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1.
Theor Appl Genet ; 132(9): 2591-2604, 2019 Sep.
Article in English | MEDLINE | ID: mdl-31177292

ABSTRACT

KEY MESSAGE: Genome-wide association on winter survival was conducted using data from image-based phenotyping method. Nine QTL were observed and three of them with candidate gene identified. Winter survival is an essential trait of winter wheat (Triticum aestivum L.) grown in regions with high risk of winterkill. We characterized a diversity panel of 450 Canadian wheat varieties that included mostly winter-growth habit wheats to identify key genetic factors that contribute to higher winter survival under field conditions. To more accurately quantify winter survival differences among varieties, image-based phenotyping methods, captured by unmanned aerial vehicle (UAV) and on ground level, were used to estimate the winter survival of each varieties. Winter survival index was developed to correct for emergence when evaluating winter survival. Winter survival measurement estimated by visual estimation, UAV imagery and ground imagery showed strong correlation with each other and had comparable broad-sense heritability. Genome-wide association studies resulted in the identification of seven quantitative trait loci (QTL) for winter survival including Vrn-A1. By using the recently released annotated sequence of the wheat genome and the available RNA-Seq data, two putative candidate genes underlying the QTL for winter survival were identified. However, our study showed that certain QTL was unique to specific winter survival measurement. Collectively, our study demonstrated the feasibility of using UAV-based imagery for the identification of loci associated with winter survival in wheat. The complexity of in-field condition make our result a valuable complement to indoor frost-tolerance studies in the identification of genetic factors not directly linked to freezing tolerance.


Subject(s)
Aircraft/instrumentation , Plant Proteins/genetics , Polymorphism, Single Nucleotide , Quantitative Trait Loci , Seasons , Triticum/growth & development , Triticum/genetics , Chromosome Mapping/methods , Chromosomes, Plant/genetics , Genome-Wide Association Study , Image Processing, Computer-Assisted/methods , Phenotype , Remote Sensing Technology
2.
Front Plant Sci ; 9: 343, 2018.
Article in English | MEDLINE | ID: mdl-29662497

ABSTRACT

Cacao (Theobroma cacao) is a globally important crop, and its yield is severely restricted by disease. Two of the most damaging diseases, witches' broom disease (WBD) and frosty pod rot disease (FPRD), are caused by a pair of related fungi: Moniliophthora perniciosa and Moniliophthora roreri, respectively. Resistant cultivars are the most effective long-term strategy to address Moniliophthora diseases, but efficiently generating resistant and productive new cultivars will require robust methods for screening germplasm before field testing. Marker-assisted selection (MAS) and genomic selection (GS) provide two potential avenues for predicting the performance of new genotypes, potentially increasing the selection gain per unit time. To test the effectiveness of these two approaches, we performed a genome-wide association study (GWAS) and GS on three related populations of cacao in Ecuador genotyped with a 15K single nucleotide polymorphism (SNP) microarray for three measures of WBD infection (vegetative broom, cushion broom, and chirimoya pod), one of FPRD (monilia pod) and two productivity traits (total fresh weight of pods and % healthy pods produced). GWAS yielded several SNPs associated with disease resistance in each population, but none were significantly correlated with the same trait in other populations. Genomic selection, using one population as a training set to estimate the phenotypes of the remaining two (composed of different families), varied among traits, from a mean prediction accuracy of 0.46 (vegetative broom) to 0.15 (monilia pod), and varied between training populations. Simulations demonstrated that selecting seedlings using GWAS markers alone generates no improvement over selecting at random, but that GS improves the selection process significantly. Our results suggest that the GWAS markers discovered here are not sufficiently predictive across diverse germplasm to be useful for MAS, but that using all markers in a GS framework holds substantial promise in accelerating disease-resistance in cacao.

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