Benefits and limitations of genome-wide association studies

Genetic insights into the role of mitochondria-related genes in mental disorders: An integrative multi-omics analysis

BACKGROUND

Mitochondrial dysfunction has been implicated in the development of mental disorders, yet the underlying mechanisms remain unclear. In this study, we employed summary-data-based Mendelian randomization (SMR) analysis to explore the associations between mitochondrial-related genes and seven common mental disorders across gene expression, DNA methylation, and protein levels.

METHOD

Summary statistics from genome-wide association studies were used for seven mental disorders, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, anxiety, bipolar disorder, major depressive disorder, post-traumatic stress disorder, and schizophrenia (SCZ). Instrumental variables associated with 1136 mitochondria-related genes were derived from summary statistics for DNA methylation, gene expression, and protein quantitative trait loci. SMR analyses and colocalization analyses were then conducted across these three biological levels to explore the associations with each of the seven mental disorders.

RESULTS

We identified mitochondria-related genes associated with mental disorders with multi-omics evidence: RMDN1 for ADHD, and ACADVL, ETFA, MMAB, and PPA2 for SCZ. Specifically, an increase of one standard deviation in the level of RMDN1 was linked to a 12 % decrease in the risk of developing ADHD (OR = 0.88, 95 % CI: 0.83-0.94). Increased levels of ETFA (OR = 1.79, 95 % CI: 1.24-2.60) and MMAB (OR = 1.10, 95 % CI: 1.05-1.16) were significantly associated with increased risk of SCZ. Conversely, high levels of ACADVL (OR = 0.50, 95 % CI: 0.33-0.77) and PPA2 (OR = 0.68, 95 % CI: 0.55-0.85) were associated with a reduced risk of SCZ.

CONCLUSIONS

These findings suggested that dysfunction in mitochondria-related genes may underlie the molecular mechanisms of ADHD and SCZ, providing novel biomarkers for diagnosis and therapeutic interventions.

Single-cell eQTL analysis identifies genetic variation underlying metabolic dysfunction-associated steatohepatitis

Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly recognized for its medical and socioeconomic impacts, driven by diverse genetic and environmental factors. Here, to address the urgent need for individually tailored therapies, we show results from single-cell expression quantitative trait locus (sc-eQTL) analysis on liver biopsies from 25 patients with MASLD and 23 controls. This approach identified over 3,500 sc-eQTLs across major liver cell types and cell-state-interacting eQTLs (ieQTLs) with significant enrichment for disease heritability (for MASLD trait, ieQTL enrichment odds ratio 10.27). We integrated transcription factors as upstream regulators of ieQTLs, revealing 601 functional units ('quartets') composed of transcription factors, cell states, SNP components of ieQTL (ieSNPs) and Gene component of ieQTL (ieGenes). From these results, we pinpoint the loss of an eQTL in EFHD1 during hepatocyte maladaptation associated with genotype-specific regulation by FOXO1, further contributing to the risk of MASLD. Our approach underscores the role of eQTL analysis in capturing crucial genetic variations that influence gene expression and clinical outcomes in complex diseases.

The Single Nucleotide Substitution T → A rs2072580 Damages the CREB1 Binding Site in the Bidirectional SART3/ISCU Promoter

BACKGROUND/OBJECTIVES

The regulatory SNPs (rSNPs) that disturb the binding of transcription factors (TFs) and alter the transcription levels of genes play a paramount role in the formation of different traits and are associated with many pathologies. The search for allele-specific events in RNA-seq and ChIP-seq data is a powerful genome-wide approach to detect rSNPs. Using this approach, we have identified the T → A rs2072580 substitution in the bidirectional SART3/ISCU promoter as a potential rSNP and demonstrated its association with colorectal cancer, relying on International Cancer Genome Consortium data. The goal of this work was to identify the TF binding site that is affected by the T → A substitution and to study the effect of this substitution on reporter gene expression in different plasmid constructs.

METHODS

Electrophoretic mobility shift assay (EMSA), cross-competition analysis and supershift assay, plasmid construction, and dual luciferase reporter assay.

RESULTS

The T → A rs2072580 substitution is shown to damage the binding site for ubiquitous TF CREB1 and to significantly decrease the activity of the heterologous promoter carrying the cassettes of two or three repeated CREB binding sites inserted upstream of it. However, the substitution disturbing the CREB1 binding site within the bidirectional promoter shared by SART3 and ISCU inhibits the promoter activity of only the SART3 gene but has no effect on the activity of the ISCU promoter.

CONCLUSIONS

The performed comprehensive functional analysis of the T → A rs2072580 in the bidirectional SART3/ISCU promoter unambiguously implies it is an rSNP. These results form the background for further studies of this rSNP and its potential significance for various pathologies.

Cross-species modeling of plant genomes at single-nucleotide resolution using a pretrained DNA language model

Interpreting function and fitness effects in diverse plant genomes requires transferable models. Language models (LMs) pretrained on large-scale biological sequences can capture evolutionary conservation and offer cross-species prediction better than supervised models through fine-tuning limited labeled data. We introduce PlantCaduceus, a plant DNA LM that learns evolutionary conservation patterns in 16 angiosperm genomes by modeling both DNA strands simultaneously. When fine-tuned on a small set of labeled Arabidopsis data for tasks such as predicting translation initiation/termination sites and splice donor/acceptor sites, PlantCaduceus demonstrated remarkable transferability to maize, which diverged 160 Mya. The model outperformed the best existing DNA language model by 1.45-fold in maize splice donor prediction and 7.23-fold in maize translation initiation site prediction. In variant effect prediction, PlantCaduceus showed performance comparative to state-of-the-art protein LMs. Mutations predicted to be deleterious by PlantCaduceus showed threefold lower average minor allele frequencies compared to those identified by multiple sequence alignment-based methods. Additionally, PlantCaduceus successfully identifies well-known causal variants in both Arabidopsis and maize. Overall, PlantCaduceus is a versatile DNA LM that can accelerate plant genomics and crop breeding applications.

Generative prediction of causal gene sets responsible for complex traits

The relationship between genotype and phenotype remains an outstanding question for organism-level traits because these traits are generally complex. The challenge arises from complex traits being determined by a combination of multiple genes (or loci), which leads to an explosion of possible genotype-phenotype mappings. The primary techniques to resolve these mappings are genome/transcriptome-wide association studies, which are limited by their lack of causal inference and statistical power. Here, we develop an approach that combines transcriptional data endowed with causal information and a generative machine learning model designed to strengthen statistical power. Our implementation of the approach-dubbed transcriptome-wide conditional variational autoencoder (TWAVE)-includes a variational autoencoder trained on human transcriptional data, which is incorporated into an optimization framework. Given a trait phenotype, TWAVE generates expression profiles, which we dimensionally reduce by identifying independently varying generalized pathways (eigengenes). We then conduct constrained optimization to find causal gene sets that are the gene perturbations whose measured transcriptomic responses best explain trait phenotype differences. By considering several complex traits, we show that the approach identifies causal genes that cannot be detected by the primary existing techniques. Moreover, the approach identifies complex diseases caused by distinct sets of genes, meaning that the disease is polygenic and exhibits distinct subtypes driven by different genotype-phenotype mappings. We suggest that the approach will enable the design of tailored experiments to identify multigenic targets to address complex diseases.

The Non-Coding Regulatory Variant rs2863002 at chr11p11.2 Increases Neuroblastoma Risk by Affecting HSD17B12 Expression and Lipid Metabolism

A Genome-wide association study (GWAS) on a European-American cohort identified chr11p11.2 as a neuroblastoma predisposition locus. Combining in-house and public genomic data from neuroblastoma cell lines, this work implicates rs2863002 as the candidate causal variant at the 11p11.2 locus, confirming its cis-regulatory activity through a luciferase reporter assay. The genetic association of rs2863002 with neuroblastoma risk is validated in an Italian case-control cohort. Using ChIP-qPCR, Hi-C, and CRISPR genome editing, this work deciphers the regulatory mechanisms at the risk locus, demonstrating that the rs2863002-C risk allele regulates HSD17B12 expression and reduces GATA3 binding affinity. In vitro functional assays and targeted lipidomic analyses reveal the involvement of the rs2863002-C risk allele in tumorigenicity and modulation of lipid metabolism in neuroblastoma cells through HSD17B12 regulation. This study provides new insights into the genetic basis of neuroblastoma and underscores the importance of post-GWAS functional characterization of risk loci in uncovering relevant biological findings for understanding complex diseases.

