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The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism

The Foundational Data Initiative for Parkinson Disease (FOUNDIN-PD) is an international collaboration producing fundamental resources for Parkinson disease (PD). FOUNDIN-PD generated a multi-layered molecular dataset in a cohort of induced pluripotent stem cell (iPSC) lines differentiated to dopamin...

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Autores principales: Bressan, Elisangela, Reed, Xylena, Bansal, Vikas, Hutchins, Elizabeth, Cobb, Melanie M., Webb, Michelle G., Alsop, Eric, Grenn, Francis P., Illarionova, Anastasia, Savytska, Natalia, Violich, Ivo, Broeer, Stefanie, Fernandes, Noémia, Sivakumar, Ramiyapriya, Beilina, Alexandra, Billingsley, Kimberley J., Berghausen, Joos, Pantazis, Caroline B., Pitz, Vanessa, Patel, Dhairya, Daida, Kensuke, Meechoovet, Bessie, Reiman, Rebecca, Courtright-Lim, Amanda, Logemann, Amber, Antone, Jerry, Barch, Mariya, Kitchen, Robert, Li, Yan, Dalgard, Clifton L., Rizzu, Patrizia, Hernandez, Dena G., Hjelm, Brooke E., Nalls, Mike, Gibbs, J. Raphael, Finkbeiner, Steven, Cookson, Mark R., Van Keuren-Jensen, Kendall, Craig, David W., Singleton, Andrew B., Heutink, Peter, Blauwendraat, Cornelis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025424/
https://www.ncbi.nlm.nih.gov/pubmed/36950378
http://dx.doi.org/10.1016/j.xgen.2023.100261
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author Bressan, Elisangela
Reed, Xylena
Bansal, Vikas
Hutchins, Elizabeth
Cobb, Melanie M.
Webb, Michelle G.
Alsop, Eric
Grenn, Francis P.
Illarionova, Anastasia
Savytska, Natalia
Violich, Ivo
Broeer, Stefanie
Fernandes, Noémia
Sivakumar, Ramiyapriya
Beilina, Alexandra
Billingsley, Kimberley J.
Berghausen, Joos
Pantazis, Caroline B.
Pitz, Vanessa
Patel, Dhairya
Daida, Kensuke
Meechoovet, Bessie
Reiman, Rebecca
Courtright-Lim, Amanda
Logemann, Amber
Antone, Jerry
Barch, Mariya
Kitchen, Robert
Li, Yan
Dalgard, Clifton L.
Rizzu, Patrizia
Hernandez, Dena G.
Hjelm, Brooke E.
Nalls, Mike
Gibbs, J. Raphael
Finkbeiner, Steven
Cookson, Mark R.
Van Keuren-Jensen, Kendall
Craig, David W.
Singleton, Andrew B.
Heutink, Peter
Blauwendraat, Cornelis
author_facet Bressan, Elisangela
Reed, Xylena
Bansal, Vikas
Hutchins, Elizabeth
Cobb, Melanie M.
Webb, Michelle G.
Alsop, Eric
Grenn, Francis P.
Illarionova, Anastasia
Savytska, Natalia
Violich, Ivo
Broeer, Stefanie
Fernandes, Noémia
Sivakumar, Ramiyapriya
Beilina, Alexandra
Billingsley, Kimberley J.
Berghausen, Joos
Pantazis, Caroline B.
Pitz, Vanessa
Patel, Dhairya
Daida, Kensuke
Meechoovet, Bessie
Reiman, Rebecca
Courtright-Lim, Amanda
Logemann, Amber
Antone, Jerry
Barch, Mariya
Kitchen, Robert
Li, Yan
Dalgard, Clifton L.
Rizzu, Patrizia
Hernandez, Dena G.
Hjelm, Brooke E.
Nalls, Mike
Gibbs, J. Raphael
Finkbeiner, Steven
Cookson, Mark R.
Van Keuren-Jensen, Kendall
Craig, David W.
Singleton, Andrew B.
