INVESTIGADORES
RONDANINI Deborah Paola
artículos
Título:
Unraveling the impact on agronomic traits of the genetic architecture underlying plant-density responses in canola
Autor/es:
MENENDEZ, YESICA C; SANCHEZ, DIEGO H; SNOWDON, ROD J; RONDANINI, DEBORAH P; BOTTO, JAVIER F
Revista:
JOURNAL OF EXPERIMENTAL BOTANY
Editorial:
OXFORD UNIV PRESS
Referencias:
Año: 2021 vol. 72 p. 5426 - 5441
ISSN:
0022-0957
Resumen:
Plant density defines vegetative architecture and the competition for light between individuals. Brassica napus (canola, rapeseed) presents a radically different plant architecture compared to traditional crops commonly cultivated at high density, and can act as a model system of indeterminate growth. Using a panel of 152 spring-type accessions and a double-haploid population of 99 lines from a cross between the cultivars Lynx and Monty, we performed genome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping for 12 growth and yield traits at two contrasting plant densities of 15 and 60 plants m?2. The most significant associations were found for time to flowering, biomass at harvest, plant height, silique and seed numbers, and seed yield. These were generally independent of plant density, but some density-dependent associations were found in low-density populations. RNA-seq transcriptomic analysis revealed distinctive latent gene-regulatory responses to simulated shade between Lynx and Monty. Having identified candidate genes within the canola QTLs, we further examined their influence on density responses in Arabidopsis lines mutated in certain homologous genes. The results suggested that TCP1 might promote growth independently of plant density, while HY5 could increase biomass and seed yield specifically at high plant density. For flowering time, the results suggested that PIN genes might accelerate flowering in plant a density-dependent manner whilst FT, HY5, and TCP1 might accelerate it in a density-independent. This work highlights the advantages of using agronomic field experiments together with genetic and transcriptomic approaches to decipher quantitative complex traits that potentially mediate improved crop productivity.