Mechanistic insights into Down syndrome comorbidities via convergent RNA-seq and TWAS signals

Down syndrome (DS) is caused by trisomy of chromosome 21 and is associated with diverse clinical manifestations, yet the molecular pathways linking chromosome-21 dosage effects to DS comorbidities remain poorly defined. Here we address this gap by applying a network-based, integrative framework that combines whole-blood transcriptomic data with gene-trait associations to uncover mechanistic insights into DS-associated conditions. First, we performed matrix factorization using PLIER on Human Trisome Project (HTP) RNA-Seq profiles from 304 trisomy-21 (T21) and 95 euploid (D21) individuals, deriving 156 biologically interpretable gene modules. We then identified 92 modules whose activity differed significantly between T21 and D21 and annotated these with prior-knowledge and KEGG pathways. To connect modules to clinical traits, we integrated PrediXcan-derived TWAS results from the UK Biobank, revealing 25 T21-specific modules with significant gene-trait associations (FDR < 0.1), including modules linked to cardiovascular, hematological, immune, metabolic, and neurological phenotypes relevant to DS. Using HTP clinical records as a replication cohort, 13 of these modules reliably predicted comorbidity status (AUC > 0.65, mAPS > 0.65). Most notably module 37, an interferon-stimulated gene network, whose elevated expression robustly distinguished DS individuals with pulmonary hypertension (AUC = 0.76, mAPS = 0.73). Overall, our study demonstrates that integrating blood-derived gene modules with population-scale genetic data uncovers coherent molecular signatures underlying DS comorbidities, identifies candidate biomarkers and therapeutic targets (e.g., ISG15, IFITs, MX1 ), and highlights the power of combining transcriptomic and genetic evidence to elucidate complex disease mechanisms.

Abnormal association between neural activity and genetic expressions of impulsivity in attention deficit hyperactivity disorder: an Adolescent Brain Cognitive Development study

BACKGROUND

Impulsivity in highly heritable attention deficit hyperactivity disorder (ADHD) has been studied using neural activity via fMRI or genetic data, but rarely with multivariate methods linking both. We investigated coupled neural activity and gene expression signatures, using parallel independent component analysis (pICA) and Adolescent Brain Cognitive Development data.

METHODS

Children with ADHD (n = 394; 63% males) and healthy controls (n = 1,000; 47% males) of European ancestry were included. The subjects were randomly divided into 80% discovery and 20% replication datasets with demographic stratification. We analyzed neural activity and gene expressions from the discovery datasets using pICA and extracted paired independent components (pICs). The loading coefficients of the pICs were utilized to predict behavioral and cognitive data for stop signal task (SST) in replication datasets.

RESULTS

We identified three pICs estimated from gene expression in the cortex, cerebellum, and nucleus accumbens. Significant neural activity was mainly localized to the orbital/inferior/middle frontal gyri, rectal gyrus, precuneus, inferior temporal gyrus, inferior parietal lobule, and cerebellum. Significant gene components were associated with immunoglobulin, taste receptor, and immunity-related terms and were overlapped with ADHD-related genes. The extracted fMRI-/Gene-ICs were significantly correlated with mean reaction time, stop signal reaction time of SST, and behavioral inhibition with a large boost in sensitivity when both the paired fMRI-/Gene-ICs and their interaction were used in a multimodal regression analysis.

CONCLUSION

We reported biologically plausible pairs of neural activity and gene sets using pICA, which were significantly associated with ADHD impulsivity-related behavioral and cognitive data.

EXPLORING THE POTENTIAL OF BEND7 AS AN IMMUNOMODULATORY BIOMARKER IN SEPSIS THROUGH INTEGRATIVE GENOMIC AND TRANSCRIPTOMIC ANALYSIS

ABSTRACT

Background: Sepsis is a life-threatening condition driven by a dysregulated immune response to infection. Identifying the genetic factors underlying sepsis pathogenesis remains a major challenge in developing effective treatments. Methods: The Summary data-based Mendelian Randomization method was used to integrate Genome-Wide Association Studies and expression quantitative trait loci data to identify sepsis-related genes. These genes were intersected with prognostic gene sets from Gene Expression Omnibus transcriptomic datasets and validated using an independent dataset. Comprehensive single-cell RNA sequencing analysis, including cell clustering, differential expression analysis, cell-cell communication mapping, and pseudotime trajectory analysis, was performed to explore the roles of the identified genes within the sepsis microenvironment. Results: Intersection of Summary data-based Mendelian Randomization and Gene Expression Omnibus gene sets, followed by validation, identified two risk genes and five protective genes as significantly differentially expressed. The risk gene BEND7, predominantly expressed in platelets, was further analyzed using single-cell RNA sequencing, revealing strong interactions with immune cells, particularly monocytes and neutrophils, via the intercellular adhesion molecule signaling pathway. Functional enrichment analysis suggested that BEND7-positive platelets play a role in immune modulation and platelet activation. Conclusion: BEND7 was identified as a platelet-specific gene involved in immune regulation during sepsis. Targeting BEND7-positive platelets may present new therapeutic opportunities in sepsis management.

A genetic variant associated with aquaporin 3 expression is correlated to in-hospital death in COVID-19 patients with extracellular hyperosmolality

Hyperosmolality is increasingly recognized as a factor contributing to severe COVID-19. Recently, a genetic variant near the aquaporin 3 (AQP3) water channel was associated with severe COVID-19 [rs60840586:G; odds ratio (OR): 1.07, P = 2.5 × 10-9]. The variant is known to increase gene expression of AQP3 in several organs, including the lung [normalized expression scores (NES) = 0.33, P = 4.1 × 10-20] in GTEx. In this study, we investigated 576 patients in the Biobanque Quebecoise de la COVID-19 (BQC-19) with both genetic and clinical data available. We estimated plasma osmolality using the formula: eOSM = 2 × [Na+] + 2 × [K+] + [Urea] + [Glucose]. Using a logistic regression of mortality against eOSM, genotype at rs60840586, sex, age, and the first 10 genetic principal components, we confirm that hyperosmolality is associated with COVID-19 mortality (OR = 2.06 [95% CI = 1.62-2.65], P = 9.13 × 10-9). Interestingly, we found that the risk of death linked to hyperosmolality is influenced by the AQP3 variant rs60840586:G genotype (OR = 1.95 [95% CI = 1.22-3.28], P = 0.0075). However, the rs60840586 genotype did not independently affect mortality in this cohort. These findings suggest that the body's ability to regulate and accommodate hyperosmolality may be disrupted by overexpression of AQP3, potentially worsening outcomes in COVID-19. Given the role of AQP3 in water transport and homeostasis, further defining the functionality of its variants may provide key insights into COVID-19 severity and guide clinical management strategies, particularly in critically ill patients with hyperosmolality.NEW & NOTEWORTHY A genetic variant near water channel AQP3, linked to severe COVID-19, amplifies the risk of death in patients with elevated plasma osmolality. In patients hospitalized with COVID-19, we show that although the variant does not affect systemic osmolality directly, it interacts with hyperosmolality to increase mortality risk. These findings highlight a potential mechanism where AQP3 overexpression disrupts cellular water handling during critical illness, offering new insight into the role of water balance in COVID-19 pathophysiology.

Genetic architecture of ideotype-related traits in middle American beans through single trait, multi-trait and epistatic genome-wide analyses

Common bean is one of the major legume crops for direct human consumption. The genetic improvement of common bean is a primary approach to increase crop adaptability to climate change conditions while maintaining productivity. A diverse panel of middle American beans was evaluated over three growing seasons (2021, 2022 and 2023) for agronomic traits considered in the crop ideotype such as flowering, maturity, pigment content, lodging, and yield. A study of the genetic regions controlling trait variation was carried out using single-trait and simultaneous (joint) multi-trait GWAS approaches. Additionally, genome-wide epistatic interactions were also analyzed. Several previously reported and novel regions were identified as significant for individual traits in the single and multi-year analyses with varying percentages of individual (7-52%) and collective (10 - 59%) phenotypic variance explained. In the single-trait and multi-year analyses, markers detected for lodging showed the highest average of percentage of variance explained (52%) followed by other traits with percentages between 11 and 19%. For yield, new loci were found with estimated effects between -96.19 to 90.96 kg/Ha in the multi-year data. In the multi-trait analyses, marginal loci on Pv02 and another on Pv04 were identified to have interaction effects on flowering and yield. A significant locus on Pv04 showed a common effect between lodging and maturity, characterized by several SNPs. Significant epistatic interactions were found along different chromosomes for all the evaluated traits, with some loci having interactions with multiple regions. In flowering, an interaction between loci on Pv01 and Pv04 explained up to 10.5% of the phenotypic variation, followed by interactions between Pv06 and Pv10 for chlorophyll b and between Pv03 and Pv08 for yield explaining around 6% of the trait variation. Multiple transcription factors were identified as candidate genes, particularly in the pairs of combinations of epistatic effects. Based on the homology analyses of the candidate genes, several showed potential roles in the genetic control of the agronomic traits, particularly for flowering, maturity, and yield. Our results demonstrate the applicability of various approaches for common bean and show a comprehensive and expanded panorama of the genetic basis of agronomic traits. The results provide new resources for crop improvement that can be leveraged in multiple approaches such as selection, modeling and predicting crop performance and genetic gain.

