The apple(Malus×domestica Borkh.)is one of the world’s most widely grown and valuable fruit crops.With demand for apples year round,storability has emerged as an important consideration for apple breeding progra...The apple(Malus×domestica Borkh.)is one of the world’s most widely grown and valuable fruit crops.With demand for apples year round,storability has emerged as an important consideration for apple breeding programs.Soft scald is a cold storage-related disorder that results in sunken,darkened tissue on the fruit surface.Apple breeders are keen to generate new cultivars that do not suffer from soft scald and can thus be marketed year round.Traditional breeding approaches are protracted and labor intensive,and therefore marker-assisted selection(MAS)is a valuable tool for breeders.To advance MAS for storage disorders in apple,we used genotyping-by-sequencing(GBS)to generate high-density genetic maps in two F1 apple populations,which were then used for quantitative trait locus(QTL)mapping of soft scald.In total,900 million DNA sequence reads were generated,but after several data filtering steps,only 2%of reads were ultimately used to create two genetic maps that included 1918 and 2818 single-nucleotide polymorphisms.Two QTL associated with soft scald were identified in one of the bi-parental populations originating from parent 11W-12-11,an advanced breeding line.This study demonstrates the utility of next-generation DNA sequencing technologies for QTL mapping in F1 populations,and provides a basis for the advancement of MAS to improve storability of apples.展开更多
The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration.Currently,soybean pubescence is classified visually,which is a labor-intensive and time-consuming act...The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration.Currently,soybean pubescence is classified visually,which is a labor-intensive and time-consuming activity.Additionally,the three classes of phenotypes(tawny,light tawny,and gray)may be difficult to visually distinguish,especially the light tawny class where misclassification with tawny frequently occurs.The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow,develop a set of indices for distinguishing pubescence classes,and test a machine learning(ML)classification approach.A principal component analysis(PCA)on hyperspectral soybean plot data identified clusters related to pubescence classes,while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability.Aerial images from 2018,2019,and 2020 were analyzed in this study.A 60-plot test(2019)of genotypes with known pubescence was used as reference data,while whole-field images from 2018,2019,and 2020 were used to examine the broad applicability of the classification methodology.Two indices,a red/blue ratio and blue normalized difference vegetation index(blue NDVI),were effective at differentiating tawny and gray pubescence types in high-resolution imagery.A ML approach using a support vector machine(SVM)radial basis function(RBF)classifier was able to differentiate the gray and tawny types(83.1%accuracy and kappa=0:740 on a pixel basis)on images where reference training data was present.The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel,indicating limitations of using aerial imagery for pubescence classification in some environmental conditions.High-throughput classification of gray and tawny pubescence types is possible using aerial imagery,but light tawny soybeans remain difficult to classify and may require training data from each field season.展开更多
基金This project was funded by the A-Base research(NOI-1238)of Agriculture and Agri-Food CanadaThis research was also supported in part by funding from the Canada Research Chairs program(SM)and the National Sciences and Engineering Research Council of Canada(SM,KM).
文摘The apple(Malus×domestica Borkh.)is one of the world’s most widely grown and valuable fruit crops.With demand for apples year round,storability has emerged as an important consideration for apple breeding programs.Soft scald is a cold storage-related disorder that results in sunken,darkened tissue on the fruit surface.Apple breeders are keen to generate new cultivars that do not suffer from soft scald and can thus be marketed year round.Traditional breeding approaches are protracted and labor intensive,and therefore marker-assisted selection(MAS)is a valuable tool for breeders.To advance MAS for storage disorders in apple,we used genotyping-by-sequencing(GBS)to generate high-density genetic maps in two F1 apple populations,which were then used for quantitative trait locus(QTL)mapping of soft scald.In total,900 million DNA sequence reads were generated,but after several data filtering steps,only 2%of reads were ultimately used to create two genetic maps that included 1918 and 2818 single-nucleotide polymorphisms.Two QTL associated with soft scald were identified in one of the bi-parental populations originating from parent 11W-12-11,an advanced breeding line.This study demonstrates the utility of next-generation DNA sequencing technologies for QTL mapping in F1 populations,and provides a basis for the advancement of MAS to improve storability of apples.
基金Canada First Research Excellence Fund,Food from Thought:Agricultural Systems for a Healthy Planet(CFREF-2015-00004)For the additional funding,thanks are due to Natural Sciences and Engineering Research Council(NSERC)Collaborative Research and Development Grant(CRD)#CRDPJ 513541-17,which is cofunded by CanGro Genetics Inc.and Huron Commodities Inc.
文摘The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration.Currently,soybean pubescence is classified visually,which is a labor-intensive and time-consuming activity.Additionally,the three classes of phenotypes(tawny,light tawny,and gray)may be difficult to visually distinguish,especially the light tawny class where misclassification with tawny frequently occurs.The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow,develop a set of indices for distinguishing pubescence classes,and test a machine learning(ML)classification approach.A principal component analysis(PCA)on hyperspectral soybean plot data identified clusters related to pubescence classes,while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability.Aerial images from 2018,2019,and 2020 were analyzed in this study.A 60-plot test(2019)of genotypes with known pubescence was used as reference data,while whole-field images from 2018,2019,and 2020 were used to examine the broad applicability of the classification methodology.Two indices,a red/blue ratio and blue normalized difference vegetation index(blue NDVI),were effective at differentiating tawny and gray pubescence types in high-resolution imagery.A ML approach using a support vector machine(SVM)radial basis function(RBF)classifier was able to differentiate the gray and tawny types(83.1%accuracy and kappa=0:740 on a pixel basis)on images where reference training data was present.The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel,indicating limitations of using aerial imagery for pubescence classification in some environmental conditions.High-throughput classification of gray and tawny pubescence types is possible using aerial imagery,but light tawny soybeans remain difficult to classify and may require training data from each field season.
