John Danesh

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism–. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases–. Here we present a genome-wide association study of 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 predominantly population-based cohorts. We discover over 400 independent loci and assign likely causal genes at two-thirds of these using detailed manual curation of highly plausible biological candidates. We highlight the importance of sample- and participant characteristics, such as fasting status and sample type, that can have significant impact on genetic associations, revealing direct and indirect associations on glucose and phenylalanine. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing for the first time the metabolic associations of an understudied phenotype, intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetoacetate and hypertension. Our publicly available results …

Myeloproliferative neoplasms (MPNs) are chronic cancers characterized by overproduction of mature blood cells. Their causative somatic mutations, for example, JAK2V617F, are common in the population, yet only a minority of carriers develop MPN. Here we show that the inherited polygenic loci that underlie common hematological traits influence JAK2V617F clonal expansion. We identify polygenic risk scores (PGSs) for monocyte count and plateletcrit as new risk factors for JAK2V617F positivity. PGSs for several hematological traits influenced the risk of different MPN subtypes, with low PGSs for two platelet traits also showing protective effects in JAK2V617F carriers, making them two to three times less likely to have essential thrombocythemia than carriers with high PGSs. We observed that extreme hematological PGSs may contribute to an MPN diagnosis in the absence of somatic driver mutations. Our study …

Restless legs syndrome (RLS) affects up to 10% of older adults. Their health care is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find novel entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 cases and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including 3 on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg= 0.96). Locus annotation prioritized druggable genes such as glutamate-receptors 1 and 4 and Mendelian randomization indicated RLS as a causal risk factor of diabetes. Machine-learning approaches combining genetic and non-genetic information performed best in risk prediction (AUC= 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up, and provided evidence that gene-environment interactions are likely relevant to RLS risk prediction.

Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank’s study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.

Restless legs syndrome (RLS) affects up to 1 in 10 older adults. Their health care is impeded by delayed diagnosis and insufficient treatment options. To advance disease prediction and find novel entry points for therapy, we performed a meta-analysis of genome-wide association studies (GWAS) in 116,647 cases and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci to 164, comprising 196 independent lead SNPs, with the first sex-specific GWAS on RLS revealing largely overlapping genetic predispositions of the sexes (rg= 0.96). Locus annotation prioritized druggable genes such as glutamate-receptors 1 and 4. Mendelian randomization indicated RLS as a causal risk factor of diabetes. Machine-learning approaches combining genetic and environmental information performed best in risk prediction (AUC= 0.82-0.91). Our study identified targets for drug development, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up, and provided evidence that gene-environment interaction is likely highly relevant for RLS risk prediction.

Garrod’s concept of “chemical individuality” has contributed to comprehension of molecular origins of human diseases. Untargeted high throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. Here we studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals. We identified 2,599 variant-metabolite associations (P< 1.25 x10-11) within 330 genomic regions, with rare variants (MAF≤ 1%) explaining 9.4% of associations. Jointly modelling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters (Genetically Influenced Metabotypes). We assigned causal genes for 62.4% of GIMs, providing new insights into fundamental metabolite physiology and their clinical relevance, including metabolite guided discovery of potential adverse drug effects (DPYD, SRD5A2). We show strong enrichment of Inborn Errors of Metabolism (IEM)-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of IEMs. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential aetiological relationships.

In the abstract of the article cited above, the clinical trial number for DISCHARGE (NCT02400229) was inadvertently omitted. The abstract has been revised to include the ClinicalTrials.gov identification. The editors apologize for the error. The online version of the article (https://doi.org/10.2337/dc23-0710) has been updated to correct the error.

IntroductionBlood pressure (BP) is a highly heritable trait with over 2000 underlying genomic loci identified to date. Although the kidney plays a key role, little is known about specific cell types involved in the genetic regulation of BP.MethodsHere, we applied stratified linkage disequilibrium score (LDSC) regression to connect BP genome-wide association studies (GWAS) results to specific cell types of the mature human kidney. We used the largest single-stage BP genome-wide analysis to date, including up to 1,028,980 adults of European ancestry, and single-cell transcriptomic data from 14 mature human kidneys, with mean age of 41 years.ResultsOur analyses prioritized myofibroblasts and endothelial cells, among the total of 33 annotated cell type, as specifically involved in BP regulation (P < 0.05/33, i.e., 0.001515). Enrichment of heritability for systolic BP (SBP) was observed in myofibroblast cells in mature …