Biology subjects

Makarious, M. B.

Publications and source records attributed to Makarious, M. B..

17 recordsLinked to original sources

bioRxiv2025

Long-Read epigenetic clocks identify improved brain aging predictions

Epigenetic clocks are widely used to estimate biological aging, yet most are built from array-based data from peripheral tissues of predominantly European-ancestry individuals, limiting their generalizability. Here, we present aging clocks on DNA methylation from Oxford Nanopore long-read sequencing (LRS), leveraging over 28 million CpG sites from prefrontal cortex samples across individuals of African and European ancestry. These models were developed using GenoML, an automated machine learning platform for multi-omics data that leverages a diverse catalog of existing model architectures. Our long-read-informed clocks were developed using promoter-based and whole-genome window-based features, yielding models for each individual cohort as well as a combined-cohort clock. Each of these models demonstrated favorable performance compared to existing methylation clocks and was externally validated in a cohort of Colombian individuals. We further performed enrichment analyses and nominated both shared and cohort-specific pathways, cell types, and transcription factor binding motifs which may be implicated in aging and were not fully explained by cell type proportions or postmortem interval. Altogether, our findings highlight the power of long-read methylation data for constructing accurate, ancestry-aware aging clocks and emphasize the importance of inclusive training datasets.

bioinformatics↗

bioRxiv2025

Random forest model improves annotation and discovery of variants of uncertain significance in Alzheimer's and other neurological disorders

Variants of uncertain significance (VUS) are a bottleneck for genetic discovery and complicate clinical decision-making in Alzheimers disease and related neurological disorders (ADRD). We developed MoVUS: Model for Variants of Unknown Significance, a random-forest approach that integrates functional predictors to classify missense VUS. MoVUS leverages a balanced random forest model trained on dbNSFP v5.1a with high-confidence ClinVar and HGMD labels, using harmonized functional prediction rankscores. MoVUS produced confident, explainable calls, with [~]98% accuracy (AUC [~]0.998), prioritizing potentially pathogenic candidates and down-ranked likely benign variants on independent validation sets of ClinVar-only and HGMD-only variants. In our discovery analyses on ADRD-implicated variants in dbNSFP and from independent collaborator cohorts, we achieved high-confidence classifications on a majority of the unknown variants (average of 55% of discovery variants). We also had access to medical records and family trees for some variants, further validating our findings. Across held-out and external datasets, MoVUS reports high accuracy alongside confidence scores and helps prioritize actionable candidates, and reduces bias by considering multiple scores for each variant. To facilitate use, we developed a web app for users to browse across 100+ ADRD genes. MoVUS provides transparent, reproducible triage for research follow-up by pairing consensus predictors with SHAP-based visualizations and explanations.

genetics↗

bioRxiv2025

Dissecting the biological impact of GBA1 mutations using multi-omics in an isogenic setting

GBA1 is a risk gene for multiple neurodegenerative diseases, including Lewy Body Dementia and Parkinsons disease, and biallelic pathogenic variants in the gene result in the lysosomal storage disorder Gaucher disease. GBA1 encodes the enzyme glucocerebrosidase (GCase), and alterations in the gene result in reduced enzymatic activity, which affects lysosome function downstream. Induced pluripotent stem cells (iPSCs) are a useful tool for testing the functional consequences of gene variants in an isogenic setting. Additionally, they can be used to perform multiomic studies to explore biological effects independent of disease mechanisms. Using CRISPR-edited isogenic KOLF2.1J iPSC lines containing pathogenic GBA1 variants D409H (p.D448H), D409V (p.D448V) and GBA1 knockout line generated by the iPSC Neurodegenerative Disease Initiative (iNDI), we examined potential molecular mechanisms and downstream consequences of GCase reduction. In this study, we confirm that this isogenic series behaves as expected for loss of function variants, despite the known difficulties with GBA1 editing. We identified that there are limited overlapping results across cell types suggesting potential different downstream effects caused by GBA1 variants. Additionally, we note that RNA-based quantitation may not be the best method to characterize GCase mechanisms, but protein and metabolomic analyses may be used to evaluate differences across genotypes.

