scholarly journals Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1221 ◽  
Author(s):  
Sirjan Sapkota ◽  
J. Lucas Boatwright ◽  
Kathleen Jordan ◽  
Richard Boyles ◽  
Stephen Kresovich
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Grain Composition ◽  
Genomic Heritability ◽  
Gross Energy ◽  
High Prediction ◽  
Highly Correlated ◽  
Sorghum Grain

Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits—amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.

scholarly journals Multi-trait regressor stacking increased genomic prediction accuracy of sorghum grain composition

2020 ◽  
Author(s):  
Sirjan Sapkota ◽  
Jon Lucas Boatwright ◽  
Kathleen Jordan ◽  
Richard Boyles ◽  
Stephen Kresovich
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Recombinant Inbred Lines ◽  
Animal Nutrition ◽  
Cereal Crop ◽  
Grain Composition ◽  
Genomic Heritability ◽  
Gross Energy ◽  
Highly Correlated ◽  
Sorghum Grain

AbstractCereal grains, primarily composed of starch, protein, and fat, are major source of staple for human and animal nutrition. Sorghum, a cereal crop, serves as a dietary staple for over half a billion people in the semi-arid tropics of Africa and South Asia. Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. Therefore, the use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits: amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Predicting whole environments by training with remaining environments in BMORS yielded higher average prediction accuracy than from STSE model. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.

scholarly journals Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongdong Li ◽  
Zhiqiang Zhou ◽  
Xiaohuan Lu ◽  
Yong Jiang ◽  
Guoliang Li ◽  
...  
Keyword(s):  
Grain Yield ◽  
Recombinant Inbred ◽  
Fixed Effects ◽  
Prediction Accuracy ◽  
Genetic Architecture ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Hybrid Performance

Heterosis contributes a big proportion to hybrid performance in maize, especially for grain yield. It is attractive to explore the underlying genetic architecture of hybrid performance and heterosis. Considering its complexity, different from former mapping method, we developed a series of linear mixed models incorporating multiple polygenic covariance structures to quantify the contribution of each genetic component (additive, dominance, additive-by-additive, additive-by-dominance, and dominance-by-dominance) to hybrid performance and midparent heterosis variation and to identify significant additive and non-additive (dominance and epistatic) quantitative trait loci (QTL). Here, we developed a North Carolina II population by crossing 339 recombinant inbred lines with two elite lines (Chang7-2 and Mo17), resulting in two populations of hybrids signed as Chang7-2 × recombinant inbred lines and Mo17 × recombinant inbred lines, respectively. The results of a path analysis showed that kernel number per row and hundred grain weight contributed the most to the variation of grain yield. The heritability of midparent heterosis for 10 investigated traits ranged from 0.27 to 0.81. For the 10 traits, 21 main (additive and dominance) QTL for hybrid performance and 17 dominance QTL for midparent heterosis were identified in the pooled hybrid populations with two overlapping QTL. Several of the identified QTL showed pleiotropic effects. Significant epistatic QTL were also identified and were shown to play an important role in ear height variation. Genomic selection was used to assess the influence of QTL on prediction accuracy and to explore the strategy of heterosis utilization in maize breeding. Results showed that treating significant single nucleotide polymorphisms as fixed effects in the linear mixed model could improve the prediction accuracy under prediction schemes 2 and 3. In conclusion, the different analyses all substantiated the different genetic architecture of hybrid performance and midparent heterosis in maize. Dominance contributes the highest proportion to heterosis, especially for grain yield, however, epistasis contributes the highest proportion to hybrid performance of grain yield.

Diverse sources of resistance to Spodoptera litura (F.) in groundnut

2019 ◽  
Vol 79 (01S) ◽  
Author(s):  
M. A. Saleem ◽  
G. K. Naidu ◽  
H. L. Nadaf ◽  
P. S. Tippannavar
Keyword(s):  
Recombinant Inbred ◽  
Spodoptera Litura ◽  
Hot Spot ◽  
Recombinant Inbred Lines ◽  
Insect Pest ◽  
Inbred Lines ◽  
Leaf Damage ◽  
Botanical Variety ◽  
Resistant Genotypes ◽  
Botanical Varieties

Spodoptera litura an important insect pest of groundnut causes yield loss up to 71% in India. Though many effective chemicals are available to control Spodoptera, host plant resistance is the most desirable, economic and eco-friendly strategy. In the present study, groundnut mini core (184), recombinant inbred lines (318) and elite genotypes (44) were studied for their reaction to Spodoptera litura under hot spot location at Dharwad. Heritable component of variation existed for resistance to Spodoptera in groundnut mini core, recombinant inbred lines and elite genotypes indicating scope for selection of Spodoptera resistant genotypes. Only 29 (15%) genotypes belonging to hypogaea, fastigiata and hirsuta botanical varieties under mini core set, 15 transgressive segregants belonging to fastigiata botanical variety among 318 recombinant inbred lines and three genotypes belonging to hypogaea and fastigiata botanical varieties under elite genotypes showed resistance to Spodoptera litura with less than 10% leaf damage. Negative correlation existed between resistance to Spodoptera and days to 50 per cent flowering indicating late maturing nature of resistant genotypes. Eight resistant genotypes (ICG 862, ICG 928, ICG 76, ICG 2777, ICG 5016, ICG 12276, ICG 4412 and ICG 9905) under hypogaea botanical variety also had significantly higher pod yield. These diverse genotypes could serve as potential donors for incorporation of Spodoptera resistance in groundnut.

scholarly journals QTL analysis of seed dormancy in Arabidopsis using recombinant inbred lines and MQM mapping

Heredity ◽  
1997 ◽  
Vol 79 (2) ◽  
pp. 190-200 ◽  
Author(s):  
Wybe van der Schaar ◽  
Carlos Alonso-Blanco ◽  
Karen M Léon-Kloosterziel ◽  
Ritsert C Jansen ◽  
Johan W van Ooijen ◽  
...  
Keyword(s):  
Seed Dormancy ◽  
Qtl Analysis ◽  
Recombinant Inbred ◽  
Recombinant Inbred Lines ◽  
Inbred Lines

scholarly journals A novel non-trichome based whitefly resistance QTL in Solanum galapagense

Euphytica ◽  
2021 ◽  
Vol 217 (3) ◽  
Author(s):  
Joris Santegoets ◽  
Marcella Bovio ◽  
Wendy van’t Westende ◽  
Roeland E. Voorrips ◽  
Ben Vosman
Keyword(s):  
Frankliniella Occidentalis ◽  
Recombinant Inbred Lines ◽  
Glandular Trichomes ◽  
Glandular Trichome ◽  
Trialeurodes Vaporariorum ◽  
Inbred Lines ◽  
Adult Survival ◽  
Greenhouse Whitefly ◽  
Major Threat ◽  
Type Iv

AbstractThe greenhouse whitefly Trialeurodes vaporariorum is a major threat in tomato cultivation. In greenhouse grown tomatoes non-trichome based whitefly resistance may be better suited than glandular trichome based resistance as glandular trichomes may interfere with biocontrol, which is widely used. Analysis of a collection of recombinant inbred lines derived from a cross between Solanum lycopersicum and Solanum galapagense showed resistance to the whitefly T. vaporariorum on plants without glandular trichomes type IV. The resistance affected whitefly adult survival (AS), but not oviposition rate. This indicates that S. galapagense, in addition to trichome based resistance, also carries non-trichome based resistance components. The effectiveness of the non-trichome based resistance appeared to depend on the season in which the plants were grown. The resistance also had a small but significant effect on the whitefly Bemisia tabaci, but not on the thrips Frankliniella occidentalis. A segregating F2 population was created to map the non-trichome based resistance. Two Quantitative trait loci (QTLs) for reduced AS of T. vaporariorum were mapped on chromosomes 12 and 7 (explaining 13.9% and 6.0% of the variance respectively). The QTL on chromosome 12 was validated in F3 lines.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

scholarly journals Fermentation of Whole Grain Sorghum (Sorghum bicolor (L.) Moench) with Different Dry Matter Concentrations: Effect on the Apparent Total Tract Digestibility of Energy, Crude Nutrients and Minerals in Growing Pigs

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1199
Author(s):  
Reinhard Puntigam ◽  
Julia Slama ◽  
Daniel Brugger ◽  
Karin Leitner ◽  
Karl Schedle ◽  
...  
Keyword(s):  
Dry Matter ◽  
Latin Square ◽  
Grain Sorghum ◽  
Whole Grains ◽  
Growing Pigs ◽  
Whole Grain ◽  
Inorganic P ◽  
Gross Energy ◽  
Sorghum Grain ◽  
Sorghum Bicolor L

This study investigated the effects of sorghum ensiled as whole grains with different dry matter concentrations on the apparent total tract digestibility (ATTD) of energy, crude nutrients and minerals in growing pigs. Whole grain sorghum batches with varying dry matter (DM) concentrations of 701 (S1), 738 (S2) and 809 g kg−1 (S3) due to different dates of harvest from the same arable plot, were stored in air-tight kegs (6 L) for 6 months to ensure complete fermentation. Subsequently, 9 crossbred barrows (34.6 ± 1.8 kg; (Duroc x Landrace) × Piétrain)) were used in a 3 × 3 Latin square feeding experiment. Diets were based on the respective sorghum grain silage and were supplemented with additional amino acids, minerals and vitamins to meet or exceed published feeding recommendations for growing pigs. The ATTD of gross energy, dry matter, organic matter, nitrogen-free extracts, and crude ash were higher in S1 compared to S3 treatments (p ≤ 0.05), while S2 was intermediate. Pigs fed S1 showed significantly higher ATTD of phosphorus (P) compared to all other groups while ATTD of calcium was unaffected irrespective of the feeding regime. In conclusion, growing pigs used whole grain sorghum fermented with a DM concentration of 701 g kg−1 (S1) most efficiently. In particular, the addition of inorganic P could have been reduced by 0.39 g kg−1 DM when using this silage compared to the variant with the highest DM value (809 g kg−1).

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