scholarly journals Late-maturity α-amylase (LMA): exploring the underlying mechanisms and end-use quality effects in wheat

Planta ◽  
2021 ◽  
Vol 255 (1) ◽  
Author(s):  
Ashley E. Cannon ◽  
Elliott J. Marston ◽  
Alecia M. Kiszonas ◽  
Amber L. Hauvermale ◽  
Deven R. See
Keyword(s):  
Wheat Breeding ◽  
Protein Accumulation ◽  
Comprehensive Overview ◽  
Breeding Programs ◽  
Future Directions ◽  
Open Questions ◽  
Late Maturity ◽  
End Use ◽  
Molecular Aspects ◽  
Underlying Mechanisms

Abstract Main conclusion A comprehensive understanding of LMA from the underlying molecular aspects to the end-use quality effects will greatly benefit the global wheat industry and those whose livelihoods depend upon it. Abstract Late-maturity α-amylase (LMA) leads to the expression and protein accumulation of high pI α-amylases during late grain development. This α-amylase is maintained through harvest and leads to an unacceptable low falling number (FN), the wheat industry’s standard measure for predicting end-use quality. Unfortunately, low FN leads to significant financial losses for growers. As a result, wheat researchers are working to understand and eliminate LMA from wheat breeding programs, with research aims that include unraveling the genetic, biochemical, and physiological mechanisms that lead to LMA expression. In addition, cereal chemists and quality scientists are working to determine if and how LMA-affected grain impacts end-use quality. This review is a comprehensive overview of studies focused on LMA and includes open questions and future directions.

scholarly journals Biometric Systems De-Identification: Current Advancements and Future Directions

2021 ◽  
Vol 1 (3) ◽  
pp. 470-495
Author(s):  
Md Shopon ◽  
Sanjida Nasreen Tumpa ◽  
Yajurv Bhatia ◽  
K. N. Pavan Kumar ◽  
Marina L. Gavrilova
Keyword(s):  
Information Security ◽  
System Development ◽  
User Profile ◽  
Information Privacy ◽  
Biometric System ◽  
Comprehensive Overview ◽  
Future Directions ◽  
Behavioral Biometrics ◽  
Open Questions ◽  
Biometric Systems

Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.

scholarly journals Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

2021 ◽  
Author(s):  
Karansher S Sandhu ◽  
Meriem Aoun ◽  
Craig Morris ◽  
Arron H Carter
Keyword(s):  
Deep Learning ◽  
Grain Yield ◽  
Cross Validation ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Quality Traits ◽  
Learning Models ◽  
Breeding Programs ◽  
Wheat Breeding Program ◽  
End Use

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Hence, testing is delayed until later stages in the breeding program. Delayed phenotyping results in advancement of inferior end-use quality lines into the program. Genomic selection provides an alternative to predict performance using genome-wide markers. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015-19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron), were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models performed superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trailing. Furthermore, the superior performance of machine and deep learning models strengthen the idea to include them in large scale breeding programs for predicting complex traits.

Characterization of the high α -amylase levels in grain of the wheat cultivar BD 159

1994 ◽  
Vol 45 (5) ◽  
pp. 1003 ◽  
Author(s):  
DJ Mares ◽  
K Mrva ◽  
JF Panozzo
Keyword(s):  
Amylase Activity ◽  
Wheat Cultivar ◽  
New South ◽  
Wheat Breeding ◽  
Cool Temperature ◽  
Wheat Varieties ◽  
Breeding Programs ◽  
South Wales ◽  
Late Maturity

TThe advanced wheat breeding line BD 159, from Victoria, exhibited a wide variation in falling number values at trial sites in 1990 when corresponding values for standard cultivars were uniformly high. The variable and unpredictable behaviour of BD 159 appears to be typical of a number of advanced lines and parental stocks from Australian breeding programs. The grain samples of BD 159 with low falling numbers had elevated levels of a-amylase which was distributed evenly in the proximal and distal halves of the grains. This distribution pattern, which was quite distinct from the steep gradient in a-amylase activity typical of germinated grains, and the absence of any evidence of sprouting indicated that the anomalous behaviour of BD 159 is a new and different form of the late maturity a-amylase syndrome previously described in wheat varieties such as Spica and Lerma 52. The high levels of a-amylase were reproduced at Narrabri in northern New South Wales when plants were transplanted from the field and allowed to ripen in a cool temperature glasshouse. Plants which were left to ripen in the field produced grain with a very low a-amylase activity.

