Genomics of flowering time to accelerate breeding of drought-tolerant pearl millet for Senegal

Abstract

Distinct selection strategies are required for two categories of traits targeted by breeding programs. While directional selection increases the mean of a desired trait (e.g., grain yield), stabilizing selection is necessary to maintain the optimal state of an acquired trait (e.g., flowering time). Balancing these strategies requires an optimized breeding framework that enables the de novo creation of elite gene pools to guide cross-design and accelerate genetic gain, especially in under-resourced programs. This thesis hypothesizes that trait-informed elite definition, grounded in trait architecture, enhances the precision of breeding programs.The Chapter 1 frames the challenge of managing multiple traits under different selection pressures in traditional and emerging millet programs. The Chapter 2 develops and compares molecular inference strategies to define cis- and trans-elite types. We show that the QGI-based similarity method outperforms other approaches, particularly for known QTL regions. Importantly, this finding highlights that uncovering the genetic basis of key traits is critical to applying this framework effectively. To address this, Chapters 3 and 4 focus on dissecting the genetic control of flowering time, a central adaptive trait in pearl millet. In Chapter 3, a forward genetics approach using genome-wide association studies (GWAS) in West African germplasm revealed an oligogenic architecture, with key loci including Phytochrome C (PhyC) driving ecotypic divergence. Population structure analysis further indicated the existence of shared gene pools at the regional level, offering opportunities for collaborative breeding. In Chapter 4, a reverse genetics approach was used to characterize the gene regulatory network underlying flowering. Expression profiling of early (Souna) and late (Sanio) genotypes showed that flowering is regulated by differential activation of the GI–CaHd3a pathway, modulated by photoreceptors such as CaPhyC and CaPhyA, leading to divergent regulatory dynamics between ecotypes. Together, these results provide the genomic and regulatory basis for trait-informed elite inference and support a breeding strategy that simultaneously conserves elite backgrounds while introducing desired traits. This integrative framework, which leverages population structure, trait architecture, and molecular regulation, demonstrates how quantitative, molecular, and functional genetics can be combined to enhance selection strategies in pearl millet and similar crops facing climate-related challenges.