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Polygenic scores contribution to Parkinson’s disease comorbidities
(Oxford University Press (OUP), 2025)
Carlos F Hernández
;
Camilo Villaman
;
Cristian Tejos
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REPETTO LISBOA, MARIA GABRIELA
;
Costin Leu
Comorbidities are common in Parkinson’s disease and significantly impact the disease progression and management. While polygenic scores have been widely used to assess genetic risk for complex diseases, their role in comorbidity presentation in Parkinson’s disease remains unclear. This study investigates whether genetic predisposition to comorbidities, as measured by polygenic scores, differs between individuals with Parkinson’s disease and the general population and explores how genetic risk influences disease onset and sex-related differences. We analysed data from 4144 individuals with Parkinson’s disease and 370 480 individuals from the general population in the UK Biobank, focusing on four comorbidities with high-quality genome-wide association study data: Type 2 diabetes, major depressive disorder, migraine headaches and epilepsy. We first compared polygenic score distributions between individuals with Parkinson’s disease and the general population. While our findings indicate that comorbidities and polygenic risk scores do not significantly differ between individuals with Parkinson’s disease and the general population, we show an association with disease onset and sex-specific differences. Individuals with earlier disease onset (50–70 years old) had higher genetic risk for major depressive disorder (odds ratio: 2.19, P-value: 1.27 × 10⁻¹⁵) and epilepsy (odds ratio: 1.58, P-value: 0.00845). Additionally, a female participant with Parkinson’s disease exhibited higher genetic risk scores for major depressive disorder (odds ratio: 1.5, P-value: 0.0119) and migraine headaches (odds ratio: 2.1, P-value: 0.0155), while a male participant displayed higher genetic risk scores for Type 2 diabetes (odds ratio: 2.7, P-value: 2.11 × 10⁻¹⁷). Comorbidity-polygenic score did not differ between people with versus without Parkinson’s disease, yet within Parkinson’s disease, a higher genetic burden for specific comorbidities was linked to earlier onset and sex-specific presentation, implicating common variants as modifiers of clinical heterogeneity rather than the primary disease risk. These results enhance our understanding of the genetic influences shaping the broader clinical presentation of Parkinson’s disease and highlight the need for further research into the interplay between genetic risk factors, comorbidities and disease heterogeneity.
Scopus© Citations 2 3
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Polygenic score analysis identifies distinct genetic risk profiles in Alzheimer’s disease comorbidities
(Springer Science and Business Media LLC, 2025-04-03)
Carlos F. Hernández
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Camilo Villaman
;
Costin Leu
;
Dennis Lal
;
Ignacio Mata
4
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Evaluating novel in silico tools for accurate pathogenicity classification in epilepsy‐associated genetic missense variants
(2024)
Ludovica Montanucci
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Tobias Brünger
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Christian M. Boßelmann
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Alina Ivaniuk
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Eduardo Pérez‐Palma
<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>Determining the pathogenicity of missense variants in clinical genetic tests for individuals with epilepsy is crucial for guiding personalized treatment. However, achieving a definitive pathogenic classification remains challenging, with most missense variants still classified as variants of uncertain significance (VUS) and with the availability of many computational tools which may provide conflicting predictions. Here, we aim to evaluate the performance of state‐of‐the‐art computational tools in pathogenicity prediction of missense variants in epilepsy‐associated genes. This will assist in selecting the most appropriate tool and critically assess their use in clinical setting.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We assessed the performance of nine in silico pathogenicity prediction tools for missense variants in epilepsy‐associated genes on three carefully curated data sets. The first two data sets comprise missense variants in epilepsy associated genes that have been uploaded to ClinVar in the last year and were, therefore, not part of the training set of any of the nine considered tools. These two data sets are based on two different lists of epilepsy‐associated genes and comprise ~700 and ~ 250 missense variants, respectively. The third data set includes ~400 missense variants within epilepsy‐associated genes for which the functional effects have been determined experimentally and are therefore used here to infer pathogenicity. These three data sets represent the best available approximation to blind and independent test sets.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Among the nine assessed tools, AlphaMissense (area under the curve [AUC]: .93, .88, and .95) and REVEL (AUC: .93, .88, and .93) showed the best classification performance, also outperforming other tools in the number of classified variants.</jats:p></jats:sec><jats:sec><jats:title>Significance</jats:title><jats:p>We show which recently developed prediction tools achieve higher performance in epilepsy‐associated genes and should be integrated, therefore, into the American College of Medical Genetics and Genomics/Association of Molecular Pathology (AGMC/AMP) variant classification process. Periodic reevaluation of genetic test results with newly developed or updated tools should be incorporated into standard clinical practice to improve diagnostic yield and better inform precision medicine.</jats:p></jats:sec>
Scopus© Citations 2
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SimText: a text mining framework for interactive analysis and visualization of similarities among biomedical entities
(2021)
Marie Macnee
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PEREZ PALMA, EDUARDO ESTEBAN
