Metabolic diversity in human populations and correlation with genetic and ancestral geographic distances

Gang Peng; Andrew J Pakstis; Neeru Gandotra; Tina M Cowan; Hongyu Zhao; Kenneth K Kidd; Curt Scharfe

doi:10.1016/j.ymgme.2022.10.002

Metabolic diversity in human populations and correlation with genetic and ancestral geographic distances

Mol Genet Metab. 2022 Nov;137(3):292-300. doi: 10.1016/j.ymgme.2022.10.002. Epub 2022 Oct 13.

Authors

Gang Peng¹, Andrew J Pakstis², Neeru Gandotra², Tina M Cowan³, Hongyu Zhao¹, Kenneth K Kidd², Curt Scharfe⁴

Affiliations

¹ Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.
² Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
³ Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
⁴ Department of Genetics, Yale University School of Medicine, New Haven, CT, USA. Electronic address: curt.scharfe@yale.edu.

Abstract

DNA polymorphic markers and self-defined ethnicity groupings are used to group individuals with shared ancient geographic ancestry. Here we studied whether ancestral relationships between individuals could be identified from metabolic screening data reported by the California newborn screening (NBS) program. NBS data includes 41 blood metabolites measured by tandem mass spectrometry from singleton babies in 17 parent-reported ethnicity groupings. Ethnicity-associated differences identified for 71% of NBS metabolites (29 of 41, Cohen's d > 0.5) showed larger differences in blood levels of acylcarnitines than of amino acids (P < 1e-4). A metabolic distance measure, developed to compare ethnic groupings based on metabolic differences, showed low positive correlation with genetic and ancient geographic distances between the groups' ancestral world populations. Several outlier group pairs were identified with larger genetic and smaller metabolic distances (Black versus White) or with smaller genetic and larger metabolic distances (Chinese versus Japanese) indicating the influence of genetic and of environmental factors on metabolism. Using machine learning, comparison of metabolic profiles between all pairs of ethnic groupings distinguished individuals with larger genetic distance (Black versus Chinese, AUC = 0.96), while genetically more similar individuals could not be separated metabolically (Hispanic versus Native American, AUC = 0.51). Additionally, we identified metabolites informative for inferring metabolic ancestry in individuals from genetically similar populations, which included biomarkers for inborn metabolic disorders (C10:1, C12:1, C3, C5OH, Leucine-Isoleucine). This work sheds new light on metabolic differences in healthy newborns in diverse populations, which could have implications for improving genetic disease screening.

Keywords: Biochemical genetics; Inborn metabolic disorders; Metabolism; Newborn screening; Population genetics; Public health.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Amino Acids / genetics
Biomarkers
Humans
Infant, Newborn
Metabolism, Inborn Errors* / diagnosis
Metabolism, Inborn Errors* / epidemiology
Metabolism, Inborn Errors* / genetics
Neonatal Screening / methods
Tandem Mass Spectrometry / methods

Substances

Amino Acids
Biomarkers

Grants and funding

R01 HD102537/HD/NICHD NIH HHS/United States