scholarly journals Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes

Genes ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1270 ◽  
Author(s):  
Jia Guo ◽  
Jahangir Khan ◽  
Sumit Pradhan ◽  
Dipendra Shahi ◽  
Naeem Khan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Genomic Prediction ◽  
Genotyping By Sequencing ◽  
Triticum Aestivum L ◽  
Yield Component ◽  
Soft Wheat ◽  
Response To Selection ◽  
Best Linear Unbiased ◽  
Trait Model ◽  
Variable Water

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.

scholarly journals Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods

2019 ◽  
Vol 10 ◽  
Author(s):  
Osval A. Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Roberto Tuberosa ◽  
Marco Maccaferri ◽  
Giuseppe Sciara ◽  
...  
Keyword(s):  
Deep Learning ◽  
Durum Wheat ◽  
Genomic Prediction ◽  
Best Linear Unbiased Predictor ◽  
Learning Methods ◽  
Best Linear Unbiased

scholarly journals Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

2021 ◽  
Author(s):  
Vu Thanh Nguyen Thanh ◽  
Nguyen Hong Nguyen ◽  
Phuc Huu Tran ◽  
Oanh Thi Phuong Kim ◽  
Sang Van Nguyen
Keyword(s):  
Deep Learning ◽  
Disease Resistance ◽  
Survival Time ◽  
Genomic Prediction ◽  
Best Linear Unbiased Prediction ◽  
Edwardsiella Ictaluri ◽  
Linear Unbiased Prediction ◽  
Survival Status ◽  
Striped Catfish ◽  
Best Linear Unbiased

Assessments of genomic prediction accuracies using machine and deep learning methods are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a non-linear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e., KAML) and deep learning (i.e., DeepGP) together with the four common methods (PBLUP, GBLUP, ssGBLUP and BayesR) were conducted for two main disease resistance traits (i.e., survival status coded as 0 and 1 and survival time, i.e., days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 Single Nucleotide Polymorphism (SNPs). The results showed that KAML outperformed GBLUP and ssGBLUP, with the increases in the prediction accuracies for both traits by 5.1 - 47.7%. However, the prediction accuracies obtained from KAML were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 0.2 33.2% in all the methods used. On the other hand, there were no significant increases in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001) were not largely different from those obtained from the whole set of 14,154 SNPs. In all our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that there are prospects for the application of genomic selection to increase disease resistance to Edwardsiella ictaluri in striped catfish breeding programs

scholarly journals Seeding Rate Effects on Hybrid Spring Wheat Yield, Yield Components and Quality

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1240
Author(s):  
Peder K. Schmitz ◽  
Joel K. Ransom
Keyword(s):  
Spring Wheat ◽  
Yield Components ◽  
Wheat Yield ◽  
Triticum Aestivum L ◽  
Yield Component ◽  
Economic Feasibility ◽  
Protein Yield ◽  
Seeding Rate ◽  
Hard Red Spring Wheat ◽  
End Use

Agronomic practices, such as planting date, seeding rate, and genotype, commonly influence hard red spring wheat (HRSW, Triticum aestivum L. emend. Thell.) production. Determining the agronomic optimum seeding rate (AOSR) of newly developed hybrids is needed as they respond to seeding rates differently from inbred cultivars. The objectives of this research were to determine the AOSR of new HRSW hybrids, how seeding rate alters their various yield components, and whether hybrids offer increased end-use quality, compared to conventional cultivars. The performance of two cultivars (inbreds) and five hybrids was evaluated in nine North Dakota environments at five seeding rates in 2019−2020. Responses to seeding rate for yield and protein yield differed among the genotypes. The AOSR ranged from 3.60 to 5.19 million seeds ha−1 and 2.22 to 3.89 million seeds ha−1 for yield and protein yield, respectively. The average AOSR for yield for the hybrids was similar to that of conventional cultivars. However, the maximum protein yield of the hybrids was achieved at 0.50 million seeds ha−1 less than that of the cultivars tested. The yield component that explained the greatest proportion of differences in yield as seeding rates varied was kernels spike−1 (r = 0.17 to 0.43). The end-use quality of the hybrids tested was not superior to that of the conventional cultivars, indicating that yield will likely be the determinant of the economic feasibility of any future released hybrids.

