Whole-genome landscape of plasma metabolome
Study population
CaucasianN = 191,681
AFRN = 3,296
AMRN = 138
EASN = 175
SASN = 3,848
Plasma metabolomics
N = 313
Whole genome sequencing data
Coding region
PTV, Inframe, Missense, Synonymous
Intergenic
Upstream
5'UTR
Exon
Intron
Exon
3'UTR
Downstream
Intergenic
Proximal regulatory non-coding region
Splice, Intron, 3'UTR, 5'UTR, Upstream, Downstream
What can you explore from this website
Comprehensive genetic architecture of human plasma metabolites
Whole genome sequencing
Imputed array-based GWAS
Whole exome sequencing
Single variant analysis
Aggregate-based analysis
-log10(P)
Protein-coding gene
RNA gene
Pseudogene
Intergenic region
Blood metabolites are critical biomarkers linking genetic variation to diverse health outcomes, exhibiting significant variability across individuals. In this study, we present a comprehensive whole genome sequencing (WGS) analysis of 313 plasma metabolic biomarkers from up to 199,138 individuals in the UK Biobank. This website serves as an open-access resource of genome-wide associations, integrating genetic associations derived from imputed array, whole exome sequencing (WES), and WGS datasets. These data were generated using the genomic and metabolomic data from the UK Biobank accessed as part of application 19542.
You can easily explore an overview of the genetic architecture of specific metabolic trait here.
You can download significant results from this study and the full summary statistics generated using single variant analysis and aggregate-based analysis here.
For more detailed information regarding the data sources and analytical methods used, please refer to the corresponding publication. If you use any findings from this website, kindly cite the following paper:
Wang, Y. X. et al. Whole-genome landscape of plasma metabolic biomarkers.
You can easily explore an overview of the genetic architecture of specific metabolic trait here.
You can download significant results from this study and the full summary statistics generated using single variant analysis and aggregate-based analysis here.
For more detailed information regarding the data sources and analytical methods used, please refer to the corresponding publication. If you use any findings from this website, kindly cite the following paper:
Wang, Y. X. et al. Whole-genome landscape of plasma metabolic biomarkers.