Mainstreaming Diagnostic Genetic Testing and Precision Medicine for Autism Spectrum Disorder: The Role of Child and Adolescent Psychiatrists

Autism Spectrum Disorder (ASD) is a neurodevelopmental psychiatric condition that shares significant clinical and genetic overlap with intellectualdisability (ID) and other neurodevelopmental disorders. Genetic testingin ASD lags far behind that for ID, even though Professional Societiesrecommend genetic testing for all ASD individuals and insurance reimbursement is relatively good. The core competencies for child and adolescent psychiatrists include determining the etiology and diagnosisfor all childhood psychopathology, including ID and ASD. Child psychiatrists should recommend and order genetic testing by exomeor genome sequencing on all children with ASD.

CAV1 unveils a novel therapeutic target for nephrolithiasis by modulating CaSR and ER stress

Nephrolithiasis is a complex disease resulted from abnormal crystal deposition in renal tissues. The crystal-cell interaction represents a critical step in kidney stone formation, involving numerous genes and proteins. We previously identified endoplasmic reticulum (ER) stress as a key biological process in the crystal-cell interactions, the precise mechanism of which has remained unclear. In the present study, we found that calcium oxalate monohydrate (COM) crystals induced an overload of intracellular Ca2+ and an upregulation of calcium-sensing receptor (CaSR) expression in the renal tubular epithelial cells HK-2, both of which were reversed by the CaSR inhibitor NPS2390 that also mitigated the COM-induced ER stress. The protein-protein interaction (PPI) network analysis of the genome-wide association studies (GWAS) data and the microarray data from kidney stone patients revealed that caveolin-1 (CAV1), epidermal growth factor receptor (EGFR), and the focal adhesion pathway formed a crucial intersection within the interactional networks. COM exposure induced HK-2 apoptosis, accompanied by a decrease in CAV1 protein levels and damage to EGFR-AKT signaling pathway, which was reversed by CAV1 overexpression. COM did not significantly affect CAV1 mRNA levels. Treatment with the proteasome inhibitor MG-132 prevented the downregulation of CAV1. CAV1 overexpression also inhibited ER stress and the upregulation of CaSR induced by COM. Similar results were observed in in vivo experiments. In conclusion, the present study suggests that CAV1 may be a promising target for nephrolithiasis therapy by modulating CaSR and ER stress.

Genomics of Attention Deficit Hyperactivity Disorder: What the Clinician Needs to Know

This report provides an update on current knowledge and applications of genomic research in attention deficit hyperactivity disorder (ADHD). The history, principles, and underlying assumptions for genetic studies on psychiatric disorders are reviewed. Recent DNA sequencing and genome-wide association studies have revealed common and rare genetic variants associated with ADHD. Communication of genetic knowledge in meetings with patients and their relatives and common misconceptions are addressed. The importance of recognizing genetic syndromes masquerading as ADHD or other common psychiatric disorders is emphasized and how genetic information can be used to improve diagnosis and therapy are discussed.

Genetic architecture of morphological adaptation and plasticity in insects: gaps, biases, and future directions

Insects exhibit a vast array of morphological specializations. Recent eco-evo-devo studies have provided a fresh perspective into how insect morphology can respond to the environment, both plastically and adaptively. Here, we performed a systematic literature analysis to identify biases and gaps in research on the molecular mechanisms underlying insect morphological adaptation and plasticity. We found that plasticity studies are increasingly present in the literature, while adaptation studies lag behind. Additionally, we observed a disproportionate focus on a few insect orders and specific traits like wings and body size. We highlight the need to explore the broader insect diversity, including understudied groups and unexplored traits like reproductive organs, as well as utilize advanced methods to capture subtle morphological variation. Studying a wider range of species with diverse morphologies and ecological features, as well as implementing modern genome-wide tools, can reveal the full spectrum of mechanisms underlying morphological adaptation and plasticity in insects.

Cross-tissue transcriptome-wide association study reveals novel psoriasis susceptibility genes

Background

Psoriasis is a chronic, immune-mediated inflammatory skin disorder with a strong genetic component. Although numerous GWAS have identified risk loci, many associated variants lie in non-coding regions, complicating functional interpretation.

Objective

This study aimed to identify novel psoriasis susceptibility genes by integrating large-scale GWAS and eQTL data using a cross-tissue TWAS approach.

Methods

We integrated psoriasis GWAS summary statistics from the FinnGen database with GTEx V8 eQTL data. A cross-tissue TWAS was performed using UTMOST, followed by validation with single-tissue TWAS via FUSION. Conditional and joint analyses were conducted to delineate independent genetic signals, and gene-based analysis was performed using MAGMA. Causal relationships were evaluated using Mendelian randomization (MR) and Bayesian colocalization analyses. Key SNPs were functionally characterized using CADD, GERP++, and RegulomeDB for pathogenicity prediction and regulatory potential assessment. Finally, functional network analysis was conducted using GeneMANIA.

Results

The cross-tissue TWAS identified 259 genes significantly associated with psoriasis (p < 0.05), with 12 remaining significant after FDR correction. Single-tissue TWAS validation revealed 655 significant genes, with an overlap of three protein-coding candidates: POLI, NFKB1, and ZFYVE28. Cross-validation with MAGMA refined the candidate set to NFKB1 and ZFYVE28. MR and colocalization analyses supported a causal relationship for NFKB1 in Skeletal Muscle, Transverse Colon, and Cultured Fibroblasts, and for ZFYVE28 in Subcutaneous Adipose Tissue and Esophageal Mucosa tissues. Functional annotation identified key SNPs including rs4235405, rs3774960, and rs1598856 for NFKB1, and rs1203786 for ZFYVE28, with varying degrees of pathogenicity and regulatory potential. GeneMANIA network analysis further implicated NFKB1 in NF-κB signaling and ZFYVE28 in vesicle-mediated transport.

Conclusion

Our integrative multi-omics approach identifies NFKB1 and ZFYVE28 as novel psoriasis susceptibility genes, providing potential biomarkers and therapeutic targets for this complex disease.

Differential gene expression study in whole blood identifies candidate genes for psychosis in African American individuals

Genome-wide association has identified regions of the genome that mediate risk for psychosis. It is possible that variants in these regions confer risk by altering gene expression. This work has predominantly been conducted in individuals of European descent and has focused narrowly on schizophrenia rather than psychosis as a syndrome. In the present study we investigated alterations in gene expression in African American individuals with a range of psychotic diagnoses to increase understanding of the etiology in an underserved population. We performed RNA-seq in whole bloody to survey the transcriptome in 126 patients with a psychosis-spectrum disorder and 217 healthy controls and applied differential gene expression analyses across the genome while controlling for age, sex, population stratification and batch. We found 18 differentially expressed genes (DEGs), some of the locations of the corresponding genes overlap with previously implicated regions for psychosis, but many of which were novel associations. Enrichment analysis of nominally significant genes (p < 0.05) revealed overrepresentation of biological processes relating to platelet, immune and cellular function, and sensory perception. Weighted gene co-expression network analysis, applied to identify modules of co-expressed genes associated with psychosis, revealed 10 modules, one of which was significantly associated with psychosis. This module was significantly enriched for DEGs, and for platelet function. These results support the potential role of immune function in the etiology of psychosis, identify novel candidate gene expression phenotypes that correspond to both established and new genomic regions, in individuals of African American ancestry.

Congenital Heart Diseases: Recent Insights into Epigenetic Mechanisms

Congenital Heart Diseases (CHDs) are a heterogeneous group of structural abnormalities affecting the heart and major arteries, which are present in at least 1% of births worldwide. Studies have linked CHD to both genetic and environmental factors. In this regard, it has been demonstrated that changes in the epigenetic pattern impact the expression of key genes involved in proper cardiac development. Therefore, it is suggested that aberrant epigenetic mechanisms may contribute to the development of these pathologies. Here, we review and summarize the main epigenetic mechanisms involved in CHD. Moreover, cardiac development and the importance of the environment and CHD are also addressed.

Genome-wide association studies in a large Korean cohort identify quantitative trait loci for 36 traits and illuminate their genetic architectures

Genome-wide association studies (GWAS) have predominantly focused on European ancestry populations, limiting biological discoveries across diverse populations. Here we report GWAS findings from 153,950 individuals across 36 quantitative traits in the Korean Cancer Prevention Study-II (KCPS2) Biobank. We discovered 301 previously unreported genetic loci in KCPS2, including an association between thyroid-stimulating hormone and CD36. Meta-analysis with the Korean Genome and Epidemiology Study, Biobank Japan, Taiwan Biobank, and UK Biobank identified 4588 loci that were not significant in any contributing GWAS. We describe differences in genetic architectures across these East Asian and European samples. We also highlight East Asian specific associations, including a known pleiotropic missense variant in ALDH2, which fine-mapping identified as a likely causal variant for multiple traits. Our findings provide insights into the genetic architecture of complex traits in East Asian populations and highlight how broadening the population diversity of GWAS samples can aid discovery.

