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Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders
O’Meara, Mika Isabella Grace. 2024. Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders. Unpublished masters dissertation. Stellenbosch : Stellenbosch University [online]. Available: https://scholar.sun.ac.za/handle/10019.1/131878 Thesis (MScAgric...
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867614087303659520 |
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| access_status_str | Open Access |
| author | O’Meara, Mika Isabella Grace |
| author2 | McGregor, Nathaniel |
| author_browse | McGregor, Nathaniel O’Meara, Mika Isabella Grace |
| author_facet | McGregor, Nathaniel O’Meara, Mika Isabella Grace |
| author_sort | O’Meara, Mika Isabella Grace |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | O’Meara, Mika Isabella Grace. 2024. Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders. Unpublished masters dissertation. Stellenbosch : Stellenbosch University [online].
Available: https://scholar.sun.ac.za/handle/10019.1/131878
Thesis (MScAgric)--Stellenbosch University, 2024. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/131878 |
| institution | Stellenbosch University (South Africa) |
| last_indexed | 2026-06-10T12:46:27.621Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/131878 Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders O’Meara, Mika Isabella Grace McGregor, Nathaniel Lochner, Christine Stellenbosch University. Faculty of Agrisciences. Dept. of Genetics. Obsessive-compulsive disorder -- Genetic aspects Mental illness -- Diagnosis Obsessive-compulsive disorder -- Evaluation -- Diagnosis Genomes -- Measurement Neuropsychiatry -- Data processing UCTD O’Meara, Mika Isabella Grace. 2024. Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders. Unpublished masters dissertation. Stellenbosch : Stellenbosch University [online]. Available: https://scholar.sun.ac.za/handle/10019.1/131878 Thesis (MScAgric)--Stellenbosch University, 2024. ENGLISH ABSTRACT: Neuropsychiatric disorders, such as obsessive-compulsive disorder (OCD), are characterised by genetic complexity and clinical heterogeneity, which hinder the translation of genetic discoveries into clinical practice. To address this challenge, this study utilises the GWAS-based marker selection tool (GMStool), to identify genetic markers predictive of OCD severity using the Yale-Brown Obsessive-compulsive Scale (YBOCS). This study provides a narrative review of current tools and methods for genetic marker identification, highlighting the limitations of existing approaches and the potential of GMStool to enhance genetic marker selection and prediction in genetic studies. Secondly, this study reviewed the application of the YBOCS in both clinical and genetic research focusing on its use in assessing the severity of OCD and treatment outcomes. The psychometric properties of the YBOCS were discussed and its potential to predict the severity of OCD based on genetic markers was explored. Additionally, gaps in the research regarding the genetic contributions to OCD symptom severity were highlighted. Finally, the GMStool was applied to genetic and clinical data from a cohort of adults with OCD and matched healthy control participants (n = 153) to identify genetic markers associated with OCD severity. Inclusion criteria for the OCD individuals required a primary diagnosis of OCD according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria, with the severity of obsessive and compulsive symptoms being evaluated using the YBOCS which provided an overall score. Machine learning models, including Ridge regression best linear unbiased prediction (RRB), Random Forest (RF), Deep neural network (DNN) and convolutional neural networks (CNN) were employed to analyse the data, with the RRB model yielding the best results, explaining approximately 7.3% of the genetic variance in OCD severity. This study identified several genetic markers associated with OCD severity, though no single nucleotide polymorphism (SNP) reached genome-wide significance after correcting for multiple testing. Key genes identified include OTU deubiquitinase, ubiquitin aldehyde binding 2 (OTUB2) gene, involved in the ubiquitin-proteasome system (UPS) of which has previously been linked to schizophrenia; long intergenic non-protein coding RNA 1622 (LINC01622), a regulatory long intergenic non-protein coding RNA (lincRNA); serine/threonine-protein kinase TAO3 (TAOK3), a kinase influencing brain signalling, previously associated with autism and schizophrenia; and aarF domain-containing kinase 1 protein (ADCK1), implicated in mitochondrial function and linked to compulsive behaviours. This study represents a pioneering effort in genetic research on the severity of OCD. The findings confirm the genetic complexity and the heterogeneity of OCD and its severity, emphasising the challenges in identifying genetic markers for the disorder. The identified pathways, particularly those involving the UPS and mitochondrial function, highlight potential mechanisms and pathways that may contribute to the severity of OCD, offering new insights into the shared genetic architecture of OCD and other neuropsychiatric disorders. This study highlights the potential use of machine learning tools, like GMStool, in