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Predicting trait regulators by identifying co-localization of DNA binding and GWAS variants in regulatory regions

View ORCID ProfileGerald Quon, Soheil Feizi, Daniel Marbach, Melina Claussnitzer, Manolis Kellis
doi: https://doi.org/10.1101/467852
Gerald Quon
1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
2Broad Institute of MIT and Harvard, MIT, Cambridge, MA, USA
5Department of Molecular and Cellular Biology, University of California, Davis, Davis, CA, USA
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  • ORCID record for Gerald Quon
  • For correspondence: manoli@mit.edu gquon@ucdavis.edu
Soheil Feizi
1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
2Broad Institute of MIT and Harvard, MIT, Cambridge, MA, USA
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Daniel Marbach
1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
2Broad Institute of MIT and Harvard, MIT, Cambridge, MA, USA
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Melina Claussnitzer
1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
2Broad Institute of MIT and Harvard, MIT, Cambridge, MA, USA
3Gerontology Division, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 USA
4Nutritional Medicine, Technical University Munich, Gregor-Mendel-Str. 2. 85350 Freising-Weihenstephan, Germany
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Manolis Kellis
1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
2Broad Institute of MIT and Harvard, MIT, Cambridge, MA, USA
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  • For correspondence: manoli@mit.edu gquon@ucdavis.edu
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Abstract

Genomic regions associated with complex traits and diseases are primarily located in non-coding regions of the genome and have unknown mechanism of action. A critical step to understanding the genetics of complex traits is to fine-map each associated locus; that is, to find the causal variant(s) that underlie genetic associations with a trait. Fine-mapping approaches are currently focused on identifying genomic annotations, such as transcription factor binding sites, which are enriched in direct overlap with candidate causal variants. We introduce CONVERGE, the first computational tool to search for co-localization of GWAS causal variants with transcription factor binding sites in the same regulatory regions, without requiring direct overlap. As a proof of principle, we demonstrate that CONVERGE is able to identify five novel regulators of type 2 diabetes which subsequently validated in knockdown experiments in pancreatic beta cells, while existing fine-mapping methods were unable to find any statistically significant regulators. CONVERGE also recovers more established regulators for total cholesterol compared to other fine-mapping methods. CONVERGE is therefore unique and complementary to existing fine-mapping methods and is useful for exploring the regulatory architecture of complex traits.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted November 11, 2018.
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Predicting trait regulators by identifying co-localization of DNA binding and GWAS variants in regulatory regions
Gerald Quon, Soheil Feizi, Daniel Marbach, Melina Claussnitzer, Manolis Kellis
bioRxiv 467852; doi: https://doi.org/10.1101/467852
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Predicting trait regulators by identifying co-localization of DNA binding and GWAS variants in regulatory regions
Gerald Quon, Soheil Feizi, Daniel Marbach, Melina Claussnitzer, Manolis Kellis
bioRxiv 467852; doi: https://doi.org/10.1101/467852

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