scholarly journals Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains

2018 ◽  
Author(s):  
Julien Frouin ◽  
Axel Labeyrie ◽  
Arnaud Boisnard ◽  
Gian Attilio Sacchi ◽  
Nourollah Ahmadi
Keyword(s):  
Genomic Prediction ◽  
Reference Population ◽  
Critical Issue ◽  
Breeding Population ◽  
Arsenic Content ◽  
High Concentration ◽  
Breeding Method ◽  
Rice Varieties ◽  
Arsenic Tolerance ◽  
Rice Grains

AbstractThe high concentration of arsenic in the paddy fields and, consequently, in the rice grains is a critical issue in many rice-growing areas. Breeding arsenic tolerant rice varieties that prevent As uptake and its accumulation in the grains is a major mitigation options. However, the genetic control of the trait is complex, involving large number of gene of limited individual effect, and raises the question of the most efficient breeding method. Using data from three years of experiment in a naturally arsenic-reach field, we analysed the performances of the two major breeding methods: conventional, quantitative trait loci based, selection targeting loci involved in arsenic tolerance, and the emerging, genomic selection, predicting genetic values without prior hypotheses on causal relationships between markers and target traits. We showed that once calibrated in a reference population the accuracy of genomic prediction of arsenic content in the grains of the breeding population was rather high, ensuring genetic gains per time unite close to phenotypic selection. Conversely, selection targeting quantitative loci proved to be less robust as, though in agreement with the literature on the genetic bases of arsenic tolerance, few target loci identified in the reference population could be validated in the breeding population.

scholarly journals Genomic prediction of arsenic tolerance and grain yield in rice. Contribution of trait-specific markers and multi environment models

2020 ◽  
Author(s):  
Nourollah Ahmadi ◽  
Tuong-Vi Cao ◽  
Julien Frouin ◽  
Gareth J. Norton ◽  
Adam H. Price
Keyword(s):  
Grain Yield ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Computing Time ◽  
Predictive Ability ◽  
Arsenic Content ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Base Line ◽  
Rice Varieties

AbstractMany rice-growing areas are affected by high concentrations of arsenic (As). Rice varieties that prevent As uptake and/or accumulation can mitigate As threats to human health. Genomic selection is known to facilitate rapid selection of superior genotypes for complex traits. We explored the predictive ability (PA) of genomic prediction with single-environment models, accounting or not for trait-specific markers, multi-environment models, and multi-trait and multi-environment models, using the genotypic (1600 K SNP) and phenotypic (grain arsenic content, grain yield and days to flowering, observed under two irrigation systems over two years) data of the Bengal and Assam Aus Panel (BAAP). Under the base-line single environment model, PA of up to 0.707 and 0.654 was obtained for grain yield and grain As respectively, the three prediction methods (BL, GBLUP and RKHS) considered performed similarly, and marker selection based on linkage disequilibrium allowed to reduce the number of SNP to 17 K, without negative effect on PA of genomic predictions. Single environment models giving distinct weight to trait-specific markers in the genomic relationship matrix outperformed the base-line models up to 32%. Multi-environment models, accounting for G × E interactions, and multi-trait and multi-environment models outperformed the base-line models by up to 47% and 61%, respectively. Among the multi-trait and multi-environment models, the Bayesian multi-output regressor stacking function obtained the highest PA (0.831 for grain As) with much higher efficiency for computing time. These findings pave the way for breeding for As-tolerance in the progenies of biparental crosses involving members of the BAAP. It also applies to breeding for other complex traits evaluated under multiple environments.

scholarly journals Predicting the accuracy of genomic predictions

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Jack C. M. Dekkers ◽  
Hailin Su ◽  
Jian Cheng
Keyword(s):  
Genomic Prediction ◽  
Reference Data ◽  
Selection Index ◽  
Target Population ◽  
Reference Population ◽  
Breeding Population ◽  
Population Parameter ◽  
Deterministic Method ◽  
Breeding Programs ◽  
Index Approach

