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Smith CJ, Strausz S, Spence JP, Ollila HM, Pritchard JK. Haplotype Analysis Reveals Pleiotropic Disease Associations in the HLA Region. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.29.24311183. [PMID: 39132491 PMCID: PMC11312630 DOI: 10.1101/2024.07.29.24311183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The human leukocyte antigen (HLA) region plays an important role in human health through involvement in immune cell recognition and maturation. While genetic variation in the HLA region is associated with many diseases, the pleiotropic patterns of these associations have not been systematically investigated. Here, we developed a haplotype approach to investigate disease associations phenome-wide for 412,181 Finnish individuals and 2,459 traits. Across the 1,035 diseases with a GWAS association, we found a 17-fold average per-SNP enrichment of hits in the HLA region. Altogether, we identified 7,649 HLA associations across 647 traits, including 1,750 associations uncovered by haplotype analysis. We find some haplotypes show trade-offs between diseases, while others consistently increase risk across traits, indicating a complex pleiotropic landscape involving a range of diseases. This study highlights the extensive impact of HLA variation on disease risk, and underscores the importance of classical and non-classical genes, as well as non-coding variation.
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Taeubert MJ, Kuipers TB, Zhou J, Li C, Wang S, Wang T, Tobi EW, Belsky DW, Lumey LH, Heijmans BT. Adults prenatally exposed to the Dutch Famine exhibit a metabolic signature associated with a broad spectrum of common diseases. BMC Med 2024; 22:309. [PMID: 39075494 PMCID: PMC11287851 DOI: 10.1186/s12916-024-03529-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/12/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND Exposure to famine in the prenatal period is associated with an increased risk of metabolic disease, including obesity and type 2 diabetes. We employed nuclear magnetic resonance (NMR) metabolomic profiling to identify the metabolic changes that are associated with survival of prenatal famine exposure during the Dutch Famine at the end of World War II and subsequently assess their link to disease. METHODS NMR metabolomics data were generated from serum in 480 individuals prenatally exposed to famine (mean 58.8 years, 0.5 SD) and 464 controls (mean 57.9 years, 5.4 SD). We tested associations of prenatal famine exposure with levels of 168 individual metabolic biomarkers and compared the metabolic biomarker signature of famine exposure with those of 154 common diseases. RESULTS Prenatal famine exposure was associated with higher concentrations of branched-chain amino acids ((iso)-leucine), aromatic amino acid (tyrosine), and glucose in later life (0.2-0.3 SD, p < 3 × 10-3). The metabolic biomarker signature of prenatal famine exposure was positively correlated to that of incident type 2 diabetes from the UK Biobank (r = 0.77, p = 3 × 10-27), also when re-estimating the signature of prenatal famine exposure among individuals without diabetes (r = 0.67, p = 1 × 10-18). Remarkably, this association extended to 115 common diseases for which signatures were available (0.3 ≤ r ≤ 0.9, p < 3.2 × 10-4). Correlations among metabolic signatures of famine exposure and disease outcomes were attenuated when the famine signature was adjusted for body mass index. CONCLUSIONS Prenatal famine exposure is associated with a metabolic biomarker signature that strongly resembles signatures of a diverse set of diseases, an observation that can in part be attributed to a shared involvement of obesity.
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Affiliation(s)
- M Jazmin Taeubert
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Thomas B Kuipers
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Jiayi Zhou
- Butler Columbia Aging Center, Columbia University, New York, NY, USA
| | - Chihua Li
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China
| | - Shuang Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Tian Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Elmar W Tobi
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Daniel W Belsky
- Butler Columbia Aging Center, Columbia University, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - L H Lumey
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Bastiaan T Heijmans
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
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Lorincz-Comi N, Yang Y, Li G, Zhu X. MRBEE: A bias-corrected multivariable Mendelian randomization method. HGG ADVANCES 2024; 5:100290. [PMID: 38582968 PMCID: PMC11053334 DOI: 10.1016/j.xhgg.2024.100290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/08/2024] Open
Abstract
Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies. However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using bias-corrected estimating equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.
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Affiliation(s)
- Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Gen Li
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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Elmas A, Spehar K, Do R, Castellano JM, Huang KL. Associations of Circulating Biomarkers with Disease Risks: A Two-Sample Mendelian Randomization Study. Int J Mol Sci 2024; 25:7376. [PMID: 39000484 PMCID: PMC11242355 DOI: 10.3390/ijms25137376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/30/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024] Open
Abstract
Circulating biomarkers play a pivotal role in personalized medicine, offering potential for disease screening, prevention, and treatment. Despite established associations between numerous biomarkers and diseases, elucidating their causal relationships is challenging. Mendelian Randomization (MR) can address this issue by employing genetic instruments to discern causal links. Additionally, using multiple MR methods with overlapping results enhances the reliability of discovered relationships. Here, we report an MR study using multiple methods, including inverse variance weighted, simple mode, weighted mode, weighted median, and MR-Egger. We use the MR-base resource (v0.5.6) from Hemani et al. 2018 to evaluate causal relationships between 212 circulating biomarkers (curated from UK Biobank analyses by Neale lab and from Shin et al. 2014, Roederer et al. 2015, and Kettunen et al. 2016 and 99 complex diseases (curated from several consortia by MRC IEU and Biobank Japan). We report novel causal relationships found by four or more MR methods between glucose and bipolar disorder (Mean Effect Size estimate across methods: 0.39) and between cystatin C and bipolar disorder (Mean Effect Size: -0.31). Based on agreement in four or more methods, we also identify previously known links between urate with gout and creatine with chronic kidney disease, as well as biomarkers that may be causal of cardiovascular conditions: apolipoprotein B, cholesterol, LDL, lipoprotein A, and triglycerides in coronary heart disease, as well as lipoprotein A, LDL, cholesterol, and apolipoprotein B in myocardial infarction. This Mendelian Randomization study not only corroborates known causal relationships between circulating biomarkers and diseases but also uncovers two novel biomarkers associated with bipolar disorder that warrant further investigation. Our findings provide insight into understanding how biological processes reflecting circulating biomarkers and their associated effects may contribute to disease etiology, which can eventually help improve precision diagnostics and intervention.
