Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank

Nag, Abhishek; Dhindsa, Ryan S.; Middleton, Lawrence; Jiang, Xiao; Vitsios, Dimitrios; Wigmore, Eleanor M.; Allman, Erik; Reznichenko, Anna; Carss, Keren; Smith, Katherine R.; Wang, Quanli; Challis, Benjamin; Paul, Dirk S.; Harper, Andrew; Petrovski, Slavé

doi:10.1016/j.ajhg.2023.02.002

Cited by 12 publications

(10 citation statements)

References 48 publications

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“…Comparison of our results to those from published studies that focused on rare variant aggregation testing of metabolite levels [14][15][16][17][18][19][20]24 (Methods) showed that 32 of the 73 identified unique genes (44%) had not been reported as significant in any of these studies. Moreover, 115 of all 192 detected gene-metabolite associations (60%) were novel (Supplementary Table 4).…”

Section: Identification and Properties Of 192 Significant Gene-metabo...mentioning

confidence: 50%

“…Gene-based aggregation testing of rare, putatively damaging variants in population studies can address this challenge. Previously, such studies have focused almost exclusively on the circulating metabolome [14][15][16][17][18][19][20] . We have recently shown that GWAS of paired plasma and urine metabolomes not only reveal many more associations, but also enable specific insights into renal metabolite handling 2 .…”

Section: Introductionmentioning

confidence: 99%

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Coupling of metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and resulting traits and diseases

Scherer,

Fässler,

Borisov

et al. 2023

Preprint

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Genetic studies of the metabolome can uncover enzymatic and transport processes shaping human metabolism. Using WES-based rare variant aggregation testing to detect genes associated with levels of 1,294 plasma and 1,396 urine metabolites, we discovered 235 gene-metabolite associations, many previously unreported. Validation through genetic and new computational approaches (in silicogene knockouts in whole-body models of human metabolism) provided orthogonal evidence that population-based studies of rare, damaging variants in the heterozygous state permit inferences usually obtained from inborn errors of metabolism. Allelic series of functional variants in transporters responsible for transcellular sulfate reabsorption (SLC13A1, SLC26A1) exhibited graded effects on plasma sulfate and human height, and pinpointed alleles that strongly increased risk for dozens of musculoskeletal traits and diseases in the population. We present a powerful approach to identify new players in incompletely characterized human metabolic reactions, and to reveal metabolic readouts of disease risk to inform disease prevention and treatment.

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Section: Identification and Properties Of 192 Significant Gene-metabo...mentioning

confidence: 50%

Section: Introductionmentioning

confidence: 99%

Coupling of metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and resulting traits and diseases

Scherer,

Fässler,

Borisov

et al. 2023

Preprint

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“…Genetic association effect estimates for the common variants (CVs) were derived using drug-target MR with plasma TGs as the exposure. The PTV estimates were extracted from the ptv5pcnt collapsing models from the Astra Zeneca PheWAS portal (27). Gene variant-metabolite association pairs with an association strength of P ≥ 0.10 were excluded from the analyses.…”

Section: Genetic Mimicry Analysesmentioning

confidence: 99%

Drug-target Mendelian randomization analysis supports lowering plasma ANGPTL3, ANGPTL4, and APOC3 levels as strategies for reducing cardiovascular disease risk

Landfors,

Henneman,

Chorell

et al. 2024

Preprint

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Background and Aims: APOC3, ANGPTL3, and ANGPTL4 are circulating proteins that are actively pursued as pharmacological targets to treat dyslipidemia and reduce the risk of atherosclerotic cardiovascular disease. Here, we used human genetic data to compare the predicted therapeutic and adverse effects of APOC3, ANGPTL3, and ANGPTL4 inactivation. Methods: We conducted drug-target Mendelian randomization analyses using variants in proximity to the genes associated with circulating protein levels to compare APOC3, ANGPTL3, and ANGPTL4 as drug targets. We obtained exposure and outcome data from large-scale genome-wide association studies and used generalized least squares to correct for linkage disequilibrium-related correlation. We evaluated six primary cardiometabolic endpoints and screened for potential side effects across 694 disease-related endpoints, 43 clinical laboratory tests, and 11 internal organ MRI measurements. Results: Genetically lowering circulating ANGPTL4 levels reduced the odds of coronary artery disease (CAD) (odds ratio, 0.57 per s.d. protein [95%CI,0.47-0.70]) and type 2 diabetes (T2D) (odds ratio, 0.73 per s.d. protein [95%CI,0.57-0.94]). Genetically lowering circulating APOC3 levels also reduced the odds of CAD (odds ratio, 0.90 per s.d. protein [95%CI,0.82-0.99]). Genetically lowered ANGPTL3 levels via common variants were not associated with CAD. However, meta-analysis of loss-of-function variants revealed that ANGPTL3 inactivation protected against CAD (odds ratio, 0.71 per allele [95%CI,0.62-0.82]). Analysis of lowered ANGPTL3, ANGPTL4, and APOC3 levels did not identify important safety concerns. Conclusion: Human genetic evidence suggests that therapies aimed at reducing circulating levels of ANGPTL3, ANGPTL4, and APOC3 reduce the risk of CAD. ANGPTL4 lowering may also reduce the risk of T2D.

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“…EHR-linked biobanks often report quantitative lab results of blood- and urine-based biochemical markers. Many of these traits have a strong genetic basis, and they can be used as intermediate phenotypes in the analysis of complex diseases, offering additional information in the investigation of disease connections ( Wong et al 2011 , Kanai et al 2018 , Sinnott-Armstrong et al 2021 , Julkunen et al 2023 , Nag et al 2023 ). Given the polygenic predictive power of such continuous endophenotypes, integrating them into studies of non-mendelian disorders allows for improved interpretability at the molecular level, beyond what genetic pleiotropy can uncover ( Smith et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

Woerner,

Sriram,

Nam

et al. 2024

Bioinformatics

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Motivation Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Results Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities. Availability and implementation The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

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Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank

Cited by 12 publications

References 48 publications

Coupling of metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and resulting traits and diseases

Coupling of metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and resulting traits and diseases

Drug-target Mendelian randomization analysis supports lowering plasma ANGPTL3, ANGPTL4, and APOC3 levels as strategies for reducing cardiovascular disease risk

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

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