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PubMed2026

Cross-tissue Mendelian randomization prioritizes RAB27B as a brain-derived candidate protein for postpartum depression.

OBJECTIVE: Postpartum depression (PPD) is one of the most common and debilitating complications of childbirth, yet the candidate proteins linking genetic risk to disease remain poorly defined. Building on recent genome-wide association studies (GWAS), we sought to integrate cross-tissue proteogenomic data to identify candidate proteins for PPD and explore therapeutic opportunities. METHODS: We conducted two-sample Mendelian randomization (MR) using genome-wide significant cis-protein QTLs from brain (n = 608 proteins), cerebrospinal fluid (CSF; n = 214), and plasma (n = 612). PPD summary statistics were obtained from FinnGen R8 (13,657 cases, 236,178 controls) and replicated in an independent GWAS. Phenome-wide association (PheWAS) was used to assess pleiotropy. Potential therapeutic targets were evaluated through DSigDB drug repurposing, molecular docking, and molecular dynamics simulations. RESULTS: Among all proteins tested, RAB27B was the only brain-derived protein surpassing Bonferroni correction (OR = 1.60; 95% CI: 1.30-1.96; P = 6.6 × 10⁻⁶), whereas no significant proteins were identified in CSF or plasma. This association was replicated in an independent GWAS (OR = 1.27; 95% CI: 1.02-1.58; P = 0.037). PheWAS identified no pleiotropic associations at genome-wide significance. In silico drug repurposing identified pregnenolone as a candidate ligand with computationally predicted stable binding to RAB27B, providing a starting point for future experimental validation. CONCLUSION: This study provides the first cross-tissue proteogenomic evidence that RAB27B is a brain-derived, reproducible candidate protein genetically associated with PPD. By extending GWAS signals to functional protein-level mechanisms and therapeutic inference, our findings nominate RAB27B and pregnenolone as promising directions for postpartum psychiatric research.

Humans↗

PubMed2025

Polygenic Risk Scores Predicting Estimated GFR Validated With Iohexol Clearance.

INTRODUCTION: Genome-wide association studies (GWAS) have identified hundreds of single nucleotide variants (SNVs) associated with estimated glomerular filtration rate (eGFR). eGFR has been used as a proxy phenotype because of the complexity and cost of measured GFR (mGFR) in large studies. Because eGFR is influenced by non-GFR factors, these GWAS results may be biased compared with a hypothetical study using mGFR. We aimed to investigate this by comparing aggregate measures of genetic effects on mGFR and eGFR. METHODS: We studied 1492 persons from the Renal Iohexol Clearance Survey (RENIS) cohort, a representative sample of the general population in Northern Norway without preexisting cardiovascular disease, kidney disease, or diabetes. We measured iohexol-clearance, and genotyping was performed with a microarray chip enriched for GFR-related SNVs. We compared the performance of 3 published polygenic risk scores (PGS) developed for creatinine-based eGFR (eGFRcr), narrow-sense heritability (h2) and the mean effect of SNVs on mGFR, eGFRcr, cystatin C-based eGFR (eGFRcys) and eGFRcr-cys. RESULTS: The performance of the PGS differed for mGFR and the 3 eGFRs, with best performance for prediction of eGFRcr (P < 0.05). However, when the beta coefficients of the SNVs in the 3 PGS were estimated in the RENIS-cohort, their magnitude was 11% to 46% greater for mGFR than for the 3 eGFR methods in 8 of 9 comparisons (P < 0.05). mGFR had higher h2 (0.47) than eGFRcr (0.21), eGFRcys (0.37), and eGFRcr-cys (0.42). CONCLUSIONS: SNVs with non-GFR effects on creatinine and cystatin-C influence GWAS results. The results of GWAS using eGFR should be validated using experimental and other more precise methods.

chronic kidney disease↗

PubMed2026

A novel reusable transcriptome-wide association study workflow used to map key genes linked to important cattle traits.

Transcriptome-wide association studies (TWAS) are a powerful approach for studying the genes underlying complex traits by directly integrating GWAS and gene expression datasets. In cattle, they have been previously applied to identify genes driving fertility, milk production, and health. However, these studies have also highlighted several challenges, from difficulties in reproducing these complex analyses to limitations from poor genotype calls, especially when called directly from RNA sequencing data. To address these and other challenges, for the H2020 BovReg Project, we have developed a streamlined, species-agnostic, and reusable Nextflow TWAS workflow to integrate transcriptomic and GWAS summary statistic datasets. Our workflow first generates accurate genotype calls and gene expression prediction models from transcriptomic datasets and then applies these tools to impute gene expression levels into GWAS cohorts, enabling the association of genes with traits of interest. We explore optimal strategies for calling genetic variants directly from transcriptomic data and illustrate that using imputation approaches specifically designed for low-pass sequencing data can improve variant calling over previously adopted methods. We demonstrate the utility of our TWAS workflow by applying it to both novel and publicly available GWAS cohorts for cattle, detecting novel gene-trait associations for complex traits. Using a new transcriptome annotation of the cattle genome generated for the BovReg project we also illustrate how previously un-assayable associations can be detected. The results and the workflow we present, provide a new resource for the community and contribute to a better understanding of the molecular drivers of complex traits in cattle with the goal of eventually leveraging this information in future breeding decisions.

Animals↗

PubMed2025

Distinguishing specific from broad genetic associations between external correlates and common factors.

MOTIVATION: Within the genomic structural equation modelling (genomic SEM) framework, common factors are often used to index shared genetic etiology across constellations of genome-wide associations studies (GWASs) phenotypes. A standard common pathway model, in which a genetic association is estimated between an external GWAS phenotype and a common factor, assumes that all genetic associations between the external GWAS phenotype and the individual indicator phenotypes are mediated through the factor. This assumption can be tested using the QTrait statistic, which compares the common pathway model to an independent pathways model that allows for direct genetic associations between the external GWAS phenotype and the individual indicators of the factor. However, QTrait is not designed to identify either the magnitude or the source of this heterogeneity. RESULTS: We expand upon the QTrait approach by describing an effect size index that quantifies the degree to which the common pathways model is violated, and we provide a systematic approach for empirically identifying specific direct pathways between an external trait and indicator traits. Our method comprises a series of omnibus tests and outlying indicator detection algorithms indexing the heterogeneity of associations between the genetic component of external traits and the individual indicators of common factors. We provide a set of automated functions which we apply to investigate the patterns of genetic associations across a set of external correlates with respect to indicators of general cognitive ability and case-control and proxy GWAS indices of Alzheimer's disease. AVAILABILITY AND IMPLEMENTATION: The Genomic SEM R package and the QTrait function is available at https://github.com/GenomicSEM/GenomicSEM. The QTrait function tutorial is available at https://github.com/GenomicSEM/GenomicSEM/wiki/8.-Tutorials. To ensure reproducibility of the analyses presented in this manuscript, the exact version of the QTrait function used, along with input data and scripts, has been archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17186083).

Genome-Wide Association Study↗

PubMed2025

FINEMAP-miss: fine-mapping genome-wide association studies with missing genotype information.

MOTIVATION: The most informative genome-wide association studies (GWAS) are meta-analyses that have combined multiple studies to increase the GWAS sample size. Statistical fine-mapping is a key downstream analysis of GWAS to jointly evaluate the probability of causality of all variants in a genomic region of interest. Current fine-mapping methods are miscalibrated in the meta-analysis setting due to variation in sample size across the variants. RESULTS: We introduce FINEMAP-miss, a new fine-mapping method that extends the FINEMAP model to account for variant-specific missingness. We show that FINEMAP-miss is well-calibrated in meta-analysis simulations where the standard fine-mapping fails. Compared to the summary statistics imputation approach, FINEMAP-miss provides clear improvement when the causal variants have low imputation information or when the sample size or complexity of the meta-analysis setting increase. We successfully apply FINEMAP-miss on a breast cancer GWAS meta-analysis where neither the standard fine-mapping nor the summary statistics imputation are applicable. AVAILABILITY: An open source implementation of FINEMAP-miss as an R package ("finemapmiss") is available at https://github.com/JoonasKartau/finemapmiss. The archived version of FINEMAP-miss used for this publication can be found on Zenodo at https://doi.org/10.5281/zenodo.17492622. SUPPLEMENTARY INFORMATION: is available at the journal's web site.

