Fentanyl or morphine for persistent dyspnoea in COPD: a multicentre randomised crossover trial.

Circulating metabolites are key phenotypic markers linking genetic variation to diverse health outcomes, influenced by both genetic and environmental factors. Understanding the genetic architecture of the blood metabolome could provide valuable insights into metabolic regulation and disease pathogenesis. Previous genome-wide association studies (GWASs) and whole-exome sequencing (WES) have identified numerous genetic loci associated with blood metabolites. However, most metabolomics GWASs have relied on imputed genotyping arrays, which primarily capture common variants, leaving rare variants insufficiently and inaccurately characterized. While WES has made significant strides in sequencing depth, it is limited to exonic regions, covering only 2–3% of the genome. Emerging evidence underscores the substantial regulatory effects of noncoding variants, which contribute significantly to trait heritability. Therefore, it is crucial to comprehensively investigate the genetic architecture of the metabolome within the context of the entire genome, as a critical step toward elucidating disease mechanisms and advancing precision medicine. The advancement of whole-genome sequencing (WGS) technologies has enabled comprehensive interrogation of the entire genome, facilitating the identification of genetic determinants underlying complex human traits. However, few studies have comprehensively investigated the genetic architecture of the blood metabolome at scale and from the perspective of the whole genome, leaving key questions unresolved. For example, can a large-scale WGS study provide a more comprehensive genetic landscape of blood metabolic markers and reveal additional associations? How might the whole-genome landscape help achieve the high-resolution fine-mapping of functional variants, and would those functional variants deepen the understanding of the biological mechanisms underlying the metabolic changes and pathways? Furthermore, could these genetic insights enhance causal inferences between metabolites and diseases, thereby pinpointing promising therapeutic targets? The availability of WGS data from nearly 200,000 individuals in the UK Biobank (UKB), combined with metabolomics and enriched phenotypic information, provides an unparalleled opportunity to answer these questions. Here, we present a comprehensive genetic landscape of the nuclear magnetic resonance (NMR)-based plasma metabolome in 199,138 UKB individuals across five ancestry groups. We analyzed 249 metabolic measures assessed by Nightingale Health and 64 biologically plausible ratios, predominantly comprising lipids and lipoproteins. We employed a two-stage genome-wide association meta-analysis, conducting single-variant analyses and rare-variant aggregate-based tests to elucidate the genetic architecture of plasma metabolites (Fig. ). We offer insights into ketone body metabolism pathway and inborn errors of metabolism (IEMs). The integrative analytical framework, combining multi-omics data through colocalization, Mendelian Randomization (MR), and druggability assessment analyses, provided insights into metabolite-disease associations and guided therapeutic target discovery. Our open-access platform () provides a valuable resource for advancing the understanding of metabolic genetics and its implications for human health and drug discovery.