genomics↗

bioRxiv2025

CARDBiomedBench: A Benchmark for Evaluating Large Language Model Performance in Biomedical Research

BackgroundsBiomedical research requires sophisticated understanding and reasoning across multiple specializations. While large language models (LLMs) show promise in scientific applications, their capability to safely and accurately support complex biomedical research remains uncertain. MethodsWe present CARDBiomedBench, a novel question-and-answer benchmark for evaluating LLMs in biomedical research. For our pilot implementation, we focus on neurodegenerative diseases (NDDs), a domain requiring integration of genetic, molecular, and clinical knowledge. The benchmark combines expert-annotated question-answer (Q/A) pairs with semi-automated data augmentation, drawing from authoritative public resources including drug development data, genome-wide association studies (GWAS), and Summary-data based Mendelian Randomization (SMR) analyses. We evaluated seven private and open-source LLMs across ten biological categories and nine reasoning skills, using novel metrics to assess both response quality and safety. ResultsOur benchmark comprises over 68,000 Q/A pairs, enabling robust evaluation of LLM performance. Current state-of-the-art models show significant limitations: models like Claude-3.5-Sonnet demonstrates excessive caution (Response Quality Rate: 25% [95% CI: 25% {+/-} 1], Safety Rate: 76% {+/-} 1), while others like ChatGPT-4o exhibits both poor accuracy and unsafe behavior (Response Quality Rate: 37% {+/-} 1, Safety Rate: 31% {+/-} 1). These findings reveal fundamental gaps in LLMs ability to handle complex biomedical information. ConclusionCARDBiomedBench establishes a rigorous standard for assessing LLM capabilities in biomedical research. Our pilot evaluation in the NDD domain reveals critical limitations in current models ability to safely and accurately process complex scientific information. Future iterations will expand to other biomedical domains, supporting the development of more reliable AI systems for accelerating scientific discovery.

bioinformatics↗

bioRxiv2024

Long-read sequencing of hundreds of diverse brains provides insight into the impact of structural variation on gene expression and DNA methylation

Structural variants (SVs) drive gene expression in the human brain and are causative of many neurological conditions. However, most existing genetic studies have been based on short-read sequencing methods, which capture fewer than half of the SVs present in any one individual. Long-read sequencing (LRS) enhances our ability to detect disease-associated and functionally relevant structural variants; however, its application in large-scale genomic studies has been limited by challenges in sample preparation and high costs. Here, we leverage a new scalable wet-lab protocol and computational pipeline for whole-genome Oxford Nanopore Technologies sequencing and apply it to neurologically normal control samples from the North American Brain Expression Consortium (NABEC) (European ancestry) and Human Brain Collection Core (HBCC) (African or African admixed ancestry) cohorts. Through this work, we present a publicly available long-read resource from 351 human brain samples (median N50: 27 Kbp and at an average depth of ~40x genome coverage). We discover approximately 234,905 SVs and produce locally phased assemblies that cover 95% of all protein-coding genes in GRCh38. To resolve cis-regulatory effects, we develop ASM-LR, a method for allele-specific methylation analysis from long-read data, revealing both strong and subtle regulatory effects, including numerous novel methylation QTLs masked in unphased models. Our results highlight the power of haplotype-resolved methylation to uncover regulatory mechanisms and establish a foundational resource for exploring how genetic variation shapes gene expression and epigenetic architecture across diverse ancestries.

genomics↗

bioRxiv2024

CNV-Finder: Streamlining Copy Number Variation Discovery

Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is significant in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates efficient prioritization of samples for time-consuming or costly subsequent analyses such as Multiplex Ligation-dependent Probe Amplification (MLPA), short-read, and long-read whole genome sequencing. We incorporate four genes to establish our methods--Parkin (PRKN), Leucine Rich Repeat And Ig Domain Containing 2 (LINGO2), Microtubule Associated Protein Tau (MAPT), and alpha-Synuclein (SNCA)--which may be relevant to neurological diseases such as Alzheimers disease (AD), Parkinsons disease (PD), Progressive Supranuclear Palsy (PSP), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinsons Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finders interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using visual inspection, MLPA, short-read, and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied size, probe density, and noise. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub (https://github.com/GP2code/CNV-Finder; DOI 10.5281/zenodo.14182563). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.

bioinformatics↗

bioRxiv2024

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 users genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIHs Center for Alzheimers and Related Dementias (CARD) and the Global Parkinsons Genetics Program (GP2). GenoTools was used to process and analyze the UK Biobank and major Alzheimers Disease (AD) and Parkinsons 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.

genomics↗

bioRxiv2023

Federated Learning for multi-omics: a performance evaluation in Parkinson's disease

While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated Learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinsons Disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 {+/-} 0.009, 0.014 {+/-} 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies. The Bigger PictureThe wide-scale application of artificial intelligence and computationally intensive analytical approaches in the biomedical and clinical domain is largely restricted by access to sufficient training data. This data scarcity exists due to the isolated nature of biomedical and clinical institutions, mandated by patient privacy policies in the health system or government legislation. Federated Learning (FL), a machine learning approach that facilitates collaborative model training is a promising strategy to address these restrictions. Therefore, understanding the limitations of cooperatively trained FL models, and their performance differences to similar, centrally trained models, is crucial to valuing their implementation in the broader biomedical research community.

bioinformatics↗

bioRxiv2022

Genome-wide analysis of Structural Variants in Parkinson's Disease using Short-Read Sequencing data

Parkinsons disease is a complex neurodegenerative disorder, affecting approximately one million individuals in the USA alone. A significant proportion of risk for Parkinsons disease is driven by genetics. Despite this, the majority of the common genetic variation that contributes to disease risk is unknown, in-part because previous genetic studies have focussed solely on the contribution of single nucleotide variants. Structural variants represent a significant source of genetic variation in the human genome. However, because assay of this variability is challenging, structural variants have not been cataloged on a genome-wide scale, and their contribution to the risk of Parkinsons disease remains unknown. In this study, we 1) leveraged the GATK-SV pipeline to detect and genotype structural variants in 7,772 short-read sequencing data and 2) generated a subset of matched whole-genome Oxford Nanopore Technologies long-read sequencing data from the PPMI cohort to allow for comprehensive structural variant confirmation. We detected, genotyped, and tested 3,154 "high-confidence" common structural variant loci, representing over 412 million nucleotides of non-reference genetic variation. Using the long-read sequencing data, we validated three structural variants that may drive the association signals at known Parkinsons disease risk loci, including a 2kb intronic deletion within the gene LRRN4. Further, we confirm that the majority of structural variants in the human genome cannot be detected using short-read sequencing alone, encompassing on average around 4 million nucleotides of inaccessible sequence per genome. Therefore, although these data provide the most comprehensive survey of the contribution of structural variants to the genetic risk of Parkinsons disease to date, this study highlights the need for large-scale long-read datasets to fully elucidate the role of structural variants in Parkinsons disease.

genetics↗

bioRxiv2022

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

BackgroundThe clinical manifestations of Parkinsons disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to address this critical need. Methods and FindingsWe used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinsons Disease Progression Marker Initiative (PPMI) (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinsons Disease Biomarker Program (PDBP) (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression five years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 {+/-} 0.01 for the slower progressing group (PDvec1), 0.87 {+/-} 0.03 for moderate progressors, and 0.95 {+/-} 0.02 for the fast progressing group (PDvec3). We identified serum neurofilament light (Nfl) as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent validation cohort, released the analytical code, and developed models in an open science manner. ConclusionsOur data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes that might have been masked by cohort heterogeneity. We anticipate that machine learning models will improve patient counseling, clinical trial design, allocation of healthcare resources, and ultimately individualized patient care.