scholarly journals Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 266
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deuk Hwan Lee ◽  
Chungil Cho ◽  
Taejeong Choi ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Beef Cattle ◽  
Genomic Prediction ◽  
Quantitative Trait ◽  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Best Linear Unbiased

The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.

scholarly journals Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses

2021 ◽  
Author(s):  
Maria Y. Gonzalez ◽  
Yusheng Zhao ◽  
Yong Jiang ◽  
Nils Stein ◽  
Antje Habekuss ◽  
...  
Keyword(s):  
Genetic Resources ◽  
Mosaic Virus ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Ex Situ ◽  
Yellow Mosaic Virus ◽  
Plant Responses ◽  
Resource Center ◽  
Digital Resource ◽  
Best Linear Unbiased

AbstractKey messageGenomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers.AbstractPhenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against theBarley yellow mosaic virusesfor populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions forBarley yellow mosaic virus(BaYMV) and of 1771 accessions forBarley mild mosaic virus(BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to ~ 5% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.

scholarly journals A review of deep learning applications for genomic selection

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Osval Antonio Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Paulino Pérez-Rodríguez ◽  
José Alberto Barrón-López ◽  
Johannes W. R. Martini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Plant Breeding ◽  
Genomic Selection ◽  
Genomic Prediction ◽  
Mixed Model ◽  
Prediction Models ◽  
Genetic Effect ◽  
Training Data ◽  
Additive Genetic Effect ◽  
Main Body

Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. Main body We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. Conclusions The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

scholarly journals Yield component variation in winter wheat grown under drought stress

2000 ◽  
Vol 80 (4) ◽  
pp. 739-745 ◽  
Author(s):  
B. L. Duggan ◽  
D. R. Domitruk ◽  
D. B. Fowler
Keyword(s):  
Drought Stress ◽  
Winter Wheat ◽  
Grain Yield ◽  
Triticum Aestivum L ◽  
Yield Component ◽  
Yield Potential ◽  
High Yield ◽  
Crop Water ◽  
Kernel Number ◽  
High Yield Potential

Crops produced in the semiarid environment of western Canada are subjected to variable and unpredictable periods of drought stress. The objective of this study was to determine the inter-relationships among yield components and grain yield of winter wheat (Triticum aestivum L) so that guidelines could be established for the production of cultivars with high yield potential and stability. Five hard red winter wheat genotypes were grown in 15 field trials conducted throughout Saskatchewan from 1989–1991. Although this study included genotypes with widely different yield potential and yield component arrangements, only small differences in grain yield occurred within trials under dryland conditions. High kernel number, through greater tillering, was shown to be an adaptation to low-stress conditions. The ability of winter wheat to produce large numbers of tillers was evident in the spring in all trials; however, this early season potential was not maintained due to extensive tiller die-back. Tiller die-back often meant that high yield potential genotypes became sink limiting with reduced ability to respond to subsequent improvements in growing season weather conditions. As tiller number increased under more favourable crop water conditions genetic limits in kernels spike−1 became more identified with yield potential. It is likely then, that tillering capacity per se is less important in winter wheat than the development of vigorous tillers with numerous large kernels spike−1. For example, the highest yielding genotype under dryland conditions was a breeding line, S86-808, which was able to maintain a greater sink capacity as a result of a higher number of larger kernels spike−1. It appears that without yield component compensation, a cultivar can be unresponsive to improved crop water conditions (stable) or it can have a high mean yield, but it cannot possess both characteristics. Key words: Triticum aestivum L., wheat, drought stress, kernel weight, kernel number, spike density, grain yield

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