The Genetic Background of Ankylosing Spondylitis Reveals a Distinct Overlap with Autoimmune Diseases: A Systematic Review

Background: Ankylosing Spondylitis (AS) is a rare autoinflammatory disorder affecting 0.1-1.4% of the population, with increasing recognition over the past 20 years. Although the specific causes of AS remain unclear, the presence of the HLA-B27 gene is associated with increased risk, though only 1-5% of carriers develop the disease. Despite extensive research, no definitive lab tests exist, and many patients are diagnosed years after symptom onset. Methods: In the present study, in order to investigate the disease's genetic background in correlation with autoimmune diseases, a metanalysis has been performed following PRISMA guidelines using Scopus and PubMed publications towards extracting single-nucleotide polymorphisms (SNPs) of high importance for the disease. Moreover, the polymorphisms have been annotated and analyzed using information from several databases, including PubMed, LitVar2, ClinVar, and Gene Ontology. Results: From 1940 screened titles and abstracts, 57,909 studies were selected, with 539 meeting the inclusion criteria. The genetic background of AS is described through 794 genetic variants, of which 76 SNPs are directly associated with AS (Classes A and B), predominantly located in intronic regions. ERAP1 and IL23R emerged as key genes implicated in AS, while chromosomes 1, 2, and 5 accumulated the most associated SNPs. Functional enrichment revealed strong associations with immune regulation and interleukin signaling pathways, particularly IL6 and IL10 signaling. IL-6 promotes inflammation in AS, while IL-10 tries to suppress it, acting as an anti-inflammatory cytokine. Of the 78 AS-related SNPs, 16 were unique to AS, while 66 were common to autoimmune diseases, especially rheumatoid arthritis (RA) and psoriasis (PsO), suggesting genetic overlap between these diseases. Conclusions: This study creates a comprehensive genetic map of AS-associated SNPs, highlighting key pathways and genetic overlap with autoimmune diseases. These findings contribute to understanding disease mechanisms and could guide therapeutic interventions, advancing precision medicine in AS management.

Mapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning

Genome-wide association studies (GWAS) have identified thousands of disease-associated loci, yet their interpretation remains limited by the heterogeneity of underlying biological processes. We propose Joint Pleiotropic and Epigenomic Partitioning (J-PEP), a clustering framework that integrates pleiotropic SNP effects on auxiliary traits and tissue-specific epigenomic data to partition disease-associated loci into biologically distinct clusters. To benchmark J-PEP against existing methods, we introduce a metric-Pleiotropic and Epigenomic Prediction Accuracy (PEPA)-that evaluates how well the clusters predict SNP-to-trait and SNP-to-tissue associations using off-chromosome data, avoiding overfitting. Applying J-PEP to GWAS summary statistics for 165 diseases/traits (average N =290K), we attained 16-30% higher PEPA than pleiotropic or epigenomic partitioning approaches with larger improvements for well-powered traits, consistent with simulations; these gains arise from J-PEP's tendency to upweight correlated structure-signals present in both auxiliary trait and tissue data-thereby emphasizing shared components. For type 2 diabetes (T2D), J-PEP identified clusters refining canonical pathological processes while revealing underexplored immune and developmental signals. For hypertension (HTN), J-PEP identified stromal and adrenal-endocrine processes that were not identified in prior analyses. For neutrophil count, J-PEP identified hematopoietic, hepatic-inflammatory, and neuroimmune processes, expanding biological interpretation beyond classical immune regulation. Notably, integrating single-cell chromatin accessibility data refined bulk-based clusters, enhancing cell-type resolution and specificity. For T2D, single-cell data refined a bulk endocrine cluster to pancreatic islet β-cells, consistent with established β-cell dysfunction in insulin deficiency; for HTN, single-cell data refined a bulk endocrine cluster to adrenal cortex cells, consistent with a GO enrichment for neutrophil-mediated inflammation that implicates feedback between aldosterone production in the adrenal gland and local immune signaling. In conclusion, J-PEP provides a principled framework for partitioning GWAS loci into interpretable, tissue-informed clusters that provide biological insights on complex disease.

Genome-scale metabolic modelling identifies reactions mediated by SNP-SNP interactions associated with yeast sporulation

Genome-scale metabolic models (GEMs) are powerful tools used to understand the functional effects of genetic variants. However, the impact of single nucleotide polymorphisms (SNPs) in transcription factors and their interactions on metabolic fluxes remains largely unexplored. Using gene expression data from a yeast allele replacement panel grown during sporulation, we constructed co-expression networks and SNP-specific GEMs. Analysis of co-expression networks revealed that during sporulation, SNP-SNP interactions impact the connectivity of metabolic regulators involved in glycolysis, steroid and histidine biosynthesis, and amino acid metabolism. Further, genome-scale differential flux analysis identified reactions within six major metabolic pathways associated with sporulation efficiency variation. Notably, autophagy was predicted to act as a pentose pathway-dependent compensatory mechanism supplying critical precursors like nucleotides and amino acids, enhancing sporulation. Our study highlights how transcription factor polymorphisms interact to shape metabolic pathways in yeast, offering insights into genetic variants associated with metabolic traits in genome-wide association studies.

Genome-wide association study reveals the genetic mechanism of wing bone strength in Cornish White

Broiler products mainly refer to breast and leg meat, and also include wings. The price of wings is relatively high, about twice that of breast and leg meat. However, in this study, the incidence of wing bone deformity reaches more than 10 %. Such a high fracture rate reduces the quality and benefits of wing products. It is necessary to study the wing bones, find the relevant gene loci and enhance the strength of wing bones. A total of 436 Cornish White chickens were used as experimental birds. The weight, length, width, strength and toughness of ulna, radius, humerus and tibia, as well as growth and breast muscle traits, were measured, and correlation analysis was performed. A genome-wide association study (GWAS) was performed and a linear mixed model was used to analyze 39,000 SNPs from a self-developed broiler microarray. The genetic parameters of the traits were calculated. The results showed that the coefficient of variation of the toughness of the wing bone was greater than 20 %, and that of the ulna was 34.45 %. Wing bone traits were negatively correlated with feed conversion ratio and positively correlated with tibia traits and growth traits, reaching 0.8, and weakly correlated with breast muscle traits (P > 0.05). The heritability of wing bone traits was low, and the heritability of humerus strength was the highest (0.27). There were 27 significant SNP in the GWAS results, with the lowest P value being 2.263 × 10-7. Eight bone-related genes, represented by nucleotide binding oligomerization domain containing 1, were annotated and enriched in the guanine nucleotide exchange factor activity pathway, which may affect bone strength by regulating bone homeostasis through FERM, ARH/RhoGEF and pleckstrin domain proteins. The research lays the foundation for further improving broiler product quality and animal welfare.

Endophenotype-Informed Association Analyses for Liver Fat Accumulation and Metabolic Dysfunction in the Fels Longitudinal Study

The identification of causal genomic regions for liver fat accumulation in the context of metabolic dysfunction remains a challenging goal. This study aimed to identify potential endophenotypes for liver fat content and employ them in bivariate linkage searches for pleiotropic genetic regions where targeted association analysis is more likely to reveal significant variants. Multiple metabolic risk and adiposity distribution traits were assessed using the endophenotype ranking value. The top-ranked endophenotypes were then used in a bivariate linkage analysis, paired with liver fat content. Quantitative trait loci (QTLs) identified as significant or suggestive were targeted for measured genotype association analyses. The highest-ranked endophenotypes for liver fat accumulation were insulin resistance (IR), visceral adipose tissue (VAT), and high-density lipoprotein cholesterol (HDL-C). The univariate linkage analysis for liver fat content identified one significant QTL at chromosome 17p13.2 (Logarithm of odds score (LOD) = 2.90, p = 1.29 × 10-4). The bivariate linkage analysis pairing liver fat with IR and VAT improved the localization of two suggestive QTLs at 13q21.31 (LOD = 2.11, p = 9.03 × 10-4), and 6q21 (LOD = 2.35, p = 5.07 × 10-4), respectively. Targeted association analyses within the -1-LOD score regions of these QTLs revealed 17 marginally significant single nucleotide polymorphisms (SNPs) associated with liver fat content or its combination with the selected endophenotypes. The endophenotype-informed linkage analysis successfully identified regions suitable for the targeted association analysis of liver fat content, either alone or in combination with IR or VAT, leading to the discovery of marginally significant variants with potential for future functional studies.

Genome-wide association analysis in peanut accessions uncovers the genetic basis regulating oil and fatty acid variation

BACKGROUND

The cultivated peanut, Arachis hypogaea L., is a critical oil and food crop worldwide. Improving seed oil quality in peanut has long been an aim of breeders. However, our knowledge of the genetic basis of selecting for seed nutritional traits is limited. Based on AhFAD2A and AhFAD2B, scientists have now developed higher oleic acid (80-84%) in peanut. Decoding the genetic makeup behind natural variation in kernel oil and fatty acid concentrations is crucial for molecular breeding-based nutrient quantity and quality manipulation.

RESULTS

Herein, we recognized 87 quantitative trait loci (QTLs) in 45 genomic regions for the concentrations of oil, oleic acid, and linoleic acid, as well as the oleic acid to linoleic acid (O/L) ratio via a genome-wide association study (GWAS) involving 499 peanut accessions. Eight QTLs explained more than 15% of the phenotypic variation in peanut accessions. Among the 45 potential genes significantly related to the four traits, only three genes displayed annotation to the fatty acid pathway. Furthermore, on the basis of pleiotropism or linkage data belonging to the identified singular QTLs, we generated a trait-locus axis to better elucidate the genetic background behind the observed oil and fatty acid concentration association. Expression analysis indicated that arahy.AV6GAN and arahy.NNA8KD have higher expressions in the seeds.