scholarly journals High molecular weight glutenin gene diversity in Aegilops tauschii demonstrates unique origin of superior wheat quality

2021 ◽  
Author(s):  
Emily Delorean ◽  
LiangLiang Gao ◽  
Jose Fausto Cervantes Lopez ◽  
The Open Wild Wheat Consortium ◽  
Brande Wulff ◽  
...  
Keyword(s):  
Gene Diversity ◽  
Aegilops Tauschii ◽  
Tetraploid Wheat ◽  
Wheat Breeding ◽  
D Genome ◽  
Breeding Programs ◽  
Lineage 2 ◽  
End Use ◽  
Genomic Resource ◽  
New Alleles

Abstract Central to the diversity of wheat products was the origin of hexaploid bread wheat, which added the D-genome of Aegilops tauschii to tetraploid wheat giving rise to superior dough properties in leavened breads. The polyploidization, however, imposed a genetic bottleneck, with only limited diversity introduced in the wheat D-subgenome. To understand genetic variants for quality, we sequenced 273 accessions spanning the known diversity of Ae. tauschii. We discovered 45 haplotypes in Glu-D1, a major determinant of quality, relative to the two predominant haplotypes in wheat. The wheat allele 2+12 was found in Ae. tauschii Lineage 2, the donor of the wheat D-subgenome. Conversely, the superior quality wheat allele 5+10 allele originated in Lineage 3, a recently characterized lineage of Ae. tauschii, showing a unique origin of this important allele. These two wheat alleles were also quite similar relative to the total observed molecular diversity in Ae. tauschii at Glu-D1. Ae. tauschii is thus a reservoir for unique Glu-D1 alleles and provides the genomic resource to begin utilizing new alleles for end-use quality improvement in wheat breeding programs.

Haplotype analyses in wheat for complex traits: tracking the chromosome 3B and 7B regions associated with late maturity alpha amylase (LMA) in breeding programs

2009 ◽  
Vol 60 (5) ◽  
pp. 463 ◽  
Author(s):  
M. D. McNeil ◽  
D. Diepeveen ◽  
R. Wilson ◽  
I. Barclay ◽  
R. McLean ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Relationships ◽  
Artificial Chromosome ◽  
Wheat Breeding ◽  
Breeding Programs ◽  
Discriminating Power ◽  
Late Maturity ◽  
Australian Wheat ◽  
Wheat Lines ◽  
Chromosome 3B

The quantitative trait loci (QTLs) on chromosomes 7BL and 3BS from Halberd have been used as a major source of tolerance to late maturity α amylase (LMA) within Australian wheat breeding programs. New simple sequence repeat (SSR) markers identified from the sequencing of Bacterial Artificial Chromosome (BAC) clones from the wheat cv. Renan library, and known SSRs, were used to characterise these major QTLs. The reduction or elimination of the LMA defect in wheat cultivars is a major goal for wheat breeding programs and is confounded by the complexity in measuring the trait unambiguously. In this haplotyping study focussing on two significant chromosomal regions, markers and combinations of markers were investigated for their ability to discriminate between 39 Australian and Mexican wheat lines differing in levels of LMA. Genetic relationships among these wheat lines estimated by cluster analysis of molecular marker data were combined with phenotypic information in order to calibrate the genotypes of the wheat lines against their LMA phenotype. It was evident that some SSRs from the respective QTLs had greater discriminating power than others to identify LMA phenotypes. Discrimination was not, however, absolute and a statistical analysis of the data defined a risk factor associated with particular combinations of alleles, for use in early selection or backcrossing.

scholarly journals Genetic control of seed dormancy and pre-harvest sprouting in wheat

2006 ◽  
Vol 63 (6) ◽  
pp. 564-566 ◽  
Author(s):  
Claudinei Andreoli ◽  
Manoel Carlos Bassoi ◽  
Dionisio Brunetta
Keyword(s):  
Seed Dormancy ◽  
Bimodal Distribution ◽  
Wheat Breeding ◽  
Breeding Programs ◽  
Control Seed ◽  
Grain Color ◽  
The Mean ◽  
The Cross ◽  
End Use ◽  
Financial Losses

