Analysing biological pathways in genome-wide association studies

By Kai Wang, Mingyao Li, Hakon Hakonarson. 2010

815 citations recorded by the source corpus.

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Citations in the corpus

Inferring gene ontologies from pairwise similarity data (2014, background): “…by researchers in a wide variety of situations, such as understanding the function of genes discovered in a Genome Wide Association Study (Holmans et al ., 2009; Wang et al ., 2010) or computationally predicting functions for uncharacterized genes (Pena-Castillo et al. , 2008; Yan et al ., 2010).”
Functionally-informed fine-mapping and polygenic localization of complex trait heritability (2019, methodology): “Fifth, sets of SNPs causally explaining 50% of common SNP heritability can potentially be used for gene and pathway enrichment analysis 55,56 .”
Biological interpretation of genome-wide association studies using predicted gene functions (2015, background): “Primary biliary cirrhosis [23, 13] Ventricular conduction [24, 2] Mean corpuscular volume [25, 1] Chronic kidney disease [26, 1] Corneal structure [27, 4] Phospholipid levels (plasma) [30, 1] Triglycerides [33, 4] Mean platelet volume [37, 2] Type 1 diabetes [39, 3] LDL cholesterol [40, 4] Red blood”
Beyond GWASs: illuminating the dark road from association to function. (2013, methodology): “Pathway-based analyses are another strategy being used for prioritizing genes from GWAS-identified regions (reviewed in Wang et al.46).”
Prioritizing candidate disease genes by network-based boosting of genome-wide association data. (2011, background): “This might be because a very large number of genes are involved in the diseases, or it might be because rarer variants cause a greater fraction of the heredity than previously thought (Dickson et al. 2010; Wang et al. 2010a).”
Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems (2016, background): “…been repeatedly observed that different genetic drivers of a trait can be recognized by their aggregation in networks of pairwise protein or gene interactions (Califano et al., 2012; Greene et al., 2015; Hanahan and Weinberg, 2011; Kim and Przytycka, 2012; Ramanan et al., 2012; Wang et al., 2010).”