Yu Zhou, Yidong Zhu, Haihai Wang, Yongcai Huang, Yongrui Wu, Wenqin Wang
Maize ( Zea mays L.) is central to global food security and agricultural sustainability, serving as an important source of food, animal feed, and renewable bioenergy. However, improving maize productivity remains challenging because many key agronomic traits are complex, polygenic characteristics shaped by genetic networks and environmental interactions. Quantitative trait locus (QTL) mapping has become an essential tool for dissecting these genetic architectures, and thousands of QTLs associated with important maize traits have been identified. This review provides a comprehensive examination of modern QTL mapping methodologies, including innovative population designs, high-throughput phenotyping systems, advanced genotyping technologies, and robust statistical approaches. We also discuss how emerging technologies, such as AI/machine learning (ML) and multi-omics integration, are transforming QTL analysis by improving the precision of gene discovery and trait prediction. These advances are reshaping maize breeding strategies and providing new opportunities to develop high-yielding, climate-resilient maize varieties that can help address the challenges of population growth and climate change.