Nicole Kuznetsov

IntroductionAlthough there have been significant advances in the genetic etiology of Parkinson’s disease (PD), most of this research has been conducted in European populations. This study aimed to characterize the genetic landscape of PD in South Africa.MethodsWe recruited 689 PD probands across South Africa. These participants were genotyped using the NeuroBooster array in collaboration with the Global Parkinson’s Genetics Program (GP2). An in-house computational workflow was used to identify pathogenic missense variants, which were validated using Sanger sequencing. The CNV-Finder pipeline was used to screen for copy number variations (CNVs), which were validated using Multiplex Ligation-dependent Probe Amplification (MLPA). Previously identified pathogenic variants were used as positive controls in our analysis.ResultsWe identified 16 unique missense variants, confirming 15 with Sanger sequencing, in 47 individuals across seven well-established PD genes. Variants in GBA1 and PRKN were the most frequent. Additionally, 18 variants of unknown significance were found in known genes. Furthermore, seven new CNVs were identified, five in PRKN and two in SNCA, of which six were validated using MLPA. One of the CNVs was in the 5’ untranslated region of SNCA. When combining this data with previous findings, 3.5% of the probands have a genetic basis for their disease.ConclusionsWe conducted the largest pathogenic variant screening to date in this unique and underrepresented population, and our findings highlight the rare contribution of known pathogenic variants. This study underscores the importance of expanding genetic research in African cohorts to improve global understanding of PD etiology.

Introduction: Although there have been significant advances in the genetic etiology of Parkinson’s disease (PD), most of this research has been conducted in European populations. This study aimed to characterize the genetic landscape of PD in South Africa.Methods: We recruited 689 PD probands across South Africa. These participants were genotyped using the NeuroBooster array in collaboration with the Global Parkinson’s Genetics Program (GP2). An in-house computational workflow was used to identify pathogenic missense variants. These were validated using Sanger sequencing. The CNV-Finder pipeline was used to screen for copy number variations (CNVs), which were validated using Multiplex Ligation-dependent Probe Amplification (MLPA). Previously identified pathogenic variants were used as positive controls in our analysis.Results: We identified 16 unique missense variants, confirming 15 with Sanger sequencing, in 47 individuals across seven well-established PD genes. Variants in GBA1 and PRKN were the most frequent. Additionally, 18 variants of unknown significance were found in known genes. Furthermore, seven new CNVs were identified, five in PRKN and two in SNCA, of which six were validated using MLPA. One of the CNVs was in the 5’ untranslated region of SNCA. When combining this data with previous findings, 3.5% of the probands have a genetic basis for their disease.Conclusions: We conducted the largest pathogenic variant screening to date in this unique and underrepresented population, and our findings highlight the rare contribution of known pathogenic variants. This study underscores the importance of expanding genetic research in African cohorts to improve global understanding of PD etiology.

Motivation: Copy Number Variations (CNVs) play pivotal roles in complex disease etiology, often requiring large sample sizes to analyze disease associations. While genotyping arrays offer a cost-effective approach for CNV detection using Log R Ratio (LRR) and B Allele Frequency (BAF) signals, existing independent array-based callers suffer from high false positive rates and noise susceptibility, burdening manual validation. Results: We present CNV-Finder, a deep learning pipeline employing Long Short-Term Memory (LSTM) networks for large-scale CNV identification within user-defined genomic regions. Trained on expert-annotated samples from the Global Parkinson’s Genetics Program across four neurodegenerative disease-associated genes (PRKN, LINGO2, MAPT, SNCA), CNV-Finder integrates human feedback to iteratively improve performance. In benchmarking across 105 936 samples spanning 11 ancestries and nearly 150 cohorts, the model achieved 91% and 89% visual confirmation rates for PRKN deletions and duplications at high-confidence thresholds. In two validation cohorts, CNV-Finder nominated 83% fewer candidates than a popular Hidden Markov Model-based caller while maintaining higher confirmation rates. Validation through MLPA, short-read, and long-read sequencing demonstrated robust performance, generalizing to diverse signatures including homozygous deletions and SNCA triplications absent from training. Our findings highlight human expertise’s value in complex loci like 17q21.31. Availability and implementation: CNV-Finder is freely available at https://github.com/nvk23/CNV-Finder.

