A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis

Comprehensive genetic analysis based on multi - omics reveals novel therapeutic targets for mitral valve prolapse and drug molecular dynamics simulation

OBJECTIVE

Mitral valve prolapse (MVP), the most prevalent primary valvular disease, serves as a direct risk factor for multiple cardiovascular disorders and exhibits a high prevalence in the general population. As no specific pharmacological therapies currently exist for MVP, the identification of precise therapeutic targets is imperative.

METHOD

We conducted comprehensive causal genetic inference by integrating genetic data from expression quantitative trait loci (eQTL) and genome-wide association studies (GWAS). Analytical approaches included Mendelian Randomization (MR), colocalization analysis, Summary-data-based Mendelian Randomization (SMR), Linkage Disequilibrium Score Regression (LDSC), and High-Definition Likelihood (HDL) analysis. Protein quantitative trait loci (pQTL) were utilized to validate gene expression. Replication analyses were performed using additional exposure datasets. Methylation quantitative trait loci (mQTL) were employed to elucidate regulatory roles of methylation sites on genes and disease pathogenesis. Phenome-Wide Association Study (PheWAS) was conducted to predict potential adverse effects of gene-targeted therapies. Drug candidates targeting identified genes were predicted via the Drug Signature Database (DSigDB) and validated through molecular docking. Core targets were identified using the STRING database, followed by molecular dynamics simulations.

RESULT

Two-sample MR analysis showed that genetically predicted 266 genes had positive or negative causal relationships with MVP. Colocalization analysis indicated that 9 genes had a posterior probability greater than 0.75. Subsequent SMR analysis excluded the gene GAPVD1. HDL analysis showed that except for the gene PTPN1, the remaining 7 genes were all significantly genetically associated with MVP, and LDSC analysis further showed that only NMB was associated with MVP. Validation using pQTL data confirmed that increased NMB protein expression reduced the risk of MVP. Replication analysis further verified this conclusion. In addition, SMR analysis of methylation sites for 8 genes indicated that multiple methylation sites played a key role in gene regulation of mitral valve prolapse. PheWAS results showed that targeted therapy for 8 genes did not detect other causal associations at the genome-wide significance level. Molecular docking showed that quercetin had good binding ability with 8 target genes. The STRING database identified 3 core target proteins, and molecular dynamics simulations further verified the binding ability of quercetin with core target proteins.

CONCLUSION

This study successfully predicted the potential of multiple druggable genes as effective therapeutic targets for MVP through genetic methods, validated the potential of quercetin as a drug, and provided new ideas for drug treatment strategies for MVP.

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.

Genome-wide association testing beyond SNPs

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

Linking Genome-Wide Association Studies to Pharmacological Treatments for Psychiatric Disorders

Importance

Large-scale genome-wide association studies (GWAS) should ideally inform the development of pharmacological treatments, but whether GWAS-identified mechanisms of disease liability correspond to the pathophysiological processes targeted by current pharmacological treatments is unclear.

Objective

To investigate whether functional information from a range of open bioinformatics datasets can elucidate the relationship between GWAS-identified genetic variation and the genes targeted by current treatments for psychiatric disorders.

Design, Setting, and Participants

Associations between GWAS-identified genetic variation and pharmacological treatment targets were investigated across 4 psychiatric disorders-attention-deficit/hyperactivity disorder, bipolar disorder, schizophrenia, and major depressive disorder. Using a candidate set of 2232 genes listed as targets for all approved treatments in the DrugBank database, each gene was independently assigned 2 scores for each disorder-one based on its involvement as a treatment target and the other based on the mapping between GWAS-implicated single-nucleotide variants (SNVs) and genes according to 1 of 4 bioinformatic data modalities: SNV position, gene distance on the protein-protein interaction (PPI) network, brain expression quantitative trail locus (eQTL), and gene expression patterns across the brain. Study data were analyzed from November 2023 to September 2024.