CONCLUSION

This natural population consisting of 499 peanut accessions combined with high-density SNPs will provide a better choice for identifying peanut QTLs/genes in the future. Together, our results provide strong evidence for the genetic mechanism behind oil biosynthesis in peanut, facilitating future advances in multiple fatty acid component generation via pyramiding of desirable QTLs.

DNA-damage-associated protein co-expression network in cardiomyocytes informs on tolerance to genetic variation and disease

Cardiovascular disease (CVD) is associated with genetic variants and environmental factors. A consequence of multiple risk factors is DNA damage. To examine how DNA damage influences the cardiomyocyte proteome and its relationship to CVD risk, we treated human induced pluripotent stem cell (iPSC)-derived cardiomyocytes with the DNA-damaging agent doxorubicin (DOX). A network constructed from 4,178 proteins reveals 12 co-expressed modules with 403 hub proteins. Five modules correlate with DOX and associate with RNA processing, chromatin regulation, and metabolism. DOX-correlated hub proteins are depleted for proteins that vary in expression across individuals due to genetic variation but are enriched for proteins encoded by loss-of-function intolerant genes. While not enriched for known CVD risk proteins, DOX-correlated hub proteins are enriched for the physical protein interactors of CVD risk proteins. These data demonstrate that protein connectivity in DNA-damage-associated modules influences the tolerance to genetic variation and supports the use of dynamic networks to explore complex traits.

Novel Metabolites Genetically Linked to Salt Sensitivity of Blood Pressure: Evidence from mGWAS in Chinese Population

This study aims to identify genetically influenced metabolites (GIMs) associated with SSBP and elucidate their regulatory pathways through metabolome genome-wide association studies (mGWASs). Untargeted metabolomics and genome-wide genotyping were performed on 54 participants from the Systematic Epidemiological Study of Salt Sensitivity (EpiSS). The mGWAS was conducted on 970 plasma metabolites, and their potential biological mechanisms were explored. The multivariable logistic regression model and mendelian randomization (MR) were employed to investigate the association and causal relationship between GIMs and SSBP. Metabolomic analysis was performed on 100 subjects in the replication analysis to validate the GIMs identified in the discovery set and their causal association with SSBP. The mGWAS revealed associations between 1485 loci and 18 metabolites. After performing linkage disequilibrium analysis, 368 independent mQTLs were identified and annotated to 141 genes. These functional genes were primarily implicated in the signal transduction of sinoatrial node and atrial cardiac muscle cells. Five key genes were identified using CytoHubba, including CAMK2A, TIAM1, RYR2, RBFOX1, and NRXN3. One-sample MR analysis revealed 14 GIMs with causal associations to SSBP, with LysoPC (0:0/22:5n-3) positively associated with SSBP (p < 0.05). The causal relationship between Phe-lle and SSBP was validated in the replication analysis. This study elucidates the genetic regulatory mechanisms underlying metabolites and identifies GIMs that are causally associated with SSBP. These findings provide insights into identifying metabolic biomarkers of SSBP and characterizing its genetic and metabolic regulation mechanisms.

Identification of potential therapeutic targeting in ovarian aging from genetic screening with clinical validation

PURPOSE

To screen drug targets of ovarian aging from a genetic perspective.

METHODS

Systematic analyses were conducted with cis-expression quantitative trait loci data of druggable genes extracted as instrument variables. Summary statistics were from large genome-wide association studies for age at menopause. The following colocalization analysis was utilized to examine whether identified genes and ovarian aging shared causal variants. Furthermore, clinical validation was conducted by comparing expression of identified genes in granulosa cells from women with normal or diminished ovarian reserve (DOR) who went through in vitro fertilization (IVF) and by evaluating correlation of targeted gene expression with ovarian function and IVF outcomes. Moreover, single-nuclear RNA (snRNA) seq and drug database were analyzed to find target cells within the ovary and potential drugs targeting identified genes.

RESULTS

Systematic analyses identified five therapeutic targets of ovarian aging, including four protective factors (BRCA1, KLHL18, PNP, SRPK1) and one risk factor (PDIA3). The change in expression level of four protective factors has been verified in clinical validation. Particularly, both BRCA1 and SRPK1 have been downregulated among advanced-aged women with DOR and were positively correlated with anti-Müllerian hormone and antral follicle count. Specific target cells and potential small molecule targeted drugs of these genes were identified through snRNA analysis and searching in the drug database.

CONCLUSIONS

By systematic genetic analyses combined with clinical validation, we identified five potential druggable genes for ovarian aging, providing theoretical basis and promising direction of therapeutic genetic targets for ovarian aging in the future.

AI-Driven Advances in Parkinson's Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes

Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor dysfunctions that severely compromise patients' quality of life. While pharmacological treatments provide symptomatic relief in the early stages, advanced PD often requires neurosurgical interventions, such as deep brain stimulation (DBS) and focused ultrasound (FUS), for effective symptom management. A significant challenge in optimizing these therapeutic strategies is the early identification and recruitment of suitable candidates for clinical trials. This review explores the role of artificial intelligence (AI) in advancing neurosurgical and neuroscience interventions for PD, highlighting the ways in which AI-driven platforms are transforming clinical trial design and patient selection. Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. These innovations improve trial efficiency, broaden treatment options, and enhance patient outcomes. However, integrating AI into clinical trial frameworks presents challenges such as data standardization, regulatory hurdles, and the need for extensive validation. Addressing these obstacles will require collaboration among neurosurgeons, neuroscientists, AI specialists, and regulatory bodies to establish ethical and effective guidelines for AI-driven technologies in PD neurosurgical research. This paper emphasizes the transformative potential of AI and technological innovation in shaping the future of PD neurosurgery, ultimately enhancing therapeutic efficacy and patient care.

Reproducibility of genetic risk factors identified for long COVID using combinatorial analysis across US and UK patient cohorts with diverse ancestries

BACKGROUND

Long COVID is a major public health burden causing a diverse array of debilitating symptoms in tens of millions of patients globally. In spite of this overwhelming disease prevalence, staggering cost, severe impact on patients' lives and intense global research efforts, study of the disease has proved challenging due to its complexity. Genome-wide association studies (GWAS) have identified only four loci potentially associated with the disease, although these results did not statistically replicate between studies. A previous combinatorial analysis study identified a total of 73 genes that were highly associated with two long COVID cohorts in the predominantly (> 91%) white European ancestry Sano GOLD population, and we sought to reproduce these findings in the independent and ancestrally more diverse All of Us (AoU) population.

METHODS

We assessed the reproducibility of the 5343 long COVID disease signatures from the original study in the AoU population. Because the very small population sizes provide very limited power to replicate findings, we initially tested whether we observed a statistically significant enrichment of the Sano GOLD disease signatures that are also positively correlated with long COVID in the AoU cohort after controlling for population substructure.

RESULTS

For the Sano GOLD disease signatures that have a case frequency greater than 5% in AoU, we consistently observed a significant enrichment (77-83%, p < 0.01) of signatures that are also positively associated with long COVID in the AoU cohort. These encompassed 92% of the genes identified in the original study. At least five of the disease signatures found in Sano GOLD were also shown to be individually significantly associated with increased long COVID prevalence in the AoU population. Rates of signature reproducibility are strongest among self-identified white patients, but we also observe significant enrichment of reproducing disease associations in self-identified black/African-American and Hispanic/Latino cohorts. Signatures associated with 11 out of the 13 drug repurposing candidates identified in the original Sano GOLD study were reproduced in this study.

CONCLUSION

These results demonstrate the reproducibility of long COVID disease signal found by combinatorial analysis, broadly validating the results of the original analysis. They provide compelling evidence for a much broader array of genetic associations with long COVID than previously identified through traditional GWAS studies. This strongly supports the hypothesis that genetic factors play a critical role in determining an individual's susceptibility to long COVID following recovery from acute SARS-CoV-2 infection. It also lends weight to the drug repurposing candidates identified in the original analysis. Together these results may help to stimulate much needed new precision medicine approaches to more effectively diagnose and treat the disease. This is also the first reproduction of long COVID genetic associations across multiple populations with substantially different ancestry distributions. Given the high reproducibility rate across diverse populations, these findings may have broader clinical application and promote better health equity. We hope that this will provide confidence to explore some of these mechanisms and drug targets and help advance research into novel ways to diagnose the disease and accelerate the discovery and selection of better therapeutic options, both in the form of newly discovered drugs and/or the immediate prioritization of coordinated investigations into the efficacy of repurposed drug candidates.

