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Integrative multi-omics analyses identify cell-type disease genes and regulatory networks across schizophrenia and Alzheimer’s disease

Mufang Ying, Peter Rehani, Panagiotis Roussos, View ORCID ProfileDaifeng Wang
doi: https://doi.org/10.1101/2020.06.11.147314
Mufang Ying
1Department of Statistics, University of Wisconsin - Madison, Madison, WI, 53706, USA
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Peter Rehani
2Department of Integrative Biology, University of Wisconsin - Madison, Madison, WI, 53706, USA
6Waisman Center, University of Wisconsin – Madison, Madison, WI, 53705, USA
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Panagiotis Roussos
3Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
4Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
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Daifeng Wang
5Department of Biostatistics and Medical Informatics, University of Wisconsin – Madison, Madison, WI, 53706, USA
6Waisman Center, University of Wisconsin – Madison, Madison, WI, 53705, USA
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  • ORCID record for Daifeng Wang
  • For correspondence: daifeng.wang@wisc.edu
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Abstract

Strong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer’s disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 12, 2020.
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Integrative multi-omics analyses identify cell-type disease genes and regulatory networks across schizophrenia and Alzheimer’s disease
Mufang Ying, Peter Rehani, Panagiotis Roussos, Daifeng Wang
bioRxiv 2020.06.11.147314; doi: https://doi.org/10.1101/2020.06.11.147314
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Integrative multi-omics analyses identify cell-type disease genes and regulatory networks across schizophrenia and Alzheimer’s disease
Mufang Ying, Peter Rehani, Panagiotis Roussos, Daifeng Wang
bioRxiv 2020.06.11.147314; doi: https://doi.org/10.1101/2020.06.11.147314

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