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  • team
  • department
  • center
  • program_project
  • nrc
  • whocc
  • project
  • software
  • tool
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  • Assistant Professor
  • Associate Professor
  • Clinical Research Assistant
  • Clinical Research Nurse
  • Clinician Researcher
  • Department Manager
  • Dual-education Student
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  • Honorary Professor
  • Lab assistant
  • Master Student
  • Non-permanent Researcher
  • Nursing Staff
  • Permanent Researcher
  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Prize
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  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
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Published in Nature communications - 21 Oct 2019

Gallois A, Mefford J, Ko A, Vaysse A, Julienne H, Ala-Korpela M, Laakso M, Zaitlen N, Pajukanta P, Aschard H,

Link to Pubmed [PMID] – 31636271

Link to DOI – 10.1038/s41467-019-12703-7

Nat Commun 2019 10; 10(1): 4788

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.