Our long-term goal is to understand the mechanisms by which sequence variations in enhancers affect gene expression. Genome-wide association study (GWAS) and expression quantitative trait loci (eQTL) mapping have revealed thousands of sequence variants that are associated with common diseases and gene expression variations. A large portion of the associated variants is located far away from genes, making them difficult to interpret. Given its abundance and essential role in gene regulation, sequence variants in transcriptional enhancers could be the cause of many phenotypic variations. Currently, identifying such variants remains a challenge because of several hurdles: i) rudimentary annotation of tissue-specific enhancers;ii) lack of strategies to precisely pinpoint the identity and location of transcription factor binding sites (TFBSs) within an enhancer;and iii lack of strategies to assign enhancer targets. By addressing these hurdles, the objective of this project is to design and test a computational framework that enables systematic and rapid screen of enhancer sequence variants that cause complex diseases. As an ultimate test of our approach, we will apply our computational strategy to screen and characterize enhancer variants that are associated with a common autoimmune disease, Type 1 Diabetes. To make the methods developed in this project useful to a much broader community of users, we will develop an open-source software suite and a database dedicated to the analysis and curation of regulatory mutations in enhancers. It is anticipated that the outcomes of this project will have an important positive impact because it promises to significantly accelerate the discovery and systematic documentation of causal genetic variants in the noncoding portion of the human genome.

Public Health Relevance

Thousands of genetic variants have been associated with human diseases but very few successful findings of actual etiologic variants have been reported. The need for computational and high throughput methods is essential to accelerate the discovery of disease-causing variants.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM104369-01A1
Application #
8624762
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Krasnewich, Donna M
Project Start
2014-07-01
Project End
2018-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Iowa
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
City
Iowa City
State
IA
Country
United States
Zip Code
52242
Li, Yan; Gao, Long; Hadland, Brandon et al. (2017) CD27 marks murine embryonic hematopoietic stem cells and type II prehematopoietic stem cells. Blood 130:372-376
Yu, Wenbao; He, Bing; Tan, Kai (2017) Identifying topologically associating domains and subdomains by Gaussian Mixture model And Proportion test. Nat Commun 8:535
Li, Fengyin; He, Bing; Ma, Xiaoke et al. (2017) Prostaglandin E1 and Its Analog Misoprostol Inhibit Human CML Stem Cell Self-Renewal via EP4 Receptor Activation and Repression of AP-1. Cell Stem Cell 21:359-373.e5
Tober, Joanna; Maijenburg, Marijke M W; Li, Yan et al. (2017) Maturation of hematopoietic stem cells from prehematopoietic stem cells is accompanied by up-regulation of PD-L1. J Exp Med :
He, Bing; Xing, Shaojun; Chen, Changya et al. (2016) CD8+ T Cells Utilize Highly Dynamic Enhancer Repertoires and Regulatory Circuitry in Response to Infections. Immunity 45:1341-1354
Gao, Tianshun; He, Bing; Liu, Sheng et al. (2016) EnhancerAtlas: a resource for enhancer annotation and analysis in 105 human cell/tissue types. Bioinformatics 32:3543-3551
Holmfeldt, Per; Ganuza, Miguel; Marathe, Himangi et al. (2016) Functional screen identifies regulators of murine hematopoietic stem cell repopulation. J Exp Med 213:433-49
He, Bing; Tan, Kai (2016) Understanding transcriptional regulatory networks using computational models. Curr Opin Genet Dev 37:101-108
Huang, Jianfei; Wang, Kai; Wei, Peng et al. (2016) FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics. Genetics 202:919-29
Teng, Li; He, Bing; Wang, Jiahui et al. (2015) 4DGenome: a comprehensive database of chromatin interactions. Bioinformatics 31:2560-4

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