Genome-Wide Association Study↗

PubMed2026

Exploring the shared genetic architecture of sarcopenia using genomic structural equation modeling.

Sarcopenia is a common age-associated condition characterized by the progressive loss of skeletal muscle mass, strength, and physical functionality. While large-scale genome-wide association studies (GWAS) have previously addressed isolated traits of sarcopenia, the multifactorial genetic architecture underlying this condition remains largely undefined. To characterize the common genetic basis of sarcopenia-related traits, genomic structural equation modeling (Genomic-SEM) was implemented. Multiple post-GWAS analytic approaches were integrated to pinpoint susceptibility loci. These analyses encompassed identifying enriched genetic pathways and relevant genomic elements, as well as cell-type-specific enrichment in skeletal muscle satellite stem cells, mesenchymal stem cells, and skeletal muscle satellite cells in limb muscle. Furthermore, based on the integrated GWAS data of sarcopenia-related traits, polygenic risk score (PRS) analysis was conducted to evaluate risk associations at the chromosomal level. A well-fitted Genomic-SEM successfully integrated the GWAS data, revealing the shared genetic architecture of sarcopenia-related traits. We identified 110 single nucleotide polymorphisms (SNPs) reaching genome-wide significance (p < 5 × 10-8), of which 9 represent novel discoveries. Subsequent fine-mapping procedures and gene-set analyses identified 15 causal variants alongside 77 candidate susceptibility genes. This study provides a comprehensive genetic characterization of sarcopenia via Genomic-SEM, offering new insights into the etiological pathways underlying sarcopenia.

Sarcopenia↗

PubMed2026

Genetic architecture of endometriosis: risk factors, comorbidities and clinical implications.

BACKGROUND: In 1999, Dr Susan Treloar and colleagues conducted a landmark twin study in Australia and reported their estimate of 51% for the heritability of endometriosis. This important result led several groups to begin mapping genetic factors contributing to increased endometriosis risk. Despite early challenges, advances in genome-wide association studies (GWAS) have identified multiple genetic risk factors and some target genes implicated in follow-up studies on genetic regulation of transcription. Access to large publicly available genetic datasets and analysis with endometriosis GWAS results is also providing new opportunities to answer important questions about comorbid conditions associated with endometriosis and their implications for clinical practice. OBJECTIVE AND RATIONALE: The objective of the review is to summarize the last 25 years of genetic studies in endometriosis, outline contributions to our understanding of the disease, and suggest future directions to accelerate biological insights from genetic studies to improve clinical outcomes. SEARCH METHODS: A comprehensive review of scientific literature on the genetics of endometriosis was conducted through searches in PubMed and Google Scholar up to June 2026. Search terms included "endometriosis AND (genetics OR GWAS OR genetic risk factors)", For studies addressing the functional characterization of genetic risk loci, additional searches employed the terms "endometriosis AND (genotype-phenotype associations OR colocalization OR eQTL OR mQTL OR multi omics methods)". To identify studies examining shared genetic risk between endometriosis and comorbid conditions, the search strategy included "endometriosis AND (genetic correlation OR colocalization OR Mendelian randomisation)". Publications reporting discoveries related to genetic risk factors for endometriosis and studies interpreting their biological and clinical significance were critically evaluated, and 144 publications were discussed in the review. OUTCOMES: Discovery of genetic risk factors started slowly and has accelerated in recent years with developments in technology and international collaborations to combine data and increase statistical power. GWAS have mapped 80 genetic risk factors that implicate gene regulation of hormonal targets, development of the reproductive tract, regulation of cell proliferation, and regulation of epithelial cell differentiation. In common with most other complex diseases, effects of individual common genetic risk factors are small. However, several examples demonstrate that small effect sizes are not a good predictor for the impact of drugs developed against genetically validated targets. Genetic risk factors implicate five genes regulating gonadotrophin release and oestrogen action, the major target pathway of current drugs for treatment of endometriosis demonstrating proof-of-principal for biologically meaningful results. Genetic correlation and Mendelian Randomization studies highlight important causal relationships between endometriosis and comorbid conditions including a possible role for testosterone during development and shared genetic risk factors for gynaecological, gastrointestinal, pain, psychiatric, and inflammatory conditions. Understanding causal relationships between endometriosis and related conditions will aid clinical management and more personalized treatments. WIDER IMPLICATIONS: Genetic studies provide novel insights into endometriosis pathogenesis and associations with related comorbid conditions. Genetic factors modifying gene regulation and disease risk likely act in specific cell types, and access to datasets from genetically informed cell-based models, single-cell and spatial omics data are needed to accelerate progress. Future studies should address critical questions of heterogeneity and disease subtypes, expand the search for genetic risk factors to non-European populations, evaluate the role of rare and structural variants, and better integrate data from functional, genomics, genetics, and clinical studies to reduce diagnostic delay, develop novel treatment strategies, and translate discoveries into personalized management strategies for affected individuals. REGISTRATION NUMBER: N/A.

