Genome-wide association summary statistics for human blood plasma glycome

The dataset contains results of genome-wide association study of human blood plasma glycome. The 113 files contain association summary statistics for 113 glycome traits, of which 36 were directly measured by UPLC technology and 77 were derived glycome traits. Description of each glycome trait can be found in the Additional notes section. This dataset is also available for graphical exploration in the genomic context at http://gwasarchive.org. 

The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.

When using downloaded data, please cite corresponding paper and this repository:

Sharapov, S. Z., Tsepilov, Y. A., Klaric, L., Mangino, M., Thareja, G., Shadrina, A. S., … Aulchenko, Y. (2019). Defining the genetic control of human blood plasma N-glycome using genome-wide association study. Human Molecular Genetics. http://doi.org/10.1093/hmg/ddz054

Sodbo Sharapov, Yakov Tsepilov, Lucija Klaric, Massimo Mangino, Gaurav Thareja, Mirna Simurina, Concetta Dagostino, Julia Dmitrieva, Marija Vilaj, FranoVuckovic, Tamara Pavic, Jerko Stambuk, Irena Trbojevic-Akmacic, Jasminka Kristic, Jelena Simunovic, Ana Momcilovic, Harry Campbell, Malcolm Dunlop, Susan Farrington, Maria Pucic-Bakovic, Christian Gieger, Massimo Allegri, Edouard Louis, Michel Georges, Karsten Suhre, Tim Spector, Frances MK Williams, Gordan Lauc, Yurii Aulchenko. (2018). Genome-wide association summary statistics for human blood plasma glycome (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1298406

Funding

This work was supported by the European Community’s Seventh Framework Programme funded project PainOmics (Grant agreement # 602736) and by the European Structural and Investments funding for the "Croatian National Centre of Research Excellence in Personalized Healthcare" (contract #KK.01.1.1.01.0010).

The work of SSh was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Programme.

The work of YT was supported by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project #0324-2018-0017).

Karsten Suhre and Gaurav Thareja are supported by ‘Biomedical Research Program’ funds at Weill Cornell Medicine - Qatar, a program funded by the Qatar Foundation. We thank all staff at Weill Cornell Medicine - Qatar and Hamad Medical Corporation, and especially all study participants who made the QMDiab study possible.

The SOCCS study was supported by grants from Cancer Research UK (C348/A3758, C348/A8896, C348/ A18927); Scottish Government Chief Scientist Office (K/OPR/2/2/D333, CZB/4/94); Medical Research Council (G0000657-53203, MR/K018647/1); Centre Grant from CORE as part of the Digestive Cancer Campaign (http://www.corecharity.org.uk).

TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.

Column headers:

SNP: SNP rsID

CHR: chromosome

POS: position (GRCh37 build) 

OTHER_ALLELE: reference allele (coded as "0")

EFFECT_ALLELE: effective allele (coded as "1")

EAF: effective allele frequency 

N: sample size

BETA: effect size of effective allele

SE: standard error of effect size

PVAL: P-value of association (without GC correction)

IMPUTATION: imputation quality