bioinformatics↗

bioRxiv2022

The IPDGC/GP2 Hackathon - an open science event for training in data science, genomics, and collaboration using Parkinson's disease data

BackgroundOpen science and collaboration are necessary to facilitate the advancement of Parkinsons disease (PD) research. Hackathons are collaborative events that bring together people with different skill sets and backgrounds to generate resources and creative solutions to problems. These events can be used as training and networking opportunities. ObjectiveTo coordinate a virtual hackathon to develop novel PD research tools. Methods49 early career scientists from 12 countries collaborated in a virtual 3-day hackathon event in May 2021, during which they built tools and pipelines with a focus on PD. Resources were created with the goal of helping scientists accelerate their own research by having access to the necessary code and tools. ResultsEach team was allocated one of nine different projects, each with a different goal. These included developing post-genome-wide association studies (GWAS) analysis pipelines, downstream analysis of genetic variation pipelines, and various visualization tools. ConclusionHackathons are a valuable approach to inspire creative thinking, supplement training in data science, and foster collaborative scientific relationships, which are foundational practices for early career researchers. The resources generated can be used to accelerate research on the genetics of PD.

genetics↗

bioRxiv2021

Multi-Modality Machine Learning Predicting Parkinsons Disease

BackgroundPersonalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multi-modal data is key moving forward. We build upon previous work to deliver multi-modal predictions of Parkinsons Disease (PD). MethodsWe performed automated ML on multi-modal data from the Parkinsons Progression Marker Initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinsons Disease Biomarker Program (PDBP) dataset. Finally, networks were built to identify gene communities specific to PD. FindingsOur initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification, increased the diagnosis prediction accuracy (balanced accuracy) and other metrics. Combining data modalities outperforms the single biomarker paradigm. UPSIT was the largest contributing predictor for the classification of PD. The transcriptomic data was used to construct a network of disease-relevant transcripts. InterpretationWe have built a model using an automated ML pipeline to make improved multi-omic predictions of PD. The model developed improves disease risk prediction, a critical step for better assessment of PD risk. We constructed gene expression networks for the next generation of genomics-derived interventions. Our automated ML approach allows complex predictive models to be reproducible and accessible to the community. FundingNational Institute on Aging, National Institute of Neurological Disorders and Stroke, the Michael J. Fox Foundation, and the Global Parkinsons Genetics Program. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSPrior research into predictors of Parkinsons disease (PD) has either used basic statistical methods to make predictions across data modalities, or they have focused on a single data type or biomarker model. We have done this using an open-source automated machine learning (ML) framework on extensive multi-modal data, which we believe yields robust and reproducible results. We consider this the first true multi-modality ML study of PD risk classification. Added value of this studyWe used a variety of linear, non-linear, kernel, neural networks, and ensemble ML algorithms to generate an accurate classification of both cases and controls in independent datasets using data that is not involved in PD diagnosis itself at study recruitment. The model built in this paper significantly improves upon our previous models that used the entire training dataset in previous work1. Building on this earlier work, we showed that the PD diagnosis can be refined using improved algorithmic classification tools that may yield potential biological insights. We have taken careful consideration to develop and validate this model using public controlled-access datasets and an open-source ML framework to allow for reproducible and transparent results. Implications of all available evidenceTraining, validating, and tuning a diagnostic algorithm for PD will allow us to augment clinical diagnoses or risk assessments with less need for complex and expensive exams. Going forward, these models can be built on remote or asynchronously collected data which may be important in a growing telemedicine paradigm. More refined diagnostics will also increase clinical trial efficiency by potentially refining phenotyping and predicting onset, allowing providers to identify potential cases earlier. Early detection could lead to improved treatment response and higher efficacy. Finally, as part of our workflow, we built new networks representing communities of genes correlated in PD cases in a hypothesis-free manner, showing how new and existing genes may be connected and highlighting therapeutic opportunities.

