Genome-wide association studies articles from across Nature Portfolio

Genetic prediction of type 1 diabetes is one of the most successful for complex traits. A machine learning approach now improves this further and discovers multiple non-linear locus–locus interactions and molecular subclusters with differing clinical features.

This study identifies structural variants (SVs) from long-read assemblies, develops a new reference panel and web application to impute SVs from single-nucleotide polymorphism-level data, and identifies associations between SVs and complex traits.

High-resolution analyses of blood-derived whole-genome sequence data from UK Biobank detect new mosaic chromosomal alterations and identify rare protein-coding variants associated with clonal expansions of copy-neutral loss-of-heterozygosity mutations.

Genome-wide analyses of brain white matter connectome topology reveal shared genetic architecture and causal links with fluid intelligence.

The genetic basis of many circulating lipid species is poorly understood. Here, the authors analyze nearly 1,000 lipid traits, identifying 217 associated loci and causal gene-lipid relationships, revealing new insights into lipid metabolism and potential therapeutic targets.

Mitochondrial dysfunction is linked to disease, but its causal role is unclear. Here, the authors use inherited genetic variation to predict mtDNA transcript levels across tissues and link these predicted changes to human health, highlighting mitochondrial processes as a potential driver of common disease risk.

Genome-wide association and fine-mapping analyses of type 1 diabetes (T1D) identify multiple genetic risk signals. Furthermore, a machine learning model, T1GRS, improves the prediction of T1D in individuals with complex risk profiles and identifies genetic subgroups.

Genetic prediction of type 1 diabetes is one of the most successful for complex traits. A machine learning approach now improves this further and discovers multiple non-linear locus–locus interactions and molecular subclusters with differing clinical features.

Through international collaboration and genetic analyses with short-read and long-read genome sequencing, an intronic repeat expansion in the GOLGA8A gene has been identified, with long CT-dimer-rich expansions strongly associated with a rare subtype of frontotemporal lobar degeneration.

An integrated analysis of 23,634 individuals identified 235 circulating metabolites associated with future risk of type 2 diabetes and revealed their potential genetic and environmental determinants. These findings provide a foundation for understanding the metabolic landscape underlying type 2 diabetes risk, informing preventative therapies that target specific metabolic pathways.