Multi-hop question answering (QA) requires integrating evidence across multiple documents and reasoning over interdependent intermediate steps, posing significant challenges for retrieval-augmented generation (RAG) systems. While recent RAG-based approaches interleave retrieval and reasoning to improve factual grounding, they often struggle with complex multi-hop questions due to retrieval–reason mismatch, ignored dependencies among sub-questions, and error propagation during decomposition. In this work, we propose SDRAG, a sub-question dependency-aware RAG framework that explicitly models the intrinsic logic of multi-hop questions as a dependency graph. SDRAG first decomposes the question into fine-grained sub-questions and explicitly models their dependency relations, then refines the graph by pruning unreliable or redundant nodes, and finally traverses the pruned graph to coordinate retrieval and reasoning. To improve graph construction quality, we further introduce a supervised fine-tuning strategy that independently trains task-specific LoRA adapters for decomposition, dependency prediction, and pruning. Experiments on three common multi-hop QA benchmarks show that SDRAG consistently outperforms strong RAG-based baselines, while additional analyses demonstrate the effectiveness of dependency modeling, graph pruning, and fine-tuning.