Genetic discoveries from genome-wide association studies have led to important insights on human disease mechanisms. In particular, disease-associated variants are enriched in regions of the genome that are active in gene regulation. However, most of these analyses have focused on individual variants with the strongest evidence of association, and on broadly defined functional annotations, which provide limited scope for understanding disease mechanisms. In this proposal we analyze a broader set of genome-wide variants, in conjunction with functionally specialized annotations with potential mechanistic interpretations, such as context-specific regulatory elements or binding sites for specific transcription factors. We utilize methods that ascribe heritability to specific segments of the genome, leveraging polygenic signals distributed across the entire genome instead of a limited number of known genetic associations. These methods can pinpoint disease heritability to smaller and more specific subsets of the genome defined by precise context-specific functional annotations. In addition to highlighting specific mechanisms of disease, localizing to precise annotations will offer the ability to identify causal variants. We will take advantage of large databases of genetic data, in addition to a vast array of functional genomics data from ENCODE and other consortia. Specifically, we will (1) develop new statistical methods to partition disease heritability across functional categories, (2) build new annotations enriched for disease heritability from existing functional data sets, and (3) develop new computational methods to integrate ATAC-seq data with ENCODE data. The proposal represents a collaboration between Drs. Alkes Price, Soumya Raychaudhuri, and Nick Patterson, bringing together expertise in functional genomics, human disease genetics, and polygenic modeling. The investigators have a strong track record of integrating functional genomic data with human genetic data in recent publications.

Public Health Relevance

Defining how disease alleles influence precise disease mechanisms is critical to understanding disease mechanisms and deriving therapeutics. In this proposal we will devise strategies to interpret genetic data on human diseases in the context of functionally specialized annotations (FSAs) derived from large-scale ENCODE data. These functionally specialized annotations will implicate specific cellular mechanisms that can be prioritized for mechanistic investigation.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01HG009379-04
Application #
9851888
Study Section
Special Emphasis Panel (ZHG1)
Program Officer
Pazin, Michael J
Project Start
2017-02-01
Project End
2021-01-31
Budget Start
2020-02-01
Budget End
2021-01-31
Support Year
4
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Harvard University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
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Davenport, Emma E; Amariuta, Tiffany; Gutierrez-Arcelus, Maria et al. (2018) Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial. Genome Biol 19:168
Gusev, Alexander; Mancuso, Nicholas; Won, Hyejung et al. (2018) Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet 50:538-548
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Nigrovic, Peter A; Raychaudhuri, Soumya; Thompson, Susan D (2018) Review: Genetics and the Classification of Arthritis in Adults and Children. Arthritis Rheumatol 70:7-17
Loh, Po-Ru; Kichaev, Gleb; Gazal, Steven et al. (2018) Mixed-model association for biobank-scale datasets. Nat Genet 50:906-908
Finucane, Hilary K; Reshef, Yakir A; Anttila, Verneri et al. (2018) Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat Genet 50:621-629
Palamara, Pier Francesco; Terhorst, Jonathan; Song, Yun S et al. (2018) High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. Nat Genet 50:1311-1317
Reshef, Yakir A; Finucane, Hilary K; Kelley, David R et al. (2018) Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk. Nat Genet 50:1483-1493
Hormozdiari, Farhad; Gazal, Steven; van de Geijn, Bryce et al. (2018) Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat Genet 50:1041-1047

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