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Affiliation(s)
- Abdulkadir Elmas
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kevin Spehar
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ron Do
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joseph M. Castellano
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kuan-Lin Huang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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5
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Elmas A, Spehar K, Do R, Castellano JM, Huang KL. Associations of Circulating Biomarkers with Disease Risks: a Two-Sample Mendelian Randomization Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.30.24309729. [PMID: 39006413 PMCID: PMC11245069 DOI: 10.1101/2024.06.30.24309729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Background Circulating biomarkers play a pivotal role in personalized medicine, offering potential for disease screening, prevention, and treatment. Despite established associations between numerous biomarkers and diseases, elucidating their causal relationships is challenging. Mendelian Randomization (MR) can address this issue by employing genetic instruments to discern causal links. Additionally, using multiple MR methods with overlapping results enhances the reliability of discovered relationships. Methods Here we report an MR study using multiple methods, including inverse variance weighted, simple mode, weighted mode, weighted median, and MR Egger. We use the MR-base resource (v0.5.6)1 to evaluate causal relationships between 212 circulating biomarkers (curated from UK Biobank analyses by Neale lab and from Shin et al. 2014, Roederer et al. 2015, and Kettunen et al. 2016)2-4 and 99 complex diseases (curated from several consortia by MRC IEU and Biobank Japan). Results We report novel causal relationships found by 4 or more MR methods between glucose and bipolar disorder (Mean Effect Size estimate across methods: 0.39) and between cystatin C and bipolar disorder (Mean Effect Size: -0.31). Based on agreement in 4 or more methods, we also identify previously known links between urate with gout and creatine with chronic kidney disease, as well as biomarkers that may be causal of cardiovascular conditions: apolipoprotein B, cholesterol, LDL, lipoprotein A, and triglycerides in coronary heart disease, as well as lipoprotein A, LDL, cholesterol, and apolipoprotein B in myocardial infarction. Conclusions This Mendelian Randomization study not only corroborates known causal relationships between circulating biomarkers and diseases but also uncovers two novel biomarkers associated with bipolar disorder that warrant further investigation. Our findings provide insight into understanding how biological processes reflecting circulating biomarkers and their associated effects may contribute to disease etiology, which can eventually help improve precision diagnostics and intervention.
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Affiliation(s)
- Abdulkadir Elmas
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kevin Spehar
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ron Do
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joseph M. Castellano
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kuan-lin Huang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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Dwivedi SL, Heslop-Harrison P, Amas J, Ortiz R, Edwards D. Epistasis and pleiotropy-induced variation for plant breeding. PLANT BIOTECHNOLOGY JOURNAL 2024. [PMID: 38875130 DOI: 10.1111/pbi.14405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 05/07/2024] [Accepted: 05/24/2024] [Indexed: 06/16/2024]
Abstract
Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.
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Affiliation(s)
| | - Pat Heslop-Harrison
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- Department of Genetics and Genome Biology, Institute for Environmental Futures, University of Leicester, Leicester, UK
| | - Junrey Amas
- Centre for Applied Bioinformatics, School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - David Edwards
- Centre for Applied Bioinformatics, School of Biological Sciences, University of Western Australia, Perth, WA, Australia
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Dziedzic M, Józefczuk E, Guzik TJ, Siedlinski M. Interplay Between Plasma Glycine and Branched-Chain Amino Acids Contributes to the Development of Hypertension and Coronary Heart Disease. Hypertension 2024; 81:1320-1331. [PMID: 38587181 PMCID: PMC11095885 DOI: 10.1161/hypertensionaha.123.22649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/28/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND Higher levels of plasma glycine are linked to a reduced risk, while increased levels of total branched-chain amino acids (tBCAAs) are associated with a higher risk of essential hypertension and coronary heart disease (CHD). As these metabolic components are interconnected, analyzing the tBCAAs/glycine ratio may help to understand their interplay in the pathogenesis of cardiovascular disease. METHODS The Cox regression approach was combined with the development of novel genetic tools for assessments of associations between plasma metabolomic data (glycine, tBCAAs, and tBCAAs/glycine ratio) from the UK Biobank and the development of hypertension and CHD. Genome-wide association study was performed on 186 523 White UK Biobank participants to identify new independent genetic instruments for the 2-sample Mendelian randomization analyses. P-gain statistic >10 identified instruments associated with tBCAAs/glycine ratio significantly stronger compared with individual amino acids. Outcomes of genome-wide association study on hypertension and CHD were derived from the UK Biobank (nonoverlapping sample), FinnGen, and CARDIoGRAMplusC4D. RESULTS The tBCAAs/glycine ratio was prospectively associated with a higher risk of developing hypertension and CHD (hazard ratio quintile Q5 versus Q1, 1.196 [95% CI, 1.109-1.289] and 1.226 [95% CI, 1.160-1.296], respectively). Mendelian randomization analysis demonstrated that tBCAAs/glycine ratio (P-gain >10) was a risk factor for hypertension (meta-analyzed inverse-variance weighted causal estimate 0.45 log odds ratio/SD (95% CI, 0.26-0.64) and CHD (0.48 [95% CI, 0.29-0.67]) with an absolute effect significantly larger compared with the effect of glycine (-0.06 [95% CI, -0.1 to -0.03] and -0.08 [95% CI, -0.11 to -0.05], respectively) or tBCAAs (0.22 [95% CI, 0.09-0.34] and 0.12 [95% CI, 0.01-0.24], respectively). CONCLUSIONS The total BCAAs/glycine ratio is a key element of the metabolic signature contributing to hypertension and CHD, which may reflect biological pathways shared by glycine and tBCAAs.
