Biology subjects

Iwaki, H.

Publications and source records attributed to Iwaki, H..

18 recordsLinked to original sources

bioRxiv2026

Epithelial Glutamate Retention Protects against Colitis

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract that encompasses ulcerative colitis and Crohns disease. Here we identify the cystine/glutamate antiporter xCT as being markedly upregulated in the inflamed intestinal epithelium of patients with IBD. To clarify its functional contribution to disease pathogenesis, we performed genetic loss-of-function study and found that inhibition of xCT confers robust protection against dextran sulfate sodium (DSS)-induced colitis in mice. Intestinal epithelial cell (IEC)-specific deletion of xCT markedly attenuated colitis severity, demonstrating that epithelial xCT upregulation acts as a disease-exacerbating factor in IBD. Mechanistically, xCT deficiency preserved intracellular glutamate levels and protein polyglutamylation, thereby maintaining epithelial barrier integrity and protecting IECs from inflammatory injury. Consistently, pharmacological inhibition of glutamine synthetase, which increases intracellular glutamate, exerted a potent anti-inflammatory effect on the DSS-induced colitis. These findings identify intracellular glutamate retention in IECs as a previously unrecognized mechanism of epithelial protection and highlight both inhibition of xCT-dependent glutamate efflux and suppression of glutamine synthetase as potential therapeutic strategies for IBD.

molecular biology↗

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

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↗

bioRxiv2022

Application of Aligned-UMAP to longitudinal biomedical studies

Longitudinal multi-dimensional biological datasets are ubiquitous and highly abundant. These datasets are essential to understanding disease progression, identifying subtypes, and drug discovery. Discovering meaningful patterns or disease pathophysiologies in these datasets is challenging due to their high dimensionality, making it difficult to visualize hidden patterns. Several methods have been developed for dimensionality reduction, but they are limited to cross-sectional datasets. Recently proposed Aligned-UMAP, an extension of the UMAP algorithm, can visualize high-dimensional longitudinal datasets. In this work, we applied Aligned-UMAP on a broad spectrum of clinical, imaging, proteomics, and single-cell datasets. Aligned-UMAP reveals time-dependent hidden patterns when color-coded with the metadata. We found that the algorithm parameters also play a crucial role and must be tuned carefully to utilize the algorithms potential fully. Altogether, based on its ease of use and our evaluation of its performance on different modalities, we anticipate that Aligned-UMAP will be a valuable tool for the biomedical community. We also believe our benchmarking study becomes more important as more and more high-dimensional longitudinal data in biomedical research becomes available. Highlights- explored the utility of Aligned-UMAP in longitudinal biomedical datasets - offer insights on optimal uses for the technique - provide recommendations for best practices In BriefHigh-dimensional longitudinal data is prevalent yet understudied in biological literature. High-dimensional data analysis starts with projecting the data to low dimensions to visualize and understand the underlying data structure. Though few methods are available for visualizing high dimensional longitudinal data, they are not studied extensively in real-world biological datasets. A recently developed nonlinear dimensionality reduction technique, Aligned-UMAP, analyzes sequential data. Here, we give an overview of applications of Aligned-UMAP on various biomedical datasets. We further provide recommendations for best practices and offer insights on optimal uses for the technique.

bioinformatics↗

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

Four-dimensional tractography animates neural propagations via distinct interhemispheric pathways

ObjectiveTo visualize and validate the dynamics of interhemispheric neural propagations induced by single-pulse electrical stimulation (SPES). MethodsThis methodological study included three patients with drug-resistant focal epilepsy who underwent measurement of cortico-cortical spectral responses (CCSRs) during bilateral stereo-electroencephalography recording. We delivered SPES to 83 electrode pairs and analyzed CCSRs recorded at 268 nonepileptic electrode sites. Diffusion-weighted imaging (DWI) tractography localized the interhemispheric white matter pathways as streamlines directly connecting two electrode sites. We localized and visualized the putative SPES-related fiber activation, at each 1-ms time window, based on the propagation velocity defined as the DWI-based streamline length divided by the early CCSR peak latency. ResultsThe resulting movie, herein referred to as four-dimensional tractography, delineated the spatiotemporal dynamics of fiber activation via the corpus callosum and anterior commissure. Longer streamline length was associated with delayed peak latency and smaller amplitude of CCSRs. The cortical regions adjacent to each fiber activation site indeed exhibited CCSRs at the same time window. ConclusionsOur four-dimensional tractography successfully animated neural propagations via distinct interhemispheric pathways. SignificanceOur novel animation method has the potential to help investigators in addressing the mechanistic significance of the interhemispheric network dynamics supporting physiological function.

neuroscience↗

bioRxiv2020

Large-scale pathway-specific polygenic risk, transcriptomic community networks and functional inferences in Parkinson disease

Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological pathways underlying PD using the largest currently available cohorts of genetic data and gene expression data from International Parkinsons Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership - Parkinsons disease initiative (AMP-PD), among other sources. We placed these insights into a cellular context. We applied large-scale pathway-specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk in a cohort of 457,110 individuals by focusing on a compilation of 2,199 publicly annotated gene sets representative of curated pathways, of which we nominate 46 pathways associated with PD risk. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data, including 4,331 individuals. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for adult dopaminergic neurons, serotonergic neurons, and radial glia. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of 1,612 PD patients, which revealed 54 connecting networks associated with PD. Our analyses highlight several promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

genetics↗

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

Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning

BackgroundAlzheimers disease (AD) is a common, age-related, neurodegenerative disease that impairs a persons ability to perform day-to-day activities. Diagnosing AD is challenging, especially in the early stages. Many patients still go undiagnosed, partly due to the complex heterogeneity in disease progression. This highlights a need for early prediction of the disease course to assist its treatment and tailor therapy options to the disease progression rate. Recent developments in machine learning techniques provide the potential to not only predict disease progression and trajectory of AD but also to classify the disease into different etiological subtypes. Methods and findingsThe work shown here clusters participants in distinct and multifaceted progression subgroups of AD and discusses an approach to predict the progression rate from baseline diagnosis. We observed that the myriad of clinically reported symptoms summarized in the proposed AD progression space corresponds directly with memory and cognitive measures, which are routinely used to monitor disease onset and progression. Our analysis demonstrated accurate prediction of disease progression after four years from the first 12 months of post-diagnosis clinical data (Area Under the Curve of 0.96 (95% confidence interval (CI), 0.92-1.0), 0.81 (95% CI, 0.74-0.88) and 0.98 (95% CI, 0.96-1.0) for slow, moderate and fast progression rate patients respectively). Further, we explored the long short-term memory (LSTM) neural networks to predict the trajectory of an individual patients progression. ConclusionThe machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries. With additional information about the progression rate of AD at hand, providers may further individualize the treatment plans. The predictive tests discussed in this study not only allow for early AD diagnosis but also facilitate the characterization of distinct AD subtypes relating to trajectories of disease progression. These findings are a crucial step forward for early disease detection. These models can be used to design improved clinical trials for AD research.

neuroscience↗

bioRxiv2019

Cloning of Two Gene Clusters Involved in the Catabolism of 2,4-Dinitrophenol by Paraburkholderia sp. Strain KU-46 and Characterization of the Initial DnpAB Enzymes and a Two-Component Monooxygenase DnpC1C2

