Your browser doesn't support javascript.
loading
Metabolic diversity in human populations and correlation with genetic and ancestral geographic distances.
Peng, Gang; Pakstis, Andrew J; Gandotra, Neeru; Cowan, Tina M; Zhao, Hongyu; Kidd, Kenneth K; Scharfe, Curt.
Afiliación
  • Peng G; 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.
  • Pakstis AJ; Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
  • Gandotra N; Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
  • Cowan TM; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  • Zhao H; 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.
  • Kidd KK; Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
  • Scharfe C; Department of Genetics, Yale University School of Medicine, New Haven, CT, USA. Electronic address: curt.scharfe@yale.edu.
Mol Genet Metab ; 137(3): 292-300, 2022 11.
Article en En | MEDLINE | ID: mdl-36252453
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.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Errores Innatos del Metabolismo Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans / Newborn Idioma: En Revista: Mol Genet Metab Asunto de la revista: BIOLOGIA MOLECULAR / BIOQUIMICA / METABOLISMO Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Errores Innatos del Metabolismo Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans / Newborn Idioma: En Revista: Mol Genet Metab Asunto de la revista: BIOLOGIA MOLECULAR / BIOQUIMICA / METABOLISMO Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos