Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS
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.
The authors thank B. Cole, M. Hall, and D. Cousminer for useful conversations, and Kenyaita Hodge and Michelle Leonard for the HEPG2 capture C library preparation. We also thank the reviewers for their constructive feedback. Funding for this work was provided by National Institutes of Health Grants LM010098, DK112217, ES013508, R21 HD089824, and the Children’s Hospital of Philadelphia Center for Spatial and Functional Genomics and Daniel B. Burke Endowed Chair for Diabetes Research.
- Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological) 57(1):289–300Google Scholar
- Bush WS, Dudek SM, Ritchie MD (2009) Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pac Symp Biocomput 2009:368–379Google Scholar
- DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium, Mexican American Type 2 Diabetes (MAT2D) Consortium, Type 2 Diabetes Genetic Exploration by Nex-generation sequencing in muylti-Ethnic Samples (T2D-GENES) Consortium, Mahajan A (2014) Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 46(3):234–244Google Scholar
- Hagberg AA, Schult DA, Swart PJ (2008) Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th Python in science conference (SciPy2008), pp 11–15Google Scholar
- Hinrichs AS, Karolchik D, Baertsch R, Barber GP, Bejerano G, Clawson H et al (2006) The UCSC genome browser database: update. Nucleic Acids Res 34(Database issue):D590-D598Google Scholar
- Manduchi E, Chesi A, Hall MA, Grant SFA, Moore JH (2018) Leveraging putative enhancer-promoter interactions to investigate two-way epistasis in Type 2 Diabetes GWAS. Pac Symp Biocomput 2018:548–558Google Scholar
- Nair AK, Muller YL, McLean NA, Abdussamad M, Piaggi P, Kobes S et al (2014) Variants associated with type 2 diabetes identified by the transethnic meta-analysis study: assessment in American Indians and evidence for a new signal in LPP. Diabetologia 57(11):2334–2338CrossRefPubMedPubMedCentralGoogle Scholar
- Tryka KA, Hao L, Sturcke A, Jin Y, Wang ZY, Ziyabari L et al (2014) NCBI’s database of genotypes and phenotypes: dbGaP. Nucleic Acids Res 42(Database issue):D975–D979Google Scholar