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Medical image segmentation is pivotal for diagnosing and analyzing brain tumors, particularly lower-grade gliomas (LGG). Accurate tumor delineation is critical for clinical decision-making and treatment planning, yet this task remains challenging due to the complex structure of brain tissues and the heterogeneity of tumor characteristics. In this paper, we propose Genomic Attention U-Net (GenAU-net), an enhanced segmentation framework that integrates genomic clustering data into the widely used Attention U-Net architecture. By incorporating patient-specific genomic information, GenAU-net achieves a more personalized approach to LGG MRI segmentation, demonstrating a DICE score of 0.827 on a public LGG dataset. Leveraging genomic data not only improves segmentation performance but also opens avenues for an individualized diagnosis and treatment strategy.Clinical relevance-This research underscores the potential of incorporating genomic information for more accurate LGG segmentation in brain MRI. By providing richer context in the segmentation process, GenAU-net could help clinicians better identify tumor boundaries, optimize surgical resection or radiation therapy plans, and ultimately guide tailored patient care, improving outcomes and survival rates.