Main Outcomes and Measures

Gene scores for pharmacological treatments and GWAS-implicated genes were compared using a measure of weighted similarity applying a stringent null hypothesis-testing framework that quantified the specificity of the match by comparing identified associations for a particular disorder with a randomly selected set of treatments.

Results

Incorporating information derived from functional bioinformatics data in the form of a PPI network revealed links for bipolar disorder (P permutation [P-perm] = 7 × 10-4; weighted similarity score, empirical [ρ-emp] = 0.1347; mean [SD] weighted similarity score, random [ρ-rand] = 0.0704 [0.0163]); however, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeded null expectations. Exploratory analysis assessing the overlap between the GWAS-identified genetic architecture and treatment targets across disorders identified that most disorder pairs and mapping methods did not show a significant correspondence.

Conclusions and Relevance

In this bioinformatic study, the relatively low degree of correspondence across modalities suggests that the genetic architecture driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms currently used for targeting symptom manifestations through pharmacological treatments. Novel approaches incorporating insights derived from GWAS based on refined phenotypes including treatment response may assist in mapping disorder risk genes to pharmacological treatments in the long term.

Transcriptome-wide Mendelian randomization during CD4+ T cell activation reveals immune-related drug targets for cardiometabolic diseases

Immunity has shown potentials in informing drug development for cardiometabolic diseases, such as type 2 diabetes (T2D) and coronary artery disease (CAD). Here, we performed a transcriptome-wide Mendelian randomization (MR) study to estimate the putative causal effects of 11,021 gene expression profiles during CD4+ T cells activation on the development of T2D and CAD. Robust MR and colocalization evidence was observed for 162 genes altering T2D risk and 80 genes altering CAD risk, with 12% and 16% respectively demonstrating CD4+ T cell specificity. We observed temporal causal patterns during T cell activation in 69 gene-T2D pairs and 34 gene-CAD pairs. These genes were eight times more likely to show robust genetic evidence. We further identified 25 genes that were targets for drugs under clinical investigation, including LIPA and GCK. This study provides evidence to support immune-to-metabolic disease connections, and prioritises immune-mediated drug targets for cardiometabolic diseases.

Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance

Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs.

Leveraging the Genetics of Psychiatric Disorders to Prioritize Potential Drug Targets and Compounds

Background

Genetics has the potential to inform biologically relevant drug treatment and repurposing which may ultimately improve patient care. In this study, we combine methods which leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds.

Methods

We used the largest available genome-wide association studies, in European ancestry, of four psychiatric disorders [i.e., attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] along with genes encoding drug targets. With this data, we conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We then conducted a series of molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder.

Findings

We reveal candidate drug targets shared across multiple disorders as well as disorder-specific targets. Drug prioritization indicated genetic support for several currently used psychotropic medications including the antipsychotic paliperidone as the top ranked drug for schizophrenia. We also observed genetic support for other commonly used psychotropics (e.g., clozapine, risperidone, duloxetine, lithium, and valproic acid). Opportunities for drug repurposing were revealed such as cholinergic drugs for ADHD, estrogens for depression, and gabapentin enacarbil for schizophrenia. Our findings also indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission.

Interpretation

Here we present a series of complimentary and comprehensive analyses that highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders. Our findings present novel opportunities for refining psychiatric treatment.

Circulating Proteins and IgA Nephropathy: A Multiancestry Proteome-Wide Mendelian Randomization Study

Key Points

Background

The therapeutic options for IgA nephropathy are rapidly evolving, but early diagnosis and targeted treatment remain challenging. We aimed to identify circulating plasma proteins associated with IgA nephropathy by proteome-wide Mendelian randomization studies across multiple ancestry populations.

Methods

In this study, we applied Mendelian randomization and colocalization analyses to estimate the putative causal effects of 2615 proteins on IgA nephropathy in Europeans and 235 proteins in East Asians. Following two-stage network Mendelian randomization, multitrait colocalization analysis and protein-altering variant annotation were performed to strengthen the reliability of the results. A protein–protein interaction network was constructed to investigate the interactions between the identified proteins and the targets of existing medications.

