1. A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context
- Author
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Noah Zaitlen, Päivi Pajukanta, Hanna Julienne, Joel Mefford, Hugues Aschard, Arthur Ko, Mika Ala-Korpela, Amaury Vaysse, Apolline Gallois, Markku Laakso, Département de Biologie Computationnelle - Department of Computational Biology, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), University of California (UC), Baker Heart and Diabetes Institute (AUSTRALIA), University of Oulu, University of Eastern Finland, University of Bristol [Bristol], Monash University [Melbourne], Department of Medicine, University of Kuopio, Harvard T.H. Chan School of Public Health, This study was funded by National Institutes of Health (NIH) grants R03DE025665, R21HG007687, HL-095056, HL-28481, and U01 DK105561., Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), University of California, Institut de Génétique et Développement de Rennes (IGDR), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-IFR140-Centre National de la Recherche Scientifique (CNRS), and Département de Biologie Computationnelle - Department Computational Biology
- Subjects
0301 basic medicine ,Male ,Multivariate statistics ,MESH: Lipoproteins, LDL ,MESH: Amino Acids ,Magnetic Resonance Spectroscopy ,Metabolite ,General Physics and Astronomy ,Genome-wide association study ,030105 genetics & heredity ,Lipoproteins, VLDL ,IDL ,Genome-wide association studies ,MESH: Lipoproteins, HDL ,Cohort Studies ,chemistry.chemical_compound ,Pleiotropy ,2.1 Biological and endogenous factors ,Gene Regulatory Networks ,Amino Acids ,Aetiology ,lcsh:Science ,cardiovascular genetics ,MESH: Cohort Studies ,ComputingMilieux_MISCELLANEOUS ,MESH: Gene Regulatory Networks ,MESH: Aged ,Metabolic Syndrome ,[STAT.AP]Statistics [stat]/Applications [stat.AP] ,Multidisciplinary ,MESH: Middle Aged ,MESH: Polymorphism, Single Nucleotide ,Fatty Acids ,Genetic Pleiotropy ,Single Nucleotide ,Middle Aged ,metabolomics ,MESH: Fatty Acids ,Lipoproteins, LDL ,[STAT]Statistics [stat] ,Metabolome ,Lipoproteins, HDL ,[STAT.ME]Statistics [stat]/Methodology [stat.ME] ,VLDL ,MESH: Metabolome ,HDL ,MESH: Metabolic Syndrome ,Lipoproteins ,Science ,MESH: Genetic Pleiotropy ,Context (language use) ,Computational biology ,Biology ,MESH: Lipoproteins, IDL ,computational biology and bioinformatics ,Polymorphism, Single Nucleotide ,General Biochemistry, Genetics and Molecular Biology ,Article ,LDL ,03 medical and health sciences ,Metabolomics ,Genetics ,Humans ,MESH: Lipoproteins, VLDL ,Polymorphism ,Genetic association ,Aged ,[SDV.GEN]Life Sciences [q-bio]/Genetics ,MESH: Humans ,MESH: Magnetic Resonance Spectroscopy ,Human Genome ,General Chemistry ,Cardiovascular genetics ,MESH: Male ,Computational biology and bioinformatics ,030104 developmental biology ,chemistry ,Lipoproteins, IDL ,[SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics ,genome-wide association studies ,MESH: Genome-Wide Association Study ,lcsh:Q ,[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] ,Genome-Wide Association Study - Abstract
Genetic studies of metabolites have identified thousands of variants, many of which are associated with downstream metabolic and obesogenic disorders. However, these studies have relied on univariate analyses, reducing power and limiting context-specific understanding. Here we aim to provide an integrated perspective of the genetic basis of metabolites by leveraging the Finnish Metabolic Syndrome In Men (METSIM) cohort, a unique genetic resource which contains metabolic measurements, mostly lipids, across distinct time points as well as information on statin usage. We increase effective sample size by an average of two-fold by applying the Covariates for Multi-phenotype Studies (CMS) approach, identifying 588 significant SNP-metabolite associations, including 228 new associations. Our analysis pinpoints a small number of master metabolic regulator genes, balancing the relative proportion of dozens of metabolite levels. We further identify associations to changes in metabolic levels across time as well as genetic interactions with statin at both the master metabolic regulator and genome-wide level., Genome-wide association studies of metabolites have revealed hundreds of genetic associations using univariate analyses. Here, the authors use a multivariate approach to perform association analyses for 158 serum metabolites, followed by fine mapping and GxE interaction tests with statin use and age.
- Published
- 2019
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