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Affiliation(s)
- Mateusz Dziedzic
- Department of Internal Medicine (M.D., E.J., T.J.G., M.S.), Faculty of Medicine, Jagiellonian University Medical College, Cracow, Poland
| | - Ewelina Józefczuk
- Department of Internal Medicine (M.D., E.J., T.J.G., M.S.), Faculty of Medicine, Jagiellonian University Medical College, Cracow, Poland
- Center for Medical Genomics OMICRON (T.J.G., M.S.), Faculty of Medicine, Jagiellonian University Medical College, Cracow, Poland
| | - Tomasz J. Guzik
- Department of Internal Medicine (M.D., E.J., T.J.G., M.S.), Faculty of Medicine, Jagiellonian University Medical College, Cracow, Poland
- Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, United Kingdom (T.J.G., M.S.)
| | - Mateusz Siedlinski
- Department of Internal Medicine (M.D., E.J., T.J.G., M.S.), Faculty of Medicine, Jagiellonian University Medical College, Cracow, Poland
- Center for Medical Genomics OMICRON (T.J.G., M.S.), Faculty of Medicine, Jagiellonian University Medical College, Cracow, Poland
- Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, United Kingdom (T.J.G., M.S.)
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Boetto C, Frouin A, Henches L, Auvergne A, Suzuki Y, Patin E, Bredon M, Chiu A, Consortium MI, Sankararaman S, Zaitlen N, Kennedy SP, Quintana-Murci L, Duffy D, Sokol H, Aschard H. MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data. Brief Bioinform 2024; 25:bbae272. [PMID: 38856173 PMCID: PMC11163461 DOI: 10.1093/bib/bbae272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 04/16/2024] [Accepted: 05/28/2024] [Indexed: 06/11/2024] Open
Abstract
Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.
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Affiliation(s)
- Christophe Boetto
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Arthur Frouin
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Léo Henches
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Antoine Auvergne
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Yuka Suzuki
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Etienne Patin
- Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, 25-28 rue Dr Roux, 75015 Paris, France
| | - Marius Bredon
- Sorbonne Université, INSERM, Centre de recherche Saint-Antoine, CRSA, Microbiota, Gut and Inflammation Laboratory, Hôpital Saint-Antoine (UMR S938) Sorbonne Université, 27 rue Chaligny, 75012 Paris, France
| | - Alec Chiu
- Department of Human Genetics, University California Los Angeles, 695 Charles E. Young Drive South, Box 708822, Los Angeles, CA 90095-7088, United States
| | | | - Sriram Sankararaman
- Department of Human Genetics, University California Los Angeles, 695 Charles E. Young Drive South, Box 708822, Los Angeles, CA 90095-7088, United States
| | - Noah Zaitlen
- Department of Human Genetics, University California Los Angeles, 695 Charles E. Young Drive South, Box 708822, Los Angeles, CA 90095-7088, United States
| | - Sean P Kennedy
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Lluis Quintana-Murci
- Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, 25-28 rue Dr Roux, 75015 Paris, France
- Chair of Human Genomics and Evolution, Collège de France, 11 Pl. Marcelin Berthelot, 75005 Paris, France
| | - Darragh Duffy
- Translational Immunology Unit, Institut Pasteur, Université de Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
| | - Harry Sokol
- Sorbonne Université, INSERM, Centre de recherche Saint-Antoine, CRSA, Microbiota, Gut and Inflammation Laboratory, Hôpital Saint-Antoine (UMR S938) Sorbonne Université, 27 rue Chaligny, 75012 Paris, France
- Paris Center for Microbiome Medicine, Fédération Hospitalo-Universitaire, 184 rue du Faubourg Saint-Antoine, 75571 PARIS Cedex 12, France
- Gastroenterology Department, AP-HP, Saint Antoine Hospital, 184 rue du faubourg Saint-Antoine, 75012 Paris, France
- INRAE Micalis & AgroParisTech, UMR1319, Micalis & AgroParisTech, 4 avenue Jean Jaurès, 78352 Jouy en Josas, France
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, 75015 Paris, France
- Department of Epidemiology, Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States
