scholarly journals Genomic Prediction of Yield Traits in Single-Cross Hybrid Rice (Oryza sativa L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Marlee R. Labroo ◽  
Jauhar Ali ◽  
M. Umair Aslam ◽  
Erik Jon de Asis ◽  
Madonna A. dela Paz ◽  
...  
Keyword(s):  
Genetic Distance ◽  
Genomic Prediction ◽  
Hybrid Rice ◽  
Phenotypic Selection ◽  
Production Costs ◽  
Breeding Cycle ◽  
Male Sterile ◽  
Training Set ◽  
Combining Abilities ◽  
Genomic Predictions

Hybrid rice varieties can outyield the best inbred varieties by 15 – 30% with appropriate management. However, hybrid rice requires more inputs and management than inbred rice to realize a yield advantage in high-yielding environments. The development of stress-tolerant hybrid rice with lowered input requirements could increase hybrid rice yield relative to production costs. We used genomic prediction to evaluate the combining abilities of 564 stress-tolerant lines used to develop Green Super Rice with 13 male sterile lines of the International Rice Research Institute for yield-related traits. We also evaluated the performance of their F1 hybrids. We identified male sterile lines with good combining ability as well as F1 hybrids with potential further use in product development. For yield per plant, accuracies of genomic predictions of hybrid genetic values ranged from 0.490 to 0.822 in cross-validation if neither parent or up to both parents were included in the training set, and both general and specific combining abilities were modeled. The accuracy of phenotypic selection for hybrid yield per plant was 0.682. The accuracy of genomic predictions of male GCA for yield per plant was 0.241, while the accuracy of phenotypic selection was 0.562. At the observed accuracies, genomic prediction of hybrid genetic value could allow improved identification of high-performing single crosses. In a reciprocal recurrent genomic selection program with an accelerated breeding cycle, observed male GCA genomic prediction accuracies would lead to similar rates of genetic gain as phenotypic selection. It is likely that prediction accuracies of male GCA could be improved further by targeted expansion of the training set. Additionally, we tested the correlation of parental genetic distance with mid-parent heterosis in the phenotyped hybrids. We found the average mid-parent heterosis for yield per plant to be consistent with existing literature values at 32.0%. In the overall population of study, parental genetic distance was significantly negatively correlated with mid-parent heterosis for yield per plant (r = −0.131) and potential yield (r = −0.092), but within female families the correlations were non-significant and near zero. As such, positive parental genetic distance was not reliably associated with positive mid-parent heterosis.

scholarly journals Combining ability, floral biology, and seed producibility of promising cytoplasmic male-sterile (CMS) lines for hybrid rice development

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Victoriano V. Casco ◽  
Rosemarie T. Tapic ◽  
Jerwin R. Undan ◽  
Anna Ma. Lourdes S. Latonio ◽  
Roel R. Suralta ◽  
...  
Keyword(s):  
Combining Ability ◽  
Hybrid Rice ◽  
Floral Biology ◽  
Outcrossing Rate ◽  
Floral Traits ◽  
Male Sterile ◽  
Ability Test ◽  
Cytoplasmic Male Sterile ◽  
Combining Abilities ◽  
Parental Lines

Abstract Background A combining ability analysis is a useful tool of plant breeders in screening and identifying promising parental lines with high potential for developing competitive rice hybrids. Also, one important factor that strongly determines the suitability of commercial utilization of hybrid rice parental lines is their extent of seed producibility. Methods In this study, the combining ability, floral biology and seed producibility of cytoplasmic male sterile (CMS) lines were investigated to identify good combiners with good seed production potential. The Line × Tester analysis was used to determine the general combining abilities (GCA) of hybrid rice parental lines, and Specific Combining Abilities (SCA) of the different hybrid combinations. A correlation analysis was also done to determine floral traits that influence the outcrossing rate of the CMS lines. There were 4 CMS lines, 6 restorer lines, 24 hybrid combinations and 1 check variety in a randomized complete block Design (RCBD) with 3 replicates. Results Results indicated that CMS lines IR79128B and IR102758B were good combiners and the most promising restorer lines were D2031-7-1-2R, Hanareumbyeo 2, and XTR036-54-10R. Based on specific combining ability test, the most promising combination was entry 10 (IR58025A/D2013-7-1-2R). It has the highest yield of 7496 kg ha−1, a high positive SCA score of 570.54, and highest standard heterosis of 12.9%. Based on floral traits, IR79128B was the most promising with a high positive GCA score of 186.93, panicle exertion rate of 74.8%, and a high outcrossing rate of 51%. There was a significant positive association between outcrossing rate, duration of floral opening, panicle exertion rate, and general combining ability. Conclusion The floral traits found to be significantly associated with outcrossing rate are useful selection criteria not only for identifying economically usable CMS lines but also for developing new and promising parental lines and hybrids. These CMS lines do not only give heterotic combinations but are also commercially producible, the two most important factors to the success of any hybrid rice breeding program.

scholarly journals The impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Amir Aliakbari ◽  
Emilie Delpuech ◽  
Yann Labrune ◽  
Juliette Riquet ◽  
Hélène Gilbert
Keyword(s):  
Feed Intake ◽  
Genomic Prediction ◽  
Feed Efficiency ◽  
Prediction Accuracy ◽  
Target Line ◽  
The Other ◽  
Training Set ◽  
The Impact ◽  
Genomic Predictions ◽  
Training Sets

Abstract Background Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more flexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efficiency related traits using training sets that combine animals from two different, but genetically-related lines. We compared realized prediction accuracy and prediction bias for different training set compositions for five production traits. Results Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~ 400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly affected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses. Conclusions Our results show that genomic prediction using a training set that includes animals from genetically-related lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly affected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes.

scholarly journals Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato

2021 ◽  
Vol 12 ◽  
Author(s):  
Stefan Wilson ◽  
Marcos Malosetti ◽  
Chris Maliepaard ◽  
Han A. Mulder ◽  
Richard G. F. Visser ◽  
...  
Keyword(s):  
Genetic Distance ◽  
Genomic Prediction ◽  
Random Sampling ◽  
Sampling Method ◽  
Distance Sampling ◽  
Simple Random Sampling ◽  
Coefficient Of Determination ◽  
Stratified Random Sampling ◽  
Training Set ◽  
The Impact

Training set construction is an important prerequisite to Genomic Prediction (GP), and while this has been studied in diploids, polyploids have not received the same attention. Polyploidy is a common feature in many crop plants, like for example banana and blueberry, but also potato which is the third most important crop in the world in terms of food consumption, after rice and wheat. The aim of this study was to investigate the impact of different training set construction methods using a publicly available diversity panel of tetraploid potatoes. Four methods of training set construction were compared: simple random sampling, stratified random sampling, genetic distance sampling and sampling based on the coefficient of determination (CDmean). For stratified random sampling, population structure analyses were carried out in order to define sub-populations, but since sub-populations accounted for only 16.6% of genetic variation, there were negligible differences between stratified and simple random sampling. For genetic distance sampling, four genetic distance measures were compared and though they performed similarly, Euclidean distance was the most consistent. In the majority of cases the CDmean method was the best sampling method, and compared to simple random sampling gave improvements of 4–14% in cross-validation scenarios, and 2–8% in scenarios with an independent test set, while genetic distance sampling gave improvements of 5.5–10.5% and 0.4–4.5%. No interaction was found between sampling method and the statistical model for the traits analyzed.

scholarly journals Increasing selection gain and accuracy of harvest prediction models in Jatropha through genome-wide selection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adriano dos Santos ◽  
Erina Vitório Rodrigues ◽  
Bruno Galvêas Laviola ◽  
Larissa Pereira Ribeiro Teodoro ◽  
Paulo Eduardo Teodoro ◽  
...  
Keyword(s):  
Prediction Models ◽  
Block Design ◽  
Phenotypic Selection ◽  
Breeding Population ◽  
Breeding Cycle ◽  
Selection Cycle ◽  
Genome Wide ◽  
Random Block ◽  
Random Block Design ◽  
Number Of Individuals

AbstractGenome-wide selection (GWS) has been becoming an essential tool in the genetic breeding of long-life species, as it increases the gain per time unit. This study had a hypothesis that GWS is a tool that can decrease the breeding cycle in Jatropha. Our objective was to compare GWS with phenotypic selection in terms of accuracy and efficiency over three harvests. Models were developed throughout the harvests to evaluate their applicability in predicting genetic values in later harvests. For this purpose, 386 individuals of the breeding population obtained from crossings between 42 parents were evaluated. The population was evaluated in random block design, with six replicates over three harvests. The genetic effects of markers were predicted in the population using 811 SNP's markers with call rate = 95% and minor allele frequency (MAF) > 4%. GWS enables gains of 108 to 346% over the phenotypic selection, with a 50% reduction in the selection cycle. This technique has potential for the Jatropha breeding since it allows the accurate obtaining of GEBV and higher efficiency compared to the phenotypic selection by reducing the time necessary to complete the selection cycle. In order to apply GWS in the first harvests, a large number of individuals in the breeding population are needed. In the case of few individuals in the population, it is recommended to perform a larger number of harvests.

