Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce
Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce
复制标题
无人机表型分析和机器学习可以发现调节生菜每日开花的位点
DOI：
10.1093/jxb/erab081
复制
发表时间：
2021-03-03
影响因子：
6.9
通讯作者：
Michelmore, Richard W.
中科院分区：
文献类型：
--
作者：
Han, Rongkui;Wong, Andy J. Y.;Michelmore, Richard W.
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. serriolaxL. sativa F-6 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 qDF08.1) 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.