Genetic Patterns Related with the Development and Progression of Sarcopenia and Sarcopenic Obesity: A Systematic Review

Background and Objectives: Despite their high prevalence, sarcopenia and sarcopenic obesity remain underdiagnosed worldwide, significantly impacting the health and quality of life of aging individuals. Due to their multifactorial nature, the current management strategies do not address their underlying pathogenesis. This systematic review aims to identify single-nucleotide polymorphisms (SNPs) associated with sarcopenia and/or sarcopenic obesity in humans. Materials and Methods. This systematic literature review followed the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)" guidelines and the protocol registered in PROSPERO. Extensive research was performed in six databases (PubMed, Web of Science, Cochrane Library, Scopus, ScienceDirect, and SpringerLink) using keywords such as "sarcopenia", "sarcopenic obesity", "single nucleotide polymorphisms", "SNPs", and "genetic variants". The Q-Genie and ROBINS-E tools were utilized to assess the quality of the included studies. Results: The final analysis included 12 studies, which were classified as good-quality according to the Q-Genie assessment and indicated a low to moderate risk of bias according to the ROBINS-E evaluation, collectively identifying 43 SNPs significantly associated with sarcopenia or sarcopenic obesity. Specifically, 24 SNPs were linked to sarcopenia, while 19 were associated with sarcopenic obesity. Conclusions: Understanding the implications of SNPs provides valuable insights into individual susceptibility and the variability observed across populations, potentially leading to more targeted and effective diagnostic and treatment strategies. Advancing clinical practice requires ongoing research into the genetic aspects of sarcopenia and sarcopenic obesity.

A Systematic Review of the Application of Graph Neural Networks to Extract Candidate Genes and Biological Associations

The development of high throughput technologies has resulted in the collection of large quantities of genomic and transcriptomic data. However, identifying disease-associated genes or networks from these data has remained an ongoing challenge. In recent years, graph neural networks (GNNs) have emerged as a promising analytical tool, but it is not well understood which characteristics of these models result in improved performance. We conducted a systematic search and review of publications that used GNNs to identify disease-associated biological interactions. Information was extracted about model characteristics and performance with the goal of examining the relationship between these factors and performance. Data leakage was found in 31% of these models. For node level tasks, univariate positive associations were identified between model accuracy and use of hyper parameter optimization, data leakage via hyperparameter optimization, test set size, and total dataset size. Among graph level tasks, an increase in AUC was identified in association with testing method and a decrease with optimization reporting. Data leakage may pose an issue for GNN-based approaches; the adoption of best practice guidelines and consistent reporting of model design would be beneficial for future studies.

Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel

BACKGROUND

Accurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and precision medicine. However, current methods often struggle to capture the intricate and subtle genetic interactions contributing to disease risk. This challenge may be further exacerbated by the curse of dimensionality when considering large-scale pairwise genetic combinations with limited samples. Overcoming these limitations could transform biomedicine by providing deeper insights into disease mechanisms, moving beyond black-box models and single-locus analyses, and enabling a more comprehensive understanding of cross-disease patterns.

RESULTS

We developed Ge-SAND (Genomic Embedding Self-Attention Neurodynamic Decoder), an explainable deep learning-driven framework designed to uncover complex genetic interactions at scales exceeding 106 in parallel for accurate disease risk prediction. Ge-SAND leverages genotype and genomic positional information to identify both intra- and interchromosomal interactions associated with disease phenotypes, providing comprehensive insights into pathogenic mechanisms crucial for disease risk prediction. Applied to simulated datasets and UK Biobank cohorts for Crohn's disease, schizophrenia, and Alzheimer's disease, Ge-SAND achieved up to a 20% improvement in AUC-ROC compared to mainstream methods. Beyond its predictive accuracy, through self-attention-based interaction networks, Ge-SAND provided insights into large-scale genotype relationships and revealed genetic mechanisms underlying these complex diseases. For instance, Ge-SAND identified potential genetic interaction pairs, including novel relationships such as ISOC1 and HOMER2, potentially implicating the brain-gut axis in Crohn's and Alzheimer's diseases.

CONCLUSION

Ge-SAND is a novel deep-learning approach designed to address the challenges of capturing large-scale genetic interactions. By integrating disease risk prediction with interpretable insights into genetic mechanisms, Ge-SAND offers a valuable tool for advancing genomic research and precision medicine.

Optimisation of variance component estimation and genomic prediction in a commercial crossbred population of Duroc x (Landrace x Yorkshire) three-way pigs

Crossbreeding is often used in livestock breeding, and genomic selection (GS) is implemented with the breeding goal of selecting purebreds (PB) with high genetic merit for hybridisation to produce crossbreds (CB) with generally improved performance. Previous studies have demonstrated the practicality and efficiency of using CB progeny from a commercial population as a reference population for GS, where a reference population consisting of extreme phenotypic individuals showed a predictive advantage. However, this completely extreme sampling strategy would significantly overestimate the genetic variance of traits, resulting in a significant inflation of the genomic estimated breeding values (GEBV) of PB candidates. So, we explored and optimised the variance component (VC) estimation and genomic prediction using different sampling strategies in a commercial CB population based on data from a Duroc x (Landrace x Yorkshire) pigs three-way crossbreeding system. We first compared the performance of completely extreme sampling, completely random sampling, and four mixed sampling schemes combining extreme and random sampling for VC estimation and genomic prediction for traits with high, medium, and low heritability (h2 = 0.5, 0.3, and 0.1) at different sample sizes (500-6 500). The results showed that the VC estimated from the reference populations obtained using mixed sampling strategies was more accurate than completely extreme sampling, and the mixed reference populations can carry out more accurate predictions and achieve higher response to selection. Furthermore, we applied an optimisation strategy for the mixed reference populations by solving the mixed model equation based on the VC estimated from only random CB therein, which proved to be very positive for improving the GEBV inflation caused by extreme phenotypic CB, effectively reducing the prediction bias while ensuring the prediction accuracy and response to selection. The combination of accurate VC estimation from random CB and the advantage of extreme phenotypic CB in prediction accuracy allows the mixed reference populations to achieve a superior predictive performance in GS. The optimised strategies can maximise the information from commercial CB populations in livestock genomic breeding.

Computational and functional prioritization identifies genes that rescue behavior and reduce tau protein in fly and human cell models of Alzheimer disease

Genome-wide association studies (GWASs) in Alzheimer disease (AD) have uncovered over 70 loci significantly associated with AD risk, but identifying the true causal gene(s) at these loci requires systematic functional validation that is rarely performed due to limitations of time and cost. Here, we integrate transcriptome-wide association study (TWAS) with colocalization analysis, fine-mapping, and additional annotation of AD GWAS variants to identify 123 genes at known and suggestive AD risk loci. A comparison with human AD brain transcriptome data confirmed that many of these candidate genes are dysregulated in human AD and correlate with neuropathology. We then tested all available orthologs in two well-established Drosophila AD models that express either wild-type tau or secreted β-amyloid (β42). Experimental perturbation of the 60 available candidates pinpointed 46 that modulated neuronal dysfunction in one or both fly models. The effects of 18 of these genes were concordant with the TWAS prediction, such that the direction of misexpression predicted to increase AD risk in humans exacerbated behavioral impairments in the AD fly models. Reversing the aberrant down- or upregulation of 11 of these genes (MTCH2, ELL, TAP2, HDC, DMWD, MYCL, SLC4A9, ABCA7, CSTF1, PTK2B, and CD2AP) proved neuroprotective in vivo. We further studied MTCH2 and found that it regulates steady-state tau protein levels in the Drosophila brain and reduces tau accumulation in human neural progenitor cells. This systematic, integrative approach effectively prioritizes genes at GWAS loci and reveals promising AD-relevant candidates for further investigation as risk factors or targets for therapeutic intervention.

A regulatory variant rs9379874 in T1D risk region 6p22.2 affects BTN3A1 expression regulating T cell function

OBJECTIVE

Genome-wide association studies (GWAS) have identified that 6p22.2 region is associated with type 1 diabetes (T1D) risk in the Chinese Han population. This study aims to reveal associations between this risk region and T1D subgroups and related clinical features, and further identify causal variant(s) and target gene(s) in this region.

METHODS

2608 T1D and 4814 healthy controls were recruited from East, Central, and South China. Baseline data and genotyping for rs4320356 were collected. The most likely causal variant and gene were identified by bioinformatics analysis, dual-luciferase reporter assays, expression quantitative trait loci (eQTL), and functional annotation of the non-coding region within the 6p22.2 region.

RESULTS

The leading variant rs4320356 in the 6p22.2 region was associated with T1D risk in the Chinese and Europeans. However, this variant was not significantly associated with islet function or autoimmunity. In silico analysis suggested rs9379874 was the most potential causal variant for T1D risk among thymus, spleen, and T cells, overlapping with the enhancer-related histone mark in multiple T cell subsets. Dual luciferase reporter assay and eQTL showed that the T allele of rs9379874 increased BTN3A1 expression by binding to FOXA1. Public single-cell RNA sequencing analysis indicated that BTN3A1 was related to T-cell activation, ATP metabolism, and cytokine metabolism pathways, which might contribute to T1D development.

CONCLUSION

This study indicates that a functional variant rs9379874 regulates BTN3A1 expression, expanding the genomic landscape of T1D risk and offering a potential target for developing novel therapies.