Results

Putative causal effects of 184 and 13 protein–disease pairs in European and East Asian ancestries were identified, respectively. Two protein–disease pairs showed shared causal effects across them (CFHR1 and FCRL2). Supported by the evidence from colocalization analysis, potential therapeutic targets were prioritized and four drug-repurposing opportunities were suggested. The protein–protein interaction network further provided strong evidence for existing medications and pathways that are known to be therapeutically important.

Conclusions

Our study identified a number of circulating proteins associated with IgA nephropathy and prioritized several potential drug targets that require further investigation.

Integrin signalling in joint development, homeostasis and osteoarthritis

Integrins are key regulators of cell-matrix interactions during joint development and joint tissue homeostasis, as well as in the development of osteoarthritis (OA). The signalling cascades initiated by the interactions of integrins with a complex network of extracellular matrix (ECM) components and intracellular adaptor proteins orchestrate cellular responses necessary for maintaining joint tissue integrity. Dysregulated integrin signalling, triggered by matrix degradation products such as matrikines, disrupts this delicate balance, tipping the scales towards an environment conducive to OA pathogenesis. The interplay between integrin signalling and growth factor pathways further underscores the multifaceted nature of OA. Moreover, emerging insights into the role of endocytic trafficking in regulating integrin signalling add a new layer of complexity to the understanding of OA development. To harness the therapeutic potential of targeting integrins for mitigation of OA, comprehensive understanding of their molecular mechanisms across joint tissues is imperative. Ultimately, deciphering the complexities of integrin signalling will advance the ability to treat OA and alleviate its global burden.

Multi-omics Mendelian randomization integrating GWAS, eQTL and pQTL data revealed GSTM4 as a potential drug target for migraine

INTRODUCTION

Migraine, as a complex neurological disease, brings heavy burden to patients and society. Despite the availability of established therapies, existing medications have limited efficacy. Thus, we aimed to find the drug targets that improve the prognosis of migraine.

METHOD

We used Mendelian Randomization (MR) and Summary-data-based MR (SMR) analyses to study possible drug targets of migraine by summary statistics from FinnGen cohorts (nCase = 44,616, nControl = 367,565), with further replication in UK Biobank (nCase = 26,052, nControl = 487,214). Genetic instruments were obtained from eQTLGen and UKB-PPP to verify the drug targets at the gene expression and protein levels. The additional analyses including Bayesian co-localization, the heterogeneity in dependent instruments(HEIDI), Linkage Disequilibrium Score(LDSC), bidirectional MR, multivariate MR(MVMR), heterogeneity test, horizontal pleiotropy test, and Steiger filtering were implemented to consolidate the findings further. Lastly, drug prediction analysis and phenome-wide association study(PheWAS) were employed to imply the possibility of drug targets for future clinical applications.

RESULT

The MR analysis of eQTL data showed that four drug targets (PROCR, GSTM4, SLC4A1, and TNFRSF10A) were significantly associated with migraine risk in both the FinnGen and UK Biobank cohorts. However, only GSTM4 exhibited consistent effect directions across the two outcomes(Discovery cohort: OR(95%CI) = 0.94(0.93-0.96); p = 2.70e - 10; Replication cohort: OR(95%CI) = 0.93(0.91-0.94); p = 4.21e - 17). Furthermore, GSTM4 passed the SMR at p < 0.05 and HEIDI test at p > 0.05 at both the gene expression and protein levels. The protein-level MR analysis revealed a strong correlation between genetically predicted GSTM4 with a lower incidence of migraine and its subtypes(Overall migraine: OR(95%CI) = 0.91(0.87-0.95); p = 6.98e-05; Migraine with aura(MA): OR(95%CI) = 0.90(0.85-0.96); p = 2.54e-03; Migraine without aura(MO): OR(95%CI) = 0.90(0.83-0.96); p = 2.87e-03), indicating a strong co-localization relationship (PPH4 = 0.86). Further analyses provided additional validation for the possibility of GSTM4 as a migraine treatment target.