Parkinson’s disease (PD) is a complex neurodegenerative disorder with a substantial genetic influence. To better characterize the genetic landscape of PD in South Africa, we conducted the largest genetic screening to date for pathogenic single nucleotide and copy number variations (CNVs) using genotyping array data from 689 PD probands. We identified 16 unique missense variants, confirming 15 with Sanger sequencing, in 47 individuals across seven well-established PD genes, with GBA1 and PRKN being most frequent. Also in known PD genes, 18 variants of unknown significance were found. Additionally, CNV analysis using CNV-Finder revealed seven novel CNVs, five in PRKN and two in SNCA , of which, six were validated with Multiplex Ligation-dependent Probe Amplification. The findings highlight the contribution of both rare variants and structural rearrangements to PD in this underrepresented population. This study underscores the importance of expanding genetic research in African cohorts to improve global understanding of PD etiology.

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 generalizability. Here, we present aging clocks trained using GenoML, an automated machine learning platform for clinical and multiomics data, on DNA methylation from Oxford Nanopore long-read sequencing. These models leverage over 28 million CpG sites across individuals of African and European ancestry. 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.

Background: The genetic architecture of Parkinson’s disease (PD) varies considerably across ancestries, yet most genetic studies have focused on individuals of European descent, limiting insights into the genetic landscape of PD at a global scale. Methods: We conducted a large-scale, multi-ancestry investigation of causal and risk variants in PD-related genes. Using genetic datasets from the Global Parkinson’s Genetics Program (GP2), we analyzed sequencing and genotyping data from 105,588 individuals, including 63,837 affected and 41,751 unaffected, from eleven different ancestries. Approximately 29% of individuals included were from underrepresented populations. Findings: Our findings revealed shared and ancestry-specific patterns in the prevalence and variant spectrum. Overall, ∼2% of PD individuals carried a causative variant, with substantial variations across ancestries ranging from ∼0·5% in African to ∼7% in Middle Eastern and >10% Ashkenazi Jewish ancestries. Including disease-associated GBA1 and LRRK2 risk variants raised the yield to 13·6%, largely driven by GBA1 , except in East Asians, where LRRK2 risk variants dominated. GBA1 variants were most frequent globally, albeit with substantial differences in frequencies and variant spectra. While GBA1 variants were identified across all ancestries, frequencies ranged from ∼5% in Middle Eastern and South Asian to ∼52% in African ancestry. Similarly, LRRK2 variants showed ancestry-specific enrichment, with p.G2019S most frequently seen in Middle Eastern and Ashkenazi Jewish populations, and risk variants predominating in East Asians. Notably, clinical trials targeting specific genetic variants are currently primarily based in Europe and North America. Another globally PD-relevant gene was PRKN , with variant carriers identified across almost all ancestries. Interpretation: This large-scale, multi-ancestry assessment offers crucial insights into the population-specific genetic architecture of PD. It underscores the critical need for increased ancestral diversity in PD research to improve diagnostic accuracy, enhance our understanding of disease mechanisms across populations, and ensure equitable development and application of emerging precision therapies. Funding: Aligning Science Across Parkinson’s (ASAP) Global Parkinson’s Genetics Program (GP2)

Among the more than 90 identified genetic risk loci for late-onset Alzheimer’s disease (AD) and related dementias, the apolipoprotein E gene ( APOE ) ɛ2/ɛ3/ɛ4 polymorphisms remains the longstanding benchmark for genetic disease risk with a consistently large effect across studies ^1–10 . Despite this massive signal, the exact mechanisms for how ɛ4 increases and for how ɛ2 decreases dementia risk is not well-understood. Importantly, recent trials of anti-amyloid therapies suggest less efficacy and higher risks of severe side effects in ε4 carriers ^11–13 , hampering the treatment of those with the highest unmet need. To improve our understanding of the genetic architecture of AD in the context of its main genetic driver, we performed genome-wide association studies (GWASs) stratified by ε4 and ε2 carrier status. Such insights may help to understand and overcome side effects, to impact clinical trial enrolment strategies, and to create the scientific basis for targeted mechanism-driven therapies in neurodegenerative diseases.

Backgrounds: Biomedical 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. Methods: We 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. Results: Our 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. Conclusion: CARDBiomedBench 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.

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 Alzheimer’s disease (AD), Parkinson’s 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 Parkinson’s 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-Finder’s 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.