DESCRIPTION:

List of positional candidate genes after correcting for multiple testing and controlling the false discovery rate from genome wide association studies (GWAS) retrieved from the NHGRI-EBI Catalog of published genome-wide association studies (http://www.ebi.ac.uk/gwas/). The disease/trait examined in this study, as reported by the authors, was Blood metabolite levels. The EFO term blood metabolite measurement was annotated to this set after curation by NHGRI-EBI. Intergenic SNPS were mapped to both the upstream and downstream gene. P-value uploaded. This gene set was generated using gwas2gs v. 0.1.8 and the GWAS Catalog v. 1.0.1.

LABEL:

GWAS: blood metabolite measurement

SCORE TYPE:

P-Value

DATE ADDED:

2017-05-02

DATE UPDATED:

2024-04-25

SPECIES:

AUTHORS:

SY Shin, EB Fauman, AK Petersen, J Krumsiek, R Santos, J Huang, M Arnold, I Erte, V Forgetta, TP Yang, K Walter, C Menni, L Chen, L Vasquez, AM Valdes, CL Hyde, V Wang, D Ziemek, P Roberts, L Xi, E Grundberg, M Waldenberger, JB Richards, RP Mohney, MV Milburn, SL John, J Trimmer, FJ Theis, JP Overington, K Suhre, MJ Brosnan, C Gieger, G Kastenmüller, TD Spector, N Soranzo

TITLE:

An atlas of genetic influences on human blood metabolites.

JOURNAL:

Nature genetics Jun 2014, Vol 46, pp. 543-50

ABSTRACT:

Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease. PUBMED: 24816252
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