MyMedR

Displaying all 4 publications

Abstract:

Sort:

Fulltext Global Perspectives on Returning Genetic Research Results in Parkinson Disease

Tan AH, Saffie-Awad P, Schumacher Schuh AF, Lim SY, Madoev H, Ahmad-Annuar A, et al.

Neurol Genet, 2024 Dec;10(6):e200213.
PMID: 39807215 DOI: 10.1212/NXG.0000000000200213

BACKGROUND AND OBJECTIVES: In the era of precision medicine, genetic test results have become increasingly relevant in the care of patients with Parkinson disease (PD). While large research consortia are performing widespread research genetic testing to accelerate discoveries, debate continues about whether, and to what extent, the results should be returned to patients. Ethically, it is imperative to keep participants informed, especially when findings are potentially actionable. However, research testing may not hold the same standards required from clinical diagnostic laboratories and hold significant psychosocial implications. The absence of universally recognized protocols complicates the establishment of appropriate guidelines.
METHODS: Aiming to develop recommendations on return of research results (RoR) practice within the Global Parkinson's Genetics Program (GP2), we conducted a global survey to gain insight on GP2 members' perceptions, practice, readiness, and needs surrounding RoR.
RESULTS: GP2 members (n = 191), representing 147 institutions and 60 countries across 6 continents, completed the survey. Access to clinical genetic testing services was significantly higher in high-income countries compared with low- and middle-income countries (96.6% vs 58.4%), where funding was predominantly covered by patients themselves. While 92.7% of the respondents agreed that genetic research results should be returned, levels of agreement were higher for clinically relevant results relating to pathogenic or likely pathogenic variants in genes known to cause PD or other neurodegenerative diseases. Less than 10% offered separate clinically accredited genetic testing before returning genetic research results. A total of 48.7% reported having a specific statement on RoR policy in their ethics consent form, while 53.9% collected data on participants' preferences on RoR prospectively. 24.1% had formal genetic counselling training. Notably, the comfort level in returning incidental genetic findings or returning results to unaffected individuals remains low.
DISCUSSION: Given the differences in resources and training for RoR, as well as ethical and regulatory considerations, tailored approaches are required to ensure equitable access to RoR. Several identified strategies to enhance RoR practices include improving informed consent processes, increasing capacity for genetic counselling including providing counselling toolkits for common genetic variants, broadening access to sustainable clinically accredited testing, building logistical infrastructure for RoR processes, and continuing public and health care education efforts on the important role of genetics in PD.
Fulltext Loss-of-Function Variant in the SMPD1 Gene in Progressive Supranuclear Palsy-Richardson Syndrome Patients of Chinese Ancestry

Lim SY, Tan AH, Foo JN, Tan YJ, Chew EG, Annuar AA, et al.

J Mov Disord, 2024 Apr;17(2):213-217.
PMID: 38291878 DOI: 10.14802/jmd.24009

Lysosomal dysfunction plays an important role in neurodegenerative diseases, including Parkinson's disease (PD) and possibly Parkinson-plus syndromes such as progressive supranuclear palsy (PSP). This role is exemplified by the involvement of variants in the GBA1 gene, which results in a deficiency of the lysosomal enzyme glucocerebrosidase and is the most frequently identified genetic factor underlying PD worldwide. Pathogenic variants in the SMPD1 gene are a recessive cause of Niemann-Pick disease types A and B. Here, we provide the first report on an association between a loss-of-function variant in the SMPD1 gene present in a heterozygous state (p.Pro332Arg/p.P332R, which is known to result in reduced lysosomal acid sphingomyelinase activity), with PSP-Richardson syndrome in three unrelated patients of Chinese ancestry.
Uncovering the genetic basis of Parkinson's disease globally: from discoveries to the clinic

Lim SY, Tan AH, Ahmad-Annuar A, Okubadejo NU, Lohmann K, Morris HR, et al.

Lancet Neurol, 2024 Dec;23(12):1267-1280.
PMID: 39447588 DOI: 10.1016/S1474-4422(24)00378-8

Knowledge on the genetic basis of Parkinson's disease has grown tremendously since the discovery of the first monogenic form, caused by a mutation in α-synuclein, and with the subsequent identification of multiple other causative genes and associated loci. Genetic studies provide insights into the phenotypic heterogeneity and global distribution of Parkinson's disease. By shedding light on the underlying biological mechanisms, genetics facilitates the identification of new biomarkers and therapeutic targets. Several clinical trials of genetics-informed therapies are ongoing or imminent. International programmes in populations who have been under-represented in Parkinson's disease genetics research are fostering collaboration and capacity-building, and have already generated novel findings. Many challenges remain for genetics research in these populations, but addressing them provides opportunities to obtain a more complete and equitable understanding of Parkinson's disease globally. These advances facilitate the integration of genetics into the clinic, to improve patient management and personalised medicine.
Fulltext CNV-Finder: Streamlining Copy Number Variation Discovery

Kuznetsov N, Daida K, Makarious MB, Al-Mubarak B, Brolin KA, Malik L, et al.

bioRxiv, 2024 Nov 23.
PMID: 39605431 DOI: 10.1101/2024.11.22.624040

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 of significant importance 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 the efficient prioritization of samples for subsequent, costly analyses such as short-read and long-read whole genome sequencing. We focus on five genes-Parkin (PRKN), Leucine Rich Repeat And Ig Domain Containing 2 (LINGO2), Microtubule Associated Protein Tau (MAPT), alpha-Synuclein (SNCA), and Amyloid Beta Precursor Protein (APP)-which may be relevant to neurological diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), 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 both short-read and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied sparsity, noise, and size. 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.