Identifying the molecular bases of adaptation is a key issue in evolutionary biology. Genome scan is an efficient approach for identifying important molecular variation involved in adaptation. Association mapping also offers an opportunity to gain insight into genotype–phenotype relationships. Using these two approaches coupled with environmental data should help to come up with a refined picture of the evolutionary process underlying adaptation. In this study, we first conducted a selection scan analysis on a transcription factor gene family. We focused on the MADS-box gene family, a gene family which plays a crucial role in vegetative and flower development. Twenty-one pearl millet populations were sampled along an environmental gradient in West Africa. We identified one gene, i.e. PgMADS11, using Bayesian analysis to detect selection signatures. Polymorphism at this gene was also associated with flowering time variation in an association mapping framework. Finally, we found that PgMADS11 allele frequencies were closely associated with annual rainfall. Overall, we determined an efficient way to detect functional polymorphisms associated with climate variation in non-model plants by combining genome scan and association mapping. These results should help monitor the impact of recent climatic changes on plant adaptation.