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Taeubert MJ, Kuipers TB, Zhou J, Li C, Wang S, Wang T, Tobi EW, Belsky DW, Lumey LH, Heijmans BT. Adults prenatally exposed to the Dutch Famine exhibit a metabolic signature associated with a broad spectrum of common diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.04.24305284. [PMID: 38633796 PMCID: PMC11023671 DOI: 10.1101/2024.04.04.24305284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Background Exposure to famine in the prenatal period is associated with an increased risk of metabolic disease, including obesity and type-2 diabetes. We employed nuclear magnetic resonance (NMR) metabolomic profiling to provide a deeper insight into the metabolic changes associated with survival of prenatal famine exposure during the Dutch Famine at the end of World War II and explore their link to disease. Methods NMR metabolomics data were generated from serum in 480 individuals prenatally exposed to famine (mean 58.8 years, 0.5 SD) and 464 controls (mean 57.9 years, 5.4 SD). We tested associations of prenatal famine exposure with levels of 168 individual metabolic biomarkers and compared the metabolic biomarker signature of famine exposure with those of 154 common diseases. Results Prenatal famine exposure was associated with higher concentrations of branched-chain amino acids ((iso)-leucine), aromatic amino acid (tyrosine), and glucose in later life (0.2-0.3 SD, p < 3x10-3). The metabolic biomarker signature of prenatal famine exposure was positively correlated to that of incident type-2 diabetes (r = 0.77, p = 3x10-27), also when re-estimating the signature of prenatal famine exposure among individuals without diabetes (r = 0.67, p = 1x10-18). Remarkably, this association extended to 115 common diseases for which signatures were available (0.3 ≤ r ≤ 0.9, p < 3.2x10-4). Correlations among metabolic signatures of famine exposure and disease outcomes were attenuated when the famine signature was adjusted for body mass index. Conclusions Prenatal famine exposure is associated with a metabolic biomarker signature that strongly resembles signatures of a diverse set of diseases, an observation that can in part be attributed to a shared involvement of obesity.
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Affiliation(s)
- M. Jazmin Taeubert
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Thomas B. Kuipers
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Jiayi Zhou
- Butler Columbia Aging Center, Columbia University, New York, United States
| | - Chihua Li
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, United States
| | - Shuang Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, United States
| | - Tian Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, United States
| | - Elmar W. Tobi
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Daniel W. Belsky
- Butler Columbia Aging Center, Columbia University, New York, United States
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, United States
| | - L. H. Lumey
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, United States
| | - Bastiaan T. Heijmans
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Karjalainen MK, Karthikeyan S, Oliver-Williams C, Sliz E, Allara E, Fung WT, Surendran P, Zhang W, Jousilahti P, Kristiansson K, Salomaa V, Goodwin M, Hughes DA, Boehnke M, Fernandes Silva L, Yin X, Mahajan A, Neville MJ, van Zuydam NR, de Mutsert R, Li-Gao R, Mook-Kanamori DO, Demirkan A, Liu J, Noordam R, Trompet S, Chen Z, Kartsonaki C, Li L, Lin K, Hagenbeek FA, Hottenga JJ, Pool R, Ikram MA, van Meurs J, Haller T, Milaneschi Y, Kähönen M, Mishra PP, Joshi PK, Macdonald-Dunlop E, Mangino M, Zierer J, Acar IE, Hoyng CB, Lechanteur YTE, Franke L, Kurilshikov A, Zhernakova A, Beekman M, van den Akker EB, Kolcic I, Polasek O, Rudan I, Gieger C, Waldenberger M, Asselbergs FW, Hayward C, Fu J, den Hollander AI, Menni C, Spector TD, Wilson JF, Lehtimäki T, Raitakari OT, Penninx BWJH, Esko T, Walters RG, Jukema JW, Sattar N, Ghanbari M, Willems van Dijk K, Karpe F, McCarthy MI, Laakso M, Järvelin MR, Timpson NJ, Perola M, Kooner JS, Chambers JC, van Duijn C, Slagboom PE, Boomsma DI, Danesh J, Ala-Korpela M, Butterworth AS, Kettunen J. Genome-wide characterization of circulating metabolic biomarkers. Nature 2024; 628:130-138. [PMID: 38448586 PMCID: PMC10990933 DOI: 10.1038/s41586-024-07148-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/01/2024] [Indexed: 03/08/2024]
Abstract
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. 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 the metabolic associations of 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 acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.