Abstract Background Mathematical models are needed for the design of breeding programs using genomic prediction. While deterministic models for selection on pedigree-based estimates of breeding values (PEBV) are available, these have not been fully developed for genomic selection, with a key missing component being the accuracy of genomic EBV (GEBV) of selection candidates. Here, a deterministic method was developed to predict this accuracy within a closed breeding population based on the accuracy of GEBV and PEBV in the reference population and the distance of selection candidates from their closest ancestors in the reference population. Methods The accuracy of GEBV was modeled as a combination of the accuracy of PEBV and of EBV based on genomic relationships deviated from pedigree (DEBV). Loss of the accuracy of DEBV from the reference to the target population was modeled based on the effective number of independent chromosome segments in the reference population (Me). Measures of Me derived from the inverse of the variance of relationships and from the accuracies of GEBV and PEBV in the reference population, derived using either a Fisher information or a selection index approach, were compared by simulation. Results Using simulation, both the Fisher and the selection index approach correctly predicted accuracy in the target population over time, both with and without selection. The index approach, however, resulted in estimates of Me that were less affected by heritability, reference size, and selection, and which are, therefore, more appropriate as a population parameter. The variance of relationships underpredicted Me and was greatly affected by selection. A leave-one-out cross-validation approach was proposed to estimate required accuracies of EBV in the reference population. Aspects of the methods were validated using real data. Conclusions A deterministic method was developed to predict the accuracy of GEBV in selection candidates in a closed breeding population. The population parameter Me that is required for these predictions can be derived from an available reference data set, and applied to other reference data sets and traits for that population. This method can be used to evaluate the benefit of genomic prediction and to optimize genomic selection breeding programs.

scholarly journals Genomic Prediction of Biomass Yield in Two Selection Cycles of a Tetraploid Alfalfa Breeding Population

2015 ◽  
Vol 8 (2) ◽  
Author(s):  
Xuehui Li ◽  
Yanling Wei ◽  
Ananta Acharya ◽  
Julie L. Hansen ◽  
Jamie L. Crawford ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Biomass Yield ◽  
Breeding Population

scholarly journals A study of Genomic Prediction across Generations of Two Korean Pig Populations

Animals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 672 ◽  
Author(s):  
Beatriz Castro Dias Castro Dias Cuyabano ◽  
Hanna Wackel ◽  
Donghyun Shin ◽  
Cedric Gondro
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Reference Population ◽  
Relevant Information ◽  
Production Traits ◽  
Genomic Evaluation ◽  
Genomic Breeding ◽  
Breeding Values ◽  
The Relationship ◽  
Dense Marker

Genomic models that incorporate dense marker information have been widely used for predicting genomic breeding values since they were first introduced, and it is known that the relationship between individuals in the reference population and selection candidates affects the prediction accuracy. When genomic evaluation is performed over generations of the same population, prediction accuracy is expected to decay if the reference population is not updated. Therefore, the reference population must be updated in each generation, but little is known about the optimal way to do it. This study presents an empirical assessment of the prediction accuracy of genomic breeding values of production traits, across five generations in two Korean pig breeds. We verified the decay in prediction accuracy over time when the reference population was not updated. Additionally we compared the prediction accuracy using only the previous generation as the reference population, as opposed to using all previous generations as the reference population. Overall, the results suggested that, although there is a clear need to continuously update the reference population, it may not be necessary to keep all ancestral genotypes. Finally, comprehending how the accuracy of genomic prediction evolves over generations within a population adds relevant information to improve the performance of genomic selection.

