10 Years of GWAS Discovery: Biology, Function, and Translation.

By Peter M. Visscher, Naomi R. Wray, Qian Zhang, P. Sklar, Mark I. McCarthy, M. Brown, Jian Yang. American Journal of Human Genetics, 2017

3,277 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0b256820d1e7d321e227dc41cedf3bb0b0c5d8f4

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In Molecular Biology

Citations in the corpus

Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, background): “Causal Inference Using Multiple Genetic Variants Many complex traits for which GWAS has been performed using very large sample sizes return tens or hundreds of independent genetic variants reaching the established genome wide significance level (4).”
Comparative genetic architectures of schizophrenia in East Asian and European populations (2018, neutral): “…in samples of primarily European ancestry 2,3 (EUR), but this still represents the tip of the iceberg with respect to common variant liability to the disorder: the highly polygenic nature of common variation underlying this disorder predicts that there are hundreds more loci to be discovered 4 .”
The MRC IEU OpenGWAS data infrastructure (2020, methodology): “Results The OpenGWAS infrastructure incorporates four domains - (1) data are sourced and imported before being (2) processed for format and quality control in order to be (3) served publicly and (4) connected to a range of analytical tools (Figure 1).”
A resource-efficient tool for mixed model association analysis of large-scale data (2019, background): “Over the past decade, a number of statistical methods have been developed for GWAS, facilitating the discovery of thousands of genetic loci associated with complex traits and diseases 1,2 .”
Cellular and molecular mechanisms of skin wound healing (2024, positive): “…such as these based on data from human populations have already been hugely revealing for many other human pathologies. including diabetes 249,250 , and will undoubtedly provide a novel avenue for the identification of clinically important genes controlling different aspects of wound…”
Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases (2020, methodology): “Gene features We created gene features from three main data types: (1) bulk and single-cell gene expression datasets, (2) curated biological pathways, and (3) predicted protein-protein interaction networks.”
Genetic mapping of cell type specificity for complex traits (2019, methodology): “Each dataset was processed separately according to the following steps: (1) When the obtained value was the read count, the count was converted into the count per million (CPM) to allow correction for the total number of reads per cell.”
The Polygenic and Monogenic Basis of Blood Traits and Diseases (2020, background): “resulting from the cumulative effects of many genetic loci throughout the genome, each of modest effect size (Visscher et al., 2017; Timpson et al., 2018).”
Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses (2019, result): “We find, as expected, and again as previously noted,[47] that the number of hits per study increases steadily with increasing sample size (Fig 3), but that the count also Fig 3.”