Expressive

rs10094377 - TIGD5

Magnitude 2.2 · 2 studies on file

Reported associations

  • Genetic architecture of cerebrospinal fluid and brain metabolite levels and the genetic colocalization of metabolites with human traits. - Nature genetics (2024) · Wang C, Yang C, Western D, Ali M, Wang Y, Phuah CL, Budde J, Wang L, Gorijala P, Timsina J, Ruiz A, Pastor P, Fernandez MV, Panyard DJ, Engelman CD, Deming Y, Boada M, Cano A, Garcia-Gonzalez P, Graff-Radford NR, Mori H, Lee JH, Perrin RJ, Ibanez L, Sung YJ, Cruchaga C · PubMed 39528826

    Brain metabolism perturbation can contribute to traits and diseases. We conducted a genome-wide association study for cerebrospinal fluid (CSF) and brain metabolite levels, identifying 205 independent associations (47.3% new signals, containing 11 new loci) for 139 CSF metabolites, and 32 independent associations (43.8% new signals, containing 4 new loci) for 31 brain metabolites. Of these, 96.9% (CSF) and 71.4% (brain) of the new signals belonged to previously analyzed metabolites in blood or urine. We integrated the metabolite quantitative trait loci (MQTLs) with 23 neurological, psychiatric and common human traits and diseases through colocalization to identify metabolites and biological processes implicated in these phenotypes. Combining CSF and brain, we identified 71 metabolite-trait

  • 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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