Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Prioritizing GWAS results: A review of statistical methods and recommendations for their application.

American journal of human genetics - 1 Jan 2010

Cantor Rita M, Lange Kenneth, Sinsheimer Janet S

Abstract excerpt

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Bayes Theorem
Share to Databases, Genetic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Epistasis, Genetic

Share to Genetics

Share to Meta-Analysis as Topic

Share to Models, Statistical

Share to Regression Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 20074509
DOI: 10.1016/j.ajhg.2009.11.017
PMCID: PMC2801749

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prioritizing GWAS results: A review of statistical methods and recommendations for their application.PMID 20074509Read ↗

From genetic associations to genes: methods, applications, and challenges.Semantic proximity Read ↗

Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives.Semantic proximity Read ↗

From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies?Semantic proximity Read ↗

Performing post-genome-wide association study analysis: overview, challenges and recommendationsSemantic proximity Read ↗

Prioritized subset analysis: improving power in genome-wide association studies.Semantic proximity Read ↗

Functional and genomic context in pathway analysis of GWAS data.Semantic proximity Read ↗

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseasesSemantic proximity Read ↗

Exploring genotype-phenotype data using the GWAS CatalogSemantic proximity Read ↗

Meta-analysis methods for genome-wide association studies and beyond.Semantic proximity Read ↗

MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes.Semantic proximity Read ↗

Statistical methods for genome-wide association studies.Semantic proximity Read ↗

Evaluation of Bayesian Linear Regression Models for Gene Set Prioritization in Complex DiseasesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.