Pathway-based approaches for analysis of genomewide association studies.

By Kai Wang, Mingyao Li, Maja Bucan. American Journal of Human Genetics, 2007

900 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3448bc23b911ab7446d3384422cd4db8d6ddf111

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Citations in the corpus

A human functional protein interaction network and its application to cancer data analysis (2010, background): “Pathway-based hypothesis generation has been the subject of great interest over the past few years [17].”
A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic (2009, background): “In cases where a single (or very few) common variants are expected to be associated with a disease, a variant-by-variant approach using the strongest marginal signal for each tested variant may be beneficial (as discussed in [25] and [26]).”
Five years of GWAS discovery. (2012, background): “It has become clear that human genetic variants, such as single nucleotide polymorphisms (SNPs), can in cis affect the expression of nearby genes [1], [2].”
Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics (2016, methodology): “P-values can be estimated by phenotype label permutation, but this method is both computationally intensive and requires access to genotype data of the actual study, which are rarely shared[15].”
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, background): “…genetic findings using disease-and tissue-relevant modules can increase the power to detect genetic associations with disease by combining signals that reflect similar underlying biology while simultaneously informing biological mechanism by functionally annotating genetic findings 30,109,122,183 .”
GATES: a rapid and powerful gene-based association test using extended Simes procedure. (2011, methodology): “The largest test statistic from all the SNP-based tests in a gene has been proposed as a gene-based test statistic.(12) However, the value of this statistic is expected to be positively”
Genomic Similarity and Kernel Methods II: Methods for Genomic Information (2010, positive): “The second approach, called gene set enrichment analysis, compares the entire distribution of test statistics in a pathway to the rest of the genes [25, 26] , and has been extended to association studies with SNPs [27] .”
Gene-Environment-Wide Association Studies: Emerging Approaches (2010, positive): “Although GSEA is widely used in the analysis of gene-expression data, methods for applying it in association studies have only recently been develope”
MAGMA: Generalized Gene-Set Analysis of GWAS Data (2015, neutral): “Various methods for gene and gene-set analysis are currently available [7 – 13].”
Identifying Relationships among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions (2009, background): “In fact, it is even able to identify relatedness between genes that have no established common pathways or article co-citations! In contrast, other strategies start with static gene relationships—such as (1) preconstructed molecular networks [12,16] or sets of gene with common function [11,15] or (2”
Genetic Evidence Implicates the Immune System and Cholesterol Metabolism in the Aetiology of Alzheimer's Disease (2010, background): “We also confirmed the results using gene set enrichment [15] and set-based analyses [17] to uncover sets of functionally related genes showing evidence for association with disease.”