Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations

PURPOSE OF REVIEW

Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underrepresentation of non-European populations, limiting the applicability of polygenic risk scores (PRS). This review examines recent advancements in osteoporosis genetics, highlights existing disparities, and explores strategies for more inclusive research.

RECENT FINDINGS

European-focused GWAS have identified key loci for osteoporosis, including WNT signaling (SOST, LRP5) and RUNX2 transcriptional regulation. However, fewer than 40% of these variants can be replicated in Asian and African populations. Emerging studies in non-European groups reveal population-specific loci, sex-specific associations, and gene-environment interactions. Advances in machine learning (ML)-assisted GWAS and multi-omics integration are improving genetic discovery. Expanding GWAS in diverse populations, integrating multi-omics data, refining ML-based risk models, and standardizing biobank data are essential for equitable osteoporosis research. Future efforts must prioritize clinical translation to enhance personalized osteoporosis prevention and treatment.

Sex differences in the genetic regulation of the human plasma proteome

Mechanisms underlying sex differences in the development and prognosis of many diseases remain largely elusive. Here, we systematically investigated sex differences in the genetic regulation of plasma proteome (>5800 protein targets) across two cohorts (30,307 females; 26,058 males). Plasma levels of two-thirds of protein targets differ significantly by sex. In contrast, genetic effects on protein targets are remarkably similar across sexes, with only 103 sex-differential protein quantitative loci (sd-pQTLs; for 2.9% and 0.3% of protein targets from antibody- and aptamer-based platforms, respectively). A third of those show evidence of sexual discordance, i.e., effects observed in one sex only (n = 30) or opposite effect directions (n = 1 for CDH15). Phenome-wide analyses of 365 outcomes in UK Biobank did not provide evidence that the identified sd-pQTLs accounted for sex-differential disease risk. Our results demonstrate similarities in the genetic regulation of protein levels by sex with important implications for genetically-guided drug target discovery and validation.

Integrating HiTOP and RDoC frameworks part II: shared and distinct biological mechanisms of externalizing and internalizing psychopathology

BACKGROUND

The Hierarchical Taxonomy of Psychopathology (HiTOP) and Research Domain Criteria (RDoC) frameworks emphasize transdiagnostic and mechanistic aspects of psychopathology. We used a multi-omics approach to examine how HiTOP's psychopathology spectra (externalizing [EXT], internalizing [INT], and shared EXT + INT) map onto RDoC's units of analysis.

METHODS

We conducted analyses across five RDoC units of analysis: genes, molecules, cells, circuits, and physiology. Using genome-wide association studies from the companion Part I article, we identified genes and tissue-specific expression patterns. We used drug repurposing analyses that integrate gene annotations to identify potential therapeutic targets and single-cell RNA sequencing data to implicate brain cell types. We then used magnetic resonance imaging data to examine brain regions and circuits associated with psychopathology. Finally, we tested causal relationships between each spectrum and physical health conditions.

RESULTS

Using five gene identification methods, EXT was associated with 1,759 genes, INT with 454 genes, and EXT + INT with 1,138 genes. Drug repurposing analyses identified potential therapeutic targets, including those that affect dopamine and serotonin pathways. Expression of EXT genes was enriched in GABAergic, cortical, and hippocampal neurons, while INT genes were more narrowly linked to GABAergic neurons. EXT + INT liability was associated with reduced gray matter volume in the amygdala and subcallosal cortex. INT genetic liability showed stronger causal effects on physical health - including chronic pain and cardiovascular diseases - than EXT.

CONCLUSIONS

Our findings revealed shared and distinct pathways underlying psychopathology. Integrating genomic insights with the RDoC and HiTOP frameworks advanced our understanding of mechanisms that underlie EXT and INT psychopathology.

Calbindin 2 as a Novel Biomarker and Therapeutic Target for Abdominal Aortic Aneurysm: Integrative Analysis of Human Proteomes and Genetics

BACKGROUND

Abdominal aortic aneurysm (AAA) is a clinical life-threatening issue. No pharmacological treatments are currently approved for the prevention and treatment of AAA. Therefore, identifying novel biomarkers and therapeutic targets is crucial for improving AAA management and outcomes.

METHODS

To identify plasma proteins with potential causal effects on AAA, we integrated genetic evidence from proteome-wide Mendelian randomization, genetic correlation, and colocalization analysis. The role of identified proteins in AAA was further explored through the phenome-wide association study and mediation analysis. Multiomics data analysis, including bulk RNA sequencing, single-cell/single-nucleus RNA sequencing, and spatial transcriptomics, was employed to characterize the expression patterns of these proteins. Experimental validation was performed using an AAA model in apolipoprotein E-deficient mice infused with angiotensin II. Druggability analysis was conducted to identify drug candidates, which were tested in preclinical mouse models.

RESULTS

CALB2 (calbindin 2) was identified as having a causal effect on AAA and may influence the progression of AAA through the regulation of lipid metabolism. Multiomics analysis revealed that CALB2 is predominantly expressed in the mesothelial cells of adipose tissues. Inhibition of CALB2 in an AAA mouse model alleviated AAA progression. Druggability analysis identified lenalidomide and genistein as potential therapeutic candidates, and experiments confirmed their efficacy in preventing AAA development.

CONCLUSIONS

This study identifies CALB2 as being associated with an increased risk of AAA and suggests that i might be a novel biomarker and therapeutic molecule for AAA management. Lenalidomide and genistein hold promising potential as treatments for patients with AAA.

Recent developments in population biobanks and the genetic architecture of complex disease

Population biobanks have radically transformed our understanding of complex disease genetics. Recent technological advances and the inclusion of diverse populations have accelerated the discovery and interpretation of variant associations. For instance, population-scale whole-genome sequencing now allows deep exploration of rare and structural variant associations, while multi-omics approaches integrating genome-wide association studies with proteomics, metabolomics, and advanced statistical methods like Mendelian randomization provide nuanced insights into genetic disease mechanisms. Additionally, cross-biobank collaborations and meta-analyses have been particularly impactful, dramatically increasing the statistical power for discovery. These efforts have identified novel genetic associations across numerous complex diseases, with significant contributions from non-European populations. However, data integration complexities, privacy concerns, and methodological limitations continue to constrain research. Here we review how recent advances have contributed to genetic discovery.

Exploring the effect of SGLT2 inhibitors on the risk of primary open-angle glaucoma using Mendelian randomization analysis

This study aimed to evaluate the causal effect of sodium-glucose cotransporter protein 2 (SGLT2) inhibition on primary open-angle glaucoma (POAG) and explore potential mechanisms. A drug-targeted Mendelian randomization (MR) study was conducted using genetic variation related to SGLT2 inhibition, based on SGLT2 gene expression and glycated hemoglobin levels. Genetic summary statistics for POAG were obtained from the FinnGen consortium and a multi-ancestry genome-wide association study. Glaucomatous endophenotype data were also incorporated. A two-step MR analysis was performed to examine whether pathways related to obesity, blood pressure, lipid levels, oxidative stress, and inflammation mediated the association between SGLT2 inhibition and POAG. Genetically predicted SGLT2 inhibition was associated with a reduced risk of POAG (OR: 0.28; 95% CI: 0.12 to 0.63; P = 2.22 × 10- 3), confirmed in a multi-ancestry validation cohort. It was also associated with decreased optic cup area, reduced vertical cup-disc ratio, and increased optic disc area. Mediation analysis indicated that the effect of SGLT2 inhibition on POAG was partly mediated by diastolic blood pressure (4.8%). This study suggests that SGLT2 inhibition is a promising therapeutic target for POAG. However, further large-scale randomized controlled trials are required to confirm these findings.

A Mendelian randomization analysis of cardiac MRI measurements as surrogate outcomes for heart failure and atrial fibrillation

BACKGROUND

Drug development and disease prevention of heart failure (HF) and atrial fibrillation (AF) are impeded by a lack of robust early-stage surrogates. We determined to what extent cardiac magnetic resonance (CMR) measurements act as surrogates for the development of HF or AF.

METHODS

Genetic data were sourced on the association with 21 atrial and ventricular CMR measurements. Mendelian randomization was used to determine CMR associations with AF, HF, non-ischaemic cardiomyopathy (NICM), and dilated cardiomyopathy (DCM), noting that the definition of NICM includes DCM as a subset. Additionally, for the CMR surrogates of AF and HF, we explored their association with non-cardiac traits potentially influenced by cardiac disease liability.

