Implicating candidate genes at GWAS signals by leveraging topologically associating domains

Gregory P. Way, Daniel W. Youngstrom, Kurt D. Hankenson, Casey S. Greene, Struan F.A. Grant

Research output: Contribution to journalArticle

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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=1.02x10-5) as the top-ranked pathway. In many cases, our method implicated a gene other than the nearest gene. Our molecular experiments describe a novel example: ACP2, implicated near the canonical ARHGAP1' locus. We found ACP2 to be an important regulator of osteoblast metabolism, whereas ARHGAP1 was not supported. Our results via BMD, for example, demonstrate how basic principles of three-dimensional genome organization can define biologically informed association windows.

LanguageEnglish (US)
Pages1286-1289
Number of pages4
JournalEuropean Journal of Human Genetics
Volume25
Issue number11
DOIs
StatePublished - Nov 1 2017

Fingerprint

Genome-Wide Association Study
Genes
Bone Density
Genome
Inborn Genetic Diseases
Gene Ontology
Osteoblasts

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

Implicating candidate genes at GWAS signals by leveraging topologically associating domains. / Way, Gregory P.; Youngstrom, Daniel W.; Hankenson, Kurt D.; Greene, Casey S.; Grant, Struan F.A.

In: European Journal of Human Genetics, Vol. 25, No. 11, 01.11.2017, p. 1286-1289.

Research output: Contribution to journalArticle

Way, Gregory P. ; Youngstrom, Daniel W. ; Hankenson, Kurt D. ; Greene, Casey S. ; Grant, Struan F.A./ Implicating candidate genes at GWAS signals by leveraging topologically associating domains. In: European Journal of Human Genetics. 2017 ; Vol. 25, No. 11. pp. 1286-1289
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