Validation of Grain Yield QTLs from Soft Winter Wheat Using a CIMMYT Spring Wheat Panel

Lozada, Dennis N.; Mason, R. Esten; Sukumaran, Sivakumar; Dreisigacker, Susanne

doi:10.2135/cropsci2018.04.0232

Cited by 16 publications

(12 citation statements)

References 36 publications

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“…Previously, some QTL validation studies for grain yield in wheat showed the potential of using allele specific assays such as KASP ® [52] to select for lines with high yield potential. Lozada et al [53], for instance, developed marker assays for yield and component traits and used a diverse panel of spring wheat lines from CIMMYT, Mexico to validate the effects of yield-related loci previously identified in southern US winter wheat. They eventually showed the potential of developing molecular marker assays that could select for spring wheat lines with improved yield potential.…”

Section: Discussionmentioning

confidence: 99%

Gains through selection for grain yield in a winter wheat breeding program

Lozada

Carter

2019

Preprint

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AbstractIncreased genetic gains for complex traits in plant breeding programs can be achieved through different selection strategies. The objective of this study was to compare potential gains for grain yield in a winter wheat breeding program through estimating response to selection R values across several selection approaches including phenotypic (PS), marker-based (MS), genomic (GS), and a combination of PS and GS. Five populations of Washington State University (WSU) winter wheat breeding lines evaluated from 2015 to 2018 in Lind and Pullman, WA, USA were used in the study. Selection was conducted by selecting the top 20% of lines based on observed yield (PS strategy), genomic estimated breeding values (GS), presence of yield “enhancing” alleles of the most significant single nucleotide polymorphism (SNP) markers identified from genome-wide association mapping (MS), and high observed yield and estimated breeding values (PS+GS). Overall, PS compared to other individual strategies showed the highest response. However, when combined with GS, a 23% improvement in R for yield was observed, indicating that gains could be improved by complementing traditional PS with GS. Using GS alone as a selection strategy for grain yield should be taken with caution. MS was not that successful in terms of R relative to the other selection approaches. Altogether, we demonstrated that gains through increased response to selection for yield could be achieved in the WSU winter wheat breeding program by implementing different selection strategies either exclusively or in combination.

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Section: Discussionmentioning

confidence: 99%

Gains through selection for grain yield in a winter wheat breeding program

Lozada

Carter

2019

Preprint

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“…Previously, some QTL validation studies for grain yield in wheat showed the potential of using allele specific assays such as KASP 1 [57] to select for lines with high yield potential. Lozada et al [58] developed marker assays for yield and component traits and used a diverse panel of spring wheat lines from the International Maize and Wheat Improvement Center (CIMMYT) in Mexico to validate the effects of yield-related loci previously identified in southern US winter wheat. They eventually showed the potential of developing molecular marker assays that could select for spring wheat lines with improved yield potential.…”

Section: Plos Onementioning

confidence: 99%

Gains through selection for grain yield in a winter wheat breeding program

2020

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Increased genetic gain for complex traits in plant breeding programs can be achieved through different selection strategies. The objective of this study was to compare potential gains for grain yield in a winter wheat breeding program through estimating response to selection R values across several selection approaches including phenotypic (PS), markerbased (MS), genomic (GS), and a combination of PS and GS (PS+GS). Ten populations of Washington State University (WSU) winter wheat breeding lines including a diversity panel and F5 and double haploid lines evaluated from 2015 to 2019 growing seasons for grain yield in Lind and Pullman, WA, USA were used in the study. Selection was conducted by selecting the top 20% of lines based on observed yield (PS strategy), genomic estimated breeding values (GS), presence of yield "enhancing" alleles of the most significant single nucleotide polymorphism (SNP) markers identified from genome-wide association mapping (MS), and high observed yield and estimated breeding values (PS+GS). Overall, PS compared to other individual selection strategies (MS and GS) showed the highest mean response (R = 0.61) within the same environment. When combined with GS, a 23% improvement in R for yield was observed, indicating that gains could be improved by complementing traditional PS with GS within the same environment. Validating selection strategies in different environments resulted in low to negative R values indicating the effects of genotype-by-environment interactions for grain yield. MS was not successful in terms of R relative to the other selection approaches; using this strategy resulted in a significant (P < 0.05) decrease in response to selection compared with the other approaches. An integrated PS+GS approach could result in optimal genetic gain within the same environment, whereas a PS strategy might be a viable option for grain yield validated in different environments. Altogether, we demonstrated that gains through increased response to selection for yield could be achieved in the WSU winter wheat breeding program by implementing different selection strategies either exclusively or in combination.

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“…For example, when there is a need to screen a sufficiently large plant population for a quantitative trait analysis, e.g., grain size or abiotic stress tolerance, considering replicated trials over multiple locations makes phenotyping laborious and technically challenging. In some cases, the labor-intensive and costly nature of conventional field phenotyping led many crop breeding programs to make a single measurement of final yield for replicated plots in multiple environments [ 9 , 10 , 11 ]. However, yield itself is one of the most poorly inherited traits in crop breeding [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Growth Analysis of Wheat Using Machine Vision: Opportunities and Challenges

Ajlouni

Kruse

Condori-Apfata

et al. 2020

Sensors

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Crop growth analysis is used for the assessment of crop yield potential and stress tolerance. Capturing continuous plant growth has been a goal since the early 20th century; however, this requires a large number of replicates and multiple destructive measurements. The use of machine vision techniques holds promise as a fast, reliable, and non-destructive method to analyze crop growth based on surrogates for plant traits and growth parameters. We used machine vision to infer plant size along with destructive measurements at multiple time points to analyze growth parameters of spring wheat genotypes. We measured side-projected area by machine vision and RGB imaging. Three traits, i.e., biomass (BIO), leaf dry weight (LDW), and leaf area (LA), were measured using low-throughput techniques. However, RGB imaging was used to produce side projected area (SPA) as the high throughput trait. Significant effects of time point and genotype on BIO, LDW, LA, and SPA were observed. SPA was a robust predictor of leaf area, leaf dry weight, and biomass. Relative growth rate estimated using SPA was a robust predictor of the relative growth rate measured using biomass and leaf dry weight. Large numbers of entries can be assessed by this method for genetic mapping projects to produce a continuous growth curve with fewer replicates.

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Validation of Grain Yield QTLs from Soft Winter Wheat Using a CIMMYT Spring Wheat Panel

Cited by 16 publications

References 36 publications

Gains through selection for grain yield in a winter wheat breeding program

Gains through selection for grain yield in a winter wheat breeding program

Gains through selection for grain yield in a winter wheat breeding program

Growth Analysis of Wheat Using Machine Vision: Opportunities and Challenges

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