Pre-harvest sprouting (PHS) damage leads to occasional massive losses in all wheat producing areas, causing downgrading of grain quality, that severely limits end-use applications and results in substantial financial losses to farmers and food processors. Red grain color is a traditional marker for resistance to sprouting in wheat breeding programs, however red-grained genotype alone does not always guarantee effective resistance. The objective of this work was to find genes for resistance to PHS and investigate its inheritance in Brazilian wheat cultivars. Genetic variation for dormancy was investigated in the parents, F1 and 300 F2 lines derived from the cross Frontana × OR1 and its reciprocal. The germination/dormancy sprouted grains was evaluated on fifty seeds per replication, germinated in paper towel rolls at 20ºC for 5 days. A bimodal distribution for dormancy occurred in the Frontana/OR1 and OR1/Frontana derived F2 populations. The mean ratio of dormant and non-dormant seeds of the cross and its reciprocal was 85:1115, fitting a digenic model of 1:15 (P < 0.05). In fact, all non after-ripened F1 seeds germinated. The F2 distribution indicates that two major genes, here called A,a and B,b, control seed dormancy, which it appears to be recessive to dormancy. Only the homozygous aabb is dormant. As expected, there was no effect of maternal tissues.

scholarly journals High molecular weight glutenin gene diversity in Aegilops tauschii demonstrates unique origin of superior wheat quality

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Emily Delorean ◽  
Liangliang Gao ◽  
Jose Fausto Cervantes Lopez ◽  
Ali Mehrabi ◽  
Alison Bentley ◽  
...  
Keyword(s):  
Gene Diversity ◽  
Aegilops Tauschii ◽  
Tetraploid Wheat ◽  
Wheat Breeding ◽  
D Genome ◽  
Breeding Programs ◽  
Lineage 2 ◽  
End Use ◽  
Genomic Resource ◽  
New Alleles

AbstractCentral to the diversity of wheat products was the origin of hexaploid bread wheat, which added the D-genome of Aegilops tauschii to tetraploid wheat giving rise to superior dough properties in leavened breads. The polyploidization, however, imposed a genetic bottleneck, with only limited diversity introduced in the wheat D-subgenome. To understand genetic variants for quality, we sequenced 273 accessions spanning the known diversity of Ae. tauschii. We discovered 45 haplotypes in Glu-D1, a major determinant of quality, relative to the two predominant haplotypes in wheat. The wheat allele 2 + 12 was found in Ae. tauschii Lineage 2, the donor of the wheat D-subgenome. Conversely, the superior quality wheat allele 5 + 10 allele originated in Lineage 3, a recently characterized lineage of Ae. tauschii, showing a unique origin of this important allele. These two wheat alleles were also quite similar relative to the total observed molecular diversity in Ae. tauschii at Glu-D1. Ae. tauschii is thus a reservoir for unique Glu-D1 alleles and provides the genomic resource to begin utilizing new alleles for end-use quality improvement in wheat breeding programs.

scholarly journals Molecular Insights of Fruit Quality Traits in Peaches, Prunus persica

Plants ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2191
Author(s):  
Karpagam Veerappan ◽  
Sathishkumar Natarajan ◽  
Hoyong Chung ◽  
Junhyung Park
Keyword(s):  
Fruit Quality ◽  
Prunus Persica ◽  
Color Change ◽  
Quality Traits ◽  
Breeding Programs ◽  
Nutritional Values ◽  
Sweet Cherries ◽  
Softening Process ◽  
Molecular Aspects ◽  
Underlying Mechanisms

Fleshy fruits are the most demanded fruits because of their organoleptic qualities and nutritional values. The genus Prunus is a rich source of diversified stone/drupe fruits such as almonds, apricots, plums, sweet cherries, peaches, and nectarines. The fruit-ripening process in Prunus involves coordinated biochemical and physiological changes resulting in changes in fruit texture, aroma gain, color change in the pericarp, sugar/organic acid balance, fruit growth, and weight gain. There are different varieties of peaches with unique palatable qualities and gaining knowledge in the genetics behind these quality traits helps in seedling selection for breeding programs. In addition, peaches have shorter post-harvest life due to excessive softening, resulting in fruit quality reduction and market loss. Many studies have been executed to understand the softening process at the molecular level to find the genetic basis. To summarize, this review focused on the molecular aspects of peach fruit quality attributes and their related genetics to understand the underlying mechanisms.

