ArticleFrontiers in genetics2021
Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- A reframed landscape of causal emergence.Patterns (New York, N.Y.) · 2026Article
- metacp: a versatile software package for combining dependent or independent p-values.BMC bioinformatics · 2025Article
- Drug discovery for chemotherapeutic resistance based on pathway-responsive gene sets and its application in breast cancer.Frontiers in bioinformatics · 2025Article
- Article
- Identification of New Biological Pathways Involved in Skin Aging From the Analysis of French Women Genome-Wide Data.Frontiers in genetics · 2022Article
- A Study on the Impact of Basketball on the Physical Fitness and Health of Adolescents Based on the Method of Correlation Analysis.Journal of environmental and public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar's test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum
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Registered trials
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