RESULTS

In total we find that 7 CMR measures (biventricular ejection fraction (EF) and end-systolic volume (ESV), as well as LV systolic volume (SV), end-diastolic volume (EDV), and mass to volume ratio (MVR)) associate with the development of HF, 5 with the development of NICM (biventricular EDV and ESV, LV-EF), 7 with DCM (biventricular EF, ESV, EDV, and LV end-diastolic mass (EDM), and 3 associate with AF (LV-ESV, RV-EF, RV-ESV). Higher EF of both ventricles associate with lower risk of HF and DCM, with biventricular ESV associating with all four cardiac outcomes. Higher values of biventricular EDV associate with lower risk of HF, and DCM. Exploring the associations of these CMR cardiac disease surrogates with non-cardiac traits confirms a strong link with diastolic blood pressure, as well as more specific associations with lung function (LV-ESV), HbA1c (LV-EDM), and type 2 diabetes (LV-SV).

CONCLUSIONS

The current paper identifies key CMR measurements that may act as surrogate endpoints for the development of HF (including NICM and DCM) or AF.

Decoding the causal association between immune cells and three chronic respiratory diseases: Insights from a bi-directional Mendelian randomization study

BACKGROUND

Numerous studies have indicated the correlations of immune traits and chronic respiratory diseases (CRDs). Whereas, causality is still implicative. Hence, our study was designed to investigate the causal relations utilizing bidirectional Mendelian randomization (MR) and to identify the immune traits of potential significance.

METHODS

Using GWAS datasets, we performed Mendelian randomization (MR) analyses to examine 731 immune traits associated with three CRDs: asthma, bronchiectasis and chronic obstructive pulmonary disease (COPD). Six widely applied MR approaches, along with Bayesian weighted Mendelian randomization analysis, were utilized to assess causality. Through extensive sensitivity assessments, heterogeneity and pleiotropy have been examined. For integrity, leave-one-out analysis was implemented as the final step.

RESULTS

Our study reveals 13 immune traits that may have a genetic basis for predicting the occurrence of CRDs, which include two risk traits (CD62L- myeloid dendritic cell (DC) absolute count (AC), CD8 on CD28+ CD45RA- CD8+ T cell) and four protective traits (CD39+ CD8+ %T cell, CD4 on CD39+ activated CD4 regulatory T (Treg) cell, herpes virus entry mediator (HVEM) on Central Memory (CM) CD8+ T cell, CD16 on CD14+ CD16+ monocyte) in COPD, three protective traits (IgD- CD27- %B cell, CD3 on CM CD8+ T cell, CD16 on CD14+ CD16+ monocyte) and one risk trait (CD62L- %DC) in bronchiectasis. Additionally, two risk traits (CD14- CD16- AC monocyte, CD19 on IgD+ CD38+ B cell) and one protective trait (HVEM on CD45RA- CD4+ T cell) were identified in asthma. Sensitivity analyses showed no indications of pleiotropy or signs of heterogeneity. The inverse MR assessment results gave no evidence of reverse causations, ultimately validating the soundness of the findings.

CONCLUSIONS

Our investigation identifies latent correlations of immune traits and three major CRDs, offering novel perspectives on the preventive and therapeutical strategies for CRDs.

Genome-wide association study for circulating metabolic traits in 619,372 individuals

Interpreting genetic associations with complex traits can be greatly improved by detailed understanding of the molecular consequences of these variants. However, although genome-wide association studies (GWAS) for common complex diseases routinely profile 1M+ individuals, studies of molecular phenotypes have lagged behind. We performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals, identifying 88,604 significant locus-metabolite associations and 8,774 independent lead variants, including 987 lead variants with a minor allele frequency less than 1%. We demonstrate how common and low-frequency associations converge on shared genes and pathways, bridging the gap between rare-variant burden testing and common-variant GWAS. We used Mendelian randomisation (MR) to explore putative causal links between metabolic traits, coronary artery disease and type 2 diabetes (T2D). Surprisingly, up to 85% of the tested metabolite-disease pairs had statistically significant genome-wide MR estimates, likely reflecting complex indirect effects driven by horisontal pleiotropy. To avoid these pleiotropic effects, we used cis-MR to test the phenotypic impact of inhibiting specific drug targets. We found that although plasma levels of branched-chain amino acids (BCAAs) have been associated with T2D in both observational and genome-wide MR studies, inhibiting the BCAA catabolism pathway to lower BCAA levels is unlikely to reduce T2D risk. Our publicly available results provide a valuable novel resource for GWAS interpretation and drug target prioritisation.

The Estonian Biobank's journey from biobanking to personalized medicine

Large biobanks have set a new standard for research and innovation in human genomics and implementation of personalized medicine. The Estonian Biobank was founded a quarter of a century ago, and its biological specimens, clinical, health, omics, and lifestyle data have been included in over 800 publications to date. What makes the biobank unique internationally is its translational focus, with active efforts to conduct clinical studies based on genetic findings, and to explore the effects of return of results on participants. In this review, we provide an overview of the Estonian Biobank, highlight its strengths for studying the effects of genetic variation and quantitative phenotypes on health-related traits, development of methods and frameworks for bringing genomics into the clinic, and its role as a driving force for implementing personalized medicine on a national level and beyond.

Gut microbiota, blood metabolites, & pan-cancer: a bidirectional Mendelian randomization & mediation analysis

We propose using Mendelian randomization analysis on GWAS data and MetaboAnalyst to model gut microbiota, metabolic pathways, blood metabolites, and cancer risk. We examined 473 gut microbiota, 205 pathways, 1400 metabolites, and 8 cancers. Results were validated through bidirectional two-sample Mendelian Randomization (MR), heterogeneity tests, and pathway enrichment, leading to a mediation pathway model. We identified 129 gut microbiota, 57 pathways, and 463 metabolites linked to cancer, and 34 significant plasma pathways. 15 microbiota, 8 pathways, and 58 metabolites implicated in multiple cancers. Eight plasma metabolic pathways are involved in the development of multiple types of cancer. Through Multivariate Mendelian Randomization (MVMR) and mediation analysis, we found 9 mediation pathways, offering novel targets and research directions for cancer pathogenesis and treatment.

The breadth and impact of the Global Lipids Genetics Consortium

PURPOSE OF REVIEW

This review highlights contributions of the Global Lipids Genetics Consortium (GLGC) in advancing the understanding of the genetic etiology of blood lipid traits, including total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, and non-HDL cholesterol. We emphasize the consortium's collaborative efforts, discoveries related to lipid and lipoprotein biology, methodological advancements, and utilization in areas extending beyond lipid research.

RECENT FINDINGS

The GLGC has identified over 923 genomic loci associated with lipid traits through genome-wide association studies (GWASs), involving more than 1.65 million individuals from globally diverse populations. Many loci have been functionally validated by individuals inside and outside the GLGC community. Recent GLGC studies show increased population diversity enhances variant discovery, fine-mapping of causal loci, and polygenic score prediction for blood lipid levels. Moreover, publicly available GWAS summary statistics have facilitated the exploration of lipid-related genetic influences on cardiovascular and noncardiovascular diseases, with implications for therapeutic development and drug repurposing.

SUMMARY

The GLGC has significantly advanced the understanding of the genetic basis of lipid levels and serves as the leading resource of GWAS summary statistics for these traits. Continued collaboration will be critical to further understand lipid and lipoprotein biology through large-scale genetic assessments in diverse populations.

Genome-wide association study meta-analysis provides insights into the etiology of heart failure and its subtypes

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.

A genome-wide cross-trait analysis characterizes the shared genetic architecture between lung and gastrointestinal diseases

Lung and gastrointestinal diseases often occur together, leading to more adverse health outcomes than when a disease of one of these systems occurs alone. However, the potential genetic mechanisms underlying lung-gastrointestinal comorbidities remain unclear. Here, we leverage lung and gastrointestinal trait data from individuals of European, East Asian and African ancestries, to perform a large-scale genetic cross trait analysis, followed by functional annotation and Mendelian randomization analysis to explore the genetic mechanisms involved in the development of lung-gastrointestinal comorbidities. Notably, we find significant genetic correlations between 27 trait pairs among the European population. The highest correlation is between chronic bronchitis and peptic ulcer disease. At the variant level, we identify 42 candidate pleiotropic genetic variants (3 of them previously uncharacterized) in 14 trait pairs by integrating cross-trait meta-analysis, fine-mapping and colocalization analyses. We also find 66 candidate pleiotropic genes, most of which were enriched in immune or inflammatory response-related activities. Causal inference approaches result in 4 potential lung-gastrointestinal associations. Introducing the gut microbiota as a variable establishes a relationship between the genus Parasutterella, gastro-oesophageal reflux disease and asthma. In summary, our findings highlight the genetic relationship between lung and gastrointestinal diseases, providing insights into the genetic mechanisms underlying the development of lung gastrointestinal comorbidities.