scholarly journals Deciphering the Genetics of Major End-Use Quality Traits in Wheat

2019 ◽  
Author(s):  
Sepehr Mohajeri Naraghi ◽  
Senay Simsek ◽  
Ajay Kumar ◽  
S.M. Hisam Al Rabbi ◽  
Mohammed S. Alamri ◽  
...  
Keyword(s):  
Recombinant Inbred Lines ◽  
Wheat Breeding ◽  
Snp Markers ◽  
Peak Time ◽  
Quality Traits ◽  
Gene Encoding ◽  
Breeding Programs ◽  
B1 Gene ◽  
A Subunit ◽  
End Use

AbstractImproving the end-use quality traits is one of the primary objectives in wheat breeding programs. In the current study, a population of 127 recombinant inbred lines (RILs) derived from a cross between Glenn (PI-639273) and Traverse (PI-642780) was developed and used to identify quantitative trait loci (QTL) for 16 end-use quality traits in wheat. The phenotyping of these 16 traits was performed in nine environments in North Dakota, USA. The genotyping for the RIL population was conducted using the wheat Illumina iSelect 90K SNP assay. A high-density genetic linkage map consisting of 7,963 SNP markers identified a total of 76 additive QTL (A-QTL) and 73 digenic epistatic QTL (DE-QTL) associated with these traits. Overall, 12 stable major A-QTL and three stable DE-QTL were identified for these traits, suggesting that both A-QTL and DE-QTL played an important role in controlling end-use quality traits in wheat. The most significant A-QTL (AQ.MMLPT.ndsu.1B) was detected on chromosome 1B for mixograph middle line peak time. The AQ.MMLPT.ndsu.1B A-QTL was located very close to the position of the Glu-B1 gene encoding for a subunit of high molecular weight glutenin and explained up to 24.43% of phenotypic variation for mixograph MID line peak time. A total of 23 co-localized QTL loci were detected, suggesting the possibility of the simultaneous improvement of the end-use quality traits through selection procedures in wheat breeding programs. Overall, the information provided in this study could be used in marker-assisted selection to increase selection efficiency and to improve the end-use quality in wheat.

Puroindoline genes and their effects on grain quality traits in southern Australian wheat cultivars

2004 ◽  
Vol 55 (1) ◽  
pp. 89 ◽  
Author(s):  
Karen Cane ◽  
Merrin Spackman ◽  
H. A. Eagles
Keyword(s):  
Water Absorption ◽  
Grain Quality ◽  
High Water ◽  
Major Effect ◽  
Wheat Breeding ◽  
Quality Data ◽  
Breeding Programs ◽  
Australian Wheat ◽  
End Use ◽  
Milling Yield

Grain hardness is a major determinant of the classification and end-use of wheat. Two genes, Pina-D1 and Pinb-D1, have a major effect on this trait, so for wheat breeding programs it is important to identify the alleles of these genes present in elite germplasm. This study was conducted to identify the alleles present in southern Australian germplasm, and to determine if they affected quality characteristics other than grain hardness.Only 3 genotypes were identified. These were Pina-D1a/Pinb-D1a producing soft grain, Pina-D1a/Pinb-D1b producing moderately hard grain, and Pina-D1b/Pinb-D1a producing very hard grain. WW15 was the probable source of Pina-D1a/Pinb-D1b in most cultivars; however, Halberd represented another source. An important source of Pina-D1b/Pinb-D1a was the CIMMYT line Pavon, with sources from the old Australian cultivars Gabo and Falcon probably still present in modern germplasm.In an analysis of grain quality data from the Victorian Institute for Dryland Agriculture breeding program, the Pina-D1b/Pinb-D1a genotype had a significantly higher water absorption and significantly lower milling yield than the Pina-D1a/Pinb-D1b genotype, which indicates that these genes will impede the development of hard-grained cultivars that combine high water absorption and high milling yield.

Sign in / Sign up

Export Citation Format

Share Document