This paper presents a domain-specific adaptation of GraphRAG framework for nutrigenetics, focusing on the extraction of genetic variant information relevant to personalized nutrition. By integrating Knowledge Graphs with Retrieval-Augmented Generation (RAG), we enhance biomedical knowledge discovery. A model selection study identifies optimal combinations of Large Language Models (LLMs) and embeddings, with Gemma2:9B paired with BERT achieving the highest quality score in graph construction. Evaluation against the naive RAG baseline shows significant improvements in response comprehensiveness, directness, and empowerment across diverse user profiles, including researchers, healthcare professionals, and consumers. These advancements highlight the potential of GraphRAG to accelerate hypothesis generation, support clinical decision-making, and empower individuals to make informed dietary choices based on genetic insights. These preliminary findings underscore the importance of structured knowledge representation in addressing biomedical challenges, with promising implications for advancing personalized nutrition and multi-domain biomedical applications.