Uncovering Genetic Variation in Systemic Lupus Erythematosus Risk Variants in Indigenous Peruvians

OBJECTIVE

Systemic lupus erythematosus (SLE) results in worse clinical outcomes among individuals of Amerindian descent. The genetic basis for this is uncertain, and there is a significant lack of genetic research focused on Amerindian ancestry populations. This study aims to compare the frequencies of SLE risk variants and polygenic risk scores between Indigenous Peruvians and global populations with diverse ancestral backgrounds.

METHODS

We studied 670 individuals from the Peruvian Genome Project, 2,068 individuals from the 1000 Genomes Project Phase 3 release, and 47 patients with SLE from Lima, Peru. Ancestry was inferred using admixture and RFMix. Data were imputed with the TOPMed Imputation server and annotated to hg38. We compared the frequencies of 199 SLE-associated risk variants among study participants. We also calculated SLE genetic risk scores and fixation index (FST) statistics.

RESULTS

All 199 SLE risk single-nucleotide polymorphisms had highly significant differences in frequencies across Peruvian and other continental populations (P values <0.001). Indigenous Peruvian patients have higher polygenic risk for SLE compared to European, African, South Asian, and East Asian patients. FST analysis of SLE risk variants revealed the largest FST between Peruvian patients and African patients (mean FST 0.12), and the smallest between Peruvian patients and East Asian patients (mean FST 0.09).

CONCLUSION

SLE-associated variants are common among Indigenous Peruvian patients, with varying frequencies across subpopulations. This underscores the need for ongoing genetic studies in Indigenous populations, potentially explaining SLE heterogeneity.

Shared genetic architecture of gastroesophageal reflux disease and age related phenotypes

Increasing age is a risk factor of gastroesophageal reflux disease. This study aims to uncover the shared genetic architecture of gastroesophageal reflux disease (GERD) and age-related phenotypes. Based on publicly available GWAS statistics, this genome-wide pleiotropic association research was performed with multiple genetic approaches sequentially to explore the pleiotropic associations from single-nucleotide polymorphism (SNP) and gene levels, to reveal the underlying shared genetic etiology between GERD and age-related phenotypes. This study featured shared genetic mechanisms between GERD and age-related phenotypes, including frailty index (FI), telomere length (TL), longevity, and parental lifespan (PL). Strong genetic association were observed. A set of pleiotropic loci and genes were identified by PLACO, FUMA, Bayesian colocalization and additional MAGMA analysis. Our research provided strong evidence of genetic correlation between GERD and several age-related phenotypes, especially frailty index (FI) and telomere length (TL), brought novel insight into the shared genetic architecture between GERD and aging.

Rational Redomestication for Future Agriculture

Modern agricultural practices rely on high-input, intensive cultivation of a few crop varieties with limited diversity, increasing the vulnerability of our agricultural systems to biotic and abiotic stresses and the effects of climate changes. This necessitates a paradigm shift toward a more sustainable agricultural model to ensure a stable and dependable food supply for the burgeoning global population. Leveraging knowledge from crop biology, genetics, and genomics, alongside state-of-the-art biotechnologies, rational redomestication has emerged as a targeted and knowledge-driven approach to crop innovation. This strategy aims to broaden the range of species available for agriculture, restore lost genetic diversity, and further improve existing domesticated crops. We summarize how diverse plants can be exploited in rational redomestication endeavors, including wild species, underutilized plants, and domesticated crops. Equipped with rational redomestication approaches, we propose different strategies to empower the fast and slow breeding systems distinguished by plant reproduction systems.

Nutraceuticals for Gut-Brain Axis Health: A Novel Approach to Combat Malnutrition and Future Personalised Nutraceutical Interventions

The gut-brain axis (GBA) is a bidirectional communication network between the gastrointestinal tract and the brain, modulated by gut microbiota and related biomarkers. Malnutrition disrupts GBA homeostasis, exacerbating GBA dysfunction through gut dysbiosis, impaired neuroactive metabolite production, and systemic inflammation. Nutraceuticals, including probiotics, prebiotics, synbiotics, postbiotics, and paraprobiotics, offer a promising approach to improving GBA homeostasis by modulating the gut microbiota composition and related neuroactive metabolites. This review aims to elucidate the interplay between gut microbiota-derived biomarkers and GBA dysfunction in malnutrition and evaluate the potential of nutraceuticals in combating malnutrition. Furthermore, it explores the future of personalised nutraceutical interventions tailored to individual genetic and microbiome profiles, providing a targeted approach to optimise health outcomes. The integration of nutraceuticals into GBA health management could transform malnutrition treatment and improve cognitive and metabolic health.

Genome-wide association study and transcriptomic analysis reveal the crucial role of sting1 in resistance to visceral white-nodules disease in Larimichthys polyactis

Introduction

Larimichthys polyactis is a promising marine fishery species, but visceral white-nodules disease (VWND) caused by Pseudomonas plecoglossicida causes significant losses. However, genetic resistance mechanisms to VWND remain elusive in this species.

Methods

This study combined genome-wide association study (GWAS) and transcriptome analysis to unravel resistance loci and transcriptional regulation in L. polyactis.

Results

As a result, GWAS on 946 infected fish genotyped by 100 K lipid chips identified 22 suggestive significantly associated single-nucleotide polymorphisms (SNPs), annotated 60 candidate genes, where DNA-sensing pathway were enriched. RNA-seq on liver tissues of resistant, sensitive, and control groups found immune-related pathways enriched in the comparisons of RL vs CL and RL vs SL, and autophagy-related pathways enriched in the comparisons of SL vs CL and RL vs SL. Then, the integration of GWAS and transcriptome analysis identified seven key genes associated with resistance to VWND. Among the genes, the expression levels of mRNA for genes related to the cyclic GMP-AMP synthase-stimulator of interferon genes (STING) signaling pathway, as well as the protein levels of STING1, were significantly upregulated in RL. Collectively, integrating KEGG pathway analysis, gene and protein expression analysis revealed that the importance of STING1 for VWND resistance.

Discussion

These findings deepen the available knowledge on molecular mechanisms of host genetic resistance to VWND and provide an important foundation for the selection and breeding of VWND-resistant L. polyactis.

Genetic Regulation of Alternative Polyadenylation Provides Novel Insights into Molecular Mechanisms Underlying Non-small Cell Lung Cancer

Emerging evidence emphasizes the critical role of alternative polyadenylation (APA) in posttranscriptional regulation of genes, and APA-associated genetic variants (apaQTLs) show particular relevance for multiple disease. However, genetic regulation of APA and its role in non-small cell lung cancer (NSCLC) risk have not been thoroughly studied. Here, by leveraging genotype and APA data from The Cancer Genome Atlas, the association between genetic variation and APA is determined in NSCLC samples. The identified apaQTLs are distinct from eQTLs and are preferentially enriched in functionally relevant characteristics, including poly(A) motifs, APA-associated RBP binding sites, functional elements, and known NSCLC risk loci. Moreover, genes associated with apaQTLs are broadly involved in cancer-related biological process. Of note, integration of apaQTL variants with traditional GWAS-derived PRS is proved as a potential screening tool for NSCLC. By integrating large-scale population and biological experiments, a functional apaQTL variant rs9606 in LYRM4 is identified. Mechanistically, rs9606 induces aberrant APA process of LYRM4 via allele-specific interacting with NUDT21, which lead to increased expression of oncogene LYRM4 and thus contribute to NSCLC risk. This study demonstrates the distinct contribution of APA-associated genetic variants in NSCLC risk, providing critical clues and potential targets for NSCLC etiology and clinical intervention.

Causal Variants in CHRNA1 and CHRNB1 Genes for Anti-acetylcholine Receptor Antibody Positive Myasthenia Gravis: Evidence from Bayesian Fine-Mapping and Genetic Association Study

Autoantibodies target the acetylcholine receptor (AChR) in 85% of myasthenia gravis (MG) patients. Genomic studies highlighted the association of genes encoding AChR subunits (CHRNA1 and CHRNB1) and MG in European populations. Additionally, Mendelian randomization revealed rs4151121 at the CHRNB1 locus as a potential causal variant. Here, we performed Bayesian fine-mapping of the CHRNA1 locus using GWAS summary statistics, a linkage disequilibrium matrix and functional annotations. The GWAS lead hit rs35274388 was identified as a causal variant overlapping with the promoter region (p < 0.01). Next, we performed a candidate gene study including 1038 participants from Serbia. Rs4151121 minor allele G was associated with late-onset MG (LOMG) (OR = 1.327, 95% CI = 1.084-1.625, p = 0.006, pperm = 0.007). Carriers of the rs4151121 GG and AG genotypes had an almost 1.5-fold increased risk of developing LOMG. A borderline association of the rs35274388 minor allele A with MG was observed (OR = 1.478, 95% CI = 1.009-2.166, p = 0.044, pperm = 0.060). Individuals with AA and GA genotypes also showed a nearly 1.5-fold higher risk of developing MG. In silico-identified causal variants at the CHRNA1 and CHRNB1 loci represent risk factors for MG in European populations, and to a greater extent for LOMG. Studies on non-European populations and functional research are needed to elucidate the role of AChR genes in the genetic architecture and development of MG.

