This dataset is a subset of the Hypertension exome chip analysis dataset.
Trans-ancestry Meta-Analyses Identify Rare and Common Variants Associated With Blood Pressure and Hypertension.
Surendran P, et al.
Nat Genet. 2016 Oct; 48(10): 1151–1161. doi: 10.1038/ng.3654
PMID:27618447
The ultimate goal of genetic association studies is to discover which genes and pathways have direct roles in risk of a disease or trait. Several methods now combine genetic association results with multiple kinds of evidence to generate lists of the genes that are most likely to mediate the genetic associations. The Knowledge Portals currently host results from three such methods that predict type 2 diabetes effector genes. The methods differ in the evidence types that they use as input and in the classifications or scores that they generate.
Summary statistics are available for download at the Psychiatric Genomics Consortium website.
Genome-wide association study identifies 30 loci associated with bipolar disorder.
Stahl EA, et al.
Nat Genet. 2019 May;51(5):793-803. doi: 10.1038/s41588-019-0397-8.
PMID:31043756
Summary statistics are available for download at the Psychiatric Genomics Consortium website.
Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.
Wray NR, et al.
Nat Genet. 2018 May;50(5):668-681. doi: 10.1038/s41588-018-0090-3.
PMID:29700475
Summary statistics are available for download at the GWAS Catalog.
Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing.
Kunkle BW, et al.
Nat Genet. 2019 Mar;51(3):414-430. doi: 10.1038/s41588-019-0358-2.
PMID: 30820047
Summary statistics are available for download at the GWAS Catalog.
Genome-wide association analysis of dementia and its clinical endophenotypes reveal novel loci associated with Alzheimer's disease and three causality networks: The GR@ACE project.
Moreno-Grau S, et al.
Alzheimers Dement. 2019 Oct;15(10):1333-1347. doi: 10.1016/j.jalz.2019.06.4950.
PMID:31473137
This dataset is part of the COVID19-hg GWAS meta-analyses round 4, released on October 20, 2020.
PublicationsTitle.
First author, et al.
Citation
PMID:nnn
This dataset is part of the COVID19-hg GWAS meta-analyses round 4, released on October 20, 2020.
PublicationsTitle.
First author, et al.
Citation
PMID:nnn
This dataset is part of the COVID19-hg GWAS meta-analyses round 4, released on October 20, 2020.
PublicationsTitle.
First author, et al.
Citation
PMID:nnn
This dataset is part of the COVID19-hg GWAS meta-analyses round 4, released on October 20, 2020.
PublicationsTitle.
First author, et al.
Citation
PMID:nnn