Gout, Hyperuricemia and Psoriatic Arthritis: An Evolving Conundrum

PURPOSE OF REVIEW

The co-existence of gout and psoriatic disease (PD) is long standing but more recently frequently encountered in clinical settings due to increased awareness of their shared comorbidities and clinical phenotypes, often posing diagnostic and management challenges. Here we review the overlap in gout and PD focusing on shared clinical features, common inflammatory pathophysiology and comorbidities which may prompt a diagnosis of 'Psout' and lead to changes in management.

RECENT FINDINGS

Several epidemiological studies have highlighted the increased incidence of hyperuricemia and gout in those with PD and vice versa. Although the role of monosodium urate (MSU) crystals is well recognized in activation of innate immunity via inflammasome and NETosis, it is likely that they have a role in triggering adaptive immunity via antigen presenting cells and their autocrine effect on keratinocytes in psoriasis (PSO), ultimately leading to T cell secretion of proinflammatory cytokines such as IL17. Hyperuricemia (HU) is common in PD (up to 30%) and underpins metabolic syndrome comorbidities that are common to both gout and PD. Shared clinical phenotypes and co-morbidities are routinely observed in clinical practice yet there is a paucity of evidence evaluating the effect of treating hyperuricemia/gout on PD activity, with small scale clinical trials showing a positive effect. There were no studies to our knowledge assessing gout disease activity with concurrent treatment of PD. The association between gout and PD is likely due to shared multimorbidity and perhaps to a smaller extent, the direct role of HU in triggering the release of proinflammatory cytokines in PD. There is often a significant overlap in clinical and radiological presentation of gout and Psoriatic arthritis (PsA). In those with atypical response to standard treatments of the primary condition (either gout or PsA), it would be plausible to investigate and treat for the other 'secondary' condition. This is particularly relevant and relatively feasible in those with PsA (and features of HU and multimorbidity) who respond poorly to standard immunomodulating treatments.

Sequencing and health data resource of children of African ancestry

Purpose

Individuals who self-report as Black or African American are historically underrepresented in genome-wide studies of disease risk, a disparity particularly evident in pediatric disease research. To address this gap, Cincinnati Children's Hospital Medical Center (CCHMC) established a biorepository and developed a comprehensive DNA sequencing resource including 15,684 individuals who self-identified as African American or Black and received care at CCHMC.

Methods

Participants were enrolled through the CCHMC Discover Together Biobank and sequenced. Admixture analyses confirmed the genetic ancestry of the cohort, which was then linked to electronic medical records.

Results

High-quality genome-wide genotypes from common variants accompanied by medical recordsourced data are available through the Genomic Information Commons. This dataset performs well in genetic studies. Specifically, we replicated known associations in sickle cell disease (HBB, p = 4.05 × 10-1), anxiety (PLAA3, p = 6.93 × 10-), and asthma (PCDH15, p = 5.6 × 10-1), while also identifying novel loci associated with asthma severity.

Conclusion

We present the acquisition and quality of genetic and disease-associated data and present an analytical framework for using this resource. In partnership with a community advisory council, we have co-developed a valuable framework for data use and future research.

Genetic insights into idiopathic pulmonary fibrosis: a multi-omics approach to identify potential therapeutic targets

OBJECTIVE

To identify potential therapeutic targets and evaluate the safety profiles for Idiopathic Pulmonary Fibrosis (IPF) using a comprehensive multi-omics approach.

METHOD

We integrated genomic and transcriptomic data to identify therapeutic targets for IPF. First, we conducted a transcriptome-wide association study (TWAS) using the Omnibus Transcriptome Test using Expression Reference Summary data (OTTERS) framework, combining plasma expression quantitative trait loci (eQTL) data with IPF Genome-Wide Association Studies (GWAS) summary statistics from the Global Biobank (discovery) and Finngen (duplication). We then applied Mendelian randomization (MR) to explore causal relationships. RNA-seq co-expression analysis (bulk, single-cell and spatial transcriptomics) was used to identify critical genes, followed by molecular docking to evaluate their druggability. Finally, phenome-wide MR (PheW-MR) using GWAS data from 679 diseases in the UK Biobank assessed the potential adverse effects of the identified genes.

RESULT

We identified 696 genes associated with IPF in the discovery dataset and 986 genes in the duplication dataset, with 126 overlapping genes through TWAS. MR analysis revealed 29 causal genes in the discovery dataset, with 13 linked to increased and 16 to decreased IPF risk. Summary data-based MR (SMR) confirmed six essential genes: ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1. Bulk RNA-seq showed FAM13A upregulation and SFR1 and EZH1 downregulation in IPF. Single-cell RNA-seq revealed gene expression changes across cell types. Molecular docking identified binding solid affinities for essential genes with respiratory drugs, and PheW-MR highlighted potential side effects.

CONCLUSION

We identified six key genes-ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1-as potential drug targets for IPF. Molecular docking revealed strong drug affinities, while PheW-MR analysis highlighted therapeutic potential and associated risks. These findings offer new insights for IPF treatment and further investigation of potential side effects.

The causal relationships between inflammatory cytokines, blood metabolites, and thyroid cancer: a two-step Mendelian randomization analysis

BACKGROUND

Thyroid cancer is a prevalent malignant tumor, especially with a higher incidence in women. Tumor microenvironment changes induced by inflammation and alterations in metabolic characteristics are critical in the development of thyroid cancer. Nevertheless, their causal relationships remain unclear.

METHODS

We utilized thyroid cancer GWAS data from the Global Biobank Meta-Analysis Initiative and GWAS data of 91 inflammatory cytokines and 1400 blood metabolites obtained from the GWAS Catalog to evaluate the causality between inflammatory cytokines, blood metabolites, and thyroid cancer using Mendelian randomization (MR). Initially, we identified inflammatory cytokines having a significant causal effect on thyroid cancer. Subsequently, for the identified positive blood metabolites, we applied a two-step mediation MR method to examine their mediating role in the causal effect of specific inflammatory cytokines on thyroid cancer.

RESULTS

Our forward MR analysis identified suggestive associations between 7 inflammatory cytokines and thyroid cancer risks, and found that tumor necrosis factor ligand superfamily member 14 (TNFSF14) (IVW-OR: 1.25, 95% CI 1.10-1.42, p = 0.0004) is a significant risk factor in thyroid cancer, and this causal relationship remained significant after Bonferroni correction. The reverse MR analysis identified suggestive causal associations between thyroid cancer and 3 inflammatory cytokines and ruled out the reverse causality between TNFSF14 and thyroid cancer. Then, we identified suggestive associations between 35 blood metabolites and 24 blood metabolite ratios with thyroid cancer, and found that 5-hydroxymethyl-2-furoylcarnitine (IVW-OR: 1.38, 95% CI 1.19-1.61, p = 0.00003) is a significant risk factor for thyroid cancer, with this causality remaining significant after Bonferroni correction. Finally, our two-step MR analysis indicated that Lactosyl-N-palmitoyl-sphingosine (d18:1/16:0) and X-12013 have a mediating effect in the causal relationship between TNFSF14 and thyroid cancer, with mediation proportions of 8.55% and 5.78%, respectively. Our MR analysis did not identify significant heterogeneity or horizontal pleiotropy.

CONCLUSION

This study identified some inflammatory cytokines and blood metabolites associated with thyroid cancer risk and revealed the mediating role of specific blood metabolites between TNFSF14 and thyroid cancer, highlighting the critical role of inflammatory and metabolic pathways in the pathogenesis of thyroid cancer.