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Affiliation(s)
- Minna K Karjalainen
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - Savita Karthikeyan
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Clare Oliver-Williams
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Public Health Specialty Training Programme, Cambridge, UK
| | - Eeva Sliz
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Elias Allara
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Wing Tung Fung
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UK
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Kati Kristiansson
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Matt Goodwin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Jiangsu, China
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Matt J Neville
- NIHR Oxford Biomedical Research Centre, OUHFT Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Natalie R van Zuydam
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Ayse Demirkan
- Surrey Institute for People-Centred AI, University of Surrey, Guildford, UK
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
| | - Jun Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Toomas Haller
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mika Kähönen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | - Pashupati P Mishra
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Peter K Joshi
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Erin Macdonald-Dunlop
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Jonas Zierer
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Ilhan E Acar
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Carel B Hoyng
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yara T E Lechanteur
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexander Kurilshikov
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marian Beekman
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Erik B van den Akker
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Center for Computational Biology, Leiden University Medical Center, Leiden, The Netherlands
- The Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Ivana Kolcic
- Department of Public Health, School of Medicine, University of Split, Split, Croatia
| | - Ozren Polasek
- Department of Public Health, School of Medicine, University of Split, Split, Croatia
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Folkert W Asselbergs
- Amsterdam University Medical Centers, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Anneke I den Hollander
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
- Genomics Research Center, Abbvie, Cambridge, MA, USA
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - James F Wilson
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Terho Lehtimäki
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- InFLAMES Research Flagship, University of Turku, Turku, Finland
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tonu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Fredrik Karpe
- NIHR Oxford Biomedical Research Centre, OUHFT Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Marjo-Riitta Järvelin
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Markus Perola
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Cornelia van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Johannes Kettunen
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
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Woerner J, Sriram V, Nam Y, Verma A, Kim D. Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network. Bioinformatics 2024; 40:btae126. [PMID: 38527901 PMCID: PMC10963079 DOI: 10.1093/bioinformatics/btae126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/17/2024] [Indexed: 03/27/2024] Open
Abstract
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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Affiliation(s)
- Jakob Woerner
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Vivek Sriram
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Yonghyun Nam
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Anurag Verma
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States
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12
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Varkevisser RDM, Cecil A, Prehn C, Mul D, Aanstoot HJ, Paterson AD, Wolffenbuttel BHR, van der Klauw MM. Metabolomic associations of impaired awareness of hypoglycaemia in type 1 diabetes. Sci Rep 2024; 14:4485. [PMID: 38396205 PMCID: PMC10891160 DOI: 10.1038/s41598-024-55032-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/19/2024] [Indexed: 02/25/2024] Open
Abstract
This study investigates impaired awareness of hypoglycaemia (IAH), a complication of insulin therapy affecting 20-40% of individuals with type 1 diabetes. The exact pathophysiology is unclear, therefore we sought to identify metabolic signatures in IAH to elucidate potential pathophysiological pathways. Plasma samples from 578 individuals of the Dutch type 1 diabetes biomarker cohort, 67 with IAH and 108 without IAH (NAH) were analysed using the targeted metabolomics Biocrates AbsoluteIDQ p180 assay. Eleven metabolites were significantly associated with IAH. Genome-wide association studies of these 11 metabolites identified significant single nucleotide polymorphisms (SNPs) in C22:1-OH and phosphatidylcholine diacyl C36:6. After adjusting for the SNPs, 11 sphingomyelins and phosphatidylcholines were significantly higher in the IAH group in comparison to NAH. These metabolites are important components of the cell membrane and have been implicated to play a role in cell signalling in diabetes. These findings demonstrate the potential role of phosphatidylcholine and sphingomyelins in IAH.
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Affiliation(s)
- R D M Varkevisser
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - A Cecil
- Metabolomic and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - C Prehn
- Metabolomic and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - D Mul
- Diabeter Netherlands, Center for Type 1 Diabetes Care and Research, Rotterdam, The Netherlands
| | - H J Aanstoot
- Diabeter Netherlands, Center for Type 1 Diabetes Care and Research, Rotterdam, The Netherlands
| | - A D Paterson
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
- Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - B H R Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - M M van der Klauw
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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13
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Daubney ER, D'Urso S, Cuellar-Partida G, Rajbhandari D, Peach E, de Guzman E, McArthur C, Rhodes A, Meyer J, Finfer S, Myburgh J, Cohen J, Schirra HJ, Venkatesh B, Evans DM. A Genome-Wide Association Study of Serum Metabolite Profiles in Septic Shock Patients. Crit Care Explor 2024; 6:e1030. [PMID: 38239409 PMCID: PMC10796137 DOI: 10.1097/cce.0000000000001030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2024] Open
Abstract
OBJECTIVES We sought to assess whether genetic associations with metabolite concentrations in septic shock patients could be used to identify pathways of potential importance for understanding sepsis pathophysiology. DESIGN Retrospective multicenter cohort studies of septic shock patients. SETTING All participants who were admitted to 27 participating hospital sites in three countries (Australia, New Zealand, and the United Kingdom) were eligible for inclusion. PATIENTS Adult, critically ill, mechanically ventilated patients with septic shock (n = 230) who were a subset of the Adjunctive Corticosteroid Treatment in Critically Ill Patients with Septic Shock trial (ClinicalTrials.gov number: NCT01448109). INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS A genome-wide association study was conducted for a range of serum metabolite levels for participants. Genome-wide significant associations (p ≤ 5 × 10-8) were found for the two major ketone bodies (3-hydroxybutyrate [rs2456680] and acetoacetate [rs2213037] and creatinine (rs6851961). One of these single-nucleotide polymorphisms (SNPs) (rs2213037) was located in the alcohol dehydrogenase cluster of genes, which code for enzymes related to the metabolism of acetoacetate and, therefore, presents a plausible association for this metabolite. None of the three SNPs showed strong associations with risk of sepsis, 28- or 90-day mortality, or Acute Physiology and Chronic Health Evaluation score (a measure of sepsis severity). CONCLUSIONS We suggest that the genetic associations with metabolites may reflect a starvation response rather than processes involved in sepsis pathophysiology. However, our results require further investigation and replication in both healthy and diseased cohorts including those of different ancestry.
