rs11264333 - SLC50A1

Magnitude 2.0 · 2 studies on file

Reported associations

• Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer's disease. - Nature genetics (2024) · Western D, Timsina J, Wang L, Wang C, Yang C, Phillips B, Wang Y, Liu M, Ali M, Beric A, Gorijala P, Kohlfeld P, Budde J, Levey AI, Morris JC, Perrin RJ, Ruiz A, Marquié M, Boada M, de Rojas I, Rutledge J, Oh H, Wilson EN, Le Guen Y, Reus LM, Tijms B, Visser PJ, van der Lee SJ, Pijnenburg YAL, Teunissen CE, Del Campo Milan M, Alvarez I, Aguilar M, Greicius MD, Pastor P, Pulford DJ, Ibanez L, Wyss-Coray T, Sung YJ, Cruchaga C · PubMed 39528825

  The integration of quantitative trait loci (QTLs) with disease genome-wide association studies (GWASs) has proven successful in prioritizing candidate genes at disease-associated loci. QTL mapping has been focused on multi-tissue expression QTLs or plasma protein QTLs (pQTLs). We generated a cerebrospinal fluid (CSF) pQTL atlas by measuring 6,361 proteins in 3,506 samples. We identified 3,885 associations for 1,883 proteins, including 2,885 new pQTLs, demonstrating unique genetic regulation in CSF. We identified CSF-enriched pleiotropic regions on chromosome (chr)3q28 near OSTN and chr19q13.32 near APOE that were enriched for neuron specificity and neurological development. We integrated our associations with Alzheimer's disease (AD) through proteome-wide association study (PWAS), colocali

• Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers - Nature communications (2022) · Westerman KE, Majarian TD, Giulianini F, Jang DK, Miao J, Florez JC, Chen H, Chasman DI, Udler MS, Manning AK, Cole JB · PubMed 35810165

  ABSTRACT: Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p < 4.5×10−9). Most are concentrated in a small subset (4%) of loci with

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