trait Loci expression is consistent across people who had Amyotrophic Lateral Sclerosis and those who did not. In combining public expression Quantitative Trait Loci data with Amyotrophic Lateral Sclerosis genome-wide association studies, we used Summary-data-based Mendelian Randomization to confirm that SCFD1 was the only gene that was genome-wide significant in mediating Amyotrophic Lateral Sclerosis risk via expression Quantitative Trait Loci (Summary-data-based Mendelian Randomization beta = 0.20, standard error = 0.04, P-value = 4.29 × 10−6). Using post-mortem motor cortex, we tested whether expression Quantitative Trait Loci showed significant differences in expression between Amyotrophic Lateral Sclerosis (n = 76) and controls (n = 25), genome-wide. Of 20 757 genes analysed, the two most significant expression Quantitative Trait Loci to show differential in expression between Amyotrophic Lateral Sclerosis and controls involve two known Amyotrophic Lateral Sclerosis genes (SCFD1 and VCP). Cis-acting SCFD1 expression Quantitative Trait Loci downstream of the gene showed significant differences in expression between Amyotrophic Lateral Sclerosis and controls (top expression Quantitative Trait Loci beta = 0.34, standard error = 0.063, P-value = 4.54 × 10−7). These SCFD1 expression Quantitative Trait Loci also significantly modified Amyotrophic Lateral Sclerosis survival (number of samples = 4265, hazard ratio = 1.11, 95% confidence interval = 1.05–1.17, P-value = 2.06 × 10−4) and act as an Amyotrophic Lateral Sclerosis trans-expression Quantitative Trait Loci hotspot for a wider network of genes enriched for SCFD1 function and Amyotrophic Lateral Sclerosis pathways. Using gene-set analyses, we found the genes that correlate with this trans-expression Quantitative Trait Loci hotspot significantly increase risk of Amyotrophic Lateral Sclerosis (beta = 0.247, standard deviation = 0.017, P = 0.001) and schizophrenia (beta = 0.263, standard deviation = 0.008, P-value = 1.18 × 10−5), a disease that genetically correlates with Amyotrophic Lateral Sclerosis. In summary, SCFD1 expression Quantitative Trait Loci are a major factor in Amyotrophic Lateral Sclerosis, not only influencing disease risk but are differentially expressed in post-mortem Amyotrophic Lateral Sclerosis. SCFD1 expression Quantitative Trait Loci show distinct expression profiles in Amyotrophic Lateral Sclerosis that correlate with a wider network of genes that also confer risk of the disease and modify the disease’s duration.

Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P<1×10-8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.

Background: Breast cancer is one of the most common malignancies in women. Although previous genome-wide association studies (GWAS) have identified multiple susceptibility loci, they account for only a small portion of the heritable risk. Transcriptome-wide association studies (TWAS), which integrate GWAS and expression quantitative trait loci (eQTL) data, offer a more effective strategy for uncovering functional genes involved in complex traits. This study aims to systematically identify breast cancer susceptibility genes through multiple TWAS frameworks and to elucidate their biological roles using multi-omics integration. Methods: Firstly, we performed cross-tissue TWAS for breast cancer using both UTMOST and JTI frameworks. Next, single-tissue associations were evaluated via FUSION and validated using MAGMA. We further prioritized key genes through Mendelian Randomization (MR) and colocalization analyses, followed by experimental validation. Single-cell and spatial transcriptomic data were employed to delineate gene expression patterns, intercellular communication, and spatial heterogeneity. Additionally, drug resistance profiles were constructed using TCGA transcriptomic data and verified across multiple pharmacogenomics databases. Results: Cross-tissue TWAS analysis identified 29 candidate susceptibility genes for breast cancer. The FUSION method detected 1,768 genes with FDR &amp;lt; 0.05 in at least one tissue, while MAGMA analysis revealed 354 breast cancer-associated genes. By integrating results from four analytical approaches, we prioritized 13 high-confidence susceptibility genes, including EFR3B, CASP8, and XBP1. MR and colocalization analyses further confirmed EFR3B and CASP8 as causal genes with shared genetic architecture in breast cancer. EFR3B, a susceptibility gene for both ER-positive and ER-negative breast cancer, was experimentally validated to be overexpressed in breast cancer cell lines and tumor tissues using Western blot, real-time PCR, and immunohistochemistry. Single-cell transcriptomic analysis revealed that EFR3B was predominantly expressed in specific epithelial and endothelial subpopulations within breast tumors, exhibiting notable expression heterogeneity. Analysis of VEGFA ligand-receptor signaling indicated enhanced intercellular communication between EFR3B-positive epithelial and endothelial cells. Spatial transcriptomics demonstrated heterogeneous expression of EFR3B in tumor tissues and showed that EFR3B-positive epithelial cells are spatially co-localized with vascular endothelial cells, suggesting a potential role for EFR3B in modulating the tumor microenvironment. Finally, drug sensitivity analysis revealed that high EFR3B expression is significantly associated with resistance to paclitaxel and anthracycline-based chemotherapies. Conclusions: This study identified 13 breast cancer susceptibility genes through integrative TWAS approaches, providing new insights into the genetic architecture of the disease. EFR3B emerged as a key risk gene involved in tumor microenvironment modulation and chemotherapy resistance, representing a potential therapeutic target for personalized treatment strategies in breast cancer. Citation Format: Y. Lin, S. Lin, Y. Zhang, J. She, R. Zhao, A. Qiu, L. Zhang, Q. Yang. Multi-omics analysis identifies EFR3B as a driver of chemoresistance in breast cancer through epithelial-endothelial cell communication [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS4-05-16.