Besides an industrial pollutant, 2,4-dinitrophenol (DNP) has been used illegally as a weight loss drug that had claimed human lives. Little is known about the metabolism of DNP, particularly among Gram-negative bacteria. In this study, two non-contiguous genetic loci of Paraburkholderia (formerly Burkholderia) sp. strain KU-46 genome were identified and four key initial genes (dnpA, dnpB, and dnpC1C2) were characterized to provide molecular and biochemical evidence for the degradation of DNP via the formation of 4-nitrophenol (NP), a pathway that is unique among DNP utilizing bacteria. Reverse transcription PCR analysis indicated that the dnpA gene encoding the initial hydride transferase (28 kDa), and the dnpB gene encoding a nitrite-eliminating enzyme (33 kDa), are inducible by DNP and the two genes are organized in an operon. Purified DnpA and DnpB from overexpression clones in Escherichia coli effected the transformation of DNP to NP via the formation of hydride-Meisenheimer complex of DNP. The function of DnpB appears new since all homologs of DnpB sequences in the protein database are annotated as putative nitrate ABC transporter substrate-binding proteins. The gene cluster responsible for the degradation of DNP after NP formation was designated dnpC1C2DXFER. DnpC1 and DnpC2 were functionally characterized as the respective FAD reductase and oxygenase components of the two-component NP monooxygenase. Both NP and 4-nitrocatechol were shown to be substrates, producing hydroquinone and hydroxyquinol, respectively. Elucidation of the hqdA1A2BCD gene cluster allows the delineation of the final degradation pathway of hydroquinone to {beta}-ketoadipate prior to its entry to the tricarboxylic acid cycle.\n\nImportanceThis study fills a gap in our knowledge and understanding of the genetic basis and biochemical pathway for the degradation of 2,4-dinitrophenol (DNP) in Gram-negative bacteria, represented by the prototypical Paraburkholderia sp. strain KU-46 that metabolizes DNP through the formation of 4-nitrophenol, a pathway unseen by other DNP utilizers. The newly cloned genes could serve as DNA probes in biomonitoring as well as finding application in new biocatalyst development to access green chemicals. By and large, knowledge of the diverse strategies used by microorganisms to degrade DNP will contribute to the development of bioremediation solutions since DNP is an industrial pollutant used widely in the chemical industry for the synthesis of pesticides, insecticides, sulfur dyes, wood preservatives, and explosives, etc. (119 words)

microbiology↗

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↗

bioRxiv2019

Genome-wide association study of Parkinson's disease progression biomarkers in 12 longitudinal patients' cohorts

BackgroundSeveral reports have identified different patterns of Parkinsons disease progression in individuals carrying missense variants in the GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinsons disease, however, has not been well studied. ObjectivesTo test the association between genetic variants and the clinical features and progression of Parkinsons disease on a genome-wide scale. MethodsWe accumulated individual data from 12 longitudinal cohorts in a total of 4,093 patients with 25,254 observations over a median of 3.81 years. Genome-wide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently-identified disease risk variants, were also investigated for the associations with these phenotypes. ResultsTwo variants were genome-wide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (HR 2.04 [1.58, 2.62], P-value = 3.46E-8). Rs61863020(G>A), an intergenic variant and eQTL for ADRA2A, was associated with a lower prevalence of insomnia at baseline (OR 0.63 [0,52, 0.75], P-value = 4.74E-8). In the targeted analysis, we found nine associations between known Parkinsons risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K, rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients. ConclusionsWe identified novel genetic factors associated with heterogeneity of progression in Parkinsons disease. The results provide new insights into the pathogenesis of Parkinsons disease as well as patient stratification for clinical trials.

genetics↗

bioRxiv2018

Parkinson disease age of onset GWAS: defining heritability, genetic loci and a-synuclein mechanisms

Increasing evidence supports an extensive and complex genetic contribution to Parkinsons disease (PD). Previous genome-wide association studies (GWAS) have shed light on the genetic basis of risk for this disease. However, the genetic determinants of PD age of onset are largely unknown. Here we performed an age of onset GWAS based on 28,568 PD cases. We estimated that the heritability of PD age of onset due to common genetic variation was ~0.11, lower than the overall heritability of risk for PD (~0.27) likely in part because of the subjective nature of this measure. We found two genome-wide significant association signals, one at SNCA and the other a protein-coding variant in TMEM175, both of which are known PD risk loci and a Bonferroni corrected significant effect at other known PD risk loci, INPP5F/BAG3, FAM47E/SCARB2, and MCCC1. In addition, we identified that GBA coding variant carriers had an earlier age of onset compared to non-carriers. Notably, SNCA, TMEM175, SCARB2, BAG3 and GBA have all been shown to either directly influence alpha-synuclein aggregation or are implicated in alpha-synuclein aggregation pathways. Remarkably, other well-established PD risk loci such as GCH1, MAPT and RAB7L1/NUCKS1 (PARK16) did not show a significant effect on age of onset of PD. While for some loci, this may be a measure of power, this is clearly not the case for the MAPT locus; thus genetic variability at this locus influences whether but not when an individual develops disease. We believe this is an important mechanistic and therapeutic distinction. Furthermore, these data support a model in which alpha-synuclein and lysosomal mechanisms impact not only PD risk but also age of disease onset and highlights that therapies that target alpha-synuclein aggregation are more likely to be disease-modifying than therapies targeting other pathways.

genetics↗

bioRxiv2018

Genetic variability and potential effects on clinical trial outcomes: perspectives in Parkinson’s disease

BackgroundImproper randomization in clinical trials can result in the failure of the trial to meet its primary end-point. The last [~]10 years have revealed that common and rare genetic variants are an important disease factor and sometimes account for a substantial portion of disease risk variance. However, the burden of common genetic risk variants is not often considered in the randomization of clinical trials and can therefore lead to additional unwanted variance between trial arms. We simulated clinical trials to estimate false negative and false positive rates and investigated differences in single variants and mean genetic risk scores (GRS) between trial arms to investigate the potential effect of genetic variance on clinical trial outcomes at different sample sizes.\n\nMethodsSingle variant and genetic risk score analyses were conducted in a clinical trial simulation environment using data from 5851 Parkinsons Disease patients as well as two simulated virtual cohorts based on public data. The virtual cohorts included a GBA variant cohort and a two variant interaction cohort. Data was resampled at different sizes (n = 200-5000 for the Parkinsons Disease cohort) and (n = 50-800 and n = 50-2000 for virtual cohorts) for 1000 iterations and randomly assigned to the two arms of a trial. False negative and false positive rates were estimated using simulated clinical trials, and percent difference in genetic risk score and allele frequency was calculated to quantify disparity between arms.\n\nFindingsSignificant genetic differences between the two arms of a trial are found at all sample sizes. Approximately 90% of the iterations had at least one statistically significant difference in individual risk SNPs between each trial arm. Approximately 10% of iterations had a statistically significant difference between trial arms in polygenic risk score mean or variance. For significant iterations at sample size 200, the average percent difference for mean GRS between trial arms was 130.87%, decreasing to 29.87% as sample size reached 5000. In the GBA only simulations we see an average 18.86% difference in GRS scores between trial arms at n = 50, decreasing to 3.09% as sample size reaches 2000. Balancing patients by genotype reduced mean percent difference in GRS between arms to 36.71% for the main cohort and 2.00% for the GBA cohort at n = 200. When adding a drug effect to the simulations, we found that unbalanced genetics with an effect on the chosen measurable clinical outcome can result in high false negative rates among trials, especially at small sample sizes. At a sample size of n = 50 and a targeted drug effect of -0.5 points in UPDRS per year, we discovered 33.9% of trials resulted in false negatives.\n\nInterpretationsOur data support the hypothesis that within genetically unmatched clinical trials, particularly those below 1000 participants, heterogeneity could confound true therapeutic effects as expected. This is particularly important in the changing environment of drug approvals. Clinical trials should undergo pre-trial genetic adjustment or, at the minimum, post-trial adjustment and analysis for failed trials. Clinical trial arms should be balanced on genetic risk variants, as well as cumulative variant distributions represented by GRS, in order to ensure the maximum reduction in trial arm disparities. The reduction in variance after balancing allows smaller sample sizes to be utilized without risking the large disparities between trial arms witnessed in typical randomized trials. As the cost of genotyping will likely be far less than greatly increasing sample size, genetically balancing trial arms can lead to more cost-effective clinical trials as well as better outcomes.

genomics↗

bioRxiv2018

Parkinson’s disease genetics: identifying novel risk loci, providing causal insights and improving estimates of heritable risk

We performed the largest genome-wide association study of PD to date, involving the analysis of 7.8M SNPs in 37.7K cases, 18.6K UK Biobank proxy-cases, and 1.4M controls. We identified 90 independent genome-wide significant signals across 78 loci, including 38 independent risk signals in 37 novel loci. These variants explained 26-36% of the heritable risk of PD. Tests of causality within a Mendelian randomization framework identified putatively causal genes for 70 risk signals. Tissue expression enrichment analysis suggested that signatures of PD loci were heavily brain-enriched, consistent with specific neuronal cell types being implicated from single cell expression data. We found significant genetic correlations with brain volumes, smoking status, and educational attainment. In sum, these data provide the most comprehensive understanding of the genetic architecture of PD to date by revealing many additional PD risk loci, providing a biological context for these risk factors, and demonstrating that a considerable genetic component of this disease remains unidentified.

genetics↗