comorbid conditions↗

PubMed2025

Genome topology analysis and transcriptomics of human osteoclasts reveals enhancer-promoter interactions at loci for bone traits and diseases.

Genome-wide association studies (GWAS) relevant to osteoporosis have identified hundreds of loci; however, understanding how these variants influence the phenotype is complicated because most reside in non-coding DNA sequence that serves as transcriptional enhancers and repressors. To advance knowledge on these regulatory elements in osteoclasts (OCs), we performed Micro-C analysis, which informs on the genome topology of these cells and integrated the results with transcriptome and GWAS data to further define loci linked to BMD. Using blood cells isolated from 4 healthy participants aged 31-61 yr, we cultured OC in vitro and generated a Micro-C chromatin conformation capture dataset. We characterized chromatin loops (CLs) in OC from among more than 69 million chromatin interactions identified in the genome. Of the CL identified in OC, >16 000 were unique compared to precursor cells. When sentinel single nucleotide polymorphisms from osteoporosis and bone-related GWAS and those in linkage disequilibrium at r 2 > 0.6 were mapped to CL for OC, 12 588 of these variants were observed within chromatin contact regions. Notable in differential gene ontology enrichment analyses of the topology data for OC and precursors were pathways regulating pluripotency of stem cells, Wnt signaling, nucleotide-binding oligomerization domain (NOD)-like receptor signaling and chemokine signaling. These data, in combination with other 3D genome architecture and epigenetic data (eg, histone modifications and chromatin accessibility), will be useful in modeling to predict genome-wide, which enhancers regulate which genes in OC. This data will therefore also be informative for resolving GWAS hits. In conclusion, we have generated a high-resolution genome topology dataset for human OC and have used this to identify CLs relevant to studies of the genetics of osteoporosis. This data will serve as a powerful resource to inform future functional studies of OC biology.

BMD↗

PubMed2025

Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.

Coronary microvascular disease (CMVD) is an underdiagnosed but significant contributor to the burden of ischemic heart disease, characterized by angina and myocardial infarction. The development of risk prediction models such as polygenic risk scores (PRS) for CMVD has been limited by a lack of large-scale genome-wide association studies (GWAS). However, there is significant overlap between CMVD and enrollment criteria for coronary artery disease (CAD) GWAS. In this study, we developed CMVD PRS models by selecting variants identified in a CMVD GWAS and applying weights from an external CAD GWAS, using CMVD-associated loci as proxies for the genetic risk. We integrated plasma proteomics, clinical measures from perfusion PET imaging, and PRS to evaluate their contributions to CMVD risk prediction in comprehensive machine and deep learning models. We then developed a novel unsupervised endotyping framework for CMVD from perfusion PET-derived myocardial blood flow data, revealing distinct patient subgroups beyond traditional case-control definitions. This imaging-based stratification substantially improved classification performance alongside plasma proteomics and PRS, achieving AUROCs between 0.65 and 0.73 per class, significantly outperforming binary classifiers and existing clinical models, highlighting the potential of this stratification approach to enable more precise and personalized diagnosis by capturing the underlying heterogeneity of CMVD. This work represents the first application of imaging-based endotyping and the integration of genetic and proteomic data for CMVD risk prediction, establishing a framework for multimodal modeling in complex diseases.

