A new AI-assisted data standard accelerates interoperability in biomedical research.

In this paper, we leveraged Large Language Models(LLMs) to accelerate data wrangling and automate labor-intensive aspects of data discovery and harmonization. This work promotes interoperability standards and enhances data discovery, facilitating AI-readiness in biomedical science with the generation of Common Data Elements (CDEs) as key to harmonizing multiple datasets. Thirty-one studies, various ontologies, and medical coding systems served as source material to create CDEs from which available metadata and context was sent as an API request to 4th-generation OpenAI GPT models to populate each metadata field. A human-in-the-loop (HITL) approach was used to assess quality and accuracy of the generated CDEs. To regulate CDE generation, we employed ElasticSearch and HITL to avoid duplicate CDEs and instead, added them as potential aliases for existing CDEs. The generated CDEs are foundational to assess the interoperability potential of datasets by determining how many data set column headers can be correctly mapped to CDEs as well as quantifying compliance with permissible values and data types. Subject matter experts reviewed generated CDEs and determined that 94.0% of generated metadata fields did not require manual revisions. Data tables from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Global Parkinson's Genetic Program (GP2) were used as test cases for interoperability assessments. Column headers from all test cases were successfully mapped to generated CDEs at a rate of 32.4% via elastic search.The interoperability score, a metric for dataset compatibility to CDEs and other connected datasets, based on relevant criteria such as data field completeness and compliance with common harmonization standards averaged 53.8 out of 100 for test cases. With this project, we aim to automate the most tedious aspects of data harmonization, enhancing efficiency and scalability in biomedical research while decreasing activation energy for federated research.

Gut-Brain Nexus: Mapping Multi-Modal Links to Neurodegeneration at Biobank Scale.

Alzheimer's disease (AD) and Parkinson's disease (PD) are influenced by genetic and environmental factors. Using data from UK Biobank, SAIL Biobank, and FinnGen, we conducted an unbiased, population-scale study to: 1) Investigate how 155 endocrine, nutritional, metabolic, and digestive system disorders are associated with AD and PD risk prior to their diagnosis, considering known genetic influences; 2) Assess plasma biomarkers' specificity for AD or PD in individuals with these conditions; 3) Develop a multi-modal classification model integrating genetics, proteomics, and clinical data relevant to conditions affecting the gut-brain axis. Our findings show that certain disorders elevate AD and PD risk before AD and PD diagnosis including: insulin and non-insulin dependent diabetes mellitus, noninfective gastro-enteritis and colitis, functional intestinal disorders, and bacterial intestinal infections, among others. Polygenic risk scores revealed lower genetic predisposition to AD and PD in individuals with co-occurring disorders in the study categories, underscoring the importance of regulating the gut-brain axis to potentially prevent or delay the onset of neurodegenerative diseases. The proteomic profile of AD/PD cases was influenced by comorbid endocrine, nutritional, metabolic, and digestive systems conditions. Importantly, we developed multi-modal prediction models integrating clinical, genetic, proteomic and demographic data, the combination of which performs better than any single paradigm approach in disease classification. This work aims to illuminate the intricate interplay between various physiological factors involved in the gut-brain axis and the development of AD and PD, providing a multifactorial systemic understanding that goes beyond traditional approaches.

The LRRK2 p.L1795F variant causes Parkinson's disease in the European population.

Pathogenic variants in the LRRK2 gene represent the most common cause of autosomal dominant Parkinson's disease (PD) worldwide. We identified the LRRK2 p.L1795F variant in 14 White/European ancestry PD patients, including two families with multiple affected carriers and seven additional affected individuals with familial PD using genotyping and sequencing data from more than 50,000 individuals through GP2, AMP-PD, PDGENEration, and CENTOGENE. All variant carriers were of White/European ancestry, and those with available genotyping data shared a common haplotype. The clinical presentation of p.L1795F carriers resembles that of other LRRK2 pathogenic variant carriers. Combined with published functional evidence showing strongly enhanced LRRK2 kinase activity, our findings provide conclusive evidence that the LRRK2 p.L1795F variant is pathogenic. It represents a rare cause of PD in the European population but needs to be included in genetic testing efforts and considered for ongoing gene-specific clinical trials.

NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations.

BACKGROUND: Commercial genome-wide genotyping arrays have historically neglected coverage of genetic variation across populations. OBJECTIVE: We aimed to create a multi-ancestry genome-wide array that would include a wide range of neuro-specific genetic content to facilitate genetic research in neurological disorders across multiple ancestral groups, fostering diversity and inclusivity in research studies. METHODS: We developed the Illumina NeuroBooster Array (NBA), a custom high-throughput and cost-effective platform on a backbone of 1,914,934 variants from the Infinium Global Diversity Array and added custom content comprising 95,273 variants associated with more than 70 neurological conditions or traits, and we further tested its performance on more than 2000 patient samples. This novel platform includes approximately 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related genome-wide association study loci across diverse populations. RESULTS: In this article, we describe NBA's potential as an efficient means for researchers to assess known and novel disease genetic associations in a multi-ancestry framework. The NBA can identify rare genetic variants and accurately impute more than 15 million common variants across populations. Apart from enabling sample prioritization for further whole-genome sequencing studies, we envisage that NBA will play a pivotal role in recruitment for interventional studies in the precision medicine space. CONCLUSIONS: From a broader perspective, the NBA serves as a promising means to foster collaborative research endeavors in the field of neurological disorders worldwide. Ultimately, this carefully designed tool is poised to make a substantial contribution to uncovering the genetic etiology underlying these debilitating conditions. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Lack of genetic evidence for NLRP3 inflammasome involvement in Parkinson's disease pathogenesis.

Activation of the NLRP3 inflammasome has been implicated in Parkinson's disease (PD) based on in vitro and in vivo studies. Clinical trials targeting the NLRP3 inflammasome in PD are ongoing. However, the evidence supporting NLRP3's involvement in PD from human genetics data is limited. We analyzed common and rare variants in NLRP3 inflammasome-related genes in PD cohorts, performed pathway-specific polygenic risk score (PRS) analyses, and studied causal associations using Mendelian randomization (MR) with the NLRP3 components and the cytokines IL-1ß and IL-18. Our findings showed no associations of common or rare variants, nor of the pathway PRS with PD. MR suggests that altering the expression of the NLRP3 inflammasome, IL-1ß, or IL-18, does not affect PD risk or progression. Therefore, our results do not support a role for the NLRP3 inflammasome in PD pathogenesis or as a target for drug development.

Assessing the lack of diversity in genetics research across neurodegenerative diseases: A systematic review of the GWAS Catalog and literature.

The under-representation of non-European cohorts in neurodegenerative disease genome-wide association studies (GWAS) hampers precision medicine efforts. Despite the inherent genetic and phenotypic diversity in these diseases, GWAS research consistently exhibits a disproportionate emphasis on participants of European ancestry. This study reviews GWAS up to 2022, focusing on non-European or multi-ancestry neurodegeneration studies. We conducted a systematic review of GWAS results and publications up to 2022, focusing on non-European or multi-ancestry neurodegeneration studies. Rigorous article inclusion and quality assessment methods were employed. Of 123 neurodegenerative disease (NDD) GWAS reviewed, 82% predominantly featured European ancestry participants. A single European study identified over 90 risk loci, compared to a total of 50 novel loci in identified in all non-European or multi-ancestry studies. Notably, only six of the loci have been replicated. The significant under-representation of non-European ancestries in NDD GWAS hinders comprehensive genetic understanding. Prioritizing genomic diversity in future research is crucial for advancing NDD therapies and understanding. HIGHLIGHTS: Eighty-two percent of neurodegenerative genome-wide association studies (GWAS) focus on Europeans. Only 6 of 50 novel neurodegenerative disease (NDD) genetic loci have been replicated. Lack of diversity significantly hampers understanding of NDDs. Increasing diversity in NDD genetic research is urgently required. New initiatives are aiming to enhance diversity in NDD research.