Cardiovascular disease and type 2 diabetes mellitus represent overlapping diseases where a large portion of the variation attributable to genetics remains unexplained. An important player in their pathogenesis is peroxisome proliferator-activated receptor γ (PPARγ) that is involved in lipid and glucose metabolism and maintenance of metabolic homeostasis. We used a functional genomics methodology to interrogate human chromatin immunoprecipitation-sequencing, genome-wide association studies, and expression quantitative trait locus data to inform selection of candidate functional single nucleotide polymorphisms (SNPs) falling in PPARγ motifs. We derived 27 328 chromatin immunoprecipitation-sequencing peaks for PPARγ in human adipocytes through meta-analysis of 3 data sets. The PPARγ consensus motif showed greatest enrichment and mapped to 8637 peaks. We identified 146 SNPs in these motifs. This number was significantly less than would be expected by chance, and Inference of Natural Selection from Interspersed Genomically coHerent elemenTs analysis indicated that these motifs are under weak negative selection. A screen of these SNPs against genome-wide association studies for cardiometabolic traits revealed significant enrichment with 16 SNPs. A screen against the MuTHER expression quantitative trait locus data revealed 8 of these were significantly associated with altered gene expression in human adipose, more than would be expected by chance. Several SNPs fall close, or are linked by expression quantitative trait locus to lipid-metabolism loci including CYP26A1. We demonstrated the use of functional genomics to identify SNPs of potential function. Specifically, that SNPs within PPARγ motifs that bind PPARγ in adipocytes are significantly associated with cardiometabolic disease and with the regulation of transcription in adipose. This method may be used to uncover functional SNPs that do not reach significance thresholds in the agnostic approach of genome-wide association studies.

Genome-wide association studies (GWAS) significantly enhance our ability to identify trait-associated genomic variants by considering the host genome. Moreover, the hologenome refers to the host organism's collective genetic material and its associated microbiome. In this study, we utilized the hologenome framework, called Hologenome-wide association studies (HWAS), to dissect the architecture of complex traits, including milk yield, methane emissions, rumen physiology in cattle, and gut microbial composition in pigs. We employed four statistical models: (1) GWAS, (2) Microbial GWAS (M-GWAS), (3) HWAS-CG (hologenome interaction estimated using COvariance between Random Effects Genome-based restricted maximum likelihood (CORE-GREML)), and (4) HWAS-H (hologenome interaction estimated using the Hadamard product method). We applied Bonferroni correction to interpret the significant associations in the complex traits. The GWAS and M-GWAS detected one and sixteen significant SNPs for milk yield traits, respectively, whereas the HWAS-CG and HWAS-H each identified eight SNPs. Moreover, HWAS-CG revealed four, and the remaining models identified three SNPs each for methane emissions traits. The GWAS and HWAS-CG detected one and three SNPs for rumen physiology traits, respectively. For the pigs' gut microbial composition traits, the GWAS, M-GWAS, HWAS-CG, and HWAS-H identified 14, 16, 13, and 12 SNPs, respectively. We further explored these associations through SNP annotation and by analyzing biological processes and functional pathways. Additionally, we integrated our GWA results with expression quantitative trait locus (eQTL) data using transcriptome-wide association studies (TWAS) and summary-based Mendelian randomization (SMR) methods for a more comprehensive understanding of SNP-trait associations. Our study revealed hologenomic variability in agriculturally important traits, enhancing our understanding of host-microbiome interactions.

This Month in The Journal

Integrating genome-wide association study and expression quantitative trait loci data identifies NEGR1 as a causal risk gene of major depression disorder

To identify risk variants associated with gene expression in peripheral blood and to identify genes whose expression change may contribute to the susceptibility to periodontitis. We systematically integrated the genetic associations from a recent large-scale periodontitis GWAS and blood expression quantitative trait loci (eQTL) data using Sherlock, a Bayesian statistical framework. We then validated the potential causal genes in independent gene expression data sets. Gene co-expression analysis was used to explore the functional relationship for the identified causal genes. Sherlock analysis identified 10 genes (rs7403881 for MT1L, rs12459542 for SIGLEC5, rs12459542 for SIGLEC14, rs6680386 for S100A12, rs10489524 for TRIM33, rs11962642 for HIST1H3E, rs2814770 for AIM2, rs7593959 for FASTKD2, rs10416904 for PKN1, and rs10508204 for WDR37) whose expression may influence periodontitis. Among these genes, AIM2 was consistent significantly upregulated in periodontium of periodontitis patients across four data sets. The cis-eQTL (rs2814770, ~16kb upstream of AIM2) showed significant association with AIM2 (p=6.63×10-6 ) and suggestive association with periodontitis (p=7.52×10-4 ). We also validated the significant association between rs2814770 and AIM2 expression in independent expression data set. Pathway analysis revealed that genes co-expressed with AIM2 were significantly enriched in immune- and inflammation-related pathways. Our findings implicate that AIM2 is a susceptibility gene, expression of which in gingiva may influence periodontitis risk. Further functional investigation of AIM2 may provide new insight for periodontitis pathogenesis.

Identification of the primate-specific gene BTN3A2 as an additional schizophrenia risk gene in the MHC loci

Expanded Genome-Wide Association Study (GWAS) Identifies Nine Novel Germline Risk Loci for Waldenström Macroglobulinemia (WM) and Lymphoplasmacytic Lymphoma (LPL)