Assessment of polygenic risk score performance in East Asian populations for ten common diseases

Polygenic risk score (PRS) uses genetic variants to assess disease susceptibility. While PRS performance is well-studied in Europeans, its accuracy in East Asians is less explored. This study evaluated PRSs for ten diseases in the Health Examinees (HEXA) cohort (n = 55,870) in Korea. Single-population PRSs were constructed using PRS-CS, LDpred2, and Lassosum based on East Asian GWAS summary statistics (sample sizes: 51,442-341,204), while cross-population PRSs were developed using PRS-CSx and CT-SLEB by integrating European and East Asian GWAS data. PRS-CS consistently outperformed other single-population methods across key metrics, including the likelihood ratio test (LRT), odds ratio per standard deviation (perSD OR), net reclassification improvement (NRI), and area under the curve (AUC). Cross-population PRSs further improved predictive performance, with average increases of 1.08-fold (LRT), 1.07-fold (perSD OR), and 1.15-fold (NRI) across seven diseases with statistical significance, and a 1.01-fold improvement in AUC. Differences in R² between single- and cross-population PRSs were statistically significant for five diseases, showing an average increase of 1.13%. Cross-population PRSs achieved 87.8% of the predictive performance observed in European PRSs. These findings highlight the benefits of integrating European GWAS data while underscoring the need for larger East Asian datasets to improve prediction accuracy.

Evolution, genetic diversity, and health

Human genetic diversity in today's world has been shaped by evolutionary history, demographic shifts and environmental exposures, influencing complex traits, disease susceptibility and drug responses. Capturing this diversity is essential for advancing precision medicine and promoting equitable healthcare. Despite the great progress achieved with initiatives such as the human Pangenome and large biobanks that aim for a better representation of human diversity, important challenges remain. In this Perspective, we discuss the importance of diversity in clinical genomics through an evolutionary lens. We highlight progress and challenges and outline key clinical applications of diverse genetic data. We argue that diversifying both datasets and methodologies-integrating ancestral and environmental factors-is crucial for fully understanding the genetic basis of human health and disease.

Multi-Cohort Analysis Reveals Genetic Predispositions to Clonal Hematopoiesis as Mutation-Specific Risk Factors for Stroke

Recent observational studies have found an association between Clonal Hematopoesis (CH) and strokes but with incomplete results. This study aims to comprehensively characterize mutation-specific effects of CH on ischemic and hemorrhagic stroke subtypes and 90-day functional outcomes through publicly available genome-wide association study (GWAS) cohorts and Mendelian Randomization. TET2 is associated with an increased risk of overall stroke (OR = 1.06, P = 0.02), ischemic stroke (OR = 1.05, P = 0.03), transient ischemic attack (OR = 1.07, P = 0.01) and small vessel stroke (OR = 1.29, P = 0.01), as well as adverse 90-day modified Rankin scale (mRS ≥ 3) before (OR = 1.34, P = 0.005) and after adjusted for age, sex, and stroke severity (OR = 1.30, P = 0.02). While the presence of any CH mutation is associated with intracerebral hemorrhage (ICH) (OR = 1.21, P = 0.02), specific mutations, SRSF2 and ASXL1 are protective against ICH (OR = 0.9, P = 0.04) and nontraumatic subarachnoid hemorrhage (OR = 0.92, P = 0.03), respectively. In conclusion, the study provided genetic evidence that TET2 is strongly associated with an increased risk of ischemic stroke and poor functional recovery. Future studies clarifying the relationship between CH and hemorrhagic stroke subtypes are needed.

Large-scale multi-omics identifies drug targets for heart failure with reduced and preserved ejection fraction

Heart failure (HF) has limited therapeutic options. In this study, we differentiated the pathophysiological underpinnings of the HF subtypes-HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF)-and uncovered subtype-specific therapeutic strategies. We investigated the causal roles of the human proteome and transcriptome using Mendelian randomization on more than 420,000 participants from the Million Veteran Program (27,799 HFrEF and 27,579 HFpEF cases). We created therapeutic target profiles covering efficacy, safety, novelty, druggability and mechanism of action. We replicated findings on more than 175,000 participants of diverse ancestries. We identified 70 HFrEF and 10 HFpEF targets, of which 58 were not previously reported; notably, the HFrEF and HFpEF targets are non-overlapping, suggesting the need for subtype-specific therapies. We classified 14 previously unclassified HF loci as HFrEF. We substantiated the role of ubiquitin-proteasome system, small ubiquitin-related modifier pathway, inflammation and mitochondrial metabolism in HFrEF. Among druggable genes, IL6R, ADM and EDNRA emerged as potential HFrEF targets, and LPA emerged as a potential target for both subtypes.

Biobanking with genetics shapes precision medicine and global health

Precision medicine provides patients with access to personally tailored treatments based on individual-level data. However, developing personalized therapies requires analyses with substantial statistical power to map genetic and epidemiologic associations that ultimately create models informing clinical decisions. As one solution, biobanks have emerged as large-scale, longitudinal cohort studies with long-term storage of biological specimens and health information, including electronic health records and participant survey responses. By providing access to individual-level data for genotype-phenotype mapping efforts, pharmacogenomic studies, polygenic risk score assessments and rare variant analyses, biobanks support ongoing and future precision medicine research. Notably, due in part to the geographical enrichment of biobanks in Western Europe and North America, European ancestries have become disproportionately over-represented in precision medicine research. Herein, we provide a genetics-focused review of biobanks from around the world that are in pursuit of supporting precision medicine. We discuss the limitations of their designs, ongoing efforts to diversify genomics research and strategies to maximize the benefits of research leveraging biobanks for all.

GWAS meta-analysis of psoriasis identifies new susceptibility alleles impacting disease mechanisms and therapeutic targets

Psoriasis is a common, debilitating immune-mediated skin disease. Genetic studies have identified biological mechanisms of psoriasis risk, including those targeted by effective therapies. However, the genetic liability to psoriasis is not fully explained by variation at robustly identified risk loci. To refine the genetic map of psoriasis susceptibility we meta-analysed 18 GWAS comprising 36,466 cases and 458,078 controls and identified 109 distinct psoriasis susceptibility loci, including 46 that have not been previously reported. These include susceptibility variants at loci in which the therapeutic targets IL17RA and AHR are encoded, and deleterious coding variants supporting potential new drug targets (including in STAP2, CPVL and POU2F3). We conducted a transcriptome-wide association study to identify regulatory effects of psoriasis susceptibility variants and cross-referenced these against single cell expression profiles in psoriasis-affected skin, highlighting roles for the transcriptional regulation of haematopoietic cell development and epigenetic modulation of interferon signalling in psoriasis pathobiology.

PennPRS: a centralized cloud computing platform for efficient polygenic risk score training in precision medicine

Polygenic risk scores (PRS) are becoming increasingly vital for risk prediction and stratification in precision medicine. However, PRS model training presents significant challenges for broader adoption of PRS, including limited access to computational resources, difficulties in implementing advanced PRS methods, and availability and privacy concerns over individual-level genetic data. Cloud computing provides a promising solution with centralized computing and data resources. Here we introduce PennPRS (https://pennprs.org), a scalable cloud computing platform for online PRS model training in precision medicine. We developed novel pseudo-training algorithms for multiple PRS methods and ensemble approaches, enabling model training without requiring individual-level data. These methods were rigorously validated through extensive simulations and large-scale real data analyses involving over 6,000 phenotypes across various data sources. PennPRS supports online single- and multi-ancestry PRS training with seven methods, allowing users to upload their own data or query from more than 27,000 datasets in the GWAS Catalog, submit jobs, and download trained PRS models. Additionally, we applied our pseudo-training pipeline to train PRS models for over 8,000 phenotypes and made their PRS weights publicly accessible. In summary, PennPRS provides a novel cloud computing solution to improve the accessibility of PRS applications and reduce disparities in computational resources for the global PRS research community.

Integrating HiTOP and RDoC Frameworks Part I: Genetic Architecture of Externalizing and Internalizing Psychopathology

Background

There is considerable comorbidity between externalizing (EXT) and internalizing (INT) psychopathology. Understanding the shared genetic underpinnings of these spectra is crucial for advancing knowledge of their biological bases and informing empirical models like the Research Domain Criteria (RDoC) and Hierarchical Taxonomy of Psychopathology (HiTOP).