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Affiliation(s)
- Emily R Daubney
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Shannon D'Urso
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | | | | | - Elizabeth Peach
- Frazer Institute, University of Queensland, Brisbane, QLD, Australia
| | - Erika de Guzman
- Australian Translational Genomics Centre, Queensland University of Technology, Brisbane, QLD, Australia
| | - Colin McArthur
- Department of Critical Care Medicine, Auckland City Hospital, Auckland, New Zealand
| | - Andrew Rhodes
- Department of Adult Critical Care, St George's University Hospitals NHS Foundation Trust and St George's University of London, London, United Kingdom
| | - Jason Meyer
- The George Institute for Global Health, Sydney, NSW, Australia
- Intensive Care Unit, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Simon Finfer
- The George Institute for Global Health, Sydney, NSW, Australia
- School of Public Health, Imperial College London, London, United Kingdom
| | - John Myburgh
- The George Institute for Global Health, Sydney, NSW, Australia
- St George Hospital, Sydney, NSW, Australia
| | - Jeremy Cohen
- Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
- Intensive Care Unit, The Wesley Hospital, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Horst Joachim Schirra
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
- Griffith School of Environment and Science-Chemical Sciences, Griffith University, Brisbane, QLD, Australia
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, QLD, Australia
| | - Balasubramanian Venkatesh
- The George Institute for Global Health, Sydney, NSW, Australia
- Intensive Care Unit, Princess Alexandra Hospital, Brisbane, QLD, Australia
- Intensive Care Unit, The Wesley Hospital, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Faculty of Health, University of New South Wales, Sydney, NSW, Australia
| | - David M Evans
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Frazer Institute, University of Queensland, Brisbane, QLD, Australia
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
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14
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Zhang T, Cao Y, Zhao J, Yao J, Liu G. Assessing the causal effect of genetically predicted metabolites and metabolic pathways on stroke. J Transl Med 2023; 21:822. [PMID: 37978512 PMCID: PMC10655369 DOI: 10.1186/s12967-023-04677-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Stroke is a common neurological disorder that disproportionately affects middle-aged and elderly individuals, leading to significant disability and mortality. Recently, human blood metabolites have been discovered to be useful in unraveling the underlying biological mechanisms of neurological disorders. Therefore, we aimed to evaluate the causal relationship between human blood metabolites and susceptibility to stroke. METHODS Summary data from genome-wide association studies (GWASs) of serum metabolites and stroke and its subtypes were obtained separately. A total of 486 serum metabolites were used as the exposure. Simultaneously, 11 different stroke phenotypes were set as the outcomes, including any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), small vessel stroke (SVS), lacunar stroke (LS), white matter hyperintensities (WMH), intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH), transient ischemic attack (TIA), and brain microbleeds (BMB). A two-sample Mendelian randomization (MR) study was conducted to investigate the causal effects of serum metabolites on stroke and its subtypes. The inverse variance-weighted MR analyses were conducted as causal estimates, accompanied by a series of sensitivity analyses to evaluate the robustness of the results. Furthermore, a reverse MR analysis was conducted to assess the potential for reverse causation. Additionally, metabolic pathway analysis was performed using the web-based MetOrigin. RESULTS After correcting for the false discovery rate (FDR), MR analysis results revealed remarkable causative associations with 25 metabolites. Further sensitivity analyses confirmed that only four causative associations involving three specific metabolites passed all sensitivity tests, namely ADpSGEGDFXAEGGGVR* for AS (OR: 1.599, 95% CI 1.283-1.993, p = 2.92 × 10-5) and AIS (OR: 1.776, 95% CI 1.380-2.285, p = 8.05 × 10-6), 1-linoleoylglycerophosph-oethanolamine* for LAS (OR: 0.198, 95% CI 0.091-0.428, p = 3.92 × 10-5), and gamma-glutamylmethionine* for SAH (OR: 3.251, 95% CI 1.876-5.635, p = 2.66 × 10-5), thereby demonstrating a high degree of stability. Moreover, eight causative associations involving seven other metabolites passed both sensitivity tests and were considered robust. The association result of one metabolite (glutamate for LAS) was considered non-robust. As for the remaining metabolites, we speculate that they may potentially possess underlying causal relationships. Notably, no common metabolites emerged from the reverse MR analysis. Moreover, after FDR correction, metabolic pathway analysis identified 40 significant pathways across 11 stroke phenotypes. CONCLUSIONS The identified metabolites and their associated metabolic pathways are promising circulating metabolic biomarkers, holding potential for their application in stroke screening and preventive strategies within clinical settings.
