Genetic influences on human blood metabolites in the Japanese population

. 2022 Dec 6;26(1):105738. doi: 10.1016/j.isci.2022.105738

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Biological samples

Healthy individuals enrolled in the Nagahama study	The Nagahama Study	N/A

Deposited data

Summary statistics of GWAS	This paper	https://www.hgvd.genome.med.kyoto-u.ac.jp/repository/HGV0000020.html
Genotype reference panel	1000 Genomes Project (RRID:SCR_006828)	https://www.internationalgenome.org/home
Summary statistics of GWAS for blood levels of metabolites in the Japanese population (ToMMo)	Sakaue et al., 2021³⁵	https://jmorp.megabank.tohoku.ac.jp/202102/gwas/TGA000005
Summary statistics of GWAS for blood levels of metabolites in the European population	Shin et al., 2014⁴	http://metabolomics.helmholtz-muenchen.de/gwas/
Summary statistics of GWAS for 58 clinical measurements	Kanai et al., 2018²⁵	http://jenger.riken.jp/en/result
Summary statistics of GWAS for 42 diseases	Ishigaki et al., 2020²⁶	http://jenger.riken.jp/en/result
Summary statistics of expression QTL analysis on unfractionated peripheral blood	Ishigaki et al., 2017²³	http://jenger.riken.jp/en/result
Summary statistics of UK Biobank Pan-Ancestry	Pan-UKB team	https://pan.ukbb.broadinstitute.org

Software and algorithms

GCMS solution software	Shimadzu	Version 2.71
SHAPEIT	Delaneau et al., 2011³⁶	https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html
Minimac3	Das et al., 2016³⁷	https://genome.sph.umich.edu/wiki/Minimac3
PLINK	Purcell et al., 2007³⁸	https://www.cog-genomics.org/plink/1.9/, https://www.cog-genomics.org/plink2
METAL	Willer et al., 2010³⁹	http://csg.sph.umich.edu/abecasis/metal/download/
ANNOVAR	Wang et al., 2010⁴⁰	http://annovar.openbioinformatics.org/en/latest/
Popcorn	Brown et al., 2016⁴¹	https://github.com/brielin/Popcorn
GCTB	Zeng et al., 2018³²	http://cnsgenomics.com/software/gctb/

Other

Mass spectrometer	Shimadzu	GCMS-QP2010 Ultra