scholarly journals Construction of a weight-based seed sorting system for the third-generation hybrid rice

Rice ◽  
2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jianxin Wu ◽  
Shijun Qiu ◽  
Menglong Wang ◽  
Chunjue Xu ◽  
Xing Wang Deng ◽  
...  
Keyword(s):  
Hybrid Rice ◽  
Pollen Grains ◽  
Third Generation ◽  
Male Sterile ◽  
Specific Expression ◽  
The Third ◽  
Genes Encoding ◽  
Wild Type Male ◽  
Sorting System ◽  
Generation Hybrid

Abstract Background The third-generation hybrid rice technology can be constructed by transforming a recessive nuclear male sterile (NMS) mutant with a transgenic cassette containing three functional modules: the wild type male fertility gene to restore the fertility of the mutant, the pollen killer gene that specifically kills the pollen grains carrying the transgene, and the red fluorescence protein (RFP) gene to mark the transgenic seed (maintainer). The transgenic plant produces 1:1 NMS seeds and maintainer seeds that can be distinguished by the RFP signal. However, the RFP signals in the partially filled or pathogen-infected maintainer seeds are often too weak to be detected by RFP-based seed sorting machine, resulting in intermingling of the maintainer seeds with NMS seeds. Results Here we constructed a weight-based seed sorting system for the third-generation hybrid rice technology by silencing the genes encoding ADP-glucose pyrophosphorylase (AGP) essential for endosperm starch biosynthesis via endosperm-specific expression of artificial microRNAs (amiRNAs). In this system, the NMS seeds have normal endosperm and are heavy, but the maintainer seeds have shrunken endosperms and are light-weighted. The maintainer seeds can be easily and accurately sorted out from the NMS seeds by weight-sorting machines, so pure and fully filled NMS seeds are available. Conclusions The weight-based seed sorting system shows obvious advantages over the RFP-based seed sorting system in accuracy, efficiency, and cost for propagation of pure male sterile seeds. These characteristics will significantly increase the value and transgenic safety of the third-generation hybrid rice technology.

Transgene flow to hybrid rice and its male-sterile lines

2007 ◽  
Vol 16 (4) ◽  
pp. 491-501 ◽  
Author(s):  
Shirong Jia ◽  
Feng Wang ◽  
Lei Shi ◽  
Qianhua Yuan ◽  
Wuge Liu ◽  
...  
Keyword(s):  
Hybrid Rice ◽  
Male Sterile ◽  
Transgene Flow

Agromorphological and molecular analysis discloses wide genetic variability in sunflower breeding lines from USDA, USA

2019 ◽  
Vol 79 (02) ◽  
Author(s):  
K. T. Ramya ◽  
A. Vishnuvardhan Reddy ◽  
M. Sujatha
Keyword(s):  
Genetic Distance ◽  
Polymorphic Information Content ◽  
Distance Matrix ◽  
Coefficient Matrix ◽  
Male Sterile ◽  
Breeding Lines ◽  
Genetic Distance Matrix ◽  
Ssr Primers ◽  
Fertility Restorers ◽  
Simple Sequence

The present study investigates genetic divergence among 84 fertility restorers and 32 cytoplasmic male sterile (CMS) lines of sunflower augmented from USDA, USA along with the popular Indian parental lines using simple sequence repeats (SSR). Thirty-nine polymorphic SSR primers produced 139 alleles with an average of 3.56 alleles per locus. The polymorphic information content ranged from 0.23 to 0.69 with an average of 0.45. The average genetic distance was 0.45 and 0.42 for the R and CMS lines, respectively. Dendrogram based on the dissimilarity coefficient matrix grouped the CMS and R lines into separate clusters except for Cluster A which consisted of all CMS lines along with five R lines. Genetic distance matrix estimated from three sets of mitochondrial primers (BOX, ERIC and REP) grouped the 32 CMS lines into eight clusters. The results suggest the existence of considerable genetic diversity among the restorer and CMS lines of sunflower obtained from USDA, USA.

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