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Affiliation(s)
- Tianlong Zhang
- Department of Critical Medicine, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Yina Cao
- Department of Neurology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Jianqiang Zhao
- Department of Cardiology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Jiali Yao
- Department of Critical Care Medicine, Jinhua Hospital Affiliated to Zhejiang University, Jinhua, Zhejiang, China.
| | - Gang Liu
- Department of Infection Control, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
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15
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Fuller H, Zhu Y, Nicholas J, Chatelaine HA, Drzymalla EM, Sarvestani AK, Julián-Serrano S, Tahir UA, Sinnott-Armstrong N, Raffield LM, Rahnavard A, Hua X, Shutta KH, Darst BF. Metabolomic epidemiology offers insights into disease aetiology. Nat Metab 2023; 5:1656-1672. [PMID: 37872285 PMCID: PMC11164316 DOI: 10.1038/s42255-023-00903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/06/2023] [Indexed: 10/25/2023]
Abstract
Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.
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Affiliation(s)
- Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jayna Nicholas
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Haley A Chatelaine
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Emily M Drzymalla
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Afrand K Sarvestani
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | | | - Usman A Tahir
- Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Xinwei Hua
- Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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16
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Woerner J, Sriram V, Nam Y, Verma A, Kim D. Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.11.23289852. [PMID: 37293013 PMCID: PMC10246076 DOI: 10.1101/2023.05.11.23289852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Many diseases 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 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. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes 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 diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. 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.
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Affiliation(s)
- Jakob Woerner
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Vivek Sriram
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yonghyun Nam
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
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17
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Dervishi E, Bai X, Dyck MK, Harding JCS, Fortin F, Dekkers JCM, Plastow G. GWAS and genetic and phenotypic correlations of plasma metabolites with complete blood count traits in healthy young pigs reveal implications for pig immune response. Front Mol Biosci 2023; 10:1140375. [PMID: 36968283 PMCID: PMC10034349 DOI: 10.3389/fmolb.2023.1140375] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 02/27/2023] [Indexed: 03/11/2023] Open
Abstract
Introduction: In this study estimated genetic and phenotypic correlations between fifteen complete blood count (CBC) traits and thirty-three heritable plasma metabolites in young healthy nursery pigs. In addition, it provided an opportunity to identify candidate genes associated with variation in metabolite concentration and their potential association with immune response, disease resilience, and production traits.Methods: The blood samples were collected from healthy young pigs and Nuclear Magnetic Resonance (NMR) was used to quantify plasma metabolites. CBC was determined using the ADVIA® 2120i Hematology System. Genetic correlations of metabolite with CBC traits and single step genome-wide association study (ssGWAS) were estimated using the BLUPF90 programs.Results: Results showed low phenotypic correlation estimates between plasma metabolites and CBC traits. The highest phenotypic correlation was observed between lactic acid and plasma basophil concentration (0.36 ± 0.04; p < 0.05). Several significant genetic correlations were found between metabolites and CBC traits. The plasma concentration of proline was genetically positively correlated with hemoglobin concentration (0.94 ± 0.03; p < 0.05) and L-tyrosine was negatively correlated with mean corpuscular hemoglobin (MCH; −0.92 ± 0.74; p < 0.05). The genomic regions identified in this study only explained a small percentage of the genetic variance of metabolites levels that were genetically correlated with CBC, resilience, and production traits.Discussion: The results of this systems approach suggest that several plasma metabolite phenotypes are phenotypically and genetically correlated with CBC traits, suggesting that they may be potential genetic indicators of immune response following disease challenge. Genomic analysis revealed genes and pathways that might interact to modulate CBC, resilience, and production traits.
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Affiliation(s)
- E. Dervishi
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
| | - X. Bai
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
| | - M. K. Dyck
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
| | - J. C. S. Harding
- Department of Large Animal Clinical Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - F. Fortin
- Centre de Developpement du porc du Quebec inc (CDPQ), Quebec City, QC, Canada
| | - J. C. M. Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - G. Plastow
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
- *Correspondence: G. Plastow,
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18
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Auwerx C, Sadler MC, Woh T, Reymond A, Kutalik Z, Porcu E. Exploiting the mediating role of the metabolome to unravel transcript-to-phenotype associations. eLife 2023; 12:81097. [PMID: 36891970 PMCID: PMC9998083 DOI: 10.7554/elife.81097] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/17/2023] [Indexed: 02/17/2023] Open
Abstract
Despite the success of genome-wide association studies (GWASs) in identifying genetic variants associated with complex traits, understanding the mechanisms behind these statistical associations remains challenging. Several methods that integrate methylation, gene expression, and protein quantitative trait loci (QTLs) with GWAS data to determine their causal role in the path from genotype to phenotype have been proposed. Here, we developed and applied a multi-omics Mendelian randomization (MR) framework to study how metabolites mediate the effect of gene expression on complex traits. We identified 216 transcript-metabolite-trait causal triplets involving 26 medically relevant phenotypes. Among these associations, 58% were missed by classical transcriptome-wide MR, which only uses gene expression and GWAS data. This allowed the identification of biologically relevant pathways, such as between ANKH and calcium levels mediated by citrate levels and SLC6A12 and serum creatinine through modulation of the levels of the renal osmolyte betaine. We show that the signals missed by transcriptome-wide MR are found, thanks to the increase in power conferred by integrating multiple omics layer. Simulation analyses show that with larger molecular QTL studies and in case of mediated effects, our multi-omics MR framework outperforms classical MR approaches designed to detect causal relationships between single molecular traits and complex phenotypes.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,University Center for Primary Care and Public Health, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Marie C Sadler
