ArticleEuropean journal of human genetics : EJHG2017
Implicating candidate genes at GWAS signals by leveraging topologically associating domains.
Article in European journal of human genetics : EJHG, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors.Nature genetics · 2019Pooled it
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- Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci.PLoS computational biology · 2025Article
- Combined analyses of RNA-sequence and Hi-C along with GWAS loci-A novel approach to dissect keloid disorder genetic mechanism.PLoS genetics · 2022Article
- Genome-wide association study implicates novel loci and reveals candidate effector genes for longitudinal pediatric bone accrual.Genome biology · 2021Article
- Effect of 6p21 region on lung function is modified by smoking: a genome-wide interaction study.Scientific reports · 2020Article
- p63 establishes epithelial enhancers at critical craniofacial development genes.Science advances · 2019Article
- Review
- Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes.BioData mining · 2019Review
- 5-Hydroxymethylcytosine alterations in the human postmortem brains of autism spectrum disorder.Human molecular genetics · 2018Article
- Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS.Human genetics · 2018Article
- The TAD-pathway for GWAS signals.European journal of human genetics : EJHG · 2017Article
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5 authors at 3 institutions in 1 country.
Funding
Abstract
Genome-wide association studies (GWAS) have contributed significantly to the understanding of complex disease genetics. However, GWAS only report association signals and do not necessarily identify culprit genes. As most signals occur in non-coding regions of the genome, it is often challenging to assign genomic variants to the underlying causal mechanism(s). Topologically associating domains (TADs) are primarily cell-type-independent genomic regions that define interactome boundaries and can aid in the designation of limits within which an association most likely impacts gene function. We describe and validate a computational method that uses the genic content of TADs to prioritize candidate genes. Our method, called 'TAD_Pathways', performs a Gene Ontology (GO) analysis over genes that reside within TAD boundaries corresponding to GWAS signals for a given trait or disease. Applying our pipeline to the bone mineral density (BMD) GWAS catalog, we identify 'Skeletal System Development' (Benjamini-Hochberg adjusted P=1.02x10
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