Expressing Arsenite Antiporter PvACR3;1 in Rice (Oryza sativa L.) Decreases Inorganic Arsenic Content in Rice Grains

2019 ◽  
Vol 53 (17) ◽  
pp. 10062-10069 ◽  
Author(s):  
Yanshan Chen ◽  
Chen-Yu Hua ◽  
Jun-Xiu Chen ◽  
Bala Rathinasabapathi ◽  
Yue Cao ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Oryza Sativa L ◽  
Inorganic Arsenic ◽  
Arsenic Content ◽  
Rice Grains

Genomic prediction of weight and wool traits in a multi-breed sheep population

2014 ◽  
Vol 54 (5) ◽  
pp. 544 ◽  
Author(s):  
N. Moghaddar ◽  
A. A. Swan ◽  
J. H. J. van der Werf
Keyword(s):  
Genomic Prediction ◽  
Pearson Correlation ◽  
Reference Population ◽  
Breeding Value ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Sheep Population ◽  
Australian Sheep ◽  
Estimated Breeding Value ◽  
Breed Sheep

The objective of this study was to predict the accuracy of genomic prediction for 26 traits, including weight, muscle, fat, and wool quantity and quality traits, in Australian sheep based on a large, multi-breed reference population. The reference population consisted of two research flocks, with the main breeds being Merino, Border Leicester (BL), Poll Dorset (PD), and White Suffolk (WS). The genomic estimated breeding value (GEBV) was based on GBLUP (genomic best linear unbiased prediction), applying a genomic relationship matrix calculated from the 50K Ovine SNP chip marker genotypes. The accuracy of GEBV was evaluated as the Pearson correlation coefficient between GEBV and accurate estimated breeding value based on progeny records in a set of genotyped industry animals. The accuracies of weight traits were relatively low to moderate in PD and WS breeds (0.11–0.27) and moderate to relatively high in BL and Merino (0.25–0.63). The accuracy of muscle and fat traits was moderate to relatively high across all breeds (between 0.21 and 0.55). The accuracy of GEBV of yearling and adult wool traits in Merino was, on average, high (0.33–0.75). The results showed the accuracy of genomic prediction depends on trait heritability and the effective size of the reference population, whereas the observed GEBV accuracies were more related to the breed proportions in the multi-breed reference population. No extra gain in within-breed GEBV accuracy was observed based on across breed information. More investigations are required to determine the precise effect of across-breed information on within-breed genomic prediction.

scholarly journals Phosphorus and Zinc Fertilization Improve Zinc Biofortification in Grains and Straw of Coarse vs. Fine Rice Genotypes

Agronomy ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1155
Author(s):  
Amanullah ◽  
Inamullah ◽  
Mona S. Alwahibi ◽  
Mohamed Soliman Elshikh ◽  
Jawaher Alkahtani ◽  
...  
Keyword(s):  
Management Practices ◽  
Field Experiments ◽  
Negative Impact ◽  
Oryza Sativa L ◽  
Crop Productivity ◽  
Continuous Cropping ◽  
Rice Varieties ◽  
Rice Grains ◽  
Rice Genotypes ◽  
The Impact

Continuous cropping of rice (Oryza sativa L.) and wheat (Triticum aestivum L.) deplete soil fertility and reduce crop productivity as well as zinc (Zn) concentrations in rice grains and straw. Low Zn concentrations in rice grains have a negative impact on human health, while low Zn concertation in rice straw creates a nutritional problem for animals. The current high yielding rice varieties and hybrids remove large quantities of Zn from the soils, lowering the residual concentrations of soil Zn for the subsequent crop (e.g., wheat). Field experiments were conducted on farmers field in Malakand with the objective to evaluate the impact of various combinations of phosphorus (0, 40, 80, and 120 kg ha−1) and Zn levels (0, 5, 10, and 15 kg ha−1) on biofortification of Zn in grains and straw of rice genotypes [fine (Bamati-385) vs. coarse (Fakhre-e-Malakand and Pukhraj)]. The results revealed that Zn biofortification in rice genotypes increased with the integrated use of both nutrients (P + Zn) when applied at higher rates (80 and 120 kg P ha−1, and 10 and 15 kg Zn ha−1, respectively). The biofortification of Zn in both grains and straw was higher in the coarse than fine rice genotypes (Pukhraj > Fakhre-e-Malakand > Basmati-385). It was concluded from this study that the application of higher P and Zn levels increased Zn contents in rice parts (grains and straw) under the rice-wheat system. We also concluded from this study that Zn concentrations in rice grains and straw are influenced by plant genetic factors and Zn management practices.