Cardiovascular Disease↗

PubMed2025

Multiple Psychiatric Traits Enriched for Brain Tissues in the Early Postpartum Period.

BACKGROUND: The perinatal period is a high-risk time for onset of various psychiatric disorders. However, it is unclear how genetic risk factors for these disorders interact with biological changes associated with pregnancy and postpartum. This study evaluates whether psychiatric genome-wide association study (GWAS) results are enriched within various brain regions across the perinatal period. METHODS: Tissue-specific enrichment analyses were conducted to estimate the potential impact of GWAS loci on transcriptional changes in the brain across the perinatal period. GWAS summary statistics were obtained for 26 psychiatric phenotypes. RNA-sequencing data was acquired from four brain regions (hypothalamus, hippocampus, cerebellum, and neocortex) in mice at six timepoints (virgin, 14- and 16-days post-conception, and 1-, 3- and 10-days postpartum). RESULTS: Hippocampus and neocortex in the early postpartum period are significantly enriched (q-value < 0.05) for genetic variants associated with schizophrenia (SCZ), bipolar disorder (BD), depressive symptoms, and major depressive disorder with suicidal features. The most significant enrichment occurred in the neocortex for SCZ and BD, peaking at postpartum day 1 (SCZ p-value = 3.85 × 10-8; BD p-value = 6.65 × 10-5). In the hippocampus, BD and SCZ were enriched at postpartum day 1 (SCZ p-value = 3.27 × 10-3; BD p-value = 4.33 × 10-3). No enrichment was observed in cerebellum or hypothalamus for any of the psychiatric traits tested. CONCLUSIONS: The results accord with previous epidemiological studies and provide context in which to interpret GWAS results. Understanding the burden of genetic variants across the perinatal period may help prioritise pathways underlying onset of psychiatric disorders outside of pregnancy and postpartum periods.

Journal Article↗

PubMed2026

Optimizing Control Definitions in Opioid Use Disorder Genetic Research Using Electronic Health Records.

Amidst the opioid crisis, understanding the genetic basis of opioid use disorder (OUD) is crucial for identifying biological mechanisms and intervention points. However, genome-wide association studies (GWASs) have been hampered by inadequate sample sizes and often the use of control populations not assessed for prior opioid exposure. Because opioid exposure is a prerequisite for the development of OUD, consideration of exposure history in controls is important. Electronic health record data (EHR) paired with genomic information allow a broader sampling of patients with OUD and exposed controls. We leveraged data across two healthcare systems to evaluate the impact of using controls not screened for opioid exposure ('generic') versus minimally opioid-exposed control ('exposed'). First, at the phenotypic level, we conducted phenome-wide association studies (PheWAS) to compare the medical comorbidity profiles of OUD cases when using generic versus exposed controls. While PheWAS results for OUD-related comorbidities were more pronounced when using the generic group, 83% of the disease associations were overlapping and of similar effect sizes. Second, at the genetic level, we conducted GWAS (cases vs. generic; cases vs. exposed) and assessed differences in genetic correlations and degrees of phenotypic misclassification. Genetic results were concordant across control groups based on heritability (generic: 0.16 ± 0.07 vs. 0.10 ± 0.07), associations with the coding OPRM1 variant rs1799971 (pgeneric = 8.83E-03 vs. pexposed = 1.83E-02) and genetic correlations with prior OUD GWAS (rg-generic = 0.83 ± 0.26 vs. rg-exposed = 0.78 ± 0.27). Although GWASs were limited by sample size (Ngeneric = 6269, Nexposed = 6365), compared to an independent OUD GWAS (N = 425 944), the dilution value for the two GWAS was not different from 1, suggesting no major impact of phenotypic misclassification. This study represents the first effort to enhance OUD genetic research through optimization of control definitions using EHR data. Generic controls ascertained within the US health systems, where exposure to prescription opioids is high, offer a practical alternative for genetic studies of OUD.

