[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127378-en":3,"doc-seo-127378-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127378,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Decoding the genomic symphony - unravelling brain disorders through data integration and machine learning","Machine learning is reshaping how complex genetic architectures of brain disorders are decoded. This review evaluates strengths and limitations of ML approaches, focusing on genetic prediction, patient stratification, and modelling genetic interactions. It explains how ML can enhance polygenic risk scores using advanced techniques, while integrating functional genomics and multimodal data to confront rare variants and weak effects. The article also emphasizes embedding biological knowledge to improve interpretability, leveraging federated learning and growing phenotype-genotype datasets to challenge classical statistics.","[www.nature.com/mp](www.nature.com/mp Molecular)[ Molecular](www.nature.com/mp Molecular) Psychiatry  \nEXPERT REVIEW OPEN   \nDecoding the genomic symphony: unravelling brain disorders through data integration and machine learning  \nMatthew Bracher-Smith 1 and Valentina Escott-Price 1,2 ✉  \n© The Author(s) 2025  \n|  |  |  |\n| --- | --- | --- |\n|  | Machine learning (ML) is revolutionising our ability to decode the complex genetic architectures of brain disorders. In this review we examine the strengths and limitations of ML methods, highlighting their applications in genetic prediction, patient stratification, and the modelling of genetic interactions. We explore how ML can augment polygenic risk scores (PRS) through advanced techniques and how integrating functional genomics and multimodal data can address challenges like rare variants and weak genetic effects. Additionally, we discuss the importance of embedding biological knowledge into ML models to enhance interpretability and uncover meaningful insights. With the ongoing expansion of phenotype-genotype datasets and advances in federated learning, ML is poised to compete with and surpass classical statistical methods in disease risk prediction and identifying genetically homogenous subgroups. By balancing the strengths and weaknesses of these approaches, we provide a roadmap for |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| leveraging ML to unravel the genomic complexity of brain disorders and drive the next wave of discoveries. |  |  |\n|  | Molecular Psychiatry (2025) 30:5914–5925; [https://doi.org/10.1038/s41380-025-03330-4](https://doi.org/10.1038/s41380-025-03330-4) |  |\n|  |  |  |\n\nBACKGROUND  \nBrain disorders are complex and often highly heritable traits that can be caused by a combination of genetic, physical, psychological and environmental factors [1–3] . Such complexity is evident in their diagnosis, which is often based on symptoms. There is no clinical biomarker for schizophrenia or other psychotic disorders: these conditions are usually diagnosed after assessment by a specialist in mental health, and only a postmortem brain biopsy can confirm the presence of a specific type of dementia [4] . Differentiation between brain disorders is further challenged by a pronounced overlap in symptoms and comorbidities [5] . Neurodegenerative disorders like dementia, for example, cause a range of psychiatric symptoms, including depression and anxiety, in addition to physical difficulties like incontinence [6] . The phenotypic complexity of brain disorders is mirrored in their genetics. This includes a broad range of genetic variation which impacts risk for psychiatric disorders [7], including common and rare variants, single nucleotide changes, small insertions and deletions, and large structural rearrangements such as copy number variations (CNVs) and trisomy 21 [8–12] . While disorders like schizophrenia are characterised by a wide spectrum of genetic variation including a high burden of rare variants [13], others maybe characterised by common variants of stronger effect in genes such as LRRK2 in Parkinson’s disease (PD), or APOE in Alzheimer’s disease (AD) . This divergent genetic architecture magnifies difficulties in modelling; a single modelling approach is unlikely to work consistently across all brain disorders.  \nThe rise of additive models  \nGenome wide association studies (GWAS) have been the driving force behind cutting the Gordian knot. A focus on statistical power  \nand simple models helped to push through early quagmires in candidate gene studies and onto the first robust genetic associations with brain disorders like schizophrenia [14] . Procedures for quality control and conducting GWAS are now routine and robust. Applying hundreds of thousands of simple univariable additive models with stringent thresholds for the strength of evidence of association has ultimately been instrumental in identifying the lion’s share of","cbCaia0UI7Yrt4Ji","https://ap.wps.com/l/cbCaia0UI7Yrt4Ji","pdf",1306242,1,12,"English","en",105,"# Background\n## Genetic and phenotypic complexity in brain disorders\n## Limits of diagnosis and biomarkers\n# The rise of additive models\n## GWAS and statistical power\n## Polygenic risk scores and prediction performance\n# Integrating ML with genetics\n## ML methods for prediction, stratification, and interactions\n## Functional genomics and multimodal data integration\n# Bridging interpretability and discovery\n## Embedding biological knowledge into ML models","[{\"question\":\"Why are brain disorders difficult to model using genetics and diagnosis?\",\"answer\":\"Brain disorders involve intertwined genetic, physical, psychological, and environmental contributors, and diagnosis is often symptom-based due to limited clinical biomarkers.\"},{\"question\":\"What roles do GWAS and polygenic risk scores play in this field?\",\"answer\":\"GWAS has enabled robust common-variant associations through quality control and additive statistical models, while polygenic risk scores focus on prediction by summarizing genome-wide genotype liability into a single variable.\"},{\"question\":\"How can machine learning improve polygenic risk score approaches?\",\"answer\":\"The review describes ML techniques that augment polygenic risk scores, improving prediction and supporting applications such as genetic prediction and patient stratification.\"},{\"question\":\"What strategies are discussed to address rare variants and weak genetic effects?\",\"answer\":\"Integrating functional genomics and multimodal data is highlighted as a way to tackle challenges including rare variants and weak genetic contributions, alongside embedding biological knowledge for interpretability.\"}]","Decoding the genomic symphony - unravelling brain disorders through data integration and machine learning | PDF",1785938581,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"decoding-the-genomic-symphony-unravelling-brain-disorders-through-data-integration-and-machine-learning","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/decoding-the-genomic-symphony-unravelling-brain-disorders-through-data-integration-and-machine-learning/127378/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why are brain disorders difficult to model using genetics and diagnosis?","Question",{"text":75,"@type":76},"Brain disorders involve intertwined genetic, physical, psychological, and environmental contributors, and diagnosis is often symptom-based due to limited clinical biomarkers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What roles do GWAS and polygenic risk scores play in this field?",{"text":80,"@type":76},"GWAS has enabled robust common-variant associations through quality control and additive statistical models, while polygenic risk scores focus on prediction by summarizing genome-wide genotype liability into a single variable.",{"name":82,"@type":73,"acceptedAnswer":83},"How can machine learning improve polygenic risk score approaches?",{"text":84,"@type":76},"The review describes ML techniques that augment polygenic risk scores, improving prediction and supporting applications such as genetic prediction and patient stratification.",{"name":86,"@type":73,"acceptedAnswer":87},"What strategies are discussed to address rare variants and weak genetic effects?",{"text":88,"@type":76},"Integrating functional genomics and multimodal data is highlighted as a way to tackle challenges including rare variants and weak genetic contributions, alongside embedding biological knowledge for interpretability.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":125},"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]