Methods

We applied genomic structural equation modeling to summary statistics from 16 EXT and INT traits in European-ancestry individuals (n = 16,400 to 1,074,629). Traits included clinical (e.g., major depressive disorder, alcohol use disorder) and subclinical measures (e.g., risk tolerance, irritability). We tested five confirmatory factor models to identify the best fitting and most parsimonious genetic architecture and then conducted multivariate genome-wide association studies (GWAS) of the resulting latent factors.

Results

A two-factor correlated model, representing EXT and INT spectra, provided the best fit to the data. There was a moderate genetic correlation between EXT and INT (r = 0.37, SE = 0.02), with bivariate causal mixture models showing extensive overlap in causal variants across the two spectra (94.64%, SE = 3.27). Multivariate GWAS identified 409 lead genetic variants for EXT, 85 for INT, and 256 for the shared traits.

Conclusions

The shared genetic liabilities for EXT and INT identified here help to characterize the genetic architecture underlying these frequently comorbid forms of psychopathology. The findings provide a framework for future research aimed at understanding the shared and distinct biological mechanisms underlying psychopathology, which will help to refine psychiatric classification systems and potentially inform treatment approaches.

MorPhiC Consortium: towards functional characterization of all human genes

Recent advances in functional genomics and human cellular models have substantially enhanced our understanding of the structure and regulation of the human genome. However, our grasp of the molecular functions of human genes remains incomplete and biased towards specific gene classes. The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) Consortium aims to address this gap by creating a comprehensive catalogue of the molecular and cellular phenotypes associated with null alleles of all human genes using in vitro multicellular systems. In this Perspective, we present the strategic vision of the MorPhiC Consortium and discuss various strategies for generating null alleles, as well as the challenges involved. We describe the cellular models and scalable phenotypic readouts that will be used in the consortium's initial phase, focusing on 1,000 protein-coding genes. The resulting molecular and cellular data will be compiled into a catalogue of null-allele phenotypes. The methodologies developed in this phase will establish best practices for extending these approaches to all human protein-coding genes. The resources generated-including engineered cell lines, plasmids, phenotypic data, genomic information and computational tools-will be made available to the broader research community to facilitate deeper insights into human gene functions.

A scalable variational inference approach for increased mixed-model association power

The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration. We applied Quickdraws to 79 quantitative and 50 binary traits in 405,088 UK Biobank samples, identifying 4.97% and 3.25% more associations than REGENIE and 22.71% and 7.07% more than FastGWA. Quickdraws had costs comparable to REGENIE, FastGWA and SAIGE on the UK Biobank Research Analysis Platform service, while being substantially faster than BOLT-LMM. These results highlight the promise of leveraging machine learning techniques for scalable GWASs without sacrificing power or robustness.

Human Genetic Evidence to Inform Clinical Development of IL-6 Signaling Inhibition for Abdominal Aortic Aneurysm

BACKGROUND

Abdominal aortic aneurysm (AAA) represents a significant cause of mortality, yet no medical therapies have proven efficacious. The aim of the current study was to leverage human genetic evidence to inform clinical development of IL-6 (interleukin-6) signaling inhibition for the treatment of AAA.

METHODS

Associations of rs2228145, a missense variant in the IL6R gene region, are expressed per additional copy of the C allele, corresponding to the genetically predicted effect of IL-6 signaling inhibition. We consider genetic associations with AAA risk in the AAAgen consortium (39 221 cases and 1 086 107 controls) and UK Biobank (1963 cases and 365 680 controls). To validate against known effects of IL-6 signaling inhibition, we present associations with rheumatoid arthritis, polymyalgia rheumatica, and severe COVID-19. To explore mechanism specificity, we present associations with thoracic aortic aneurysm, intracranial aneurysm, and coronary artery disease. We further explored genetic associations in clinically relevant subgroups of the population.

RESULTS

We observed strong genetic associations with AAA risk in the AAAgen consortium, UK Biobank, and FinnGen (odds ratios: 0.91 [95% CI, 0.90-0.92], P=4×10-30; 0.90 [95% CI, 0.84-0.96], P=0.001; and 0.86 [95% CI, 0.82-0.91], P=7×10-9, respectively). The association was similar for fatal AAA but with greater uncertainty due to the lower number of events. The association with AAA was of greater magnitude than associations with coronary artery disease and even rheumatological disorders for which IL-6 inhibitors have been approved. No strong associations were observed with thoracic aortic aneurysm or intracranial aneurysm. Associations attenuated toward the null in populations with concomitant rheumatological or connective tissue disease.

CONCLUSIONS

Inhibition of IL-6 signaling is a promising strategy for treating AAA but not other types of aneurysmal disease. These findings serve to help inform clinical development of IL-6 signaling inhibition for AAA treatment.

Prioritizing effector genes at trait-associated loci using multimodal evidence

Genome-wide association studies (GWAS) yield large numbers of genetic loci associated with traits and diseases. Predicting the effector genes that mediate these locus-trait associations remains challenging. Here we present the FLAMES (fine-mapped locus assessment model of effector genes) framework, which predicts the most likely effector gene in a locus. FLAMES creates machine learning predictions from biological data linking single-nucleotide polymorphisms to genes, and then evaluates these scores together with gene-centric evidence of convergence of the GWAS signal in functional networks. We benchmark FLAMES on gene-locus pairs derived by expert curation, rare variant implication and domain knowledge of molecular traits. We demonstrate that combining single-nucleotide-polymorphism-based and convergence-based modalities outperforms prioritization strategies using a single line of evidence. Applying FLAMES, we resolve the FSHB locus in the GWAS for dizygotic twinning and further leverage this framework to find schizophrenia risk genes that converge with rare coding evidence and are relevant in different stages of life.

Exploring the Causal Relationship Between Frailty and Chronic Obstructive Pulmonary Disease: Insights From Bidirectional Mendelian Randomization and Mediation Analysis

Background

Observational studies have underscored a robust association between frailty and chronic obstructive pulmonary disease (COPD), yet the causality remains equivocal.

Methods

This study employed bidirectional two-sample Mendelian randomization (MR) analysis. Univariable MR investigated the causal relationship between frailty and COPD. Genetic correlation was assessed using linkage disequilibrium score (LDSC) regression. Multivariable MR and mediation analysis explored the influence of various confounders and their mediating effects. The primary analytic approach was inverse variance weighted (IVW).

Results

LDSC analysis revealed moderate genetic correlations between frailty and Global Biobank Meta-Analysis Initiative (GBMI) COPD (rg = 0.643, P = 6.66×10-62) as well as FinnGen COPD (rg = 0.457, P = 8.20×10-28). IVW analysis demonstrated that frailty was associated with increased risk of COPD in both the GBMI cohort (95% CI, 1.475 to 2.158; P = 2.40×10-9) and the FinnGen database (1.411 to 2.434; 9.02×10-6). Concurrently, COPD was identified as a susceptibility factor for frailty (P < 0.05). These consistent findings persisted after adjustment for potential confounders in MVMR. Additionally, mediation analysis revealed that walking pace mediated 19.11% and 15.40% of the impact of frailty on COPD risk, and 17.58% and 23.26% of the effect of COPD on frailty risk in the GBMI and FinnGen cohorts, respectively.

Conclusion

This study has strengthened the current evidence affirming a reciprocal causal relationship between frailty and COPD, highlighting walking pace as a pivotal mediator.

Modeling gene interactions in polygenic prediction via geometric deep learning

Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning-based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution and then explicitly encapsulates gene-gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared with a wide range of conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases.

Genetic variation reveals the therapeutic potential of BRSK2 in idiopathic pulmonary fibrosis

BACKGROUND

Current research underscores the need to better understand the pathogenic mechanisms and treatment strategies for idiopathic pulmonary fibrosis (IPF). This study aimed to identify key targets involved in the progression of IPF.

METHODS

We employed Mendelian randomization (MR) with three genome-wide association studies and four quantitative trait loci datasets to identify key driver genes for IPF. Prioritized targets were evaluated for respiratory insufficiency and transplant-free survival. The therapeutic efficacy of the core gene was validated in cellular and animal models. Additionally, we conducted a comprehensive evaluation of therapeutic value, pathogenic mechanisms, and safety through phenome-wide association study (PheWAS), mediation analysis, transcriptomic analyses, shared causal variant exploration, DNA methylation MR, and protein interactions.

