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It deepens the understanding of the mechanisms through which genetic variations contribute to neurological and psychiatric disorders. This systematic review overviews the methods and applications of imaging genetics in the context of neurological diseases, mentioning its potential role in personalized medicine. Following PRISMA guidelines, this review systematically analyzes 28 studies integrating genetic and neuroimaging data to explore disease mechanisms and their implications for precision medicine. Selected research included multiple neurological disorders, including frontotemporal dementia, Alzheimer\u2019s disease, bipolar disorder, schizophrenia, Parkinson\u2019s disease, and others. Voxel-based morphometry was the most common imaging technique, while frequently examined genetic variants included APOE, C9orf72, MAPT, GRN, COMT, and BDNF. Associations between these variants and regional gray matter loss (e.g., frontal, temporal, or subcortical regions) suggest that genetic risk factors play a key role in disease pathophysiology. Integrating genetic and neuroimaging analyses enhances our understanding of disease mechanisms and supports advancements in precision medicine.<\/jats:p>","DOI":"10.1007\/s12031-025-02350-7","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T20:23:21Z","timestamp":1747167801000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["The Intersection of Genetics and Neuroimaging: A Systematic Review of Imaging Genetics in Neurological Disease for Personalized Treatment"],"prefix":"10.1007","volume":"75","author":[{"given":"Mahinaz A.","family":"Mashhour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Youssef","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manal Abdel","family":"Wahed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mai S.","family":"Mabrouk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,13]]},"reference":[{"issue":"5","key":"2350_CR1","doi-asserted-by":"publisher","first-page":"e0265300","DOI":"10.1371\/journal.pone.0265300","volume":"17","author":"AH Algumaei","year":"2022","unstructured":"Algumaei AH, Algunaid RF, Rushdi MA, Yassine IA (2022) Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data. 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