Open AccessIntegrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk
Author(s) -
Xianyong Yin,
Debraj Bose,
Annie Kwon,
Sarah C. Hanks,
Anne Jackson,
Heather M. Stringham,
Ryan Welch,
Anniina Oravilahti,
Lilian Fernandes Silva,
Adam E. Locke,
Christian Fuchsberger,
Susan K. Service,
Michael R. Erdos,
Lori L. Bonnycastle,
Johanna Kuusisto,
Nathan O. Stitziel,
Ira M. Hall,
Jean Morrison,
Samuli Ripatti,
Aarno Palotie,
Nelson B. Freimer,
Francis S. Collins,
Karen L. Mohlke,
Laura J. Scott,
Eric B. Fauman,
Charles Burant,
Michael Boehnke,
Markku Laakso,
Xiaoquan Wen
Publication year - 2022
Publication title -
the american journal of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.661
H-Index - 302
eISSN - 1537-6605
pISSN - 0002-9297
DOI - 10.1016/j.ajhg.2022.08.007
Subject(s) - metabolomics , genome wide association study , transcriptome , biology , expression quantitative trait loci , computational biology , metabolite , metabolome , kegg , genetic association , genetics , disease , gene , gene expression , bioinformatics , single nucleotide polymorphism , medicine , genotype , biochemistry
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