The Genetic Control of Grain Protein Content under Variable Nitrogen Supply in an Australian Wheat Mapping Population

Mahjourimajd, Saba; Taylor, Julian; Rengel, Zed; Khabaz‐Saberi, Hossein; Kuchel, Haydn; Okamoto, Mamoru; Langridge, Peter

doi:10.1371/journal.pone.0159371

Cited by 26 publications

(26 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Protein content is a very important trait that is necessary to better understand and interpret the results from the other quality tests. However, even though protein content is highly influenced by the environment (Mahjourimajd et al, 2016), the prediction accuracies obtained in the present study would suggest that it is possible to accurately predict protein content, thus improving the overall selection for wheat quality.…”

Section: Discussionmentioning

confidence: 77%

Genome‐based prediction of multiple wheat quality traits in multiple years

et al. 2020

View full text Add to dashboard Cite

Wheat quality improvement is an important objective in all wheat breeding programs. However, due to the cost, time and quantity of seed required, wheat quality is typically analyzed only in the last stages of the breeding cycle on a limited number of samples. The use of genomic prediction could greatly help to select for wheat quality more efficiently by reducing the cost and time required for this analysis. Here were evaluated the prediction performances of 13 wheat quality traits under two multi-trait models (Bayesian multi-trait multi-environment [BMTME] and multi-trait ridge regression [MTR]) using five data sets of wheat lines evaluated in the field during two consecutive years. Lines in the second year (testing) were predicted using the quality information obtained in the first year (training). For most quality traits were found moderate to high prediction accuracies, suggesting that the use of genomic selection could be feasible. The best predictions were obtained with the BMTME model in all traits and the worst with the MTR model. The best predictions with the BMTME model under the mean arctangent absolute percentage error (MAAPE) were for test weight across the five data sets, whereas the worst predictions were for the alveograph trait

show abstract

Section: Discussionmentioning

confidence: 77%

Genome‐based prediction of multiple wheat quality traits in multiple years

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Therefore, despite evidence for the genetic origin of the negative relationship between grain yield and GPC in wheat [9,18], its expression can be highly influenced by the environment [11,19]. A previous study using six Southern and Western Australian sites showed that the inverse relationship between grain yield and GPC varied across different regions [20]. Therefore, multi-environment trial studies can provide an opportunity for the simultaneous selection of both grain yield and GPC [11,19,20].…”

Section: Introductionmentioning

confidence: 99%

Strengths and Weaknesses of National Variety Trial Data for Multi-Environment Analysis: A Case Study on Grain Yield and Protein Content

et al. 2020

Self Cite

View full text Add to dashboard Cite

Multi-environment trial studies provide an opportunity for the detailed analysis of complex traits. However, conducting trials across a large number of regions can be costly and labor intensive. The Australian National Variety Trials (NVT) provide grain yield and protein content (GPC) data of over 200 wheat varieties in many and varied environments across the Australian wheat-belt and is representative of similar trials conducted in other countries. Through our analysis of the NVT dataset, we highlight the advantages and limitations in using these data to explore the relationship between grain yield and GPC in the low yielding environments of Australia. Eight environment types (ETs), categorized in a previous study based on the time and intensity of drought stress, were used to analyze the impact of drought on the relationship between grain yield and protein content. The study illustrates the value of comprehensive multi-environment analysis to explore the complex relationship between yield and GPC, and to identify the most appropriate environments to select for a favorable relationship. However, the NVT trial design does not follow the rigor associated with a normal genotype × environment study and this limits the accuracy of the interpretation.

show abstract

“…A photoperiod gene, Ppd-D1, on wheat chromosome 2D has been shown to act pleiotropically to affect a number of traits including flowering date, spikelet number, and floret fertility (González et al, 2005). Furthermore, genetic markers associated with NUE and N-related traits have been identified near the Ppd-D1 locus in several genetic mapping studies (Laperche et al, 2007;Bogard et al, 2013;Cormier et al, 2014;Mahjourimajd et al, 2016), suggesting a link between photoperiod sensitivity and NUE. The authors also reported higher grain yields and greater preanthesis water uptake in photoperiod-sensitive (Ppd-D1b) lines under both irrigated and nonirrigated conditions, associating photoperiod response with abiotic stress adaptation.…”

Section: Introductionmentioning

confidence: 99%

“…The authors also reported higher grain yields and greater preanthesis water uptake in photoperiod-sensitive (Ppd-D1b) lines under both irrigated and nonirrigated conditions, associating photoperiod response with abiotic stress adaptation. Furthermore, genetic markers associated with NUE and N-related traits have been identified near the Ppd-D1 locus in several genetic mapping studies (Laperche et al, 2007;Bogard et al, 2013;Cormier et al, 2014;Mahjourimajd et al, 2016), suggesting a link between photoperiod sensitivity and NUE.…”

mentioning

confidence: 99%

Photoperiod Response Gene Ppd‐D1 Affects Nitrogen Use Efficiency in Soft Red Winter Wheat

et al. 2018

View full text Add to dashboard Cite

Optimal N application is critical to ensure profitable soft red winter wheat (Triticum aestivum L.) production and limit negative environmental effects. This study was conducted using 12 wheat lines grown in two Ohio and six Virginia environments to determine the effects of differentially split N rates on grain yield, yield components, and N‐related traits. Upon analysis of wheat lines in two contrasting photoperiod groups, it was discovered that wheat lines containing the photoperiod sensitivity allele (Ppd‐D1b) conferred a significant (P < 0.05) yield advantage under N‐limited conditions in Ohio and across N treatments in half of the Virginia testing environments. This resulted from increased harvest index, grains per square meter, number of kernels per spike, and floret fertility in Ppd‐D1b lines relative to lines with the insensitive allele (Ppd‐D1a). However, wheat genotypes in the photoperiod‐insensitive allelic group had significantly higher (P < 0.05) grain N concentration in all Virginia testing sites. A spring split N application of 33 kg N ha−1 at Zadoks growth stage (GS) 25 and 101 kg N ha−1 at Zadoks GS 30 produced the highest grain yields and grain N contents in five of the six Virginia environments. Nitrogen utilization efficiency was the primary contributor to variation in N use efficiency under higher N rates, whereas N uptake efficiency was the greatest contributor under low N rates. The contribution of N utilization and uptake efficiencies varied among wheat lines in the Ppd‐D1a group when 67 kg N ha−1 was applied at Zadoks GS 25 under the moderate and standard N rates.

show abstract

The Genetic Control of Grain Protein Content under Variable Nitrogen Supply in an Australian Wheat Mapping Population

Cited by 26 publications

References 23 publications

Genome‐based prediction of multiple wheat quality traits in multiple years

Genome‐based prediction of multiple wheat quality traits in multiple years

Strengths and Weaknesses of National Variety Trial Data for Multi-Environment Analysis: A Case Study on Grain Yield and Protein Content

Photoperiod Response Gene Ppd‐D1 Affects Nitrogen Use Efficiency in Soft Red Winter Wheat

Contact Info

Product

Resources

About