Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce.
J Exp Bot
; 72(8): 2979-2994, 2021 04 02.
Article
en En
| MEDLINE
| ID: mdl-33681981
ABSTRACT
Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self- and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences that open only once. An L. serriola×L. sativa F6 recombinant inbred line (RIL) population differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored using time-course image series obtained by drone-based phenotyping on two occasions. Floral pixels were identified from the images using a support vector machine with an accuracy >99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent quantitative trait loci (QTLs; Daily Floral Opening 2.1 and qDFO8.1) explaining >30% of the phenotypic variation in floral opening time were discovered. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote sensing, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Lactuca
/
Sitios de Carácter Cuantitativo
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
J Exp Bot
Asunto de la revista:
BOTANICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos