Traditional Web-based Question and Answer (Q&A) Web sites cannot easily solve non-factual questions to match askers' preference. Recent research efforts begin to study social-based Q&A systems that rely on an asker's social friends to provide answers. However, this method cannot find answerers for a question not belonging to the asker's interests. To solve this problem, we propose a distributed Q&A system incorporating both social community intelligence and global collective intelligence, named as iASK. iASK improves the response latency and answer quality in both the social domain and global domain. It uses a neural network based friend ranking method to identify answerer candidates by considering social closeness and Q&A activities. To efficiently identify answerers in the global user base, iASK builds a virtual server tree that embeds the hierarchical structure of interests, and also maps users to the tree based on user interests. To accurately locate the cooperative experts, iASK has a fine-grained reputation system to evaluate user reputation based on their cooperativeness and expertise. Experimental results from large-scale trace-driven simulation and realworld daily usages of the iASK prototype show the superior performance of iASK. It achieves high answer quality with 24% higher accuracy, short response latency with 53% less delay and effective cooperative incentives with 16% more answers compared to other social-based Q&A systems.