Cross-Kingdom DNA Methylation Dynamics: Comparative Mechanisms of 5mC/6mA Regulation and Their Implications in Epigenetic Disorders

DNA methylation, a cornerstone of epigenetic regulation, governs critical biological processes including transcriptional modulation, genomic imprinting, and transposon suppression through chromatin architecture remodeling. Recent advances have revealed that aberrant methylation patterns-characterized by spatial-temporal dysregulation and stochastic molecular noise-serve as key drivers of diverse pathological conditions, from oncogenesis to neurodegenerative disorders. However, the field faces dual challenges: (1) current understanding remains fragmented due to the inherent spatiotemporal heterogeneity of methylation landscapes across tissues and developmental stages, and (2) mechanistic insights into non-canonical methylation pathways (particularly 6mA) in non-mammalian systems are conspicuously underdeveloped. This review systematically synthesizes the evolutionary-conserved versus species-specific features of 5-methylcytosine (5mC) and N6-methyladenine (6mA) regulatory networks across three biological kingdoms. Through comparative analysis of methylation/demethylation enzymatic cascades (DNMTs/TETs in mammals, CMTs/ROS1 in plants, and DIM-2/DNMTA in fungi), we propose a unified framework for targeting methylation-associated diseases through precision epigenome editing, while identifying critical knowledge gaps in fungal methylome engineering that demand urgent investigation.

JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation

Genetic risk prediction for non-European populations is hindered by limited Genome-Wide Association Study (GWAS) sample sizes and small tuning datasets. We propose JointPRS, a data-adaptive framework that leverages genetic correlations across multiple populations using GWAS summary statistics. It achieves accurate predictions without individual-level tuning data and remains effective in the presence of a small tuning set thanks to its data-adaptive approach. Through extensive simulations and real data applications to 22 quantitative and four binary traits in five continental populations evaluated using the UK Biobank (UKBB) and All of Us (AoU), JointPRS consistently outperforms six state-of-the-art methods across three data scenarios: no tuning data, same-cohort tuning and testing, and cross-cohort tuning and testing. Notably, in the Admixed American population, JointPRS improves lipid trait prediction in AoU by 6.46%-172.00% compared to the other existing methods.

The accuracy of polygenic score models for BMI and Type II diabetes in the Native Hawaiian population

Polygenic scores (PGS) are promising in stratifying individuals based on the genetic susceptibility to complex diseases or traits. However, the accuracy of PGS models, typically trained in European- or East Asian-ancestry populations, tend to perform poorly in other ethnic minority populations and their accuracies have not been evaluated for Native Hawaiians. In particular, for body mass index (BMI) and type-2 diabetes (T2D), Polynesian-ancestry individuals such as Native Hawaiians or Samoans exhibit varied distribution from other continental populations, but are understudied, particularly in the context of PGS. Using BMI and T2D as examples of metabolic traits of importance to Polynesian populations (along with height as a comparison of a similarly highly polygenic trait), here we examine the prediction accuracies of PGS models in a large Native Hawaiian sample from the Multiethnic Cohort with up to 5300 individuals. We find evidence of lowered prediction accuracies for the PGS models in some cases, particularly for height. We also find that using the Native Hawaiian samples as an optimization cohort during training does not consistently improve PGS performance. Moreover, even the best-performing PGS models among Native Hawaiians have lowered prediction accuracy among the subset of individuals most enriched with Polynesian ancestry. Our findings indicate that factors such as admixture histories, sample size, and diversity in GWAS can influence PGS performance for complex traits among Native Hawaiian samples. This study provides an initial survey of PGS performance among Native Hawaiians and exposes the current gaps and challenges associated with improving polygenic prediction models for underrepresented minority populations.

Divide and conquer approach for genome-wide association studies

Genome-wide association studies (GWAS) are computationally intensive, requiring significant time and resources with computational complexity scaling at least linearly with sample size. Here, we present an accurate and resource-efficient pipeline for GWAS that mitigates the impact of sample size on computational demands. Our approach involves (1) randomly partitioning the cohort into equally sized sub-cohorts, (2) conducting independent GWAS within each sub-cohort, and (3) integrating the results using a novel meta-analysis technique that accounts for population structure and other confounders between sub-cohorts. Importantly, we demonstrate through simulations and real-data examples in humans that our approach effectively manages analyzing related individuals, a critical factor in real datasets, while controlling for inflated effect sizes, a phenomenon known as winner's curse. We show that our method achieves the same discovery levels as standard approaches but with significantly reduced computational costs. Additionally, it is well-suited for incremental GWAS as new samples are added over time. Our implementation within a bioinformatics workflow management system enhances reproducibility and scalability.

SURFBAT: a surrogate family based association test building on large imputation reference panels

Genotype-phenotype association tests are typically adjusted for population stratification using principal components that are estimated genome-wide. This lacks resolution when analyzing populations with fine structure and/or individuals with fine levels of admixture. This can affect power and precision, and is a particularly relevant consideration when control individuals are recruited using geographic selection criteria. Such is the case in France where we have recently created reference panels of individuals anchored to different geographic regions. To make correct comparisons against case groups, who would likely be gathered from large urban areas, new methods are needed. We present SURFBAT (a surrogate family based association test), which performs an approximation of the transmission-disequilibrium test. Our method hinges on the application of genotype imputation algorithms to match similar haplotypes between the case and control groups. This permits us to approximate local ancestry informed posterior probabilities of un-transmitted parental alleles of each case individual. This is achieved by assuming haplotypes from the imputation panel are well-matched for ancestry with the case individuals. When the first haplotype of an individual from the imputation panel matches that of a case individual, it is assumed that the second haplotype of the same reference individual can be used as a locally ancestry matched control haplotype and to approximately impute un-transmitted parental alleles. SURFBAT provides an association test that is inherently robust to fine-scale population stratification and opens up the possibility of efficiently using large imputation reference panels as control groups for association testing. In contrast to other methods for association testing that incorporate local-ancestry inference, SURFBAT does not require a set of ancestry groups to be defined, nor for local ancestry to be explicitly estimated. We demonstrate the interest of our tool on simulated datasets, as well as on a real-data example for a group of case individuals affected by Brugada syndrome.

A novel genome-wide association study method for detecting quantitative trait loci interacting with complex population structures in plant genetics

In plant genetics, most modern association analyses are performed on panels that bring together individuals from several populations, including admixed individuals whose genomes comprise chromosomal regions from different populations. These panels can identify quantitative trait loci (QTLs) with population-specific effects and epistatic interactions between QTLs and polygenic backgrounds. However, analyzing a diverse panel constitutes a challenge for statistical analysis. The statistical model must account for possible interactions between a QTL and the panel structure while strictly controlling the detection error rate. Although models to detect population-specific QTLs have already been developed, they rely on prior information about the population structure. In practice, this prior information may be missing as many genome-wide association study (GWAS) panels exhibit complex population structures. The present study introduces 2 new models for detecting QTLs interacting with complex population structures. Both incorporate an interaction term between single nucleotide polymorphism/haplotype block and genetic background into conventional GWAS models. The proposed models were compared with state-of-the-art models through simulation studies that considered QTLs with different levels of interaction with their genetic backgrounds. Results showed that models matching simulation settings were most effective for detecting corresponding QTLs while the proposed models outperformed classical models in detecting QTLs interacting with polygenes. Additionally, when applied to a soybean dataset, one of our models identified putative associated QTLs that conventional models failed to detect. The new models, implemented in the RAINBOWR package available on CRAN, are expected to help uncover complex trait genetic architectures.

Novel insight of critical genes involved in breast cancer brain metastasis: evidence from a cross-tissue transcriptome association study and validation through external clinical cohorts

BACKGROUND

Breast cancer represents the most prevalent form of tumors among females and is characterized by a significant genetic component. The brain is a frequent site of metastasis for breast cancer. Although numerous loci associated with breast cancer brain metastasis (BCBM) have been identified, the critical regulatory genes underlying BCBM remain largely unclear.

METHODS

The FinnGen R11 dataset was combined with Genotype-Tissue Expression Project (GTEx) for Transcriptome-wide Association Study (TWAS). The Unified Test for Molecular Signatures (UTMOST), Multimarker Analysis of Genomic Annotation (MAGMA), and Functional Summary-based Imputation (FUSION) were used to identify candidate genes. Summary-data-based mendelian randomization (SMR) and co-localization were performed further to elucidate the association between key genes and BCBM. Finally, multiple external cohorts were obtained to validate the findings.

RESULT

In our study, 12 new genes associated with breast cancer were identified with TWAS. Subsequently, both SMR and co-localization have shown that CAPS8 was only expressed in brain tissues including frontal cortex and cerebellar hemispheres associated with breast cancer. Potential regulation of CASP8 could occur in BCBM. Finally, the findings were ultimately validated by external clinical cohorts.

CONCLUSION

Our study identified key gene CASP8, which was associated with BCBM, providing new insights into the occurrence of BCBM.