Humans↗

PubMed2026

Genetic Basis of Pancreatic Steatosis: A Systematic Review of Comparison between African and Non-African Populations.

This systematic review compared genetic evidence of pancreatic steatosis across African and non-African populations to illuminate ancestry-specific mechanisms and precision-prevention opportunities. Following PRISMA guidelines for reporting, a search was conducted across PubMed, Scopus, Web of Science, and NHGRI-EBI GWAS Catalog for studies spanning 2011 to 31st March 2026. Eligible studies included genome-wide association studies (GWAS), polygenic risk score (PRS), and Mendelian randomization (MR) analyses that reported genetic associations with pancreatic fat phenotypes and had explicit ancestry stratification or comparison. Narrative thematic synthesis was performed due to methodological heterogeneity. Six core genetic studies (N > 120,000 participants) were included. The only multi-ethnic GWAS found a strong protective variant of African ancestry, rs73449607 (near PDX1/PLUTO), which reduced pancreatic fat (β = -0.67, P = 4.50 × 10⁻⁸) and explained 14.3% of the variance in African Americans (versus 5.3% overall). UK Biobank race-stratified PRS analyses confirmed the lowest pancreatic fat fraction in Black participants, with the strongest HbA1c PRS-fat association in this group (ρ = 0.23, P < 0.0001). European-dominant GWAS highlighted risk loci, including FUT2 rs601338 (higher fat and chronic pancreatitis risk, OR 1.26). MR studies demonstrated causal links between genetically predicted intra-pancreatic fat deposition (IPFD) and pancreatic ductal adenocarcinoma (PDAC) (OR 2.46 per SD) but not diabetes. African-ancestry genomes confer substantial protection against pancreatic steatosis, whereas non-African genomes are enriched for risk alleles that amplify the non-alcoholic fatty pancreas disease (NAFPD)-to-PDAC cascade. These ancestry-differentiated mechanisms position NAFPD as a precision medicine target.

Africa↗

PubMed2025

Genomic structural equation modeling elucidates the shared genetic architecture of allergic disorders.

BACKGROUND: The intricate shared genetic architecture underlying allergic disorders-including allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis-remains incompletely characterized. METHODS: Our study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing the shared genetic architecture of allergic disorders. Coupled with diverse post-GWAS analytical methods, we aimed to discover susceptible loci and investigate genetic associations with external traits. Furthermore, we explored enriched genetic pathways, cellular layers, and genomic elements, and investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted to assess chromosomal-level risk associations for allergic disorders. RESULTS: A well-fitted genomic SEM integrated GWAS data, revealing the shared genetic architecture of allergic disorders. We identified a total of 2038 genome-wide significant SNP loci (p < 5e-8), including 31 previously unreported loci. Fine-mapping of variants and gene sets pinpointed 2 causal variants and 31 candidate susceptible genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Preliminary findings identified four putative causal plasma protein biomarkers. CONCLUSION: Notably, this study presents the first comprehensive genetic characterization of allergic disorders through a GWAS analysis of an unmeasured composite phenotype, providing novel insights into shared etiological pathways across these conditions.

Humans↗

PubMed2025

Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci.