CONCLUSION

This study identifies GSTM4 as a potential druggable gene and promising therapeutic target for migraine.

Identifying therapeutic target genes for migraine by systematic druggable genome-wide Mendelian randomization

BACKGROUND

Currently, the treatment and prevention of migraine remain highly challenging. Mendelian randomization (MR) has been widely used to explore novel therapeutic targets. Therefore, we performed a systematic druggable genome-wide MR to explore the potential therapeutic targets for migraine.

METHODS

We obtained data on druggable genes and screened for genes within brain expression quantitative trait locis (eQTLs) and blood eQTLs, which were then subjected to two-sample MR analysis and colocalization analysis with migraine genome-wide association studies data to identify genes highly associated with migraine. In addition, phenome-wide research, enrichment analysis, protein network construction, drug prediction, and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic drugs.

RESULTS

We identified 21 druggable genes significantly associated with migraine (BRPF3, CBFB, CDK4, CHD4, DDIT4, EP300, EPHA5, FGFRL1, FXN, HMGCR, HVCN1, KCNK5, MRGPRE, NLGN2, NR1D1, PLXNB1, TGFB1, TGFB3, THRA, TLN1 and TP53), two of which were significant in both blood and brain (HMGCR and TGFB3). The results of phenome-wide research showed that HMGCR was highly correlated with low-density lipoprotein, and TGFB3 was primarily associated with insulin-like growth factor 1 levels.

CONCLUSIONS

This study utilized MR and colocalization analysis to identify 21 potential drug targets for migraine, two of which were significant in both blood and brain. These findings provide promising leads for more effective migraine treatments, potentially reducing drug development costs.

Stroke genetics and how it Informs novel drug discovery

INTRODUCTION

Stroke is one of the main causes of death and disability worldwide. Nevertheless, despite the global burden of this disease, our understanding is limited and there is still a lack of highly efficient etiopathology-based treatment. It is partly due to the complexity and heterogenicity of the disease. It is estimated that around one-third of ischemic stroke is heritable, emphasizing the importance of genetic factors identification and targeting for therapeutic purposes.

AREAS COVERED

In this review, the authors provide an overview of the current knowledge of stroke genetics and its value in diagnostics, personalized treatment, and prognostication.

EXPERT OPINION

As the scale of genetic testing increases and the cost decreases, integration of genetic data into clinical practice is inevitable, enabling assessing individual risk, providing personalized prognostic models and identifying new therapeutic targets and biomarkers. Although expanding stroke genetics data provides different diagnostics and treatment perspectives, there are some limitations and challenges to face. One of them is the threat of health disparities as non-European populations are underrepresented in genetic datasets. Finally, a deeper understanding of underlying mechanisms of potential targets is still lacking, delaying the application of novel therapies into routine clinical practice.

Subclinical Atherosclerosis in a Gout Cohort: Prevalence and Associations

The current observational, prospective study enrolled 65 patients with gout, diagnosed according to 2015 ACR/EULAR criteria [17], evaluated in Rheumatology Clinic, Emergency County Hospital Craiova, and 40 healthy subjects. This research aimed to determine the presence of subclinical carotid atherosclerosis, revealed by an increased intima media thickness and carotid plaques in gout patients, by US examination. Secondary, we aimed to search for the possible correlations displayed between the presence of subclinical carotid atherosclerosis and several disease variables. CCAIMT over 0.9mm was identified for 19 patients (29.23%), percentage statistically significant different compared to controls (7; 17.5%), p=0,0428. For 23 patients (35.38%) carotid plaques were present at US examination, more prevalent compared to controls (19; 29.23%), p=0.002. Using multivariate logistic regression, we pointed out that SUA (OR 2,103; p=0.0002), age (OR=1,051; p<0.001), disease duration (OR=1.740; p=0.0039) and LDLc (OR=1,003; p=0.0029) were independently associated to an increased IMT in patients with gout, similar results being obtained for carotid plaques. MSKUS was performed for all patients, with important results. The presence of deposits associated with an increased risk of a thick IMT; similar results were obtained for double contour sign, aggregates and tophi. A statistically significant risk was noticed for the presence of deposits (p=0.002). Regarding the presence of carotid atheroma plaques, a higher risk was associated to deposits identification, double contour sign, aggregates, tophi and PD signal. Our results sustain that carotid ultrasound is an easily accessible imagistic method that offers important predictors of atherosclerotic status.