The Neurodegenerative Disease Knowledge Portal: Propelling Discovery Through the Sharing of Neurodegenerative Disease Genomic Resources.

Although large-scale genetic association studies have proven opportunistic for the delineation of neurodegenerative disease processes, we still lack a full understanding of the pathological mechanisms of these diseases, resulting in few appropriate treatment options and diagnostic challenges. To mitigate these gaps, the Neurodegenerative Disease Knowledge Portal (NDKP) was created as an open-science initiative with the aim to aggregate, enable analysis, and display all available genomic datasets of neurodegenerative disease, while protecting the integrity and confidentiality of the underlying datasets. The portal contains 218 genomic datasets, including genotyping and sequencing studies, of individuals across ten different phenotypic groups, including neurological conditions such as Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease. In addition to securely hosting large genomic datasets, the NDKP provides accessible workflows and tools to effectively utilize the datasets and assist in the facilitation of customized genomic analyses. Here, we summarize the genomic datasets currently included within the portal, the bioinformatics processing of the datasets, and the variety of phenotypes captured. We also present example use-cases of the various user interfaces and integrated analytic tools to demonstrate their extensive utility in enabling the extraction of high-quality results at the source, for both genomics experts and those in other disciplines. Overall, the NDKP promotes open-science and collaboration, maximizing the potential for discovery from the large-scale datasets researchers and consortia are expending immense resources to produce and resulting in reproducible conclusions to improve diagnostic and therapeutic care for neurodegenerative disease patients.

GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis.

GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization, specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIH's Center for Alzheimer's and Related Dementias (CARD) and the Global Parkinson's Genetics Program (GP2). GenoTools was used to process and analyze the UK Biobank and major Alzheimer's Disease (AD) and Parkinson's Disease (PD) datasets with over 400,000 genotypes from arrays and 5000 sequences and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous QC, and conducted genetic ancestry-specific GWAS to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotype data with reproducible and scalable ancestry, QC, and GWAS pipelines.

omicSynth: An open multi-omic community resource for identifying druggable targets across neurodegenerative diseases.

Treatments for neurodegenerative disorders remain rare, but recent FDA approvals, such as lecanemab and aducanumab for Alzheimer disease (MIM: 607822), highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use summary-data-based Mendelian randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer disease, 3 amyotrophic lateral sclerosis (MIM: 105400), 5 Lewy body dementia (MIM: 127750), 46 Parkinson disease (MIM: 605909), and 9 progressive supranuclear palsy (MIM: 601104) target genes passing multiple test corrections (pSMR_multi < 2.95 × 10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics, classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these, 69.8% are expressed in the disease-relevant cell type from single-nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development, and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as riluzole in Alzheimer disease. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community.

Assessing the lack of diversity in genetics research across neurodegenerative diseases: a systematic review of the GWAS Catalog and literature.

Importance: The under-representation of participants with non-European ancestry in genome-wide association studies (GWAS) is a critical issue that has significant implications, including hindering the progress of precision medicine initiatives. This issue is particularly significant in the context of neurodegenerative diseases (NDDs), where current therapeutic approaches have shown limited success. Addressing this under-representation is crucial to harnessing the full potential of genomic medicine in underserved communities and improving outcomes for NDD patients. Objective: Our primary objective was to assess the representation of non-European ancestry participants in genetic discovery efforts related to NDDs. We aimed to quantify the extent of inclusion of diverse ancestry groups in NDD studies and determine the number of associated loci identified in more inclusive studies. Specifically, we sought to highlight the disparities in research efforts and outcomes between studies predominantly involving European ancestry participants and those deliberately targeting non-European or multi-ancestry populations across NDDs. Evidence Review: We conducted a systematic review utilizing existing GWAS results and publications to assess the inclusion of diverse ancestry groups in neurodegeneration and neurogenetics studies. Our search encompassed studies published up to the end of 2022, with a focus on identifying research that deliberately included non-European or multi-ancestry cohorts. We employed rigorous methods for the inclusion of identified articles and quality assessment. Findings: Our review identified a total of 123 NDD GWAS. Strikingly, 82% of these studies predominantly featured participants of European ancestry. Endeavors specifically targeting non-European or multi-ancestry populations across NDDs identified only 52 risk loci. This contrasts with predominantly European studies, which reported over 90 risk loci for a single disease. Encouragingly, over 65% of these discoveries occurred in 2020 or later, indicating a recent increase in studies deliberately including non-European cohorts. Conclusions and relevance: Our findings underscore the pressing need for increased diversity in neurodegenerative research. The significant under-representation of non-European ancestry participants in NDD GWAS limits our understanding of the genetic underpinnings of these diseases. To advance the field of neurodegenerative research and develop more effective therapies, it is imperative that future investigations prioritize and harness the genomic diversity present within and across global populations.