MOTIVATION: Genome-wide association studies (GWAS) have identified genetic variants, usually single-nucleotide polymorphisms (SNPs), associated with human traits, including disease and disease risk. These variants (or causal variants in linkage disequilibrium with them) usually affect the regulation or function of a nearby gene. A GWAS locus can span many genes, however, and prioritizing which gene or genes in a locus are most likely to be causal remains a challenge. Better prioritization and prediction of causal genes could reveal disease mechanisms and suggest interventions. RESULTS: We describe a new Bayesian method, termed SigNet for significance networks, that combines information both within and across loci to identify the most likely causal gene at each locus. The SigNet method builds on existing methods that focus on individual loci with evidence from gene distance and expression quantitative trait loci (eQTL) by sharing information across loci using protein-protein and gene regulatory interaction network data. In an application to cardiac electrophysiology with 226 GWAS loci, only 46 (20%) have within-locus evidence from Mendelian genes, protein-coding changes, or colocalization with eQTL signals. At the remaining 180 loci lacking functional information, SigNet selects 56 genes other than the minimum distance gene, equal to 31% of the information-poor loci and 25% of the GWAS loci overall. Assessment by pathway enrichment demonstrates improved performance by SigNet. Review of individual loci shows literature evidence for genes selected by SigNet, including PMP22 as a novel causal gene candidate.

Genome-Wide Association Study↗

PubMed2025

Sparse multitask group Lasso for genome-wide association studies.

A critical hurdle in Genome-Wide Association Studies (GWAS) involves population stratification, wherein differences in allele frequencies among subpopulations within samples are influenced by distinct ancestry. This stratification implies that risk variants may be distinct across populations with different allele frequencies. This study introduces Sparse Multitask Group Lasso (SMuGLasso) to tackle this challenge. SMuGLasso is based on MuGLasso, which formulates this problem using a multitask group lasso framework in which tasks are subpopulations, and groups are population-specific Linkage-Disequilibrium (LD)-groups of strongly correlated Single Nucleotide Polymorphisms (SNPs). The novelty in SMuGLasso is the incorporation of an additional [Formula: see text]-norm regularization for the selection of population-specific genetic variants. As MuGLasso, SMuGLasso uses a stability selection procedure to improve robustness and gap-safe screening rules for computational efficiency. We evaluate MuGLasso and SMuGLasso on simulated data sets as well as on a case-control breast cancer data set and a quantitative GWAS in Arabidopsis thaliana. We show that SMuGLasso is well suited to addressing linkage disequilibrium and population stratification in GWAS data, and show the superiority of SMuGLasso over MuGLasso in identifying population-specific SNPs. On real data, we confirm the relevance of the identified loci through pathway and network analysis, and observe that the findings of SMuGLasso are more consistent with the literature than those of MuGLasso. All in all, SMuGLasso is a promising tool for analyzing GWAS data and furthering our understanding of population-specific biological mechanisms.

Genome-Wide Association Study↗

PubMed2026

Beyond years of schooling: Genetic associations across educational milestones in two Norwegian cohorts.

Although educational attainment is heritable, years-of-education (EduYears) measures are not designed to show how genetic associations vary across discrete educational milestones. In the Norwegian Mother, Father and Child Cohort Study (MoBa; N = 120,527) and the Norwegian Twin Registry (N = 8,910), we combined genome-wide association studies (GWAS), polygenic indices (PGIs), and twin models to characterize genetic associations with high-school completion and bachelor's, master's, and doctoral attainment. In transition-specific analyses conditional on prior attainment, observed-scale common-variant heritability (h2SNP) and PGI discrimination followed an inverse-U pattern, peaking for progression from high-school completion to attainment of at least a bachelor's degree (HS → BSc + ; h2SNP ≈ 0.14; R2Tjur ≈ 0.05) and declining at postgraduate transitions. Genetic correlations (rg) with large-scale GWAS of EduYears (EA4) and intelligence were high at earlier transitions but lower at later ones (for EA4, rg ≈ 0.92 at HS → BSc+ and ≈ 0.38 at MSc → PhD). In cumulative analyses of attained status in the full cohort, the point-estimate gap between twin- and SNP-based heritability narrowed from ≈ 0.38 at high-school completion to ≈ 0.11 at PhD, while genetic overlap was lower for distant than adjacent milestones (rg ≈ 0.71 for high-school completion versus PhD and ≈ 0.92 for adjacent milestones). Sensitivity analyses using an alternative intelligence GWAS and additional PGIs showed similar transition-specific trajectories. At the doctoral transition, estimates were obtained among individuals who had attained at least a master's degree, so attenuation there must be interpreted in light of selection and range restriction. Because EduYears converts distinct credentials and progression steps into a single numerical scale, estimates expressed per additional year should not be interpreted as though one year has the same meaning across the educational sequence. Milestone-preserving analyses make that hidden structure visible and sharpen the interpretation of education GWAS and PGIs.