Involvement of Phenolic Compounds in Anaerobic Flooding Germination of Rice (Oryza sativa L.)

2016 ◽  
Vol 56 ◽  
pp. 73-81 ◽  
Author(s):  
Do Tan Khang ◽  
Pham Thi Thu Ha ◽  
Nguyen Thi Lang ◽  
Phung Thi Tuyen ◽  
Luong The Minh ◽  
...  
Keyword(s):  
Phenolic Acids ◽  
Germination Rate ◽  
Syringic Acid ◽  
Seedling Vigor ◽  
High Concentration ◽  
Rice Varieties ◽  
Shoot Height ◽  
Phenolic Profiles ◽  
Total Difference ◽  
Phenolic And Flavonoid

By this study, thirty rice varieties were evaluated for anaerobic flooding tolerance using the direct sowing method. Phenolic profiles of strong and weak tolerant varieties were identified and compared based on HPLC chromatograms. The germination rates and shoot heights of rice were recorded for calculating the seedling vigor, which indicate the tolerant ability of rice in flooding condition. The results revealed a high variation of germination rate (10.01 to 100%), shoot height (0.35 to 78.17 mm) and seedling vigor (0.05 to 72.83). There was a high correlation between (r = 0.71) germination rate in 5 cm and 10 cm flood. Phenolic and flavonoid contents of the strong tolerant cultivar significantly and proportionally increased in the flooding levels (5 cm and 10 cm). There was a total difference in terms of number of phenolic acids found in the strong and weak tolerant varieties. In particular, six phenolic acids (gallic acid, catechol, caffeic acid, syringic acid, vanillin, and ellagic acid) were only identified with high concentration in the strong tolerant cultivar. The findings suggest that the phenolics presented in the strong tolerant varieties probably have a certain function in response and adaptation to anaerobic flooding condition. Further researches on exogenous application of these phenolic acids to increase the flooding tolerant level of rice should be continued at both green house and field treatments.

Arsenate-Resistant Genes in Egyptian Rice Cultivars as Soil Pollution Sensors

2017 ◽  
Author(s):  
Elsayed E. Hafez ◽  
Ebtesam A. El. Bestawy ◽  
Mohamed A. Rashad ◽  
Sayed M. Hassan
Keyword(s):  
Soil Pollution ◽  
Messenger Rna ◽  
Basal Medium ◽  
Rice Cultivar ◽  
Rice Cultivars ◽  
Rice Varieties ◽  
Arbitrary Primers ◽  
Barr Virus ◽  
Rice Grains ◽  
Resistant Genes

The main objective of the present study was to investigate arsenate [As (V)] resistance genes in rice cultivars grown in arsenic contaminated Egyptian soil in order to genetically induce resistance against arsenic in the local rice varieties as well as defining contaminated rice grains and/or soil. Three local rice cultivars; Sakha 102-104 were cultivated on modified Murashige and Skoog Basal Medium (MS medium) containing elevated concentrations of arsenate (0.1, 1 and 10 mg/l). The three varieties showed different resistant attitudes against arsenate with Sakha 104 being the most resistant. Extracted messenger RNA (mRNA) from treated and untreated Sakha 104 plantlets was scanned using differential display to demonstrate the arsenate resistant genes using three different arbitrary primers. About 100 different RNAs with (1500 bp - 50 bp) were obtained from which seven were up-regulated genes, subjected to DNA cloning using TOPO TA system and the selected clones were sequenced. The sequence analysis described four genes out of the seven namely disease resistance protein RPM1, Epstein-Barr virus EBNA-1-like, CwfJ family protein and outer membrane lipoprotein OmlA while the other three genes were hypothetical proteins. It is concluded the four induced genes in the resistant rice cultivar considered as a direct response to arsenic soil pollution. Genes detected in the present study can be used as geno-sensors for rice grains and soil contamination with As (V). Moreover, local rice cultivars may be genetically modified with such genes to induce high resistance and to overcome arsenic soil pollution.

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