The Triticeae Toolbox: Combining Phenotype and Genotype Data to Advance Small‐Grains Breeding

Blake, Victoria; Birkett, Clay; Matthews, David E.; Hane, David L.; Bradbury, Peter J.; Jannink, Jean‐Luc

doi:10.3835/plantgenome2014.12.0099

Cited by 95 publications

(84 citation statements)

References 32 publications

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“…Marker genotypes from 768 six-row spring inbred lines at 3072 biallelic SNP loci were obtained from the Triticeae Toolbox (T3) database (Close et al 2009; Blake et al 2016). The genetic map position of markers was based on the consensus linkage map created by Muñoz-Amatriaín et al (2011).…”

Section: Methodsmentioning

confidence: 99%

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

Neyhart

Tiede

Lorenz

et al. 2017

G3 Genes|Genomes|Genetics

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Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles; however, optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, both the best and worst predicted lines, random lines, criterion-selected lines, or no lines. In the short term, we found that updating with the best predicted lines or the best and worst predicted lines resulted in high prediction accuracy and genetic gain, but in the long term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that an optimal method of updating the training population is also very practical.

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Section: Methodsmentioning

confidence: 99%

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

Neyhart

Tiede

Lorenz

et al. 2017

G3 Genes|Genomes|Genetics

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“…All of these types of populations are available in wheat and their relative merits are discussed elsewhere (Huang and Han, ). Importantly, the coupling of these populations to the new genomic and validations tools (discussed below) have provided an integrated workflow for gene discovery in wheat, exemplified by the Triticeae Toolbox from the TCAP project (Blake et al ., ).…”

Section: Use Of Natural Variation For Trait Discoverymentioning

confidence: 97%

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

2018

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Summary Improving traits in wheat has historically been challenging due to its large and polyploid genome, limited genetic diversity and in‐field phenotyping constraints. However, within recent years many of these barriers have been lowered. The availability of a chromosome‐level assembly of the wheat genome now facilitates a step‐change in wheat genetics and provides a common platform for resources, including variation data, gene expression data and genetic markers. The development of sequenced mutant populations and gene‐editing techniques now enables the rapid assessment of gene function in wheat directly. The ability to alter gene function in a targeted manner will unmask the effects of homoeolog redundancy and allow the hidden potential of this polyploid genome to be discovered. New techniques to identify and exploit the genetic diversity within wheat wild relatives now enable wheat breeders to take advantage of these additional sources of variation to address challenges facing food production. Finally, advances in phenomics have unlocked rapid screening of populations for many traits of interest both in greenhouses and in the field. Looking forwards, integrating diverse data types, including genomic, epigenetic and phenomics data, will take advantage of big data approaches including machine learning to understand trait biology in wheat in unprecedented detail.

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“…The final advantage of both the FAC‐WIN6 and the NSGC Core is data. All TCAP data are publicly available at the TCAP's online database, T3 (http://triticeaetoolbox.org/barley/; Blake et al, 2015). T3 is the successor of The Hordeum Toolbox (THT) (http://hordeumtoolbox.org//; Blake et al, 2012).…”

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confidence: 99%

Registration of the TCAP FAC-WIN6 Barley Panel for Genomewide Association Studies

Belcher

Graebner

Cuesta‐Marcos

et al. 2015

Journal of Plant Registrations

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Facultative/winter six‐row malting barley is a distinct elite germplasm pool and a valuable resource that may prove useful in meeting the challenges of climate change. To preserve its diversity and make it accessible to the research and agricultural communities, the Oregon State University and University of Minnesota barley breeding programs are publicly releasing their winter/facultative six‐row malt advanced lines named the TCAP FAC‐WIN6 (MP‐1, NSL 512632 MAP), which also function as a genomewide association studies (GWAS) panel. The FAC‐WIN6 contains 296 lines—180 facultative and 116 winter—selected for disease resistance, malt quality, and general agronomic performance. To date, all lines have data for 6892 single nucleotide polymorphism (SNP) markers and phenotypic data from six experiments (representing 3 yr, eight locations), including traits such as malt quality, disease resistance, nitrogen use efficiency, and winter hardiness. The FAC‐WIN6 is one of 24 barley and wheat mapping panels and populations from the USDA‐ARS Triticeae Coordinated Agricultural Project (TCAP). As such, all of the TCAP FAC‐WIN6 genotypic and phenotypic data can be freely downloaded from the TCAP's online database, T3 (http://triticeaetoolbox.org/barley/). Preliminary GWAS have identified novel loci for wort β‐glucan, low temperature tolerance, and disease resistance. Given these results, the FAC‐WIN6 is a singular resource both for future winter six‐row barley breeding and for identifying and deploying genes for key barley traits in all backgrounds.

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The Triticeae Toolbox: Combining Phenotype and Genotype Data to Advance Small‐Grains Breeding

Abstract: The Triticeae

Cited by 95 publications

References 32 publications

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

Registration of the TCAP FAC-WIN6 Barley Panel for Genomewide Association Studies

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