Journal Article↗

PubMed2026

Genome-wide association study of estimated glomerular filtration rate using repeated measurements in the Taiwan Biobank.

BACKGROUND: Chronic kidney disease (CKD) is a major global public health issue, with genetic factors playing a significant role in kidney function. Although genome-wide association studies (GWAS) have identified numerous loci associated with estimated glomerular filtration rate (eGFR), most studies relied on a single time-point measurement, which limits the capacity to account for within-individual measurement variability. METHODS: We performed a repeated-measurement GWAS in the prospective Taiwan Biobank (Taiwanese ancestry; n = 25,004) using two repeated creatinine-based eGFR measurements. Repeated eGFR values were analyzed using a linear mixed-effects model with a subject-specific random intercept and time-varying covariates, providing a more precise estimate of eGFR level. Identified loci underwent functional annotation (expression quantitative trait locus, deleteriousness prediction, and epigenetic markers) and were compared with results from a single-measurement GWAS. RESULTS: Six loci associated with eGFR were identified, including four previously reported regions (1q22, 4q21.1, 11p14.1, and 17q21.2) and two additional loci (6p21.32 and 15q24.2). Functional annotation implicated several candidate genes-such as MUC1/EFNA1, SHROOM3, HLA-DQB1, MPPED2, NRG4, and PGAP3/FBXL20-in the regulation of kidney function. CONCLUSION: Incorporating repeated eGFR measurements into GWAS may improve phenotypic precision for identifying genetic associations with kidney function. This study identified eGFR-associated loci and biologically plausible candidate genes in a Taiwanese population, which require further replication and functional validation.

Chronic kidney disease↗

PubMed2026

High-Density Genome-Wide Association Mapping Identifies Candidate Loci Associated with Maize Stalk Cell Wall Composition.

Maize (Zea mays L.) stalk cell wall composition is a key determinant of forage digestibility, lodging resistance, and biomass utilization efficiency. Although previous genome-wide association studies (GWAS) have identified loci associated with lignin (LIG), cellulose (CEL), and hemicellulose (HC), advances in genomic resources provide an opportunity to revisit existing phenotypic datasets at substantially higher resolution. Here, we re-analyzed a maize association panel consisting of 341 diverse inbred lines using an expanded genotype dataset containing 10.77 million SNPs, two derived compositional indices (CEL/HC and [LIG/(CEL + HC)], and six complementary GWAS models. Across all traits and models, we identified 855 unique significant SNPs associated with 579 candidate genes. Among the traits examined, LIG/(CEL + HC) yielded the greatest number of associations, suggesting that indices representing the relative balance among cell wall components may better capture the genetic architecture of cell wall composition than individual component measurements alone. Integration of multiple GWAS models with functional enrichment, haplotype, and selective sweep analyses prioritized three biologically relevant candidate genes encoding a MYB58 transcription factor, the glycosyltransferase Xt9, and a putative xyloglucan 6-xylosyltransferase. Haplotype analysis revealed significant effects of Xt9 and the xyloglucan 6-xylosyltransferase on cell wall composition, while selective sweep analysis identified Xt9 as a target of repeated selection during maize domestication, ecological adaptation, and modern breeding. Although these candidate genes provide promising targets for future investigation, the associations identified here are based on a single association panel and require functional and independent population validation. Collectively, our results demonstrate how high-density genotyping combined with complementary GWAS models can refine candidate associations and generate testable hypotheses from existing phenotypic datasets.

cell wall composition↗