Multi-ancestry genome-wide association meta-analysis of Parkinson's disease.

Although over 90 independent risk variants have been identified for Parkinson's disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson's disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations.

Evaluating the performance of polygenic risk profiling across diverse ancestry populations in Parkinson's disease.

Objective: This study aims to address disparities in risk prediction by evaluating the performance of polygenic risk score (PRS) models using the 90 risk variants across 78 independent loci previously linked to Parkinson's disease (PD) risk across seven diverse ancestry populations. Methods: We conducted a multi-stage study, testing PRS models in predicting PD status across seven different ancestries applying three approaches: 1) PRS adjusted by gender and age; 2) PRS adjusted by gender, age and principal components (PCs); and 3) PRS adjusted by gender, age and percentage of population admixture. These models were built using the largest four population-specific summary statistics of PD risk to date (base data) and individual level data obtained from the Global Parkinson's Genetics Program (target data). We performed power calculations to estimate the minimum sample size required to conduct these analyses. A total of 91 PRS models were developed to investigate cumulative known genetic variation associated with PD risk and age of onset in a global context. Results: We observed marked heterogeneity in risk estimates across non-European ancestries, including East Asians, Central Asians, Latino/Admixed Americans, Africans, African admixed, and Ashkenazi Jewish populations. Risk allele patterns for the 90 risk variants yielded significant differences in directionality, frequency, and magnitude of effect. PRS did not improve in performance when predicting disease status using similar base and target data across multiple ancestries, demonstrating that cumulative PRS models based on current known risk are inherently biased towards European populations. We found that PRS models adjusted by percentage of admixture outperformed models that adjusted for conventional PCs in highly admixed populations. Overall, the clinical utility of our models in individually predicting PD status is limited in concordance with the estimates observed in European populations. Interpretation: This study represents the first comprehensive assessment of how PRS models predict PD risk and age at onset in a multi-ancestry fashion. Given the heterogeneity and distinct genetic architecture of PD across different populations, our assessment emphasizes the need for larger and diverse study cohorts of individual-level target data and well-powered ancestry-specific summary statistics. Our current understanding of PD status unraveled through GWAS in European populations is not generally applicable to other ancestries. Future studies should integrate clinical and *omics level data to enhance the accuracy and predictive power of PRS across diverse populations.

Human brain single nucleus cell type enrichments in neurodegenerative diseases.

Background: Single-cell RNA sequencing has opened a window into clarifying the complex underpinnings of disease, particularly in quantifying the relevance of tissue- and cell-type-specific gene expression. Methods: To identify the cell types and genes important to therapeutic target development across the neurodegenerative disease spectrum, we leveraged genome-wide association studies, recent single-cell sequencing data, and bulk expression studies in a diverse series of brain region tissues. Results: We were able to identify significant immune-related cell types in the brain across three major neurodegenerative diseases: Alzheimer's disease, amyotrophic lateral sclerosis, and Parkinson's disease. Subsequently, putative roles of 30 fine-mapped loci implicating seven genes in multiple neurodegenerative diseases and their pathogenesis were identified. Conclusions: We have helped refine the genetic regions and cell types effected across multiple neurodegenerative diseases, helping focus future translational research efforts.

