|SWARTS, KELLY - Cornell University|
|BAUER, EVA - Technical University Of Munich|
|GLAUBITZ, JEFFREY - Cornell University|
|HO, TIFFANY - Cornell University|
|JOHNSON, LYNN - Cornell University|
|LI, YONGXIANG - Chinese Academy Of Agricultural Sciences|
|LI, YU - Chinese Academy Of Agricultural Sciences|
|MILLER, ZACHARY - Cornell University|
|ROMAY, MARIA CINTA - Cornell University|
|SCHOEN, CHRIS-CAROLIN - Technical University Of Munich|
|WANG, TIANYU - Chinese Academy Of Agricultural Sciences|
|ZHANG, ZHIWU - Washington State University|
|Buckler, Edward - Ed|
Submitted to: bioRxiv
Publication Type: Other
Publication Acceptance Date: 11/7/2016
Publication Date: 11/7/2016
Citation: Swarts, K., Bauer, E., Glaubitz, J., Ho, T., Johnson, L., Li, Y., Li, Y., Miller, Z., Romay, M., Schoen, C., Wang, T., Zhang, Z., Buckler IV, E.S., Bradbury, P. 2016. A large scale joint analysis of flowering time reveals independent temperate adaptations in maize. bioRxiv. https://doi.org/10.1101/086082.
Interpretive Summary: Controlling the time to flowering of maize is the key element of making it adapted to its local environment. The genetics of flowering have been studied in maize adapted all different locations to the world from the tropics, China, Europe, and the US. In this study, the knowledge of DNA sequence variation from across all the world maize varieties was integrated, and then genetic controllers were flowering time mapped. The genetic controllers of flowering in the Americas and Europe were highly overlapping, while Chinese germplasm appears to have substantially independent genetics controlling its flowering time variation. Chinese germplasm likely represents an independent temperate adaptation. Prediction accuracy of flowering time across the Americas was as high as 90% and was high across the non-Chinese germplasm. These models can be used move allelic variation across germplasm pools and increase adaptation, and they suggest there may be opportunities to improve temperate adaptation by combining American and Chinese germplasm.
Technical Abstract: Modulating days to flowering is a key mechanism in plants for adapting to new environments, and variation in days to flowering drives population structure by limiting mating. To elucidate the genetic architecture of flowering across maize, a quantitative trait, we mapped flowering in five global populations, a diversity panel (Ames) and four half-sib mapping designs, Chinese (CNNAM), US (USNAM), and European Dent (EUNAM-Dent) and Flint (EUNAM-Flint). Using whole-genome projected SNPs, we tested for joint association using GWAS, resampling GWAS and two regional approaches; Regional Heritability Mapping (RHM) (1, 2) and a novel method, Boosted Regional Heritability Mapping (BRHM). Direct overlap in significant regions detected between populations and flowering candidate genes was limited, but whole-genome cross-population predictive abilities were =0.78. Poor predictive ability correlated with increased population differentiation (r = 0.41), unless the parents were broadly sampled from across the North American temperate-tropical germplasm gradient; uncorrected GWAS results from populations with broadly sampled parents were well predicted by temperate-tropical FSTs in machine learning. Machine learning between GWAS results also suggested shared architecture between the American panels and, more distantly, the European panels, but not the Chinese panel. Machine learning approaches can reconcile non-linear relationships, but the combined predictive ability of all of the populations did not significantly enhance prediction of physiological candidates. While the North American-European temperate adaption is well studied, this study suggest independent temperate adaptation evolved in the Chinese panel, most likely in China after 1500, a finding supported by differential gene ontology term enrichment between populations.