An atlas of genetic influences on human blood metabolites
By So-Youn Shin, E. Fauman, A. Petersen, Jan Krumsiek, Rita Santos, Jie Huang, M. Arnold, Idil Erte, Vincenzo Forgetta, Tsun-Po Yang and 25 more. Nature Genetics, 2014
1,424 citations recorded by the source corpus.
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.
Subfields: Clinical Biochemistry, Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/590db87e8c57111b7a24f6a62b713b2613e229f9
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Citations in the corpus
- Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, background): “Recent technological advancement has permitted high-throughput measurement of human genome, epigenome, metabolome, transcriptome and proteome at the population level (G. T. Consortium, 2015; Kim et al., 2014; Roadmap Epigenomics et al., 2015; Shin et al., 2014; Wellcome Trust Case Control, 2007; Zil”
- Altered Bile Acid Profile Associates with Cognitive Impairment in Alzheimer’s Disease – An Emerging Role for Gut Microbiome (2018, methodology): “previous study of genetic influences on blood metabolite levels in large population-based cohorts (nw7800) [45].”