GWAS combined with transcriptomics revealed key regulatory genes for inflorescence traits and fruit set rate in Litchi (Litchi chinensis Sonn.)
Bibliographic record
Abstract
As an important tropical and subtropical fruit, litchi's inflorescence size and the number of florets per inflorescence are crucial factors affecting fruit set rate and yield. This study extensively collected a total of 219 litchi germplasm resources, including those from China and 11 other countries worldwide, systematically evaluating eight core phenotypic traits: inflorescence length (IL), inflorescence width (IW), number of secondary lateral inflorescences (NSLI), number of inflorescence internodes (NII), base to main axis length (BMAL), inverted 5th internode length (I5IL), number of female flowers per inflorescence (NFFI), and fertilization rate (FR). The research findings indicated significant correlations between these agronomic traits and fruit set rate, particularly with high fruit set rate litchi varieties showing notably lower trait expressions in IL, NSLI, NFFI, and NII compared to low fruit set rate varieties. Furthermore, through genome-wide association studies (GWAS), significant SNP loci were successfully identified for the two key traits, NSLI and NFFI, while other traits did not show significant associations. Transcriptome results revealed that differentially expressed genes between two typical inflorescence litchi varieties were mainly enriched in molecular function categories such as catalytic activity and transferase activity, affecting metabolic pathways and secondary metabolite synthesis. Joint analysis of GWAS and transcriptomics suggested that the NSLI-associated gene LITCHI016073 (UBP1-associated proteins) might regulate inflorescence development by influencing gibberellin signaling, while NFFI -associated genes LITCHI019855 (Solute carrier family), LITCHI011125 (SEC3A), LITCHI025977 (Acid phosphatase), and LITCHI023264 (Enolase) affected the development and number of female florets. The functions of these genes were further validated by transcriptome results. qRT-PCR analysis showed that in the Houxian variety (dense inflorescence), the expression levels of LITCHI016073, LITCHI011125, LITCHI025977, and LITCHI023264 during the three critical flowering stages were significantly higher than those in the Edanli variety, while the expression level of the LITCHI019855 gene was significantly lower, strongly demonstrating the important roles of these genes in inflorescence development and fruit set rate regulation. By employing large-scale sample analysis and multi-omics technologies, this study systematically unraveled the intrinsic relationships between litchi inflorescence traits and fruit set rate, providing a solid scientific basis and novel insights for litchi breeding practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".