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

Towns, Clodagh; Richer, Madeleine; Jasaityte, Simona; Stafford, Eleanor; Joubert, Julie; Antar, Tarek; Carrasco-Martinez, Alejandro; Makarious, Mary B.; Casey, Bradford; Vitale, Dan; Levine, Kristin; Leonard, Hampton; Wegel, Claire E.; Solle, Justin; Nalls, Mike A.; Blauwendraat, Cornelis; Singleton, Andrew B.; Tan, Manuela; Iwaki, Hirotaka; Morris, Huw

doi:10.1101/2022.11.25.22282764

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…Our study was conducted in individuals with European ancestry, limiting the transferability and utility of the results to other ethnic groups. Multi-ethnic GWAS and genetic studies of underrepresented populations are currently high on the agenda in the PD field (44,45). Investigating diverse populations will likely be important also in PD methylation studies, especially when methylation profiling is exploited for the development of prediction models for precision medicine.…”

Section: Discussionmentioning

confidence: 99%

Epigenome-wide association study, meta-analysis and risk profiling of whole blood in Parkinson’s disease

Lie,

Tan,

Andersen

et al. 2024

Preprint

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An increasing body of evidence indicates altered DNA methylation in Parkinson's disease (PD), yet the reproducibility and utility of such methylation changes are largely unexplored. We conducted an epigenome-wide association study (EWAS) in whole blood, including 280 PD and 279 control participants from Oslo, Norway. In meta-analysis with data from the Parkinson's Progression Markers Initiative (PPMI) and a previously published whole blood PD EWAS (total N=3068) we confirm SLC7A11 hypermethylation and nominate a novel suggestive differentially methylated CpG near LPIN1. A joint multiscore risk profiling model incorporating polygenic risk and methylation-based estimates of epigenetic PD risk, smoking and leukocyte proportions differentiated patients from control participants with an area under the receiver-operator curve or 0.82 in the Oslo cohort and 0.65 in PPMI. Our results highlight the power of DNA methylation profiling to capture multiple aspects of disease risk, indicating a biomarker potential for precision medicine in neurodegenerative disorders.

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Section: Discussionmentioning

confidence: 99%

Epigenome-wide association study, meta-analysis and risk profiling of whole blood in Parkinson’s disease

Lie,

Tan,

Andersen

et al. 2024

Preprint

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“…The reference panel for ancestry prediction models is composed of samples from the 1000 Genomes Project (1000 Genomes) 12 , the Human Genome Diversity Project (HGDP) 13 , and an Ashkenazi Jewish reference panel 14 totaling 4008 samples. The full counts per ancestry and quality control steps are detailed in Figures 3 and 4 below.…”

Section: Methodsmentioning

confidence: 99%

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

Vitale,

Koretsky,

Kuznetsov

et al. 2024

Preprint

Self Cite

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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.

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“…The latter has recently been greatly bolstered by regional [44][45][46] and global collaborations such as the Global Parkinson's Genetics Program (GP2; Fig. 2) [47][48][49][50]. It is worth noting here that in China and India (by far the two most populous countries in the world, being home to ∼2.9 billion people, and also with extensive diasporas globally [16]), the vast majority of PD patients with family pedigrees compatible with autosomal dominant inheritance remain "unsolved" (e.g., 95% of the 242 probands studied by Zhao et al [37], and 100% of 44 probands in the study by Punia et al which tested specifically for pathogenic LRRK2 variants [51]), suggesting that additional genetic determinants of PD remain to be discovered in these large populations [52,53].…”

Section: Pathogenic Variants In Some Pd Genes Are Highly Penetrant Fo...mentioning

confidence: 99%

“…Currently, the identified risk loci explain an estimated 16-36% of the heritable risk of PD [64], and efforts are ongoing to very substantially ramp up the recruitment of patients (to reach 200,000, including patients from diverse populations) [47][48][49][50], which will further advance understanding of the genetic determinants of PD on a global scale [13][14][15]65].…”

Section: What About Sporadic (So-called "Idiopathic") Pd?mentioning

confidence: 99%

Parkinson’s Disease is Predominantly a Genetic Disease

Lim,

Klein

2024

Journal of Parkinson’s Disease

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The discovery of a pathogenic variant in the alpha-synuclein (SNCA) gene in the Contursi kindred in 1997 indisputably confirmed a genetic cause in a subset of Parkinson’s disease (PD) patients. Currently, pathogenic variants in one of the seven established PD genes or the strongest known risk factor gene, GBA1, are identified in ∼15% of PD patients unselected for age at onset and family history. At first sight, the steep increase in PD prevalence exceeding that of other neurodegenerative diseases may argue against a predominant genetic etiology. Notably, the principal genetic contribution in PD is conferred by pathogenic variants in LRRK2 and GBA1 and, in both cases, characterized by an overall late age of onset and age-related penetrance. In addition, polygenic risk plays a considerable role in PD. However, it is likely that, in the majority of PD patients, a complex interplay of aging, genetic, environmental, and epigenetic factors leads to disease development.5% of PD patients unselected for age at onset and family history. At first sight, the steep increase in PD prevalence exceeding that of other neurodegenerative diseases may argue against a predominant genetic etiology. Notably, the principal genetic contribution in PD is conferred by pathogenic variants in LRRK2 and GBA1 and, in both cases, characterized by an overall late age of onset and age-related penetrance. In addition, polygenic risk plays a considerable role in PD. However, it is likely that, in the majority of PD patients, a complex interplay of aging, genetic, environmental, and epigenetic factors leads to disease development.

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

Cited by 5 publications

References 33 publications

Epigenome-wide association study, meta-analysis and risk profiling of whole blood in Parkinson’s disease

Epigenome-wide association study, meta-analysis and risk profiling of whole blood in Parkinson’s disease

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

Parkinson’s Disease is Predominantly a Genetic Disease

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