Publications

Coding Variation in ANGPTL4, LPL, and SVEP1 and the Risk of Coronary Disease.
Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators, et al.

Publications

Genetic Interactions with Age, Sex, Body Mass Index, and Hypertension in Relation to Atrial Fibrillation: The AFGen Consortium.
Weng LC, et al.
Sci Rep. 2017 Sep 12;7(1):11303. doi: 10.1038/s41598-017-09396-7

Data in this set may be accessed in a researcher workspace via the Data Discovery interface of the American Heart Association Institute for Precision Cardiovascular Medicine.
Go to the Data Discovery interface

D

Publications

Whole Exome Sequencing in Atrial Fibrillation.
Lubitz SA, et al.
PLoS Genet. 2016 Sep 2;12(9):e1006284. doi: 10.1371/journal.pgen.1006284

Publications

Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program
Sarnowski, C et al.
Am J Hum Genet. 2019 Oct 3;105(4):706-718. doi: 10.1016/j.ajhg.2019.08.010

Summary statistics are available for download at the CARDIoGRAMplusC4D Consortium website.

Publications

Association analyses based on false discovery rate implicate new loci for coronary artery disease.
Nelson, CP, et al.
Nat Genet. 2017 Sep;49(9):1385-1391. doi: 10.1038/ng.3913

Dataset

These datasets draw upon overlapping sample sets: CKDGen GWAS; CKDGen GWAS - stratified UACR associations; and CKDGen GWAS - stratified serum creatinine associations.

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Publications

1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.
Gorski M, et al.
Sci Rep. 2017 Apr 28;7:45040. doi: 10.1038/srep45040

Dataset

All CKDGen GWAS datasets draw upon overlapping sample sets.

Download summary statistics

Publications

Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria.
Teumer A, et al.
Nat Commun. 2019 Sep 11;10(1):4130. doi: 10.1038/s41467-019-11576-0

Dataset

Download summary statistics for T2D meta-analysis

Download summary statistics for bivariate analysis