PGRN-RIKEN GWAS Statistics

Overview

PGRN-RIKEN investigators are releasing the summary data from their published genome-wide association of pharmacogenomics research, in order to enable other researchers to examine particular variants or loci of interest for association with these drug response or adverse response phenotypes. The files include p-values and directions of effect for directly genotyped single nucleotide polymorphisms (SNPs).

Acknowledging the data

When using data from the downloadable results please acknowledge the source of the data as follows:

"The GWAS data was contributed by PGRN-RIKEN Investigators and was downloaded from www.pgrn.org/gwas-statistics.html".

​In addition to the above acknowledgement, please cite the relevant paper.

Download Datasets

1. Allopurinol Response GWAS Summary Statistics

Below is the summary statistics file for allopurinol induced uric acid reduction in 1,495 patients for gout treatment. The sample size and precision of the data presented should preclude de-identification of any individual subject. However, in downloading these data, do not attempt to de-identify individual subjects.

Allopurinol response GWAS summary statistics txt file READ me

Please cite:

Genome-wide association study identifies ABCG2 (BCRP) as an allopurinol transporter and a determinant of drug response.

Wen CC, Yee SW, Liang X, Hoffmann TJ, Kvale MN, Banda Y, Jorgenson E, Schaefer, C, Risch N, Giacomini KM. Clin Pharmacol Ther. 2015 May;97(5):518-25.

PubMed: 25676789; PubMed Central: PMC4479153; DOI: 10.1002/cpt.89.

2. Genomewide Association Studies for Cisplatin-Associated Ototoxicity

Below is the summary statistics file for cisplatin-associated ototoxicity in 511 patients. The sample size and precision of the data presented should preclude de-identification of any individual subject. However, in downloading these data, you do not attempt to de-identify individual subjects.

Cisplatin ototoxicity GWAS summary statistics txt file READ me

Please cite:

Variants in WFS1 and other Mendelian deafness genes are associated with cisplatin-associated ototoxicity.

Wheeler HE et al. Clin Cancer Res. 2016 Dec 30

Pubmed: 28039263 DOI: 10.1158/1078-0432.CCR-16-2809

3. Genomewide Association Studies for Anti-Hypertensive Drug Treatment and New-Onset Diabetes

Below is the summary statistics file for anti-hypertensive drug treatment-induced risk for new-onset diabetes in 552 European Americans, 471 Hispanics and 117 African Americans. The sample size and precision of the data presented should preclude de-identification of any individual subject. However, in downloading these data, you do not attempt to de-identify individual subjects.

As the dataset is quite large and exceed the storage limit (files in ~200Mb range), please contact info@pgrn.org to obtain the following txt files: 

New-Onset Diabetes – European​ txt file New-Onset Diabetes – African​ txt file New-Onset Diabetes – Hispanics​ txt file New-Onset Diabetes – Meta-Analysis txt file​ READ me

Please cite:

Genome-wide association study identifies pharmacogenomic loci linked with specific antihypertensive drug treatment and new-onset diabetes.

Chang SW et al. Pharmacogenomics J. 2016 Sep 27

Pubmed: 27670767; PubMed Central: PMC5368017; DOI: 10.1038/tpj.2016.67 

5. Genomewide Association Studies for Selective Serotonin Reuptake Inhibitor (SSRI) Response

Below are the links to the GWAS summary statistics file for selective serotonin reuptake inhibitor (SSRI) response in 499 patients with major depressive disorder. The full datasets could be applied through dbGAP.

Association analysis on remission following treatment Association analysis on remission following 8-week treatment Association analysis on response with at least a 50% reduction in the total QIDS-C16 score Association analysis on response following 8-week with at least a 50% reduction in the total QIDS-C16 score​

Please cite:

Pharmacogenomics of selective serotonin reuptake inhibitor treatment for major depressive disorder: genome-wide associations and functional genomics.

Ji Y et al. Pharmacogenomics J. 2013 Oct;13(5):456-63. doi: 10.1038/tpj.2012.32.

Pubmed: 22907730;  PubMed Central: PMC3941038; DOI: 10.1038/tpj.2012.32

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