Long-Read Sequencing Resolves a Complex Structural Variant in PRKN Parkinson's Disease.

BACKGROUND: Parkin RBR E3 ubiquitin-protein ligase (PRKN) mutations are the most common cause of young onset and autosomal recessive Parkinson's disease (PD). PRKN is located in FRA6E, which is one of the common fragile sites in the human genome, making this region prone to structural variants. However, complex structural variants such as inversions of PRKN are seldom reported, suggesting that there are potentially unrevealed complex pathogenic PRKN structural variants. OBJECTIVES: To identify complex structural variants in PRKN using long-read sequencing. METHODS: We investigated the genetic cause of monozygotic twins presenting with a young onset dystonia-parkinsonism using targeted sequencing, whole exome sequencing, multiple ligation probe amplification, and long-read sequencing. We assessed the presence and frequency of complex inversions overlapping PRKN using whole-genome sequencing data of Accelerating Medicines Partnership Parkinson's disease (AMP-PD) and United Kingdom (UK)-Biobank datasets. RESULTS: Multiple ligation probe amplification identified a heterozygous exon three deletion in PRKN and long-read sequencing identified a large novel inversion spanning over 7 Mb, including a large part of the coding DNA sequence of PRKN. We could diagnose the affected subjects as compound heterozygous carriers of PRKN. We analyzed whole genome sequencing data of 43,538 participants of the UK-Biobank and 4941 participants of the AMP-PD datasets. Nine inversions in the UK-Biobank and two in AMP PD were identified and were considered potentially damaging and likely to affect PRKN expression. CONCLUSIONS: This is the first report describing a large 7 Mb inversion involving breakpoints outside of PRKN. This study highlights the importance of using long-read sequencing for structural variant analysis in unresolved young-onset PD cases. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Sleep disturbances as risk factors for neurodegeneration later in life.

The relationship between sleep disorders and neurodegeneration is complex and multi-faceted. Using over one million electronic health records (EHRs) from Wales, UK, and Finland, we mined biobank data to identify the relationships between sleep disorders and the subsequent manifestation of neurodegenerative diseases (NDDs) later in life. We then examined how these sleep disorders' severity impacts neurodegeneration risk. Additionally, we investigated how sleep attributed risk may compensate for the lack of genetic risk factors (i.e. a lower polygenic risk score) in NDD manifestation. We found that sleep disorders such as sleep apnea were associated with the risk of Alzheimer's disease (AD), amyotrophic lateral sclerosis, dementia, Parkinson's disease (PD), and vascular dementia in three national scale biobanks, with hazard ratios (HRs) ranging from 1.31 for PD to 5.11 for dementia. These sleep disorders imparted significant risk up to 15 years before the onset of an NDD. Cumulative number of sleep disorders in the EHRs were associated with a higher risk of neurodegeneration for dementia and vascular dementia. Sleep related risk factors were independent of genetic risk for Alzheimer's and Parkinson's, potentially compensating for low genetic risk in overall disease etiology. There is a significant multiplicative interaction regarding the combined risk of sleep disorders and Parkinson's disease. Poor sleep hygiene and sleep apnea are relatively modifiable risk factors with several treatment options, including CPAP and surgery, that could potentially reduce the risk of neurodegeneration. This is particularly interesting in how sleep related risk factors are significantly and independently enriched in manifesting NDD patients with low levels of genetic risk factors for these diseases.

NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations.