RESULTS

Multiple MR results revealed that BRSK2 has a significant pathogenic impact on IPF at both transcriptional and translational levels, with a lung tissue-specific association (OR = 1.596; CI, 1.300-1.961; Pval = 8.290 × 10 - 6). BRSK2 was associated with IPF progression driven by high-risk factors, with mediation effects ranging from 34.452 to 69.665%. Elevated BRSK2 expression in peripheral blood mononuclear cells correlated with reduced pulmonary function, while increased circulating BRSK2 levels suggested respiratory failure and shorter transplant-free survival in IPF patients. BRSK2 silencing attenuated lung fibrosis progression in cellular and animal models. Transcriptomic integration identified PSMB1, CTSD, and CTSH as significant downstream effectors of BRSK2, with PSMB1 showing robust shared causal variant support (PPH4 = 0.800). Colocalization analysis and phenotype scan deepened the pathogenic association of BRSK2 with IPF, while methylation MR analysis highlighted the critical role of epigenetic regulation in BRSK2-driven IPF pathogenesis. PheWAS revealed no significant drug-related toxicities for BRSK2, and its therapeutic potential was further underscored by protein interaction analyses.

CONCLUSIONS

BRSK2 is identified as a critical pathogenic factor in IPF, with strong potential as a therapeutic target. Future studies should focus on its translational implications and the development of targeted therapies to improve patient outcomes.

metaGE: Investigating genotype x environment interactions through GWAS meta-analysis

Elucidating the genetic components of plant genotype-by-environment interactions is of key importance in the context of increasing climatic instability, diversification of agricultural practices and pest pressure due to phytosanitary treatment limitations. The genotypic response to environmental stresses can be investigated through multi-environment trials (METs). However, genome-wide association studies (GWAS) of MET data are significantly more complex than that of single environments. In this context, we introduce metaGE, a flexible and computationally efficient meta-analysis approach for jointly analyzing single-environment GWAS of any MET experiment. The metaGE procedure accounts for the heterogeneity of quantitative trait loci (QTL) effects across the environmental conditions and allows the detection of QTL whose allelic effect variations are strongly correlated to environmental cofactors. We evaluated the performance of the proposed methodology and compared it to two competing procedures through simulations. We also applied metaGE to two emblematic examples: the detection of flowering QTLs whose effects are modulated by competition in Arabidopsis and the detection of yield QTLs impacted by drought stresses in maize. The procedure identified known and new QTLs, providing valuable insights into the genetic architecture of complex traits and QTL effects dependent on environmental stress conditions. The whole statistical approach is available as an R package.

Social Determinants of Health and Lifestyle Risk Factors Modulate Genetic Susceptibility for Women's Health Outcomes

Women's health conditions are influenced by both genetic and environmental factors. Understanding these factors individually and their interactions is crucial for implementing preventative, personalized medicine. However, since genetics and environmental exposures, particularly social determinants of health (SDoH), are correlated with race and ancestry, risk models without careful consideration of these measures can exacerbate health disparities. We focused on seven women's health disorders in the All of Us Research Program: breast cancer, cervical cancer, endometriosis, ovarian cancer, preeclampsia, uterine cancer, and uterine fibroids. We computed polygenic risk scores (PRSs) from publicly available weights and tested the effect of the PRSs on their respective phenotypes as well as any effects of genetic risk on age at diagnosis. We next tested the effects of environmental risk factors (BMI, lifestyle measures, and SDoH) on age at diagnosis. Finally, we examined the impact of environmental exposures in modulating genetic risk by stratified logistic regressions for different tertiles of the environment variables, comparing the effect size of the PRS. Of the twelve sets of weights for the seven conditions, nine were significantly and positively associated with their respective phenotypes. None of the PRSs was associated with different ages at diagnoses in the time-to-event analyses. The highest environmental risk group tended to be diagnosed earlier than the low and medium-risk groups. For example, the cases of breast cancer, ovarian cancer, uterine cancer, and uterine fibroids in highest BMI tertile were diagnosed significantly earlier than the low and medium BMI groups, respectively). PRS regression coefficients were often the largest in the highest environment risk groups, showing increased susceptibility to genetic risk. This study's strengths include the diversity of the All of Us study cohort, the consideration of SDoH themes, and the examination of key risk factors and their interrelationships. These elements collectively underscore the importance of integrating genetic and environmental data to develop more precise risk models, enhance personalized medicine, and ultimately reduce health disparities.

Polygenic Score for the Prediction of Postoperative Nausea and Vomiting: A Retrospective Derivation and Validation Cohort Study

BACKGROUND

Postoperative nausea and vomiting (PONV) is a key driver of unplanned admission and patient satisfaction after surgery. Because traditional risk factors do not completely explain variability in risk, this study hypothesized that genetics may contribute to the overall risk for this complication. The objective of this research is to perform a genome-wide association study of PONV, derive a polygenic risk score for PONV, assess associations between the risk score and PONV in a validation cohort, and compare any genetic contributions to known clinical risks for PONV.

METHODS

Surgeries with integrated genetic and perioperative data performed under general anesthesia at Michigan Medicine (Ann Arbor, Michigan) and Vanderbilt University Medical Center (Nashville, Tennessee) were studied. PONV was defined as nausea or emesis occurring and documented in the postanesthesia care unit. In the discovery phase, genome-wide association studies were performed on each genetic cohort, and the results were meta-analyzed. Next, the polygenic phase assessed whether a polygenic score, derived from genome-wide association study in a derivation cohort from Vanderbilt University Medical Center, improved prediction within a validation cohort from Michigan Medicine, as quantified by discrimination (c-statistic) and net reclassification index.

RESULTS

Of 64,523 total patients, 5,703 developed PONV (8.8%). The study identified 46 genetic variants exceeding the threshold of P < 1 × 10-5, occurring with minor allele frequency greater than 1%, and demonstrating concordant effects in both cohorts. Standardized polygenic score was associated with PONV in a basic model, controlling for age and sex (adjusted odds ratio, 1.027 per SD increase in overall genetic risk; 95% CI, 1.001 to 1.053; P = 0.044), a model based on known clinical risks (adjusted odds ratio, 1.029; 95% CI, 1.003 to 1.055; P = 0.030), and a full clinical regression, controlling for 21 demographic, surgical, and anesthetic factors, (adjusted odds ratio, 1.029; 95% CI, 1.002 to 1.056; P = 0.033). The addition of polygenic score improved overall discrimination in models based on known clinical risk factors (c-statistic, 0.616 compared to 0.613; P = 0.028) and improved net reclassification of 4.6% of cases.

CONCLUSIONS

Standardized polygenic risk was associated with PONV in all three of the study's models, but the genetic influence was smaller than exerted by clinical risk factors. Specifically, a patient with a polygenic risk score greater than 1 SD above the mean has 2 to 3% greater odds of developing PONV when compared to the baseline population, which is at least an order of magnitude smaller than the increase associated with having prior PONV or motion sickness (55%), having a history of migraines (17%), or being female (83%) and is not clinically significant. Furthermore, the use of a polygenic risk score does not meaningfully improve discrimination compared to clinical risk factors and is not clinically useful.

EDITOR’S PERSPECTIVE

Genetically Enriched Clinical Trials for Precision Development of Noncancer Therapeutics: A Scoping Review

Genetically driven clinical trial enrichment has been proposed to accelerate and reduce the cost of developing new therapeutics. Usage of this approach has not been comprehensively reviewed. We searched Ovid MEDLINE, Embase, Web of Science, Cochrane Library, ClinicalTrials.gov, and WHO ICTRP for articles published between 2010 and 2023. Excluding absorption, distribution, metabolism, and elimination pharmacogenetic studies and anti-infectives, we found 95 completed, 4 terminated, and 22 ongoing prospective genetically enriched trials on 110 drugs for 48 nononcology, nonrare syndromic indications. Trial sizes ranged from 4 to 6,147 participants (median 72) and covered numerous disease areas, particularly neurology (30), metabolism (22), and psychiatry (17). Fifty-six completed studies (60%) met their primary end point. Overall, this scoping review demonstrates that genetically enriched trials are feasible and scalable across disease areas and provide critical information for further development, or attrition, of investigational drugs. Large, appropriately designed disease-, hospital-, or population-based biobanks will undoubtedly facilitate this type of precision drug development approach.

Multiome-wide Association Studies: Novel Approaches for Understanding Diseases

The rapid development of multiome (transcriptome, proteome, cistrome, imaging, and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multiome-wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association study (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.