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.,University Center for Primary Care and Public Health, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Tristan Woh
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.,University Center for Primary Care and Public Health, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,University Center for Primary Care and Public Health, Lausanne, Switzerland
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19
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Nag A, Dhindsa RS, Middleton L, Jiang X, Vitsios D, Wigmore E, Allman EL, Reznichenko A, Carss K, Smith KR, Wang Q, Challis B, Paul DS, Harper AR, Petrovski S. Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank. Am J Hum Genet 2023; 110:487-498. [PMID: 36809768 PMCID: PMC10027475 DOI: 10.1016/j.ajhg.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/30/2023] [Indexed: 02/22/2023] Open
Abstract
Genome-wide association studies (GWASs) have established the contribution of common and low-frequency variants to metabolic blood measurements in the UK Biobank (UKB). To complement existing GWAS findings, we assessed the contribution of rare protein-coding variants in relation to 355 metabolic blood measurements-including 325 predominantly lipid-related nuclear magnetic resonance (NMR)-derived blood metabolite measurements (Nightingale Health Plc) and 30 clinical blood biomarkers-using 412,393 exome sequences from four genetically diverse ancestries in the UKB. Gene-level collapsing analyses were conducted to evaluate a diverse range of rare-variant architectures for the metabolic blood measurements. Altogether, we identified significant associations (p < 1 × 10-8) for 205 distinct genes that involved 1,968 significant relationships for the Nightingale blood metabolite measurements and 331 for the clinical blood biomarkers. These include associations for rare non-synonymous variants in PLIN1 and CREB3L3 with lipid metabolite measurements and SYT7 with creatinine, among others, which may not only provide insights into novel biology but also deepen our understanding of established disease mechanisms. Of the study-wide significant clinical biomarker associations, 40% were not previously detected on analyzing coding variants in a GWAS in the same cohort, reinforcing the importance of studying rare variation to fully understand the genetic architecture of metabolic blood measurements.
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Affiliation(s)
- Abhishek Nag
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ryan S Dhindsa
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Lawrence Middleton
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Xiao Jiang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dimitrios Vitsios
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Eleanor Wigmore
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Erik L Allman
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Anna Reznichenko
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Keren Carss
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Katherine R Smith
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA
| | - Benjamin Challis
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Dirk S Paul
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Andrew R Harper
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK; Early Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK; Department of Medicine, University of Melbourne, Austin Health, Melbourne, VIC, Australia.
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20
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Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nat Commun 2023; 14:604. [PMID: 36737450 PMCID: PMC9898515 DOI: 10.1038/s41467-023-36231-7] [Citation(s) in RCA: 78] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 01/20/2023] [Indexed: 02/05/2023] Open
Abstract
Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases ( nightingalehealth.com/atlas ). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation.
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21
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Granados JC, Watrous JD, Long T, Rosenthal SB, Cheng S, Jain M, Nigam SK. Regulation of Human Endogenous Metabolites by Drug Transporters and Drug Metabolizing Enzymes: An Analysis of Targeted SNP-Metabolite Associations. Metabolites 2023; 13:metabo13020171. [PMID: 36837791 PMCID: PMC9958903 DOI: 10.3390/metabo13020171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
Drug transporters and drug-metabolizing enzymes are primarily known for their role in the absorption, distribution, metabolism, and excretion (ADME) of small molecule drugs, but they also play a key role in handling endogenous metabolites. Recent cross-tissue co-expression network analyses have revealed a "Remote Sensing and Signaling Network" of multispecific, oligo-specific, and monospecific transporters and enzymes involved in endogenous metabolism. This includes many proteins from families involved in ADME (e.g., SLC22, SLCO, ABCC, CYP, UGT). Focusing on the gut-liver-kidney axis, we identified the endogenous metabolites potentially regulated by this network of ~1000 proteins by associating SNPs in these genes with the circulating levels of thousands of small, polar, bioactive metabolites, including free fatty acids, eicosanoids, bile acids, and other signaling metabolites that act in part via G-protein coupled receptors (GPCRs), nuclear receptors, and kinases. We identified 77 genomic loci associated with 7236 unique metabolites. This included metabolites that were associated with multiple, distinct loci, indicating coordinated regulation between multiple genes (including drug transporters and drug-metabolizing enzymes) of specific metabolites. We analyzed existing pharmacogenomic data and noted SNPs implicated in endogenous metabolite handling (e.g., rs4149056 in SLCO1B1) also affecting drug ADME. The overall results support the existence of close relationships, via interactions with signaling metabolites, between drug transporters and drug-metabolizing enzymes that are part of the Remote Sensing and Signaling Network, and with GPCRs and nuclear receptors. These analyses highlight the potential for drug-metabolite interactions at the interfaces of the Remote Sensing and Signaling Network and the ADME protein network.
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Affiliation(s)
- Jeffry C. Granados
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Jeramie D. Watrous
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pharmacology, University of California San Diego, La Jolla, CA 92093, USA
| | - Tao Long
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pharmacology, University of California San Diego, La Jolla, CA 92093, USA
| | - Sara Brin Rosenthal
- Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA 92093, USA
| | - Susan Cheng
- Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Mohit Jain
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pharmacology, University of California San Diego, La Jolla, CA 92093, USA
| | - Sanjay K. Nigam
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
- Correspondence:
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