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Sarah Schumacher-Bass
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Jarrod Dalton
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Costin Leu
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Summary</jats:title> <jats:p>Literature exploration in PubMed on a large number of biomedical entities (e.g. genes, diseases or experiments) can be time-consuming and challenging, especially when assessing associations between entities. Here, we describe SimText, a user-friendly toolset that provides customizable and systematic workflows for the analysis of similarities among a set of entities based on text. SimText can be used for (i) text collection from PubMed and extraction of words with different text mining approaches, and (ii) interactive analysis and visualization of data using unsupervised learning techniques in an interactive app.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>We developed SimText as an open-source R software and integrated it into Galaxy (https://usegalaxy.eu), an online data analysis platform with supporting self-learning training material available at https://training.galaxyproject.org. A command-line version of the toolset is available for download from GitHub (https://github.com/dlal-group/simtext) or as Docker image (https://hub.docker.com/r/dlalgroup/simtext/tags.).</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>
Scopus© Citations 5 5 1
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The genomic landscape across 474 surgically accessible epileptogenic human brain lesions
(2022)
Javier A López-Rivera
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Costin Leu
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Marie Macnee
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Jean Khoury
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Lucas Hoffmann
<jats:title>Abstract</jats:title> <jats:p>Understanding the exact molecular mechanisms involved in the aetiology of epileptogenic pathologies with or without tumour activity is essential for improving treatment of drug-resistant focal epilepsy. Here, we characterize the landscape of somatic genetic variants in resected brain specimens from 474 individuals with drug-resistant focal epilepsy using deep whole-exome sequencing (&gt;350×) and whole-genome genotyping. Across the exome, we observe a greater number of somatic single-nucleotide variants in low-grade epilepsy-associated tumours (7.92 ± 5.65 single-nucleotide variants) than in brain tissue from malformations of cortical development (6.11 ± 4 single-nucleotide variants) or hippocampal sclerosis (5.1 ± 3.04 single-nucleotide variants). Tumour tissues also had the largest number of likely pathogenic variant carrying cells. low-grade epilepsy-associated tumours had the highest proportion of samples with one or more somatic copy-number variants (24.7%), followed by malformations of cortical development (5.4%) and hippocampal sclerosis (4.1%). Recurring somatic whole chromosome duplications affecting Chromosome 7 (16.8%), chromosome 5 (10.9%), and chromosome 20 (9.9%) were observed among low-grade epilepsy-associated tumours. For germline variant-associated malformations of cortical development genes such as TSC2, DEPDC5 and PTEN, germline single-nucleotide variants were frequently identified within large loss of heterozygosity regions, supporting the recently proposed ‘second hit’ disease mechanism in these genes. We detect somatic variants in 12 established lesional epilepsy genes and demonstrate exome-wide statistical support for three of these in the aetiology of low-grade epilepsy-associated tumours (e.g. BRAF) and malformations of cortical development (e.g. SLC35A2 and MTOR). We also identify novel significant associations for PTPN11 with low-grade epilepsy-associated tumours and NRAS Q61 mutated protein with a complex malformation of cortical development characterized by polymicrogyria and nodular heterotopia. The variants identified in NRAS are known from cancer studies to lead to hyperactivation of NRAS, which can be targeted pharmacologically. We identify large recurrent 1q21–q44 duplication including AKT3 in association with focal cortical dysplasia type 2a with hyaline astrocytic inclusions, another rare and possibly under-recognized brain lesion. The clinical-genetic analyses showed that the numbers of somatic single-nucleotide variant across the exome and the fraction of affected cells were positively correlated with the age at seizure onset and surgery in individuals with low-grade epilepsy-associated tumours. In summary, our comprehensive genetic screen sheds light on the genome-scale landscape of genetic variants in epileptic brain lesions, informs the design of gene panels for clinical diagnostic screening and guides future directions for clinical implementation of epilepsy surgery genetics.</jats:p>
Scopus© Citations 25 6 1
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Identification and quantification of oligogenic loss-of-function disorders
(2022)
Arthur Stefanski
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Eduardo Pérez-Palma
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Marko Mrdjen
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Megan McHugh
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Costin Leu
Scopus© Citations 3 1
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Incidence and prevalence of major epilepsy-associated brain lesions
(2022)
Javier A. López-Rivera
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Victoria Smuk
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Costin Leu
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Gaelle Nasr
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Deborah Vegh
Scopus© Citations 15 1
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Effects of copy number variations on brain structure and risk for psychiatric illness: Large‐scale studies from the<scp>ENIGMA</scp>working groups on<scp>CNVs</scp>
(2021)
Ida E. Sønderby
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Christopher R. K. Ching
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Sophia I. Thomopoulos
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Dennis Meer
;
Daqiang Sun
3Scopus© Citations 50

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