Expressive

rs11189591 - PYROXD2

Magnitude 2.2 · 2 studies on file

Reported associations

  • Metagenomic and metabolomic remodeling in nonagenarians and centenarians and its association with genetic and socioeconomic factors. - Nature aging (2023) · Xu Q, Wu C, Zhu Q, Gao R, Lu J, Valles-Colomer M, Zhu J, Yin F, Huang L, Ding L, Zhang X, Zhang Y, Xiong X, Bi M, Chen X, Zhu Y, Liu L, Liu Y, Chen Y, Fan J, Sun Y, Wang J, Cao Z, Fan C, Ehrlich SD, Segata N, Qin N, Qin H · PubMed 37118062

    A better understanding of the biological and environmental variables that contribute to exceptional longevity has the potential to inform the treatment of geriatric diseases and help achieve healthy aging. Here, we compared the gut microbiome and blood metabolome of extremely long-lived individuals (94-105 years old) to that of their children (50-79 years old) in 116 Han Chinese families. We found extensive metagenomic and metabolomic remodeling in advanced age and observed a generational divergence in the correlations with socioeconomic factors. An analysis of quantitative trait loci revealed that genetic associations with metagenomic and metabolomic features were largely generation-specific, but we also found 131 plasma metabolic quantitative trait loci associations that were cross-gener

  • Rare and common genetic determinants of metabolic individuality and their effects on human health - Unknown journal (n.d.) · Unknown authors · PubMed 36357675

    ABSTRACT: Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10−11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced meta

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