Genomic and transcriptomic insights into antipsychotic-induced changes in total cholesterol and body mass index in a multi-ancestry cohort of the US veterans

Elucidating the proteomic pathways linking childhood body mass index with type 2 diabetes: a Mendelian randomization and recall-by-genotype study

The authors' study identified putative actionable proteins that could reduce the elevated T2D risk attributable to childhood adiposity in Europeans, but their relevance in Asian populations requires further investigation.

Suggestive genome-wide associations with inflammatory biomarkers in an admixed population, including a missense variant in the OR6K6 olfactory receptor gene associated with MCP-1

New SNPs linked to inflammatory biomarkers in a highly admixed Brazilian population are identified, including a missense variant in an olfactory receptor gene linked to MCP-1, which may be biologically important for inflammation and could affect the risk of cardiometabolic diseases.

Genome-Wide Association Studies and Deep-Learning Functional Annotation of Opioid Use Disorder across Three Ancestries in the All of Us Research Program

Multivariate Genome-Wide Association Analysis Identifies Genetic Susceptibility Loci for Metabolic Syndrome in the Korean Genome and Epidemiology Study

Metabolic syndrome (MetS), characterized by a cluster of interrelated metabolic abnormalities including central obesity, elevated blood pressure, dysglycemia, hypertriglyceridemia, and reduced high-density lipoprotein cholesterol (HDL-C), substantially increases type 2 diabetes and cardiovascular disease risk. Conventional genome-wide association studies (GWASs) analyze individual traits or a dichotomized MetS status, only partially capturing its heritability and potentially overlooking variants with shared (pleiotropic) effects across correlated traits. Here, we performed a multiple-trait GWAS using data from the Korean Genome and Epidemiology Study. Using the Korea Biobank Array (K-Chip), we analyzed a discovery cohort from the Ansan and Ansung studies (1490 cases and 3856 controls) and validated the findings in CAVAS (3620 cases and 4461 controls) and HEXA (15,257 cases and 41,807 controls) cohorts. We jointly modeled six MetS-related traits using the complementary multivariate frameworks Multiple Phenotype Association Tests and Genome-wide Efficient Mixed Model Association, alongside a baseline logistic regression. Whereas logistic regression detected 2 APOA5 variants (rs662799 and rs2075291), the multivariate analyses identified 79 significant single-nucleotide polymorphisms; all were replicated in the HEXA cohort. Functional annotation prioritized 27 high-risk variants mapping to 12 genes, whereas pathway analysis (DAVID) implicated eight Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways related to lipid metabolism. Our findings demonstrate that multivariate analysis substantially improves the identification of pleiotropic susceptibility loci for MetS in the Korean population and indicates candidate genes for early detection and management.

Brain imaging-derived phenotypes influence risk of bipolar disorder: comprehensive exposome-wide association and wide-angled genetic analyses

An atlas of brain IDPs associated with bipolar disorder is established, by integrating epidemiological and genetic evidence, and candidate IDPs that were consistently associated with BD are highlighted across complementary analyses.

Genetic and epigenetic architecture of mass spectrometry-derived lipids in blood and their role in lifetime Major Depressive Disorder

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder that remains challenging to treat effectively. Difficulty predicting who will develop the disorder and which individuals will respond to treatment is most likely due to our limited mechanistic understanding and the lack of reliable biomarkers. The current literature suggests that lipid profiles differ in MDD; however, most studies do not integrate other omics layers to elucidate their potential causal relationship at the molecular level. Using phenotypic, genetic, epigenetic and mass-spectrometry lipid data from a Scottish-based cohort of approximately 1,000 participants, we examined associations with individual lipid species. We identified 77 common genetic variant associations ({beta} range = -0.46 to 0.69, p < 3.1x10-10) and 82 genome-wide DNA methylation associations ({beta} range = -1.88 to 1.39, p < 2.2x10-10). We identified 23 lipid species associated with lifetime MDD ({beta} range = -0.23 to 0.2, p < 0.05), including lipid classes such as lysophosphatidylcholine (LPC), diacylglycerol (DG) and phosphatidylcholine (PC). Our integration analysis showed that genetic variants and DNA methylation at sites annotated to the FADS gene cluster were linked with lipid species that were also associated with lifetime MDD status, including LPC(14:1) ({beta} = 0.15, p = 0.03) and PC(19:0)/(20:4) ({beta} = -0.14, p < 0.05). Our findings provide further support for the role of the FADS gene cluster in lipid metabolism and its association with MDD, while introducing DNA methylation as an additional molecular layer supporting this link.