Multi-tissue Transcriptomic and Pan-genomic Analyses Reveal Reciprocal Selective Retention Drives the Phenotypic Trade-off in Gossypium hirsutum and Gossypium barbadense

The long-standing phenotypic trade-off between Upland cotton (Gossypium hirsutum, high yield) and Sea Island cotton (G. barbadense, high quality) represents a major bottleneck in cotton breeding and industrialization. Despite rapid advances in genomics, the genomic structural variations driving the divergence of these two domestication strategies, and their subsequent impact on downstream transcriptional networks, remain unclear. This study integrates multi-tissue transcriptomic atlases spanning the full growth period with pan-genomic analyses of both Upland and Sea Island cotton, proposing a "Reciprocal Selective Retention (RSR)" strategy adopted by the two species during evolution. Transcriptomic analysis revealed that while the transcriptional chassis of the two species is highly conserved, a drastic "phase inversion" occurs during fiber development. Sea Island cotton appears to trade off for quality by specifically activating a "delayed elongation" module (at 20 DPA), whereas Upland cotton initiates a "precocious filling" program to pursue yield. Further Weighted Gene Co-expression Network Analysis (WGCNA), combined with genomic Presence/Absence Variation (PAV) analysis, identified a set of key transcription factors exhibiting a "3-vs-3" reciprocal loss pattern. Upland cotton specifically retained CRF10, WIND1, and MYB93, constructing a genetic foundation of "robust root system and strong regeneration" to support high yield. Conversely, Sea Island cotton specifically retained MYB111 and ERF105/017 to maintain long-staple characteristics and environmental buffering. Whole-genome structural variation and microsynteny analyses confirmed that these differences stem from asymmetric physical deletions (e.g., a 5 kb deletion in MYB111) or fragment sequence collapse (e.g., a 15.9 kb sequence divergence in MYB93) on homologous chromosomes. The RSR model proposed in this study not only offers a novel insight into the genetic roots of the negative correlation between yield and quality but also provides clear genomic targets for precision improvement using gene-editing technologies such as CRISPR.