Integrative multi-omics analysis identifies genetically supported druggable targets and immune cell specificity for myasthenia gravis

BACKGROUND

Myasthenia gravis (MG) is a chronic autoimmune disorder characterized by fluctuating muscle weakness. Despite the availability of established therapies, the management of MG symptoms remains suboptimal, partially attributed to lack of efficacy or intolerable side-effects. Therefore, new effective drugs are warranted for treatment of MG.

METHODS

By employing an analytical framework that combines Mendelian randomization (MR) and colocalization analysis, we estimate the causal effects of blood druggable expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) on the susceptibility of MG. We subsequently investigated whether potential genetic effects exhibit cell-type specificity by utilizing genetic colocalization analysis to assess the interplay between immune-cell-specific eQTLs and MG risk.

RESULTS

We identified significant MR results for four genes (CDC42BPB, CD226, PRSS36, and TNFSF12) using cis-eQTL genetic instruments and three proteins (CTSH, PRSS8, and CPN2) using cis-pQTL genetic instruments. Six of these loci demonstrated evidence of colocalization with MG susceptibility (posterior probability > 0.80). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci. Notably, we identified robust evidence of colocalization, with a posterior probability of 0.854, linking CTSH expression in TH2 cells and MG risk.

CONCLUSIONS

This study provides crucial insights into the genetic and molecular factors associated with MG susceptibility, singling out CTSH as a potential candidate for in-depth investigation and clinical consideration. It additionally sheds light on the immune-cell regulatory mechanisms related to the disease. However, further research is imperative to validate these targets and evaluate their feasibility for drug development.

Extracting immunological and clinical heterogeneity across autoimmune rheumatic diseases by cohort-wide immunophenotyping

OBJECTIVE

Extracting immunological and clinical heterogeneity across autoimmune rheumatic diseases (AIRDs) is essential towards personalised medicine.

METHODS

We conducted large-scale and cohort-wide immunophenotyping of 46 peripheral immune cells using Human Immunology Protocol of comprehensive 8-colour flow cytometric analysis. Dataset consisted of >1000 Japanese patients of 11 AIRDs with deep clinical information registered at the FLOW study, including rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). In-depth clinical and immunological characterisation was conducted for the identified RA patient clusters, including associations of inborn human genetics represented by Polygenic Risk Score (PRS).

RESULTS

Multimodal clustering of immunophenotypes deciphered underlying disease-cell type network in immune cell, disease and patient cluster resolutions. This provided immune cell type specificity shared or distinct across AIRDs, such as close immunological network between mixed connective tissue disease and SLE. Individual patient-level clustering dissected patients with AIRD into several clusters with different immunological features. Of these, RA-like or SLE-like clusters were exclusively dominant, showing immunological differentiation between RA and SLE across AIRDs. In-depth clinical analysis of RA revealed that such patient clusters differentially defined clinical heterogeneity in disease activity and treatment responses, such as treatment resistance in patients with RA with SLE-like immunophenotypes. PRS based on RA case-control and within-case stratified genome-wide association studies were associated with clinical and immunological characteristics. This pointed immune cell type implicated in disease biology such as dendritic cells for RA-interstitial lung disease.

CONCLUSION

Cohort-wide and cross-disease immunophenotyping elucidate clinically heterogeneous patient subtypes existing within single disease in immune cell type-specific manner.

Utilizing Human Genetics to Develop Chemoprevention for Cancer-Too Good an Opportunity to be Missed

Large-scale genetic studies are reliably identifying many risk factors for disease in the general population. Several of these genetic risk factors encode potential drug targets, and genetics has already helped to introduce targeted agents for some diseases, an example being lipid-lowering drugs to reduce the incidence of cardiovascular disease. Multiple drugs have been developed to treat cancers based on somatic mutations and genomics, but in stark contrast, there seems to be a reluctance to use germline genetic data to develop drugs to prevent malignancy, despite the large numbers of people who could benefit, the potential for lowering cancer rates, and the widespread current use of non-pharmaceutical measures to reduce cancer risk factors such as tobacco, alcohol, and infectious diseases. We argue that concerted efforts for cancer prevention based on genetics, including genes influenced by common polymorphisms that modulate cancer risk, are urgently needed. There are enormous, yet underutilized, opportunities to develop novel targeted agents for chemoprevention of cancer based on human germline genetics. Such efforts are likely to require the support of a dedicated funding program by national and international agencies. See related commentary by Winham and Sherman, p. 13.

Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.

Progress in genetics of type 2 diabetes and diabetic complications

Type 2 diabetes results from a complex interaction between genetic and environmental factors. Precision medicine for type 2 diabetes using genetic data is expected to predict the risk of developing diabetes and complications and to predict the effects of medications and life-style intervention more accurately for individuals. Genome-wide association studies (GWAS) have been conducted in European and Asian populations and new genetic loci have been identified that modulate the risk of developing type 2 diabetes. Novel loci were discovered by GWAS in diabetic complications with increasing sample sizes. Large-scale genome-wide association analysis and polygenic risk scores using biobank information is making it possible to predict the development of type 2 diabetes. In the ADVANCE clinical trial of type 2 diabetes, a multi-polygenic risk score was useful to predict diabetic complications and their response to treatment. Proteomics and metabolomics studies have been conducted and have revealed the associations between type 2 diabetes and inflammatory signals and amino acid synthesis. Using multi-omics analysis, comprehensive molecular mechanisms have been elucidated to guide the development of targeted therapy for type 2 diabetes and diabetic complications.

What are the hot topics in Japanese rheumatology? Go above and beyond

Japanese rheumatology and immunology have contributed to progress in the field and advancement of rheumatology, including postmarketing surveillance, development of IL-6-targeting therapy and concept of drug tapering, have accelerated in the 21st century. The 67th Annual Scientific Meeting of the Japan College of Rheumatology, held on Fukuoka on 24 April 2023-26 April 2023, will go ahead and beyond such an advancement. Profound discussion on future perspectives such as precision medicine, the elucidation of pathology and genome-based drug discovery by multilayered integration with various types of omics information, information on metabolome and proteome of blood metabolites, and database of target proteins and compounds for drug discovery will be discussed.

Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWASs). Fine-mapping of meta-analysis studies is typically performed as in a single-cohort study. Here, we first demonstrate that heterogeneity (e.g., of sample size, phenotyping, imputation) hurts calibration of meta-analysis fine-mapping. We propose a summary statistics-based quality-control (QC) method, suspicious loci analysis of meta-analysis summary statistics (SLALOM), that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics. We validate SLALOM in simulations and the GWAS Catalog. Applying SLALOM to 14 meta-analyses from the Global Biobank Meta-analysis Initiative (GBMI), we find that 67% of loci show suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci are significantly depleted for having nonsynonymous variants as lead variant (2.7×; Fisher's exact p = 7.3 × 10-4). We find limited evidence of fine-mapping improvement in the GBMI meta-analyses compared with individual biobanks. We urge extreme caution when interpreting fine-mapping results from meta-analysis of heterogeneous cohorts.