genomics↗

bioRxiv2020

Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into the complex genetic architecture

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer’s and Parkinson’s disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition.Competing Interest StatementThomas G. Beach is a consultant for Prothena, Vivid Genomics and Avid Radiopharmaceuticals. He is a scientific advisory board member for Vivid Genomics. John A. Hardy, Huw R. Morris, Stuart Pickering-Brown, Andrew B. Singleton, and Bryan J. Traynor hold US, EU and Canadian patents on the clinical testing and therapeutic intervention for the hexanucleotide repeat expansion of C9orf72. Michael A. Nalls is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH, Bethesda, MD, USA; as a possible conflict of interest Dr. Nalls also consults for Neuron23 Inc., Lysosomal Therapeutics Inc., Illumina Inc., the Michael J. Fox Foundation and Vivid Genomics among others. Jose A. Palma is an editorial board member of Movement Disorders, Parkinsonism & Related Disorders, BMC Neurology, and Clinical Autonomic Research. Bradley F. Boeve, James Leverenz, and Sonja W. Scholz serve on the Scientific Advisory Council of the Lewy Body Dementia Association. Sonja W. Scholz is an editorial board member for the Journal of Parkinson's Disease. Bryan J. Traynor is an editorial board member for JAMA Neurology; Journal of Neurology, Neurosurgery, and Psychiatry; Brain; and Neurobiology of Aging. Zbigniew K. Wszolek serves as a principal investigator or co-principal investigator on Abbvie, Inc. (M15-562 and M15-563), Biogen, Inc. (228PD201) grant, and Biohaven Pharmaceuticals, Inc. (BHV4157-206 and BHV3241-301). Zbigniew K. Wszolek serves as the principal investigator of the Mayo Clinic American Parkinson Disease Association (APDA) Information and Referral Center, and as co-principal investigator of the Mayo Clinic APDA Center for Advanced Research. All other authors report no competing interests.View Full Text

genomics↗

bioRxiv2020

The Parkinson's Disease GWAS Locus Browser

Parkinsons disease (PD) is a neurodegenerative disease with an often complex genetic component identifiable by genome-wide association studies (GWAS). The most recent large scale PD GWASes have identified more than 90 independent risk variants for PD risk and progression across 80 loci. One major challenge in current genomics is identifying the causal gene(s) and variant(s) from each GWAS locus. Here we present a GWAS locus browser application that combines data from multiple databases to aid in the prioritization of genes associated with PD GWAS loci. We included 92 independent genome-wide significant signals from multiple recent PD GWAS studies including the PD risk GWAS, age-at-onset GWAS and progression GWAS. We gathered data for all 2336 genes within 1Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self-ranked criteria. Our aim is that the information contained in this browser (https://pdgenetics.shinyapps.io/GWASBrowser/) will assist the PD research community with the prioritization of genes for follow-up functional studies and as potential therapeutic targets.

genetics↗

bioRxiv2019

A genome-wide genetic pleiotropy approach identified shared loci between multiple system atrophy and inflammatory bowel disease

We aimed to identify shared genetic background between multiple system atrophy (MSA) and autoimmune diseases by using the conjFDR approach. Our study showed significant genetic overlap between MSA and inflammatory bowel disease and identified DENND1B, C7, and RSP04 loci, which are linked to significant changes in methylation or expression levels of adjacent genes. We obtained evidence of enriched heritability involving immune/digestive categories. Finally, an MSA mouse model showed dysregulation of the C7 gene in the degenerating midbrain compared to wildtype mice. The results identify novel molecular mechanisms and implicate immune and gut dysfunction in MSA pathophysiology.

