Expressive

rs12134854 - SLC44A5 - ACADM

Magnitude 2.2 · 4 studies on file

Reported associations

  • Genome-Wide Association Studies of Metabolites in Patients with CKD Identify Multiple Loci and Illuminate Tubular Transport Mechanisms. - Journal of the American Society of Nephrology : JASN (2019) · Li Y, Sekula P, Wuttke M, Wahrheit J, Hausknecht B, Schultheiss UT, Gronwald W, Schlosser P, Tucci S, Ekici AB, Spiekerkoetter U, Kronenberg F, Eckardt KU, Oefner PJ, Köttgen A · PubMed 29545352

    The kidneys have a central role in the generation, turnover, transport, and excretion of metabolites, and these functions can be altered in CKD. Genetic studies of metabolite concentrations can identify proteins performing these functions. We conducted genome-wide association studies and aggregate rare variant tests of the concentrations of 139 serum metabolites and 41 urine metabolites, as well as their pairwise ratios and fractional excretions in up to 1168 patients with CKD. After correction for multiple testing, genome-wide significant associations were detected for 25 serum metabolites, two urine metabolites, and 259 serum and 14 urinary metabolite ratios. These included associations already known from population-based studies. Additional findings included an association for the uremi

  • Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine - Unknown journal (n.d.) · Unknown authors · PubMed 37277652

    ABSTRACT: The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (S

  • Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans - Unknown journal (n.d.) · Unknown authors · PubMed 31959995

    ABSTRACT: The kidneys integrate information from continuous systemic processes related to the absorption, distribution, metabolism and excretion (ADME) of metabolites. To identify underlying molecular mechanisms, we performed genome-wide association studies of the urinary concentrations of 1,172 metabolites among 1,627 patients with reduced kidney function. The 240 unique metabolite-locus associations (metabolite quantitative trait loci, mQTLs) that were identified and replicated highlight novel candidate substrates for transport proteins. The identified genes are enriched in ADME-relevant tissues and cell types, and they reveal novel candidates for biotransformation and detoxification reactions. Fine mapping of mQTLs and integration with single-cell gene expression permitted the priorit

  • Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information - Unknown journal (n.d.) · Unknown authors · PubMed 23093944

    ABSTRACT: Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical

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