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A probabilistic framework to dissect functional cell-type-specific regulatory elements and risk loci underlying the genetics of complex traits

View ORCID ProfileYue Li, Jose Davila-Velderrain, Manolis Kellis
doi: https://doi.org/10.1101/059345
Yue Li
1Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA
2The Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
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  • For correspondence: liyue@mit.edu manoli@mit.edu
Jose Davila-Velderrain
1Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA
2The Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
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Manolis Kellis
1Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA
2The Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA
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  • For correspondence: liyue@mit.edu manoli@mit.edu
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Abstract

Dissecting the physiological circuitry underlying diverse human complex traits associated with heritable common mutations is an ongoing effort. The primary challenge involves identifying the relevant cell types and the causal variants among the vast majority of the associated mutations in the noncoding regions. To address this challenge, we developed an efficient probabilistic framework. First, we propose a sparse group-guided learning algorithm to infer cell-type-specific enrichments. Second, we propose a fine-mapping Bayesian model that incorporates as Bayesian priors the sparse enrichments to infer risk variants. Using the proposed framework to analyze 32 complex human traits revealed meaningful tissue-specific epigenomic enrichments indicative of the relevant disease pathologies. The prioritized variants exhibit prominent tissue-specific epigenomic signatures and significant enrichments for eQTL and conserved elements. Together, we demonstrate the general benefits of the proposed integrative framework in elucidating meaningful tissue-specific epigenomic elements from large-scale correlated annotations and the implicated functional variants for future experimental interrogation.

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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-ND 4.0 International license.
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Posted October 24, 2017.
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A probabilistic framework to dissect functional cell-type-specific regulatory elements and risk loci underlying the genetics of complex traits
Yue Li, Jose Davila-Velderrain, Manolis Kellis
bioRxiv 059345; doi: https://doi.org/10.1101/059345
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A probabilistic framework to dissect functional cell-type-specific regulatory elements and risk loci underlying the genetics of complex traits
Yue Li, Jose Davila-Velderrain, Manolis Kellis
bioRxiv 059345; doi: https://doi.org/10.1101/059345

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