Abstract
Polymorphisms in genes related to the metabolism of vitamin B12 haven’t been examined in a Brazilian population. To (a) determine the correlation between the local genetic ancestry components and vitamin B12 levels using ninety B12-related genes; (b) determine associations between these genes and their SNPs with vitamin B12 levels; (c) determine a polygenic risk score (PRS) using significant variants. This cross-sectional study included 168 children and adolescents, aged 9–13 years old. Total cobalamin was measured in plasma. Genotyping arrays and whole exome data were combined to yield ~ 7000 SNPs in 90 genes related to vitamin B12. The Efficient Local Ancestry Inference was used to estimate local ancestry for African (AFR), Native American, and European (EUR). The association between the genotypes and vitamin B12 levels were determined with generalized estimating equation. Vitamin B12 levels were driven by positive (EUR) and negative (AFR, AMR) correlations with genetic ancestry. A set of 36 variants were used to create a PRS that explained 42% of vitamin level variation. Vitamin B12 levels are influenced by genetic ancestry and a PRS explained almost 50% of the variation in plasma cobalamin in Brazilian children and adolescents.
Highlights
Public health recommendations for the intake of micronutrients are designed to meet the requirements of the majority (97–98%) of healthy individuals within a population group[1]
Over 2300 publications on associations between single-nucleotide polymorphisms (SNPs) in candidate genes involved in nutrient metabolism or response, as well as with disease[8,9] have been published since 200110
After removing siblings and outliers of clinical and vitamins levels, 168 participants were considered for analysis in the present study
Summary
Public health recommendations for the intake of micronutrients are designed to meet the requirements of the majority (97–98%) of healthy individuals within a population group[1]. A large number of studies have focused on how genetic variation might affect micronutrient metabolism, clinical and metabolomic measurements, and phenotypic expression[4,5,6] with the goal of personalizing recommendations based on genetic variation[7]. The majority of reports found statistical correlations but effect sizes were uniformly very small (usually < 1% of total phenotype) and reproducibility between studies is low. These results can be explained by gene–gene interactions, especially between different ancestral populations, and variation in diet and environmental/lifestyle factors that underlay gene-nutrient interactions[11,12,13]. Reductionistic approaches ignore the many metabolite–protein (and gene) and protein–protein interactions (i.e., gene–gene interactions) that produce an observable and measurable phenotype[14]
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