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

Bressan, Elisângela; Reed, Xylena; Bansal, V.; Hutchins, Elizabeth; Cobb, Melanie M.; Webb, Michelle G.; Alsop, Eric; Grenn, Francis P.; Illarionova, Anastasia; Savytska, Natalia; Violich, Ivo; Broeer, Stefanie; Fernandes, Noémia; R, Sivakumar; Beilina, Alexandra; Billingsley, Kimberley; 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 A.; Gibbs, J. Raphael; Finkbeiner, Steven; Cookson, Mark; Keuren‐Jensen, Kendall Van; Craig, David W.; Singleton, Andrew B.; Heutink, Peter; Blauwendraat, Cornelis

doi:10.1016/j.xgen.2023.100261

Cited by 15 publications

(2 citation statements)

References 73 publications

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“…As the costs of omics assays continue to drop, the standard use of high-throughput DNA, RNA, protein, and metabolomics biomarkers in the clinic need to become a reality. Largescale sequencing initiatives that focus on the genomic underpinnings of neurodegenerative diseases 41,[86][87][88][89][90] will aid in the development of more targeted and cost-effective tests such as PRSs and metabolite panels. 91 Collectively, these initiatives will enable many opportunities for biomarker identification, validation in both diagnosis and early disease detection, as well as raise important ethical and technical challenges.…”

Section: Possible Solutionsmentioning

confidence: 99%

Artificial intelligence for dementia genetics and omics

Bettencourt,

Skene,

Bandres‐Ciga

et al. 2023

Alzheimer's & Dementia

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Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high‐dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia‐related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine.Highlights We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

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Section: Possible Solutionsmentioning

confidence: 99%

Artificial intelligence for dementia genetics and omics

Bettencourt,

Skene,

Bandres‐Ciga

et al. 2023

Alzheimer's & Dementia

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“…12 iPSC models of ALS have previously been used to characterize phenotypic patterns of neurodegeneration in mid-size cohorts of sporadic ALS patient iPSCderived motor neurons derived from a population of Japanese ALS and control subjects, 13 and to construct disease-associated protein-protein interaction networks for ALS cases associated with the mutant C9orf72 hexanucleotide repeat expansion. 14 iPSCs are being used to model many other neurodegenerative diseases in smaller-scale studies and through large initiatives such as FOUNDIN-PD, 15 iNDI, 12 among others 12,[16][17][18][19][20][21][22][23][24][25] . Important technical challenges arise in studies of this scale, which necessarily have many sources of variation.…”

Section: Introductionmentioning

confidence: 99%

Disease related changes in ATAC-Seq of more than 450 iPSC-derived motor neuron lines from ALS patients and controls

Tsitkov,

Valentine,

Kozareva

et al. 2023

Preprint

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Amyotrophic Lateral Sclerosis (ALS), like many other neurodegenerative diseases, is highly heritable, but with only a small fraction of cases explained by monogenic disease alleles. To better understand sporadic ALS, we report epigenomic profiles, as measured by ATAC-seq, of motor neuron cultures derived from a diverse group of 380 ALS patients and 80 healthy controls. We find that chromatin accessibility is heavily influenced by sex, the iPSC cell type of origin, ancestry, and the inherent variance arising from sequencing. Once these covariates are corrected for, we are able to identify robust ALS-specific signals in the data. Additionally, we find that the ATAC-seq data is able to predict ALS disease progression rates with similar accuracy to methods based on biomarkers and clinical status. These results suggest that iPSC-derived motor neurons recapitulate important disease-relevant epigenomic changes.

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Deep learning and deep phenotyping of HD iPSCs: Applications to study biology and test therapeutics

Finkbeiner

2024

Huntington's Disease

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

Cited by 15 publications

References 73 publications

Artificial intelligence for dementia genetics and omics

Artificial intelligence for dementia genetics and omics

Disease related changes in ATAC-Seq of more than 450 iPSC-derived motor neuron lines from ALS patients and controls

Deep learning and deep phenotyping of HD iPSCs: Applications to study biology and test therapeutics

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