facilitating the integration of genetic data into clinical practice and help bridge the gap between genetic discoveries and clinical application. AFRIKAANSE OPSOMMING: Neuropsigiatriese versteurings soos obsessiewe-kompulsiewe steuring (OKS) word gekenmerk deur genetiese kompleksiteit en kliniese heterogeniteit, wat die toepas van genetiese ontdekkings in kliniese praktyk bemoeilik. Om hierdie uitdaging aan te spreek, maak hierdie studie gebruik van die GWAS-gebaseerde merkerseleksie-instrument (GMStool), om genetiese merkers te identifiseer wat die erns van OKS kan voorspel. Hierdie studie bied 'n omvattende oorsig van huidige instrumente en metodes vir die identifikasie van genetiese merkers, wat die beperkings van bestaande benaderings en die potensiaal van GMStool om genetiese merkerseleksie en -voorspelling in genetiese studies te verbeter, beklemtoon. Tweedens is die kliniese bruikbaarheid van die Yale-Brown Obsessief-Kompulsiewe Skaal (YBOCS), die goue standaard vir die assessering van OKS-erns, geëvalueer en die toepassing daarvan in genetiese studies is ondersoek. Tweedens het hierdie studie die toepassing van die YBOCS in beide kliniese en genetiese navorsing hersien en gefokus op die gebruik daarvan in die assessering van die erns van OKS en behandelingsuitkomste. Die psigometriese eienskappe van die YBOCS is bespreek en die potensiaal daarvan om die erns van OKS te voorspel gebaseer op genetiese merkers is ondersoek. Laastens is die GMStool gebruik met genetiese en kliniese data van 'n kohort van volwassenes met OKS- en ooreenstemmende gesonde kontrole-deelnemers (n = 153) om genetiese merkers te identifiseer wat met OKS-erns geassosieer word. Insluitingskriteria vir die OCD-individue het 'n primêre diagnose van OCD vereis volgens die Diagnostiese en Statistiese Handleiding van Geestesversteurings (DSM-IV) kriteria, met die erns van obsessiewe en kompulsiewe simptome wat geëvalueer is met behulp van die YBOCS wat 'n algehele telling verskaf het. Verskeie masjienleermodelle, insluitend Ridge regression best linear unbiased prediction (RRB), Random Forest (RF), Deep neural network (DNN) en convolutional neural networks (CNN), is gebruik om die data te ontleed, met die RRB-model wat die beste resultate gelewer het en ongeveer 7.3% van die genetiese variansie in OKS-erns kon verduidelik. Hierdie studie het verskeie genetiese merkers geïdentifiseer wat met OKS-erns geassosieer is, hoewel geen enkelnukleotied polimorfisme (ENP) genoom-wye beduidendheid bereik het na korrigering vir veelvuldige toetsing nie. Die belangrikste gene wat geïdentifiseer is, sluit in OTU deubiquitinase, ubiquitin aldehyde binding 2 (OTUB2), wat ook betrokke is by die ubiquitien-proteasoomstelsel (UPS) wat voorheen met skisofrenie verbind is; lang intergeniese nie-proteïenkodering RNA 1622 (LINC01622), 'n regulatoriese lang intergeniese nie- proteïenkoderende RNA (lincRNA); serien/treonien-proteïen kinase TAO3 (TAOK3), 'n kinase wat breinseine beïnvloed, en wat voorheen met outisme en skisofrenie geassosieer is; en aarF domein- bevattende kinase 1-proteïen (ADCK1), betrokke by mitochondriale funksie en wat met kompulsiewe gedrag verbind word. Hierdie bevindinge onthul en valideer die genetiese kompleksiteit en heterogeniteit van OKS en die erns daarvan, wat die uitdagings in die identifikasie van genetiese merkers vir die steuring beklemtoon. Die geïdentifiseerde paaie, veral dié wat die UPS en mitochondriale funksie behels, beklemtoon potensiële meganismes van neurale regulering en energiemetabolisme wat moontlik tot die erns van OKS kan bydra, en bied nuwe insigte in die gedeelde genetiese argitektuur van OKS en sommige ander neuropsigiatriese toestande. Hierdie studie beklemtoon die potensiële gebruik van masjienleergereedskap, soos GMStool, om die integrasie van genetiese data in kliniese praktyk te fasiliteer en help om die gaping tussen genetiese ontdekkings en kliniese toepassing te oorbrug. Masters 2025-04-04T13:09:39Z 2025-04-04T13:09:39Z 2024-12 Thesis https://scholar.sun.ac.za/handle/10019.1/131878 Stellenbosch University 90 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Obsessive-compulsive disorder -- Genetic aspects Mental illness -- Diagnosis Obsessive-compulsive disorder -- Evaluation -- Diagnosis Genomes -- Measurement Neuropsychiatry -- Data processing UCTD O’Meara, Mika Isabella Grace Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders |
| title | Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders |
| title_full | Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders |
| title_fullStr | Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders |
| title_full_unstemmed | Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders |
| title_short | Identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders |
| title_sort | identifying optimal genetic markers for predicting quantitative measures in neuropsychiatric disorders |
| topic | Obsessive-compulsive disorder -- Genetic aspects Mental illness -- Diagnosis Obsessive-compulsive disorder -- Evaluation -- Diagnosis Genomes -- Measurement Neuropsychiatry -- Data processing UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/131878 |
| work_keys_str_mv | AT omearamikaisabellagrace identifyingoptimalgeneticmarkersforpredictingquantitativemeasuresinneuropsychiatricdisorders |