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Hum Genet. 2018 May;137(5):413-425. doi: 10.1007/s00439-018-1893-0. Epub 2018 May 24.

Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS.

Author information

1
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA. manduchi@pennmedicine.upenn.edu.
2
Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA. manduchi@pennmedicine.upenn.edu.
3
Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA. manduchi@pennmedicine.upenn.edu.
4
Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
5
Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
6
Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
7
Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
8
Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
9
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA. jhmoore@upenn.edu.

Abstract

Although Genome Wide Association Studies (GWAS) have led to many valuable insights into the genetic bases of common diseases over the past decade, the issue of missing heritability has surfaced, as the discovered main effect genetic variants found to date do not account for much of a trait's predicted genetic component. We present a workflow, integrating epigenomics and topologically associating domain data, aimed at discovering trait-associated SNP pairs from GWAS where neither SNP achieved independent genome-wide significance. Each analyzed SNP pair consists of one SNP in a putative active enhancer and another SNP in a putative physically interacting gene promoter in a trait-relevant tissue. As a proof-of-principle case study, we used this approach to identify focused collections of SNP pairs that we analyzed in three independent Type 2 diabetes (T2D) GWAS. This approach led us to discover 35 significant SNP pairs, encompassing both novel signals and signals for which we have found orthogonal support from other sources. Nine of these pairs are consistent with eQTL results, two are consistent with our own capture C experiments, and seven involve signals supported by recent T2D literature.

PMID:
29797095
PMCID:
PMC5996751
[Available on 2019-05-24]
DOI:
10.1007/s00439-018-1893-0

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