G protein-coupled receptor (GPCR) pharmacogenomics

The field of pharmacogenetics, the investigation of the influence of one or more sequence variants on drug response phenotypes, is a special case of pharmacogenomics, a discipline that takes a genome-wide approach. Massively parallel, next generation sequencing (NGS), has allowed pharmacogenetics to be subsumed by pharmacogenomics with respect to the identification of variants associated with responders and non-responders, optimal drug response, and adverse drug reactions. A plethora of rare and common naturally-occurring GPCR variants must be considered in the context of signals from across the genome. Many fundamentals of pharmacogenetics were established for G protein-coupled receptor (GPCR) genes because they are primary targets for a large number of therapeutic drugs. Functional studies, demonstrating likely-pathogenic and pathogenic GPCR variants, have been integral to establishing models used for in silico analysis. Variants in GPCR genes include both coding and non-coding single nucleotide variants and insertion or deletions (indels) that affect cell surface expression (trafficking, dimerization, and desensitization/downregulation), ligand binding and G protein coupling, and variants that result in alternate splicing encoding isoforms/variable expression. As the breadth of data on the GPCR genome increases, we may expect an increase in the use of drug labels that note variants that significantly impact the clinical use of GPCR-targeting agents. We discuss the implications of GPCR pharmacogenomic data derived from the genomes available from individuals who have been well-phenotyped for receptor structure and function and receptor-ligand interactions, and the potential benefits to patients of optimized drug selection. Examples discussed include the renin-angiotensin system in SARS-CoV-2 (COVID-19) infection, the probable role of chemokine receptors in the cytokine storm, and potential protease activating receptor (PAR) interventions. Resources dedicated to GPCRs, including publicly available computational tools, are also discussed.

The Genetic Variants Influencing Hypertension Prevalence Based on the Risk of Insulin Resistance as Assessed Using the Metabolic Score for Insulin Resistance (METS-IR)

Insulin resistance is a major indicator of cardiovascular diseases, including hypertension. The Metabolic Score for Insulin Resistance (METS-IR) offers a simplified and cost-effective way to evaluate insulin resistance. This study aimed to identify genetic variants associated with the prevalence of hypertension stratified by METS-IR score levels. Data from the Korean Genome and Epidemiology Study (KoGES) were analyzed. The METS-IR was calculated using the following formula: ln [(2 × fasting blood glucose (FBG) + triglycerides (TG)) × body mass index (BMI)]/ ln [high-density lipoprotein cholesterol (HDL-C)]. The participants were divided into tertiles 1 (T1) and 3 (T3) based on their METS-IR scores. Genome-wide association studies (GWAS) were performed for hypertensive cases and non-hypertensive controls within these tertile groups using logistic regression adjusted for age, sex, and lifestyle factors. Among the METS-IR tertile groups, 3517 of the 19,774 participants (17.8%) at T1 had hypertension, whereas 8653 of the 20,374 participants (42.5%) at T3 had hypertension. A total of 113 single-nucleotide polymorphisms (SNPs) reached the GWAS significance threshold (p < 5 × 10-8) in at least one tertile group, mapping to six distinct genetic loci. Notably, four loci, rs11899121 (chr2p24), rs7556898 (chr2q24.3), rs17249754 (ATP2B1), and rs1980854 (chr20p12.2), were significantly associated with hypertension in the high-METS-score group (T3). rs10857147 (FGF5) was significant in both the T1 and T3 groups, whereas rs671 (ALDH2) was significant only in the T1 group. The GWASs identified six genetic loci significantly associated with hypertension, with distinct patterns across METS-IR tertiles, highlighting the role of metabolic context in genetic susceptibility. These findings underscore critical genetic factors influencing hypertension prevalence and provide insights into the metabolic-genetic interplay underlying this condition.

A cross-tissue transcriptome-wide association study reveals GRK4 as a novel susceptibility gene for COPD

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disorder with environmental factors being the primary risk determinants. However, genetic factors also substantially contribute to the susceptibility and progression of COPD. Although genome-wide association studies (GWAS) have identified several loci associated with COPD susceptibility, the specific pathogenic genes underlying these loci, along with their biological functions and roles within regulatory networks, remain unclear. This lack of clarity constrains our ability to achieve a deeper understanding of the genetic basis of COPD. This study leveraged the FinnGen R11 genetic dataset, comprising 21,617 cases and 372,627 controls, along with GTEx V8 eQTLs data to conduct a cross-tissue transcriptome-wide association study (TWAS). Initially, we performed a cross-tissue TWAS analysis using the Unified Test for Molecular Signatures (UTMOST), followed by validation of the UTMOST findings in single tissues using the Functional Summary-based Imputation (FUSION) method and conditional and joint (COJO) analyses of the identified genes. Subsequently, candidate susceptibility genes were screened using Multi-marker Analysis of Genomic Annotation (MAGMA). The causal relationship between these candidate genes and COPD was further evaluated through summary data-based Mendelian randomization (SMR), colocalization analysis, and Mendelian randomization (MR). Additionally, the identified results were validated against the COPD dataset in the GWAS Catalog (GCST90399694). GeneMANIA was employed to further explore the functional significance of these susceptibility genes. In the cross-tissue TWAS analysis (UTMOST), we identified 17 susceptibility genes associated with COPD. Among these, a novel susceptibility gene, G protein-coupled receptor kinase 4 (GRK4), was validated through single-tissue TWAS (FUSION) and MAGMA analyses, with further confirmation via SMR, MR, and colocalization analyses. Moreover, GRK4 was validated in an independent dataset. This study identifies GRK4 as a potential novel susceptibility gene for COPD, which may influence disease risk by exacerbating inflammatory responses. The findings address gaps in previous single-tissue GWAS studies, revealing consistent expression and potential function of GRK4 across different tissues. However, considering the study's limitations, further investigation and validation of GRK4's role in COPD are warranted.

DNA methylation as a possible mechanism linking childhood adversity and health: results from a 2-sample mendelian randomization study

Childhood adversity is an important risk factor for adverse health across the life course. Epigenetic modifications, such as DNA methylation (DNAm), are a hypothesized mechanism linking adversity to disease susceptibility. Yet, few studies have determined whether adversity-related DNAm alterations are causally related to future health outcomes or if their developmental timing plays a role in these relationships. Here, we used 2-sample mendelian randomization to obtain stronger causal inferences about the association between adversity-associated DNAm loci across development (ie, birth, childhood, adolescence, and young adulthood) and 24 mental, physical, and behavioral health outcomes. We identified particularly strong associations between adversity-associated DNAm and attention-deficit/hyperactivity disorder, depression, obsessive-compulsive disorder, suicide attempts, asthma, coronary artery disease, and chronic kidney disease. More of these associations were identified for birth and childhood DNAm, whereas adolescent and young adulthood DNAm were more closely linked to mental health. Childhood DNAm loci also had primarily risk-suppressing relationships with health outcomes, suggesting that DNAm might reflect compensatory or buffering mechanisms against childhood adversity rather than acting solely as an indicator of disease risk. Together, our results suggest adversity-related DNAm alterations are linked to both physical and mental health outcomes, with particularly strong impacts of DNAm differences emerging earlier in development.

A genome-wide association analysis reveals new pathogenic pathways in gout

Gout is a chronic disease that is caused by an innate immune response to deposited monosodium urate crystals in the setting of hyperuricemia. Here, we provide insights into the molecular mechanism of the poorly understood inflammatory component of gout from a genome-wide association study (GWAS) of 2.6 million people, including 120,295 people with prevalent gout. We detected 377 loci and 410 genetically independent signals (149 previously unreported loci in urate and gout). An additional 65 loci with signals in urate (from a GWAS of 630,117 individuals) but not gout were identified. A prioritization scheme identified candidate genes in the inflammatory process of gout, including genes involved in epigenetic remodeling, cell osmolarity and regulation of NOD-like receptor protein 3 (NLRP3) inflammasome activity. Mendelian randomization analysis provided evidence for a causal role of clonal hematopoiesis of indeterminate potential in gout. Our study identifies candidate genes and molecular processes in the inflammatory pathogenesis of gout suitable for follow-up studies.

The genetic landscape of substance use disorders

Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.

Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score

Identifying genetic drivers of chronic diseases is necessary for drug discovery. Here, we develop a machine learning-assisted genetic priority score, which we call ML-GPS, that incorporates genetic associations with predicted disease phenotypes to enhance target discovery. First, we construct gradient boosting models to predict 112 chronic disease phecodes in the UK Biobank and analyze associations of predicted and observed phenotypes with common, rare, and ultra-rare variants to model the allelic series. We integrate these associations with existing evidence using gradient boosting with continuous feature encoding to construct ML-GPS, training it to predict drug indications in Open Targets and externally testing it in SIDER. We then generate ML-GPS predictions for 2,362,636 gene-phecode pairs. We find that the use of predicted phenotypes, which identify substantially more genetic associations than observed phenotypes across the allele frequency spectrum, significantly improves the performance of ML-GPS. ML-GPS increases coverage of drug targets, with the top 1% of all scores providing support for 15,077 gene-phecode pairs that previously had no support. ML-GPS can also identify well-known target-disease relationships, promising targets without indicated drugs, and targets for several drugs in clinical trials, including LRRK2 inhibitors for Parkinson's disease and olpasiran for cardiovascular disease.

Lipid-lowering drugs and risk of rapid renal function decline: a mendelian randomization study

BACKGROUND

Chronic kidney disease (CKD) patients face the risk of rapid kidney function decline leading to adverse outcomes like dialysis and mortality. Lipid metabolism might contribute to acute kidney function decline in CKD patients. Here, we utilized the Mendelian Randomization approach to investigate potential causal relationships between drug target-mediated lipid phenotypes and rapid renal function decline.

METHODS

In this study, we utilized two methodologies: summarized data-based Mendelian randomization (SMR) and inverse variance-weighted Mendelian randomization (IVW-MR), to approximate exposure to lipid-lowering drugs. This entailed leveraging expression quantitative trait loci (eQTL) for drug target genes and genetic variants proximal to drug target gene regions, which encode proteins associated with low-density lipoprotein (LDL) cholesterol, as identified in genome-wide association studies. The objective was to investigate causal associations with the progression of rapid kidney function decline.

RESULTS

The SMR analysis revealed a potential association between high expression of PCSK9 and rapid kidney function decline (OR = 1.11, 95% CI= [1.001-1.23]; p = 0.044). Similarly, IVW-MR analysis demonstrated a negative association between LDL cholesterol mediated by HMGCR and kidney function decline (OR = 0.74, 95% CI = 0.60-0.90; p = 0.003).

CONCLUSION

Genetically predicted inhibition of HMGCR is linked with the progression of kidney function decline, while genetically predicted PCSK9 inhibition is negatively associated with kidney function decline. Future research should incorporate clinical trials to validate the relevance of PCSK9 in preventing kidney function decline.

Genetically Proxied Antidiabetic Drug Target and Primary Open-Angle Glaucoma: A Mendelian Randomization Study

Background and Aims

Observational studies suggest that antidiabetic drugs may lower POAG risk; while the causal relationship remains unclear. Naturally occurring variation in genes encoding antidiabetics drug targets can be used as proxies to investigate long-term therapeutic effect of these drugs on POAG risk.

Methods

We performed a two-sample Mendelian randomization study to evaluate the potential effect of antidiabetic drug targets on POAG in Europeans and East Asians. To proxy antidiabetic drugs (ABCC8, PPARG, GLP1R, SLC5A2), we leveraged genetic variants located near or within drug target genes that were associated with HbA1c. The validity of our ancestry-specific genetic instrument was checked with multipul positive control outcomes. Genetic summary statistics of POAG from the International Glaucoma Genetics Consortium, Global Biobank Meta-analysis Initiative, and FinnGen consortia were analyzed for Europeans (38,164 cases and 1,576,179 controls) and East Asians (16,650 cases and 288,833 controls) separately. Inverse-variance weighted random-effects models were used as primary method.

Results

MR results provided consistent evidence of a protective effect of ABCC8 inhibition on POAG using data sets from IGG, GBMI, and FinnGen. Genetically predicted one-standard deviation reduction in HbA1c from ABCC8 inhibition were significant associated with lower risk of POAG in Europeans (OR = 0.211, 95% CI: 0.133-0.333; p < 0.001) and East Asians (OR = 0.070, 95% CI: 0.011-0.459; p = 0.0056). The association between genetically predicted ABCC8 inhibition and risk of POAG was mainly mediated through intraocular pressure. No association was found for PPARG, SLC5A2, or GLP1R. Sensitivity analyses supported this observation.

Conclusions

We found a protective effect of genetically proxied ABCC8 inhibition on POAG risk in both Europeans and East Asians, highlighting ABCC8 as a promising candidate drug target for POAG, and mechanisms underlying the protective effect should also be investigated.

Powerful mapping of cis-genetic effects on gene expression across diverse populations reveals novel disease-critical genes

While disease-associated variants identified by genome-wide association studies (GWAS) most likely regulate gene expression levels, linking variants to target genes is critical to determining the functional mechanisms of these variants. Genetic effects on gene expression have been extensively characterized by expression quantitative trait loci (eQTL) studies, yet data from non-European populations is limited. This restricts our understanding of disease to genes whose regulatory variants are common in European populations. While previous work has leveraged data from multiple populations to improve GWAS power and polygenic risk score (PRS) accuracy, multi-ancestry data has not yet been used to better estimate cis-genetic effects on gene expression. Here, we present a new method, Multi-Ancestry Gene Expression Prediction Regularized Optimization (MAGEPRO), which constructs robust genetic models of gene expression in understudied populations or cell types by fitting a regularized linear combination of eQTL summary data across diverse cohorts. In simulations, our tool generates more accurate models of gene expression than widely-used LASSO and the state-of-the-art multi-ancestry PRS method, PRS-CSx, adapted to gene expression prediction. We attribute this improvement to MAGEPRO's ability to more accurately estimate causal eQTL effect sizes (p < 3.98 × 10-4, two-sided paired t-test). With real data, we applied MAGEPRO to 8 eQTL cohorts representing 3 ancestries (average n = 355) and consistently outperformed each of 6 competing methods in gene expression prediction tasks. Integration with GWAS summary statistics across 66 complex traits (representing 22 phenotypes and 3 ancestries) resulted in 2,331 new gene-trait associations, many of which replicate across multiple ancestries, including PHTF1 linked to white blood cell count, a gene which is overexpressed in leukemia patients. MAGEPRO also identified biologically plausible novel findings, such as PIGB, an essential component of GPI biosynthesis, associated with heart failure, which has been previously evidenced by clinical outcome data. Overall, MAGEPRO is a powerful tool to enhance inference of gene regulatory effects in underpowered datasets and has improved our understanding of population-specific and shared genetic effects on complex traits.

Multiancestry transferability of a polygenic risk score for diverticulitis

OBJECTIVE

Polygenic risk scores (PRS) for diverticular disease must be evaluated in diverse cohorts. We sought to explore shared genetic predisposition across the phenome and to assess risk stratification in individuals genetically similar to European, African and Admixed-American reference samples.

METHODS

A 44-variant PRS was applied to the All of Us Research Program. Phenome-wide association studies (PheWAS) identified conditions linked with heightened genetic susceptibility to diverticular disease. To evaluate the PRS in risk stratification, logistic regression models for symptomatic and for severe diverticulitis were compared with base models with covariates of age, sex, body mass index, smoking and principal components. Performance was assessed using area under the receiver operating characteristic curves (AUROC) and Nagelkerke's R2.

RESULTS

The cohort comprised 181 719 individuals for PheWAS and 50 037 for risk modelling. PheWAS identified associations with diverticular disease, connective tissue disease and hernias. Across ancestry groups, one SD PRS increase was consistently associated with greater odds of severe (range of ORs (95% CI) 1.60 (1.27 to 2.02) to 1.86 (1.42 to 2.42)) and of symptomatic diverticulitis ((95% CI) 1.27 (1.10 to 1.46) to 1.66 (1.55 to 1.79)) relative to controls. European models achieved the highest AUROC and Nagelkerke's R2 (AUROC (95% CI) 0.78 (0.75 to 0.81); R2 0.25). The PRS provided a maximum R2 increase of 0.034 and modest AUROC improvement.

CONCLUSION

Associations between a diverticular disease PRS and severe presentations persisted in diverse cohorts when controlling for known risk factors. Relative improvements in model performance were observed, but absolute change magnitudes were modest.