Design of training populations for selective phenotyping in genomic prediction

Akdemir, Deniz; Isidro-Sánchez, Julio

doi:10.1038/s41598-018-38081-6

Cited by 93 publications

(117 citation statements)

References 64 publications

(74 reference statements)

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“…In plant breeding, phenotyping is one of the major current bottlenecks and the optimization or minimization of phenotyping costs within breeding programs is needed (Akdemir and Isidro-Sánchez, 2019). Therefore, the maximization of genomic prediction accuracy can be directly translated into reduced phenotyping costs (Akdemir and Isidro-Sánchez, 2019;Jarquin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments

Vojgani

Pook

Martini

et al. 2020

Preprint

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We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested in six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Over all studied traits, locations, and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from −0.3 to +27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP. The average increase across traits and locations was 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for “sparse testing” approaches in which only a subset of the lines/hybrids of interest is observed at each location.Key MessageThe prediction accuracy of genomic prediction of phenotypes can be increased by only including top ranked pairwise SNP interactions into the prediction models.

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Section: Introductionmentioning

confidence: 99%

Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments

Vojgani

Pook

Martini

et al. 2020

Preprint

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“…We expect this would further increase the genetic gain for the same level of investment or require less investment for the same genetic gain, but increase the complexity of optimization. Such optimizations were shown to increase the accuracy of genomic prediction up to 20% with small sample sizes in plant breeding [31,32]. Similarly, selective genotyping of cows from the distribution tails has been shown to increase the accuracy of genomic prediction by 15% [33].…”

Section: Discussionmentioning

confidence: 99%

Genomic selection for any dairy breeding program via optimized investment in phenotyping and genotyping

Obšteter

Jenko²

2020

Preprint

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Background: This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to achieve desired selection accuracy for breeders and quality for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. However, genomic selection requires a large initial investment, which limits the adoption of genomic selection for some breeding programmes. Methods: We simulated a case-study of a small dairy population with a number of scenarios under equal available resources. The conventional progeny testing scenario had 11 phenotype records per lactation. In genomic scenarios, we reduced phenotyping to between 10 and 1 phenotype records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without initial training population for genomic selection. Results: Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing investment in genotyping, despite reduced phenotyping, and with high‑genotyping scenarios not even using all the available resources. Compared to the conventional scenario, genomic scenarios also increased accuracy for young non‑phenotyped male and female candidates, and cows. Conclusions: This study shows that breeding programmes should optimize investment into phenotyping and genotyping to maximise return on investment. Our results suggest that any dairy breeding programme using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

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“…However, acquisition of accurate and precise phenotyping data on sizeable individuals presents a major bottleneck in plant breeding programmes. This has stimulated adoption of new breeding techniques that optimize phenotyping requirements for improving complex traits controlled by a number of small-effect QTL (Akdemir and Isidro-Sanchez, 2019). Genomic selection (GS) improves genetic gain by enhancing selection intensity (i) and selection accuracy (r) and reducing the breeding cycle length (L).…”

Section: Genomic Selectionmentioning

confidence: 99%

Genomic interventions for sustainable agriculture

Bohra

Jha

Godwin

et al. 2020

Plant Biotechnology Journal

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Agricultural production faces a Herculean challenge to feed the increasing global population. Food production systems need to deliver more with finite land and water resources while exerting the least negative influence on the ecosystem. The unpredictability of climate change and consequent changes in pests/pathogens dynamics aggravate the enormity of the challenge. Crop improvement has made significant contributions towards food security, and breeding climate-smart cultivars are considered the most sustainable way to accelerate food production. However, a fundamental change is needed in the conventional breeding framework in order to respond adequately to the growing food demands. Progress in genomics has provided new concepts and tools that hold promise to make plant breeding procedures more precise and efficient. For instance, reference genome assemblies in combination with germplasm sequencing delineate breeding targets that could contribute to securing future food supply. In this review, we highlight key breakthroughs in plant genome sequencing and explain how the presence of these genome resources in combination with gene editing techniques has revolutionized the procedures of trait discovery and manipulation. Adoption of new approaches such as speed breeding, genomic selection and haplotype-based breeding could overcome several limitations of conventional breeding. We advocate that strengthening varietal release and seed distribution systems will play a more determining role in delivering genetic gains at farmer's field. A holistic approach outlined here would be crucial to deliver steady stream of climate-smart crop cultivars for sustainable agriculture.

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Design of training populations for selective phenotyping in genomic prediction

Cited by 93 publications

References 64 publications

Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments

Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments

Genomic selection for any dairy breeding program via optimized investment in phenotyping and genotyping

Genomic interventions for sustainable agriculture

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