Genome-wide genotyping platforms have the capacity to capture genetic variation across different populations, but there have been disparities in the representation of population-dependent genetic diversity. The motivation for pursuing this endeavor was to create a comprehensive genome-wide array capable of encompassing a wide range of neuro-specific content for the Global Parkinson's Genetics Program (GP2) and the Center for Alzheimer's and Related Dementias (CARD). CARD aims to increase diversity in genetic studies, using this array as a tool to foster inclusivity. GP2 is the first supported resource project of the Aligning Science Across Parkinson's (ASAP) initiative that aims to support a collaborative global effort aimed at significantly accelerating the discovery of genetic factors contributing to Parkinson's disease and atypical parkinsonism by generating genome-wide data for over 200,000 individuals in a multi-ancestry context. Here, we present the Illumina NeuroBooster array (NBA), a novel, high-throughput and cost-effective custom-designed content platform to screen for genetic variation in neurological disorders across diverse populations. The NBA contains a backbone of 1,914,934 variants (Infinium Global Diversity Array) complemented with custom content of 95,273 variants implicated in over 70 neurological conditions or traits with potential neurological complications. Furthermore, the platform includes over 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related GWAS loci across diverse populations. The NBA can identify low frequency variants and accurately impute over 15 million common variants from the latest release of the TOPMed Imputation Server as of August 2023 (reference of over 300 million variants and 90,000 participants). We envisage this valuable tool will standardize genetic studies in neurological disorders across different ancestral groups, allowing researchers to perform genetic research inclusively and at a global scale.

Genetics of NLRP3 suggests lack of involvement and inefficient druggability in Parkinson's disease.

Activation of the NLRP3-inflammasome has been proposed to play a role in Parkinson's disease pathogenesis based on in vitro and in vivo studies. Currently, clinical trials targeting the NLRP3 pathway in Parkinson's disease are at early stages. However, the evidence supporting NLRP3's involvement in Parkinson's disease from human genetics data remains limited. In this study, we conducted comprehensive analyses of common and rare variants in genes related to the NLRP3-inflammasome in large Parkinson's disease cohorts. Furthermore, we performed pathway-specific analyses using polygenic risk scores and studied potential causal associations using Mendelian randomization with the NLRP3 components and the cytokines released by its activation, IL-1ß and IL-18. Our findings showed no associations of common or rare variants, nor of the pathway polygenic risk score for the NLRP3 inflammasome, with risk of Parkinson's disease. Mendelian randomization analyses suggest that altering the expression of the NLRP3 inflammasome, IL-1ß or IL-18, is not likely to affect Parkinson's disease risk, age-at-onset, or progression. Therefore, our results do not support an important role for the NLRP3 inflammasome in Parkinson's disease pathogenesis or as a strong target for drug development.

Long-read sequencing resolves a complex structural variant in PRKN Parkinson's disease.

Background: PRKN mutations are the most common cause of young onset and autosomal recessive Parkinson's disease (PD). PRKN is located in FRA6E which is one of the common fragile sites in the human genome, making this region prone to structural variants. However, complex structural variants such as inversions of PRKN are seldom reported, suggesting that there are potentially unrevealed complex pathogenic PRKN structural variants. Objectives: To identify complex structural variants in PRKN using long-read sequencing. Methods: We investigated the genetic cause of monozygotic twins presenting with a young onset dystonia-parkinsonism using targeted sequencing, whole exome sequencing, multiple ligation probe amplification, and long-read. We assessed the presence and frequency of complex inversions overlapping PRKN using whole-genome sequencing data of AMP-PD and UK-Biobank datasets. Results: Multiple ligation probe amplification identified a heterozygous exon 3 deletion in PRKN and long-read sequencing identified a large novel inversion spanning over 7Mb, including a large part of the coding DNA sequence of PRKN. We could diagnose the affected subjects as compound heterozygous carriers of PRKN. We analyzed whole genome sequencing data of 43,538 participants of the UK-Biobank and 4,941 participants of the AMP-PD datasets. Nine inversions in the UK-Biobank and two in AMP PD were identified and were considered potentially damaging and likely to affect PRKN isoforms. Conclusions: This is the first report describing a large 7Mb inversion involving breakpoints outside of PRKN. This study highlights the importance of using long-read whole genome sequencing for structural variant analysis in unresolved young-onset PD cases.

Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2).

The Global Parkinson's Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia.