scholarly journals Application of Genomic Selection at the Early Stage of Breeding Pipeline in Tropical Maize

2021 ◽  
Vol 12 ◽  
Author(s):  
Yoseph Beyene ◽  
Manje Gowda ◽  
Paulino Pérez-Rodríguez ◽  
Michael Olsen ◽  
Kelly R. Robbins ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Selection ◽  
Tropical Maize ◽  
Training Set ◽  
Maize Breeding ◽  
Pooled Data ◽  
The Third ◽  
The Mean ◽  
Dh Lines ◽  
Testing Set

In maize, doubled haploid (DH) line production capacity of large-sized maize breeding programs often exceeds the capacity to phenotypically evaluate the complete set of testcross candidates in multi-location trials. The ability to partially select DH lines based on genotypic data while maintaining or improving genetic gains for key traits using phenotypic selection can result in significant resource savings. The present study aimed to evaluate genomic selection (GS) prediction scenarios for grain yield and agronomic traits of one of the tropical maize breeding pipelines of CIMMYT in eastern Africa, based on multi-year empirical data for designing a GS-based strategy at the early stages of the pipeline. We used field data from 3,068 tropical maize DH lines genotyped using rAmpSeq markers and evaluated as test crosses in well-watered (WW) and water-stress (WS) environments in Kenya from 2017 to 2019. Three prediction schemes were compared: (1) 1 year of performance data to predict a second year; (2) 2 years of pooled data to predict performance in the third year, and (3) using individual or pooled data plus converting a certain proportion of individuals from the testing set (TST) to the training set (TRN) to predict the next year's data. Employing five-fold cross-validation, the mean prediction accuracies for grain yield (GY) varied from 0.19 to 0.29 under WW and 0.22 to 0.31 under WS, when the 1-year datasets were used training set to predict a second year's data as a testing set. The mean prediction accuracies increased to 0.32 under WW and 0.31 under WS when the 2-year datasets were used as a training set to predict the third-year data set. In a forward prediction scenario, good predictive abilities (0.53 to 0.71) were found when the training set consisted of the previous year's breeding data and converting 30% of the next year's data from the testing set to the training set. The prediction accuracy for anthesis date and plant height across WW and WS environments obtained using 1-year data and integrating 10, 30, 50, 70, and 90% of the TST set to TRN set was much higher than those trained in individual years. We demonstrate that by increasing the TRN set to include genotypic and phenotypic data from the previous year and combining only 10–30% of the lines from the year of testing, the predicting accuracy can be increased, which in turn could be used to replace the first stage of field-based screening partially, thus saving significant costs associated with the testcross formation and multi-location testcross evaluation.

scholarly journals Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program

2020 ◽  
Author(s):  
Sikiru Adeniyi Atanda ◽  
Michael Olsen ◽  
Juan Burgueño ◽  
Jose Crossa ◽  
Daniel Dzidzienyo ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Primary Objective ◽  
Breeding Program ◽  
Breeding Cycle ◽  
Training Set ◽  
Maize Breeding ◽  
Phenotypic Data ◽  
Breeding Programs

Abstract Key message Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. Abstract The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

scholarly journals Genomic Prediction of Tropical Maize Resistance to Fall Armyworm and Weevils: Genomic Selection Should Focus on Effective Training Set Determination

2020 ◽  
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Training Set ◽  
Maize Weevil ◽  
Maize Resistance ◽  
Sub Saharan

Genomic selection (GS) can accelerate variety release by shortening variety development phase when factors that influence prediction accuracies (PA) of genomic prediction (GP) models such as training set (TS) size and relationship with the breeding set (BS) are optimized beforehand. In this study, PAs for the resistance to fall armyworm (FAW) and maize weevil (MW) in a diverse tropical maize panel composed of 341 double haploid and inbred lines were estimated. Both phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) were predicted using 17 parametric, semi-parametric, and nonparametric algorithms with a 10-fold and 5 repetitions cross-validation strategy. n. For both MW and FAW resistance datasets with an RBTS of 37%, PAs achieved with BLUPs were at least as twice as higher than those realized with BLUEs. The PAs achieved with BLUPs for MW resistance traits: grain weight loss (GWL), adult progeny emergence (AP), and number of affected kernels (AK) varied from 0.66 to 0.82. The PAs were also high for FAW resistance RBTS datasets, varying from 0.694 to 0.714 (for RBTS of 37%) to 0.843 to 0.844 (for RBTS of 85%). The PAs for FAW resistance with PBTS were generally high varying from 0.83 to 0.86, except for one dataset that had PAs ranging from 0.11 to 0.75. GP models showed generally similar predictive abilities for each trait while the TS designation was determinant. There was a highly positive correlation (R=0.92***) between TS size and PAs for the RBTS approach while, for the PBTS, these parameters were highly negatively correlated (R=-0.44***), indicating the importance of the degree of kinship between the TS and the BS with the smallest TS (31%) achieving the highest PAs (0.86). This study paves the way towards the use of GS for maize resistance to insect pests in sub-Saharan Africa.

scholarly journals Rapid Cycling Genomic Selection in a Multiparental Tropical Maize Population

2017 ◽  
Vol 7 (7) ◽  
pp. 2315-2326 ◽  
Author(s):  
Xuecai Zhang ◽  
Paulino Pérez-Rodríguez ◽  
Juan Burgueño ◽  
Michael Olsen ◽  
Edward Buckler ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Grain Yield ◽  
Genomic Selection ◽  
Breeding Period ◽  
Rapid Cycling ◽  
Tropical Maize ◽  
Training Population ◽  
Genetic Gains ◽  
Maize Population ◽  
Short Period

Abstract Genomic selection (GS) increases genetic gain by reducing the length of the selection cycle, as has been exemplified in maize using rapid cycling recombination of biparental populations. However, no results of GS applied to maize multi-parental populations have been reported so far. This study is the first to show realized genetic gains of rapid cycling genomic selection (RCGS) for four recombination cycles in a multi-parental tropical maize population. Eighteen elite tropical maize lines were intercrossed twice, and self-pollinated once, to form the cycle 0 (C0) training population. A total of 1000 ear-to-row C0 families was genotyped with 955,690 genotyping-by-sequencing SNP markers; their testcrosses were phenotyped at four optimal locations in Mexico to form the training population. Individuals from families with the best plant types, maturity, and grain yield were selected and intermated to form RCGS cycle 1 (C1). Predictions of the genotyped individuals forming cycle C1 were made, and the best predicted grain yielders were selected as parents of C2; this was repeated for more cycles (C2, C3, and C4), thereby achieving two cycles per year. Multi-environment trials of individuals from populations C0, C1, C2, C3, and C4, together with four benchmark checks were evaluated at two locations in Mexico. Results indicated that realized grain yield from C1 to C4 reached 0.225 ton ha−1 per cycle, which is equivalent to 0.100 ton ha−1 yr−1 over a 4.5-yr breeding period from the initial cross to the last cycle. Compared with the original 18 parents used to form cycle 0 (C0), genetic diversity narrowed only slightly during the last GS cycles (C3 and C4). Results indicate that, in tropical maize multi-parental breeding populations, RCGS can be an effective breeding strategy for simultaneously conserving genetic diversity and achieving high genetic gains in a short period of time.

scholarly journals Analysis of Yield and Yield Related Traits Variability of Winter Wheat (Triticum aestivum L.) Cv. Izolda and Double Haploid Lines

2015 ◽  
Vol 72 (1) ◽  
pp. 33-53
Author(s):  
Janusz Kozdój ◽  
Dariusz R. Mańkowski ◽  
Monika Godzina-Sawczuk ◽  
Andrzej Czaplicki
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Field Experiments ◽  
Wheat Plant ◽  
Triticum Aestivum L ◽  
Spike Length ◽  
The Third ◽  
Highly Correlated ◽  
Dh Lines ◽  
Yield Factor

AbstractThe yield-forming potential of winter wheat is determined by several factors, namely total number of shoots per plant and total number of spikelets per spike. The field experiments were conducted during three vegetation seasons at the Plant Breeding and Acclimatization Institute – National Research Institute (PBAI–NRI), located in Radzików, Poland. The objective of this study was a comparative analysis of the structural yield-forming factor levels, which determine grain yield per spike and per plant of the DH lines and standard Izolda cultivar. Results indicate that several DH lines showed some differences in tested morphological structures of plant, yield factor levels and in grain yield per spike and per plant in comparison to standard Izolda, regardless of the year. Mean grain yield per plant of DH lines was 26.5% lower in comparison to standard Izolda only in the second year of study. It was caused by a reduction of productive tillers number. Structural yield-forming potential of DH lines was used in 38% and 59% and in case of Izolda in 47% and 61% (the second and the third year of experiment, respectively). The mean grain yield per spike of DH lines was 14.8% lower than Izolda cultivar only in third year of experiment and it was caused by about 12% lower number of grains per spike. Structural yield-forming potential of DH spikes was used in 82.4%, 85.4% and 84.9% and in case of Izolda in 83.8%, 87% and 89.5% (the first, the second and the third year of experiment, respectively). The grain yield per winter wheat plant (both DH lines and standard Izolda) was significantly correlated with the number of productive tillers per plant (r = 0.80). The grain yield per winter wheat spike (both DH lines and Izolda cultivar) was significantly and highly correlated with the number of grains per spike (r = 0.96), number of fertile spikelets per spike (r = 0.87) and the spike length (r = 0.80). Variation of spike and plant structural yield-forming factors determining grain yield levels were also analyzed. Calculated total variation coefficients values of each analyzed trait during three-year long studies were different depending on plant material – DH lines or standard Izolda. Low variation coefficients values characterized following traits (traits ranked by increasing values for DH lines and standard Izolda, respectively): total spikelets number per spike (6.6 and 6.3%), spike length (11.1 and 12.6%), fertile spikelets number per spike (13.7 and 11.7%), single grain weight (15.0 and 12.2%), shoot length (16.2 and 13.3%), grains number per spikelet (26.4 and 23.3%), total shoots number per plant (23.4 and 29.6%), grains number per spike (30.1 and 28.2%). Higher variation coefficients values were obtained for the following traits: grain yield per spike (40.0 and 35.7%), plant immature tillers number (35.8 and 42.6%), plant productive tillers number (42.2 and 43.2%), spike sterile spikelets number (46.6 and 44.7%) and number of grains per plant (58.3 and 60.5%). The highest values characterized grain yield per plant (66.9 and 60.8%).

scholarly journals B73 and related inbred lines in maize breeding

Genetika ◽  
2005 ◽  
Vol 37 (3) ◽  
pp. 245-252 ◽  
Author(s):  
Milisav Stojakovic ◽  
Goran Bekavac ◽  
Nenad Vasic
Keyword(s):  
Grain Yield ◽  
Inbred Lines ◽  
Mean Value ◽  
Yield Potential ◽  
Maize Breeding ◽  
Serbia And Montenegro ◽  
Maize Hybrids ◽  
Hybrid Production ◽  
The Mean ◽  
Per Se

Inbred lines B73 and Mol7 or some versions thereof are the most commonly used parental pair in the development of medium late and late maize hybrids in Serbia and Montenegro. Because of the ever-increasing importance of line B73 in maize hybrid production, we chose several B73-type lines and a few unrelated lines and crossed them. Using the pedigree method, progenies were developed up to the S6 generation. The grain yield potential of test crosses with Mo 17 inbred tester, as well as ear length, number of grain rows per ear and 1,000-grain mass of lines per-se were tested. Among the new inbred lines related to B73, line 260277/2 distinguished itself by a high potential for grain yield when crossed with Mo 17. Inbred lines 260465/1, 260362/1, 260747/4, 260357/13, 260151/2 and 260156/2 had a significantly longer ear than the mean value of all progenies. Compared with progeny mean, lines 260341/7, 260317/4, 260277/2 and 260187/2 had significantly more grain rows per ear, while 260362/1, 260130/5, 260277/2, 260151/2 and 260187/2 had a significantly larger 1,000-grain mass.

scholarly journals Response to Early Generation Genomic Selection for Yield in Wheat

2022 ◽  
Vol 12 ◽  
Author(s):  
David Bonnett ◽  
Yongle Li ◽  
Jose Crossa ◽  
Susanne Dreisigacker ◽  
Bhoja Basnet ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Selection ◽  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Space ◽  
Predictive Ability ◽  
Field Testing ◽  
Wheat Breeding ◽  
Gaussian Kernel ◽  
Training Set ◽  
Early Generation

We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds.

scholarly journals Improvements and inter-laboratory implementation and optimization of blood-based single-locus age prediction models using DNA methylation of the ELOVL2 promoter

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Imene Garali ◽  
Mourad Sahbatou ◽  
Antoine Daunay ◽  
Laura G. Baudrin ◽  
Victor Renault ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Prediction Models ◽  
Single Locus ◽  
Training Set ◽  
Pooled Data ◽  
Locus Model ◽  
Age Dependent ◽  
Statistical Approaches ◽  
Age Prediction ◽  
Testing Set

Abstract Several blood-based age prediction models have been developed using less than a dozen to more than a hundred DNA methylation biomarkers. Only one model (Z-P1) based on pyrosequencing has been developed using DNA methylation of a single locus located in the ELOVL2 promoter, which is considered as one of the best age-prediction biomarker. Although multi-locus models generally present better performances compared to the single-locus model, they require more DNA and present more inter-laboratory variations impacting the predictions. Here we developed 17,018 single-locus age prediction models based on DNA methylation of the ELOVL2 promoter from pooled data of four different studies (training set of 1,028 individuals aged from 0 and 91 years) using six different statistical approaches and testing every combination of the 7 CpGs, aiming to improve the prediction performances and reduce the effects of inter-laboratory variations. Compared to Z-P1 model, three statistical models with the optimal combinations of CpGs presented improved performances (MAD of 4.41–4.77 in the testing set of 385 individuals) and no age-dependent bias. In an independent testing set of 100 individuals (19–65 years), we showed that the prediction accuracy could be further improved by using different CpG combinations and increasing the number of technical replicates (MAD of 4.17).

Efficacy of some insecticide modules against major insect pests and spider population of rice, Oryza sativa L.

ENTOMON ◽  
2018 ◽  
Vol 43 (4) ◽  
pp. 257-262
Author(s):  
Atanu Seni ◽  
Bhimasen Naik
Keyword(s):  
Oryza Sativa ◽  
Grain Yield ◽  
Untreated Control ◽  
Insect Pests ◽  
Oryza Sativa L ◽  
Gall Midge ◽  
Stem Borer ◽  
The Other ◽  
The Third ◽  
Treated Plot

Experiments were carried out to assess some insecticide modules against major insect pests of rice. Each module consists of a basal application of carbofuran 3G @ 1 kg a.i ha-1 at 20 DAT and Rynaxypyr 20 SC @ 30 g a.i ha-1 at 45 DAT except untreated control. All modules differ with each other only in third treatment which was applied in 65 DAT. The third treatment includes: Imidacloprid 17.8 SL @ 27 g a.i ha-1, Pymetrozine 50 WG @ 150 g a.i ha-1, Triflumezopyrim 106 SC @ 27 g a.i ha-1, Buprofezin 25 SC @ 250 g a.i ha-1; Glamore (Imidacloprid 40+Ethiprole 40% w/w) 80 WG @ 100 g a.i. ha-1, Thiacloprid 24 SC @ 60 g a.i ha-1, Azadirachtin 0.03 EC @ 8 g a.i ha-1, Dinotefuran 20 SG@ 40 g a.i ha-1 and untreated control. All the treated plots recorded significantly lower percent of dead heart, white ear- head caused by stem borer and silver shoot caused by gall midge. Module with Pymetrozine 50 WG @ 150 g a.i ha-1 treated plot recorded significantly higher per cent reduction of plant hoppers (>80% over untreated control) and produced higher grain yield (50.75 qha-1) than the other modules. Among the different treated modules the maximum number of spiders was found in Azadirachtin 0.03 EC @ 8 g a.i ha-1 treated module plot followed by other treatments.

UPAYA MENINGKATKAN HASIL BELAJAR IPA MELALUI PENGGUNAAN VIDEO PEMBELAJARAN BAGI SISWA KELAS IV DI SDN 187/IV KOTA JAMBI

2015 ◽  
Vol 8 (2) ◽  
pp. 93
Author(s):  
Juniar Siregar
Keyword(s):  
Action Research ◽  
Research Method ◽  
Research Report ◽  
The Third ◽  
The Media ◽  
The Subject ◽  
The Mean ◽  
Classroom Action Research

This study presents a research report on improving students’ Learning results on IPA through Video. The objective was to find out whether students’ learning result improved when they are taught by using Video. It was conducted using classroom action research method. The subject of the study was the Grade IV students of SDN 187/IV Kota Jambi which is located on Jln. Adi Sucipto RT 05 Kecamatan Jambi Selatan, and the number of the students were 21 persons. The instruments used were test. In analyzing the data, the mean of the students’ score for the on fisrt sycle was 65,4 (42,85%) and the mean on cycle two was 68,5 (37,15%) and the mean of the third cycle was 81,4 (100%). Then it can be concluded that the use of video on learning IPA can improve the students’ learning result. It is suggested that teachers should use video as one of the media to improve students’ learning result on IPA.Keywords : IPA, students’ learning result, video

Effect of N management approaches and planting densities on nitrogen accumulation by transplanted rice

2005 ◽  
Vol 53 (4) ◽  
pp. 405-415 ◽  
Author(s):  
P. Janaki ◽  
T. M. Thiyagarajan
Keyword(s):  
Grain Yield ◽  
Uptake Rate ◽  
Aboveground Biomass ◽  
N Uptake ◽  
Planting Density ◽  
Transplanted Rice ◽  
The Mean ◽  
N Uptake Rate ◽  
Management Approaches ◽  
N Management

Field experiments were conducted during 1998 and 1999 in June-September with rice variety ASD18 at the wetland farm, Tamil Nadu Agricultural University, Coimbatore, India to find out theeffect of N management approaches and planting densities on N accumulation by transplanted rice in a split plot design.The main plot consisted of three plant populations (33, 66 and 100 hills m-2) and the sub-plot treatments of five N management approaches. The results revealed thatthe average N uptake in roots and aboveground biomass progressively increased with growth stages. The mean root and aboveground biomass Nuptake were 26.1 to 130.6 and 6.4 to 17.8 kg ha-1, respectively. The N uptake of grain and straw was higher in theSesbania rostratagreen manuring + 150 kg N treatment, but it was not effective in increasing the grain yield. The mean total N uptake was found to be significantly lower at 33 hills m-2(76.9 kg ha-1) and increased with an increase in planting density (100.9 and 117.2 kg ha-1at 66 and 100 hills m-2density). N application had a significant influence on N uptake and the time course of N uptake in all the SPAD-guided N approaches. A significant regression coefficient was observed between the crop N uptake and grain yield. The relationship between cumulative N uptake at the flowering stage and the grain yield was quadratic at all three densities. The N uptake rate (µN) was maximum during the active tillering to panicle initiation period and declined sharply after that. In general, µNincreased with an increase in planting density and the increase was significant up to the panicle initiation to flowering period.thereafter, the N uptake rate was similar at densities of 66 and 100 hills m-2.

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