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Huw R Morris

Publications and source records attributed to Huw R Morris.

5 recordsLinked to original sources
PubMed2026

Large-scale functional annotation establishes a reference framework for human LRRK2 variants.

Pathogenic variants in leucine-rich repeat kinase 2 (LRRK2)1are among the most frequent monogenic causes of Parkinson's disease (PD)2 and act through a gain-of-function mechanism of increased kinase activity. LRRK2-targeted therapies are in clinical development, but interpretation of the rapidly expanding catalogue of rare LRRK2 variants remains a barrier to translation. Here, we present functionally annotated data on >350 LRRK2 coding variants using a standardized cellular assay with Rab10 phosphorylation as a readout of kinase activity and integrated these data with curated genetic and clinical annotations from the Movement Disorders Society Genetic Mutation Database (MDSGene). Variants differed in activation magnitude, ranging from modest increases (e.g., p.G2019S) to strongly activating substitutions such as p.Y1699C or p.L1795F. Activating variants occurred across the full length of LRRK2, although the largest effects clustered within the ROC-COR regulatory hub, where structural analysis identified subdomains forming an allosteric scaffold controlling kinase output. All known/established pathogenic variants showed increased activity, whereas benign and likely benign variants remained within the wild-type range. Functional effect sizes correlated with pathway activation in patient-derived immune cells, altogether providing a framework for ACMG-based variant interpretation in which kinase activation can support PS3 functional evidence for reclassification of variants.

Protein phosphorylation↗
PubMed2025

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 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.

Copy Number Variation (CNV)↗
PubMed2005

Genetics of Parkinson's disease.

Twenty years ago Parkinson's disease (PD) was thought of as an environmentally determined neurodegenerative disease. It is now known that there are two autosomal dominant disease genes, alpha-synuclein and dardarin, and three genes responsible for autosomal recessive PD, parkin, DJ-1 and PINK-1. Although these gene mutations are not common, their identification has led to a new understanding of the pathogenesis of PD, and to a development in the understanding of the clinical and pathological definitions of PD and Lewy body disease. Ultimately, these advances may lead to the development of new disease-modifying therapies, but more immediately these discoveries have led to a more coherent view of the spectrum of PD and Lewy body diseases and to accurate genetic diagnosis and counselling for some families.

Genetic Predisposition to Disease↗
PubMed2004

Genome-wide analysis of the parkinsonism-dementia complex of Guam.

BACKGROUND: Parkinsonism-dementia complex (PDC) is a neurofibrillary tangle degeneration involving the deposition of Alzheimer-type tau, predominantly in the mesial temporal cortex, brainstem, and basal ganglia. It occurs in focal geographic isolates, including Guam and the Kii peninsula of Japan. The familial clustering of the disease has suggested that a genetic factor could be important in its etiology. OBJECTIVE: To determine whether a genetic locus could be identified, linked, or associated with PDC. DESIGN AND PATIENTS: We performed a genome-wide association study of 22 Guamanian PDC and 19 control subjects using 834 microsatellite markers with an approximate genome-wide marker density of 4.4 centimorgans. RESULTS: Two-point association analysis identified 17 markers (P<.015). Each of these markers then underwent conventional linkage analysis in 5 families with PDC. One marker, D20S103, generated a logarithm of odds score of greater than 1.5. Multipoint association analysis also highlighted 2 other areas on chromosome 14q (adjacent to D14S592, 59.2 megabases [M]) and chromosome 20 (adjacent to D20S470, 17.4 M) with multipoint association logarithm of the odds scores of greater than 2. The areas around D20S103, D14S592, and D20S470 were further analyzed by association using additional microsatellite markers and by conventional linkage analysis. This did not provide further evidence for the role of these areas in PDC. CONCLUSIONS: This study has not identified a single gene locus for PDC, confirming the impression of a geographic disease isolate with a complex genetic, a genetic/environmental etiology, or a purely environmental etiology.

Aged↗