Genetic architecture and analysis practices of circulating metabolites in the NHLBI Trans-Omics for Precision Medicine Program

Nannan Wang¹, Franklin P Ockerman², Laura Y Zhou³, Megan L Grove¹, Taryn Alkis¹, John Barnard⁴, Russell P Bowler⁵, Clary B Clish⁶, Shinhye Chung¹, Emily Drzymalla⁷, Anne M Evans⁸, Nora Franceschini⁹, Robert E Gerszten⁶, Madeline G Gillman¹⁰, Scott R Hutton⁸, Rachel S Kelly¹¹, Charles Kooperberg¹², Martin G Larson¹³, Jessica Lasky-Su¹¹, Deborah A Meyers¹⁴, Prescott G Woodruff¹⁵, Alexander P Reiner¹⁶, Stephen S Rich¹⁷, Jerome I Rotter¹⁸, Edwin K Silverman¹¹, Ramachandran S Vasan¹⁹, Scott T Weiss¹¹, Kari E Wong⁸, Alexis C Wood²⁰, Lang Wu²¹; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Ronit Yarden²², Thomas W Blackwell²³, Albert V Smith²³, Han Chen¹, Laura M Raffield¹⁰, Bing Yu²⁴

Affiliations

¹ Department of Epidemiology, The University of Texas Health Science Center at Houston, Houston, TX, USA.
² Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.
³ Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.
⁴ Department of Quantitative Health Sciences, Cleveland Clinic Research, Cleveland Clinic, Cleveland, OH, USA.
⁵ Department of Medicine, National Jewish Health, Denver, CO, USA.
⁶ Division of Cardiovascular Medicine, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁷ Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
⁸ Metabolon, Inc., Morrisville, NC, USA.
⁹ Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
¹⁰ Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
¹¹ Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
¹² Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
¹³ Department of Biostatistics, Boston University, Boston, MA, USA.
¹⁴ Department of Medicine, University of Arizona College of Medicine, Tucson, AZ, USA.
¹⁵ Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
¹⁶ Department of Epidemiology, University of Washington, Seattle, WA, USA.
¹⁷ Department of Genome Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.
¹⁸ Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
¹⁹ Department of Quantitative and Qualitative Health Sciences, School of Public Health, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
²⁰ USDA/ARS Children's Nutrition Center, Baylor College of Medicine, Houston, TX, USA.
²¹ Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI, USA.
²² Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, MD 20817, USA.
²³ Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
²⁴ Department of Epidemiology, The University of Texas Health Science Center at Houston, Houston, TX, USA. Electronic address: bing.yu@uth.tmc.edu.

PMID: 40972578
PMCID: PMC12487844 (available on 2026-09-18)
DOI: 10.1016/j.ajhg.2025.08.022

Genetic architecture and analysis practices of circulating metabolites in the NHLBI Trans-Omics for Precision Medicine Program

Nannan Wang et al. Am J Hum Genet. 2025.

. 2025 Sep 18:S0002-9297(25)00356-8.

doi: 10.1016/j.ajhg.2025.08.022. Online ahead of print.

Authors

Affiliations

¹ Department of Epidemiology, The University of Texas Health Science Center at Houston, Houston, TX, USA.
² Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.
³ Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.
⁴ Department of Quantitative Health Sciences, Cleveland Clinic Research, Cleveland Clinic, Cleveland, OH, USA.
⁵ Department of Medicine, National Jewish Health, Denver, CO, USA.
⁶ Division of Cardiovascular Medicine, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁷ Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
⁸ Metabolon, Inc., Morrisville, NC, USA.
⁹ Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
¹⁰ Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
¹¹ Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
¹² Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
¹³ Department of Biostatistics, Boston University, Boston, MA, USA.
¹⁴ Department of Medicine, University of Arizona College of Medicine, Tucson, AZ, USA.
¹⁵ Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
¹⁶ Department of Epidemiology, University of Washington, Seattle, WA, USA.
¹⁷ Department of Genome Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.
¹⁸ Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
¹⁹ Department of Quantitative and Qualitative Health Sciences, School of Public Health, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
²⁰ USDA/ARS Children's Nutrition Center, Baylor College of Medicine, Houston, TX, USA.
²¹ Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI, USA.
²² Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, MD 20817, USA.
²³ Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
²⁴ Department of Epidemiology, The University of Texas Health Science Center at Houston, Houston, TX, USA. Electronic address: bing.yu@uth.tmc.edu.

PMID: 40972578
PMCID: PMC12487844 (available on 2026-09-18)
DOI: 10.1016/j.ajhg.2025.08.022

Abstract

Circulating metabolite levels partly reflect the state of human health and diseases and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single-study analyses. Leveraging the rich metabolomics resources generated by the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally diverse samples. From our comparison of multiple methods, we provided a set of reasonable strategies for outlier and imputation handling to process metabolite data and show that inverse normalization by study and half-minimum imputation provide mostly similar results for pooled or meta-analysis. Following the practical analysis framework, we further performed a genome-wide association analysis on 1,135 selected metabolites using whole-genome sequencing data from 16,359 individuals passing the quality-control filters and discovered 1,775 independent loci associated with 667 metabolites. Among 160 unreported locus-metabolite pairs, we identified associations with loci locating within previously implicated metabolite-associated genes, as well as associations with loci locating in genes such as GAB3 and VSIG4 (located on the X chromosome) that may play a role in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, which were located in well-known metabolic genes such as FADS2, D2HGDH, SUGP1, and UGT2B17, strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

Keywords: GWAS; TOPMed; circulating metabolites; half-minimum imputation; inverse normal transformation; metQTLs; metabolite catalog; multiple studies analysis; sex-stratified analysis.

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Conflict of interest statement

Declaration of interests In the past 3 years, E.K.S. received grant support from Bayer and Northpond Laboratories. L.W. provided consulting services to Pupil Bio Inc., Techspert, and Galiher DeRobertis & Waxman LLP; reviewed manuscripts for the Gastroenterology Report (not related to this study); and received an honorarium. H.C. received consulting fees from Character Biosciences. L.M.R. and S.S.R. are consultants for the TOPMed Administrative Coordinating Center (through Westat). S.T.W. receives royalties from UpToDate.

Update of

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program.
Wang N, Ockerman FP, Zhou LY, Grove ML, Alkis T, Barnard J, Bowler RP, Clish CB, Chung S, Drzymalla E, Evans AM, Franceschini N, Gerszten RE, Gillman MG, Hutton SR, Kelly RS, Kooperberg C, Larson MG, Lasky-Su J, Meyers DA, Woodruff PG, Reiner AP, Rich SS, Rotter JI, Silverman EK, Ramachandran VS, Weiss ST, Wong KE, Wood AC, Wu L; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Yarden R, Blackwell TW, Smith AV, Chen H, Raffield LM, Yu B. Wang N, et al. bioRxiv [Preprint]. 2025 Feb 3:2024.07.23.604849. doi: 10.1101/2024.07.23.604849. bioRxiv. 2025. Update in: Am J Hum Genet. 2025 Sep 18:S0002-9297(25)00356-8. doi: 10.1016/j.ajhg.2025.08.022. PMID: 39211135 Free PMC article. Updated. Preprint.

References

1. Shin S-Y, Fauman EB, Petersen A-K, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang T-P, et al. (2014). An atlas of genetic influences on human blood metabolites. Nat. Genet 46, 543–550. 10.1038/ng.2982. - DOI - PMC - PubMed
1. Vattikuti S, Guo J, and Chow CC (2012). Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits. PLoS Genet. 8, e1002637. 10.1371/journal.pgen.1002637. - DOI - PMC - PubMed
1. Aguinis H, Gottfredson RK, and Joo H (2013). Best-practice recommendations for defining, identifying, and handling outliers. Organ. Res. Methods 16, 270–301. 10.1177/1094428112470848. - DOI
1. Pollet TV, and van der Meij L (2017). To remove or not to remove: the impact of outlier handling on significance testing in testosterone data. Adapt. Hum. Behav. Physiol 3, 43–60. 10.1007/s40750-016-0050-z. - DOI
1. Ratcliff R (1993). Methods for dealing with reaction time outliers. Psychol. Bull 114, 510. 10.1037/0033-2909.114.3.510. - DOI - PubMed

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Genetic architecture and analysis practices of circulating metabolites in the NHLBI Trans-Omics for Precision Medicine Program

Affiliations

Genetic architecture and analysis practices of circulating metabolites in the NHLBI Trans-Omics for Precision Medicine Program

Authors

Affiliations

Abstract

Conflict of interest statement

Update of

References

Grants and funding

LinkOut - more resources

Full Text Sources