Heutink, Peter
Blauwendraat, Cornelis
author_sort Bressan, Elisangela
collection PubMed
description The Foundational Data Initiative for Parkinson Disease (FOUNDIN-PD) is an international collaboration producing fundamental resources for Parkinson disease (PD). FOUNDIN-PD generated a multi-layered molecular dataset in a cohort of induced pluripotent stem cell (iPSC) lines differentiated to dopaminergic (DA) neurons, a major affected cell type in PD. The lines were derived from the Parkinson’s Progression Markers Initiative study, which included participants with PD carrying monogenic PD variants, variants with intermediate effects, and variants identified by genome-wide association studies and unaffected individuals. We generated genetic, epigenetic, regulatory, transcriptomic, and longitudinal cellular imaging data from iPSC-derived DA neurons to understand molecular relationships between disease-associated genetic variation and proximate molecular events. These data reveal that iPSC-derived DA neurons provide a valuable cellular context and foundational atlas for modeling PD genetic risk. We have integrated these data into a FOUNDIN-PD data browser as a resource for understanding the molecular pathogenesis of PD.
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spelling pubmed-100254242023-03-21 The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism Bressan, Elisangela Reed, Xylena Bansal, Vikas Hutchins, Elizabeth Cobb, Melanie M. Webb, Michelle G. Alsop, Eric Grenn, Francis P. Illarionova, Anastasia Savytska, Natalia Violich, Ivo Broeer, Stefanie Fernandes, Noémia Sivakumar, Ramiyapriya Beilina, Alexandra Billingsley, Kimberley J. Berghausen, Joos Pantazis, Caroline B. Pitz, Vanessa Patel, Dhairya Daida, Kensuke Meechoovet, Bessie Reiman, Rebecca Courtright-Lim, Amanda Logemann, Amber Antone, Jerry Barch, Mariya Kitchen, Robert Li, Yan Dalgard, Clifton L. Rizzu, Patrizia Hernandez, Dena G. Hjelm, Brooke E. Nalls, Mike Gibbs, J. Raphael Finkbeiner, Steven Cookson, Mark R. Van Keuren-Jensen, Kendall Craig, David W. Singleton, Andrew B. Heutink, Peter Blauwendraat, Cornelis Cell Genom Resource The Foundational Data Initiative for Parkinson Disease (FOUNDIN-PD) is an international collaboration producing fundamental resources for Parkinson disease (PD). FOUNDIN-PD generated a multi-layered molecular dataset in a cohort of induced pluripotent stem cell (iPSC) lines differentiated to dopaminergic (DA) neurons, a major affected cell type in PD. The lines were derived from the Parkinson’s Progression Markers Initiative study, which included participants with PD carrying monogenic PD variants, variants with intermediate effects, and variants identified by genome-wide association studies and unaffected individuals. We generated genetic, epigenetic, regulatory, transcriptomic, and longitudinal cellular imaging data from iPSC-derived DA neurons to understand molecular relationships between disease-associated genetic variation and proximate molecular events. These data reveal that iPSC-derived DA neurons provide a valuable cellular context and foundational atlas for modeling PD genetic risk. We have integrated these data into a FOUNDIN-PD data browser as a resource for understanding the molecular pathogenesis of PD. Elsevier 2023-02-06 /pmc/articles/PMC10025424/ /pubmed/36950378 http://dx.doi.org/10.1016/j.xgen.2023.100261 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Resource
Bressan, Elisangela
Reed, Xylena
Bansal, Vikas
Hutchins, Elizabeth
Cobb, Melanie M.
Webb, Michelle G.
Alsop, Eric
Grenn, Francis P.
Illarionova, Anastasia
Savytska, Natalia
Violich, Ivo
Broeer, Stefanie
Fernandes, Noémia
Sivakumar, Ramiyapriya
Beilina, Alexandra
Billingsley, Kimberley J.
Berghausen, Joos
Pantazis, Caroline B.
Pitz, Vanessa
Patel, Dhairya
Daida, Kensuke
Meechoovet, Bessie
Reiman, Rebecca
Courtright-Lim, Amanda
Logemann, Amber
Antone, Jerry
Barch, Mariya
Kitchen, Robert
Li, Yan
Dalgard, Clifton L.
Rizzu, Patrizia
Hernandez, Dena G.
Hjelm, Brooke E.
Nalls, Mike
Gibbs, J. Raphael
Finkbeiner, Steven
Cookson, Mark R.
Van Keuren-Jensen, Kendall
Craig, David W.
Singleton, Andrew B.
Heutink, Peter
Blauwendraat, Cornelis
The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism
title The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism
title_full The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism
title_fullStr The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism
title_full_unstemmed The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism
title_short The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism
title_sort foundational data initiative for parkinson disease: enabling efficient translation from genetic maps to mechanism
topic Resource
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025424/
https://www.ncbi.nlm.nih.gov/pubmed/36950378
http://dx.doi.org/10.1016/j.xgen.2023.100261
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