genetics↗

bioRxiv2019

Genetic modifiers of risk and age at onset in GBA associated Parkinson disease and Lewy body dementia

Parkinsons disease (PD) is a genetically complex disorder. Multiple genes have been shown to contribute to the risk of PD, and currently 90 independent risk variants have been identified by genome-wide association studies. Thus far, a number of genes (including SNCA, LRRK2, and GBA) have been shown to contain variability across a spectrum of frequency and effect, from rare, highly penetrant variants to common risk alleles with small effect sizes. Variants in GBA, encoding the enzyme glucocerebrosidase, are associated with Lewy body diseases such as PD and Lewy body dementia (LBD). These variants, which reduce or abolish enzymatic activity, confer a spectrum of disease risk, from 1.4- to >10-fold. An outstanding question in the field is what other genetic factors that influence GBA-associated risk for disease, and whether these overlap with known PD risk variants.\n\nUsing multiple, large case-control datasets, totalling 217,165 individuals (22,757 PD cases, 13,431 PD proxy cases, 622 LBD cases and 180,355 controls), we identified 1,772 PD cases, 711 proxy cases and 7,624 controls with a GBA variant (p.E326K, p.T369M or p.N370S). We performed a genome-wide association study and analysed the most recent PD-associated genetic risk score to detect genetic influences on GBA risk and age at onset. We attempted to replicate our findings in two independent datasets, including the personal genetics company 23andMe, Inc. and whole-genome sequencing data. Our analysis showed that the overall PD genetic risk score modifies risk for disease and decreases age at onset in carriers of GBA variants. Notably, this effect was consistent across all tested GBA risk variants. Dissecting this signal demonstrated that variants in close proximity to SNCA and CTSB (encoding cathepsin B) are the most significant contributors. Risk variants in the CTSB locus were identified to decrease mRNA expression of CTSB. Additional analyses suggest a possible genetic interaction between GBA and CTSB and GBA p.N370S neurons were shown to have decreased Cathepsin B expression compared to controls. These data provide a genetic basis for modification of GBA-associated PD risk and age at onset and demonstrate that variability at genes implicated in lysosomal function exerts the largest effect on GBA associated risk for disease. Further, these results have important implications for selection of GBA carriers for therapeutic interventions.

genetics↗

bioRxiv2019

Penetrance of Parkinson’s disease in LRRK2 p.G2019S carriers is modified by a polygenic risk score

BackgroundWhile the LRRK2 p.G2019S mutation has been demonstrated to be a strong risk factor for Parkinsons Disease (PD), factors that contribute to penetrance among carriers, other than aging, have not been well identified.\n\nObjectivesTo evaluate whether a cumulative genetic risk identified in the recent genome-wide study is associated with penetrance of PD among p.G2019S mutation carriers.\n\nMethodsWe included p.G2019S heterozygote carriers with European ancestry in three genetic cohorts in which the mutation carriers with and without PD were selectively recruited. We also included the carriers from two datasets: one from a case-control setting without selection of mutation carriers, and the other from a population sampling. The associations between PRS constructed from 89 variants reported in Nalls et al. and PD were tested and meta-analyzed. We also explored the interaction of age and PRS.\n\nResultsAfter excluding 8 homozygotes, 833 p.G2019S heterozygote carriers (439 PD and 394 unaffected) were analyzed. PRS was associated with a higher penetrance of PD (OR 1.34, 95% C.I. [1.09, 1.64] per +1 SD, P = 0.005). In addition, associations with PRS and penetrance were stronger in the younger participants (main effect: OR 1.28 [1.04, 1.58] per +1 SD, P = 0.022; interaction effect: OR 0.78 [0.64, 0.94] per +1 SD and +10 years of age, P = 0.008).\n\nConclusionsOur results suggest that there is a genetic contribution for penetrance of PD among p.G2019S carriers. These results have important etiologic consequences and potential impact on the selection of subjects for clinical trials.

genetics↗