Adventitious rooting(AR)is critical to the propagation,breeding,and genetic engineering of trees.The capacity for plants to undergo this process is highly heritable and of a polygenic nature;however,the basis of its g...Adventitious rooting(AR)is critical to the propagation,breeding,and genetic engineering of trees.The capacity for plants to undergo this process is highly heritable and of a polygenic nature;however,the basis of its genetic variation is largely uncharacterized.To identify genetic regulators of AR,we performed a genome-wide association study(GWAS)using 1148 genotypes of Populus trichocarpa.GWASs are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale;to help overcome this limitation,we developed a computer vision system to measure an array of traits related to adventitious root development in poplar,including temporal measures of lateral and basal root length and area.GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics and to gain statistical power.These analyses yielded a total of 277 unique associations,suggesting that genes that control rooting include regulators of hormone signaling,cell division and structure,reactive oxygen species signaling,and other processes with known roles in root development.Numerous genes with uncharacterized functions and/or cryptic roles were also identified.These candidates provide targets for functional analysis,including physiological and epistatic analyses,to better characterize the complex polygenic regulation of AR.展开更多
The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation,and p...The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation,and particularly to collect this data at a high-throughput scale with low cost.Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping,these methods commonly rely on large training sets that can be time-consuming to generate.展开更多
文摘Adventitious rooting(AR)is critical to the propagation,breeding,and genetic engineering of trees.The capacity for plants to undergo this process is highly heritable and of a polygenic nature;however,the basis of its genetic variation is largely uncharacterized.To identify genetic regulators of AR,we performed a genome-wide association study(GWAS)using 1148 genotypes of Populus trichocarpa.GWASs are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale;to help overcome this limitation,we developed a computer vision system to measure an array of traits related to adventitious root development in poplar,including temporal measures of lateral and basal root length and area.GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics and to gain statistical power.These analyses yielded a total of 277 unique associations,suggesting that genes that control rooting include regulators of hormone signaling,cell division and structure,reactive oxygen species signaling,and other processes with known roles in root development.Numerous genes with uncharacterized functions and/or cryptic roles were also identified.These candidates provide targets for functional analysis,including physiological and epistatic analyses,to better characterize the complex polygenic regulation of AR.
基金Poplar SNP dataset:Support for the Poplar GWAS data-set is provided by the U.S.Department of Energy,Office of Science Biological and Environmental Research(BER)via the Bioenergy Science Center(BESC)under Contract No.DE-PS02-06ER64304The Poplar GWAS Project used resources of the Oak Ridge Leadership Computing Facility and the Compute and Data Environment for Science at Oak Ridge National Laboratory,which is supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC05-00OR22725+1 种基金We thank the National Science Foundation Plant Genome Research Program for funding“Analysis of Genes Affecting Plant Regeneration and Transformation in Poplar,”IOS#1546900,NSF IIS#1911232,and IIS#1909038The research was also partially supported by Amazon Research Award,DARPA Contract N66001-19-2-4035,HR001120C0011 and a gift from Kuaishou Inc..
文摘The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation,and particularly to collect this data at a high-throughput scale with low cost.Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping,these methods commonly rely on large training sets that can be time-consuming to generate.
