Correcting for Genomic Inflation Leads to Loss of Power in Large-Scale Genome-Wide Association Study Meta-Analysis.

Inflation in genome-wide association studies (GWAS) summary statistics represents a major challenge, for which correction methods have been developed. These include the genomic control (GC) method, which uses the λ-value to correct summary statistics, and the linkage disequilibrium score regression (LDSR) method, which uses the LDSR intercept. By using type 2 diabetes (T2D) as an exemplar, we explore factors influencing λ-values and the impact of these corrections on association signals.

Polygenic prediction of body mass index and obesity through the life course and across ancestries.

Polygenic scores (PGSs) for body mass index (BMI) may guide early prevention and targeted treatment of obesity. Using genetic data from up to 5.1 million people (4.

Development of an MHC imputation panel highlights independent contributions of HLA amino acid residues and C4 copy number variations to SLE risk.

Objectives: Systemic lupus erythematosus (SLE) is a complex autoimmune disease strongly associated with the major histocompatibility complex (MHC) region, but precisely pinpointing the risk variants remains challenging. This study aimed to comprehensively profile SLE-driving variants using a newly developed East Asian MHC imputation reference panel, capable of simultaneously imputing diverse MHC variants, including multilevel human leukocyte antigen (HLA) variants and copy number variations (CNVs) of C4 elements, such as C4A, C4B, and human endogenous retrovirus (HERV).

Methods: Using the whole-genome-sequencing (WGS) data from ∼2000 Korean samples, we genotyped and phased MHC variants, including HLA variants and C4-related CNVs, to construct an MHC reference panel.

DrFARM: identification of pleiotropic genetic variants in genome-wide association studies.

In a standard analysis, pleiotropic variants are identified by running separate genome-wide association studies (GWAS) and combining results across traits. But such statistical approach based on marginal summary statistics may lead to spurious results. We propose a new statistical approach, Debiased-regularized Factor Analysis Regression Model (DrFARM), through a joint regression model for simultaneous analysis of high-dimensional genetic variants and multilevel dependencies.

Multidimensional Pancreatic Islet β-cell Function (PIF) Assessment Improves Predictive Effect of Diabetes Risk Scores.

Aims/hypothesis: Comprehensive assessment of pancreatic islet β-cell function (PIF) is crucial for diabetes management. We proposed a multidimensional, relative quantification system for PIF measurement.

Methods: Our novel approach evaluates PIF using three dimensions: stationary-baseline (PIF-S), load-peak (PIF-L), and accelerated-slope (PIF-A).

Impaired glucagon suppression accompanied by liver fat accumulation mediated worse blood glucose control in T2D patients.

Background: Metabolic dysfunction-associated steatotic liver disease is prevalent in type 2 diabetes (T2D) and exacerbates hyperglycemia, but its impact on postprandial glucagon suppression remains unclear.

Objective: To investigate the association between hepatic steatosis and impaired glucagon suppression during oral glucose tolerance tests (OGTT), and to evaluate the mediating role of glucagon dysregulation in linking liver fat to glycemic control.

Design And Methods: In this cross-sectional study, 604 patients with T2D underwent liver fat quantification via FibroScan Pro®-controlled attenuation parameter (CAP) and liver ultrasound.

The effect of type 2 diabetes genetic predisposition on non-cardiovascular comorbidities.

Type 2 diabetes (T2D) is epidemiologically associated with a wide range of non-cardiovascular comorbidities, yet their shared etiology has not been fully elucidated. Leveraging eight non-overlapping mechanistic clusters of T2D genetic profiles, each representing distinct biological pathways, we investigate putative causal links between cluster-stratified T2D genetic predisposition and 21 non-cardiovascular comorbidities. Most of the identified putative causal effects are driven by distinct T2D genetic clusters.

Unravelling the molecular mechanisms causal to type 2 diabetes across global populations and disease-relevant tissues.

Type 2 diabetes (T2D) is a prevalent disease that arises from complex molecular mechanisms. Here, we leverage T2D multi-ancestry genetic associations to identify causal molecular mechanisms in an ancestry- and tissue-aware manner. Using two-sample Mendelian Randomization corroborated by colocalization across four global ancestries, we analyze 20,307 gene and 1,630 protein expression levels using blood-derived -quantitative trait loci (QTLs).

Assessing the potential causal effects of 1099 plasma metabolites on 2099 binary disease endpoints.

Metabolites are small molecules that are useful for estimating disease risk and elucidating disease biology. Here, we perform two-sample Mendelian randomization to systematically infer the potential causal effects of 1099 plasma metabolites measured in 6136 Finnish men from the METSIM study on risk of 2099 binary disease endpoints measured in 309,154 Finnish individuals from FinnGen. We find evidence for 282 putative causal effects of 70 metabolites on 183 disease endpoints.

A multi-omics target study for glioblastoma multiforme (GBM) based on Mendelian randomization analysis.

Background: Glioblastoma multiforme (GBM) is the most frequent type of primary malignant brain tumor. This study utilized Mendelian randomization (MR) analysis to explore the causal link between proteins in plasma and cerebrospinal fluid and GBM.

Aims: This study aimed to identify proteins in both plasma and cerebrospinal fluid (CSF) that could serve as potential therapeutic targets for GBM.

Dissecting genetic regulation of metabolic coordination.

Understanding genetic regulation of metabolism is critical for gaining insights into the causes of metabolic diseases. Traditional metabolome-based genome-wide association studies (mGWAS) focus on static associations between single nucleotide polymorphisms (SNPs) and metabolite levels, overlooking the changing relationships caused by genotypes within the metabolic network. Notably, some metabolites exhibit changes in correlation patterns with other metabolites under certain physiological conditions while maintaining their overall abundance level.

Type 2 diabetes polygenic risk score demonstrates context-dependent effects and associations with type 2 diabetes-related risk factors and complications across diverse populations.

Polygenic risk scores (PRS) hold prognostic value for identifying individuals at higher risk of type 2 diabetes (T2D). However, further characterization is needed to understand the generalizability of T2D PRS in diverse populations across various contexts. We characterized a multi-ancestry T2D PRS among 244,637 cases and 637,891 controls across eight populations from the Population Architecture Genomics and Epidemiology (PAGE) Study and 13 additional biobanks and cohorts.

Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits.

We present multi-integration of transcriptome-wide association studies and colocalization (Multi-INTACT), an algorithm that models multiple "gene products" (e.g., encoded RNA transcript and protein levels) to implicate causal genes and relevant gene products.

Associations of Combined Genetic and Lifestyle Risks with Incident Type 2 Diabetes in the UK Biobank.

Background: Type 2 diabetes (T2D) results from a complex interplay between genetic predisposition and lifestyle factors. Both genetic susceptibility and unhealthy lifestyle are known to be associated with elevated T2D risk. However, their combined effects on T2D risk are not well studied.

Neuroticism and cerebral small vessel disease: A genetic correlation and Mendelian randomization analysis.

Objectives: The association of neuroticism and cerebral small vessel disease (CSVD) development remains unclear. In this study, we used Mendelian randomization (MR) to explore the potential role of neuroticism in CSVD development.

Methods: We collected data on ischemic stroke (IS); small vessel stroke (SVS); three neuroimaging markers altered in CSVD, including white matter hyperintensity (WMH), fractional anisotropy (FA), and mean diffusivity (MD); and three neuroticism clusters, including depressed affect, worry, sensitivity to environmental stress and adversity (SESA), from large-scale genome-wide association studies (GWAS).

Regulation of nerve cells and therapeutic potential in central nervous system injury using microglia-derived exosomes.

The intercellular communication within the central nervous system (CNS) is of great importance for in maintaining brain function, homeostasis, and CNS regulation. When the equilibrium of CNS is disrupted or injured, microglia are immediately activated and respond to CNS injury. Microglia-derived exosomes are capable of participating in intercellular communication within the CNS by transporting various bioactive substances, including nucleic acids, proteins, lipids, amino acids, and metabolites.

Multi-omics characterization of type 2 diabetes associated genetic variation.

Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 -effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits.

Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations.

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer.

A cross-ancestry genome-wide meta-analysis, fine-mapping, and gene prioritization approach to characterize the genetic architecture of adiponectin.

Previous genome-wide association studies (GWASs) for adiponectin, a complex trait linked to type 2 diabetes and obesity, identified >20 associated loci. However, most loci were identified in populations of European ancestry, and many of the target genes underlying the associations remain unknown. We conducted a cross-ancestry adiponectin GWAS meta-analysis in ≤46,434 individuals from the Metabolic Syndrome in Men (METSIM) cohort and the ADIPOGen and AGEN consortiums.

Metabolome-wide Mendelian randomization characterizes heterogeneous and shared causal effects of metabolites on human health.

Metabolites are small molecules that are useful for estimating disease risk and elucidating disease biology. Nevertheless, their causal effects on human diseases have not been evaluated comprehensively. We performed two-sample Mendelian randomization to systematically infer the causal effects of 1,099 plasma metabolites measured in 6,136 Finnish men from the METSIM study on risk of 2,099 binary disease endpoints measured in 309,154 Finnish individuals from FinnGen.