The Components of GP2’s Fifth Data Release
Author(s)
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Co-lead, Complex Disease Data Analysis
Hampton Leonard, MS
Data Tecnica International / National Institutes of Health | USA
Hampton has a background in data science and machine learning, which she applies to large multi-omic datasets in the neurodegenerative disease space. She is passionate about investigating differences on both clinical and omic levels and how these differences can affect clinical trial outcomes.
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Lead, Complex Disease Data Analysis
Mike A. Nalls, PhD
Data Tecnica International | USA
Mike founded Data Tecnica in early 2017 after over a decade of experience in large dataset analytics and methods research in healthcare and other scientific fields. 350+ peer-reviewed publications (before the age of 40) in the field of applied statistics in large datasets, brain diseases, and genomics. He is a strong advocate of open science, collaboration, and transparency in ... Read More
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Working Group Participant
Dan Vitale, MS
Data Tecnica International | USA
Dan is a data science consultant for Data Tecnica International, consulting primarily for the Laboratory of Neurogenetics at the National Institute on Aging of the National Institutes of Health. His work is focused on open science, automation and development of genetic analytic pipelines and software, and machine learning.
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Working Group Participant
Mathew Koretsky, BSc
National Institutes of Health | USA
Mathew is a postbaccalaureate fellow at the Center for Alzheimer’s Disease and Related Dementias at the National Institutes of Health. His work focuses on the development of genetic data processing pipelines as well as applying machine learning and data science techniques to genomic datasets in the neurodegenerative disease space.
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Working Group Participant
Kristin Levine, MS
Data Tecnica International | USA
Kristin is a data scientist with Data Tecnica International, consulting primarily for the Center for Alzheimer's and Related Dementias (CARD) at the National Institutes of Health. A writer turned data scientist, she is passionate about open science, democratizing research tools, and making data as clear and accessible as possible.
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Co-lead, Data and Code Dissemination
Mary B. Makarious, BSc
National Institutes of Health | USA
Mary is a graduate student at the Laboratory of Neurogenetics (LNG) at the National Institutes of Health, National Institute on Aging under Drs. Andrew Singleton and Mike Nalls. She studied Bioinformatics and Neuroscience at Loyola University in Chicago, IL, USA before coming to work at LNG, where she has been for the past 2 years. Her work involves applying machine learning an... Read More
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Working Group Participant
Zih-Hua Fang, PhD
German Center for Neurodegenerative Diseases | Germany
Zih-Hua completed her bachelor's in Taiwan, her PhD with the University of Wageningen and AgroParisTech in France, and her postdoc at the ETH Zurich. Her research interests and experience focus on animal breeding genetics and genomics. Zia-Hua has six years' experience in bioinformatics and statistical modeling and experience with short and long read whole genome sequencing.
In May 2023, GP2 announced the fifth data release on the Terra platform in collaboration with AMP® PD.
This release includes 7,462 additional new complex disease participants and 487 new monogenic disease participants, adding to the previous releases from the Complex and Monogenic Networks.
- The complex disease data (genotypes) now consists of a total of 24,935 genotyped participants (12,728 PD cases, 10,533 Controls, and 1,674 ‘Other’ phenotypes).
- The monogenic disease data (whole genome sequences) now consists of a total of 722 sequenced participants.
Individual-Level Data:
New cohorts added to GP2 in this release are:
- Arizona Brain Bank Brain and body donation program (BBDP), a US-based brain bank
- Innate and Adaptive Immunity in Parkinson Disease (IMMUNEPD), a US-based study from the University of Alabama at Birmingham
- IPDGC Africa (IPDGCAF-NG), a Nigerian cohort
- PDGENEration (PDGNRTN), a cohort from the Parkinson’s Foundation
- Genetic study of Parkinson’s disease and related movement disorders (STELLENBOS), a South African cohort from Stellenbosch University
- Malaysian Parkinson’s Genetics Cohort (UMKLM), a Malaysian cohort from the University of Malaya
- Genotype-Phenotype Correlations in Parkinson’s Disease and Related Movement Disorders (KUL), a monogenic cohort from the University of Malaysia
- Parkinson Disease and Movement Disorders Center Biorepository (PDMDC), a monogenic cohort from Northwestern University
Genetically-determined ancestry of complex disease GP2 participants is broken into ten ancestry groups; the table below details the genetically-determined ancestry of complex disease participants in this release that have passed quality control and been imputed. These numbers include samples from previous releases that have been reclustered using the new cluster file and gone through quality control along with the newly genotyped and shared samples unique to this current release.
Important to note in this release: STELLENBOS is a South African cohort and displays genetic ancestry admixture that reflects that region and its history. Some of our ancestry predictions for members of this study may change as we adjust for this new population in future releases. In this release we have provided admixture estimates for all participants so that researchers can make choices about if and how to include high admixture in their analysis as part of the tier 2 data. We have also included a list of samples that we recommend removing from analysis at this stage as their high levels of admixture lead to lower confidence in their ancestry predictions from our current model.
Genetically-determined ancestry of monogenic disease GP2 participants is estimated from the same pipeline as the complex participants. The table below details the genetically-determined ancestry of monogenic disease participants in GP2 release 5.
Future data releases will continue to grow the diversity of participants available. You can check out our dashboard to see our progress.
An important update for this release is a minor change to how we determine which participants are related. We are now using KING software. This change may have updated estimates of relatedness for some of our previously released participants. With this change, a file that details the estimated relatedness thresholds will now be made available at the release for researchers to use for their own custom filtering purposes. In addition to the projected principal components, we now include principal components calculated per ancestry available for downstream analyses.
Summary Statistics:
We are pleased to announce that new summary statistics are also available through tier 1 access for this release. Kim et al 2023 are also available through our ongoing collaboration with Neurodegenerative Disease Knowledge Portal.
- Nalls et al 2019 without 23andme in hg38 (largest European PD GWAS)
- Loesch et al 2021 (largest Latin American/Amerindian PD GWAS)
- Kim et al 2023 without 23andme (Multi-ancestry meta-analysis for PD risk including European, East Asian, Latin American/Amerindians, and African Americans)
- Rizig et al 2023 without 23andme in hg38 (largest African and African admixed PD GWAS; using GP2 release 5 data)
Copy Number Variants:
Copy number variant (CNV) calls for all genotyped samples passing quality control (gene-level plus 250kb flanking regions) have been updated to include all samples in release 5. This data has been clustered using a custom GP2 genotype clustering file (available in the utils directories under both tier 1 and tier 2 data access). Both the cluster file and the pipeline used to predict the probabilistic CNV calls can be found on the GP2 Github for use with data outside of GP2. For more information regarding clustering using the custom GP2 genotyping clustering file and the copy number variant probabilistic calls, please see “The Components of GP2’s Third Data Release” on the GP2 blog.
More information on the structure of the complex disease genotype and clinical data is detailed in the blog post “The Components of GP2’s First Data Release” as well as in the README that has been updated for this release and is available on the official GP2 Terra workspaces. The monogenic PD WGS data is also detailed in the same README.
As always, please refer to the README that accompanies each GP2 release for further details regarding pipelines, data, and analyses!
On behalf of the GP2 Complex Disease Data Analysis, Monogenic Data Analysis, and Data and Code Dissemination Working Groups.
