DeltaGen: A Comprehensive Decision Support Tool for Plant Breeders

Jahufer, M. Z. Z.; Luo, Dongwen

doi:10.2135/cropsci2017.07.0456

Cited by 43 publications

(47 citation statements)

References 41 publications

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“…Broad-sense heritability (h 2 ) was estimated using DeltaGen ( Jahufer and Luo, 2018) The long-term precipitation averages for the Waikato, Manawatu, and Canterbury environments were +4.54, −0.61, and +22.13% from the mean precipitation for the 3 yr of the study, respectively. On individual years, precipitation was higher than the long-term average by 25% in 2017 and 11.3% in 2016 in Waikato and US data subset, the autumn, summer, and spring slopes significantly deviated from zero, and the respective values for percentage gain per decade were 0.274 ± 0.12, 0.166 ± 0.063, and 0.121 ± 0.01% decade −1 (Table 1).…”

Section: Resultsmentioning

confidence: 99%

Rate of Genetic Gain for Persistence to Grazing and Dry Matter Yield in White Clover across 90 Years of Cultivar Development

et al. 2019

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537 RESEARCHW hite clover (Trifolium repens L.) is a major contributor of productivity and ecological services in pastures and rangelands worldwide. The advantages of including and maintaining white clover in a pasture are well documented (Chapman et al., 1993). In New Zealand alone, it is estimated that white clover adds approximately NZ$3 billion attributable to the seed industry, N fixation, and honey production . In grazed swards, white clover provides up to 400 kg N ha −1 via biological N fixation (Crush, 1987). In terms of area, white clover is the predominant temperate forage legume serving as a companion to temperate grasses in grazing systems (Laidlaw and Teuber, 2001). White clover in mixed sward pastures is estimated to occupy ?15 million ha in Australasia and 5 million ha in the United States. Globally, renewal of white clover is estimated at 3 to 4 million ha yr −1 (Mather et al., 1996).White clover breeding is important worldwide and is recognized as a priority in many countries with a pastoral-based livestock productivity system ( Jahufer et al., 2012). For example, the last checklist of white clover varieties (Caradus and Woodfield, 1997) described a total of 326 cultivars in relation to their maintainer institution, origin and breeding procedures used, agronomic performance, and disease susceptibility, among other characteristics, depending on the information available. This checklist comprises information collected from cultivars from 32 ABSTRACT White clover (Trifolium repens L.) is a major contributor of productivity and ecological services in pastures and rangelands worldwide. White clover breeding is recognized as a priority in many countries with a pastoral-based livestock productivity system. The objective of this study was to provide an updated estimate of the rate of change in genotypic value attributable to population improvement in white clover, using a set of 80 cultivars released between 1920 and 2010 by public and private plant breeding programs across 17 countries, in an experiment to evaluate forage yield across three locations in New Zealand. Overall, some New Zealand cultivars released in the 2000s were highly adapted to more stable testing environments. Geographic origin resulted in differential rates of gain. A segmental regression comparing the pre-1965 and post-1965 rates of gain was performed. The pre-1965 regression line for white clover dry matter yield resulted in an absolute rate of increase of 0.031 g m −2 yr −1 decade −1 or 0.087% decade −1 , whereas the post-1965 line resulted in an absolute rate of increase of 0.058 g m −2 yr −1 decade −1 or 0.162% decade −1 . White clover content resulted in a pre-1965 absolute rate of increase of 0.011% content decade −1 or 0.032% decade −1 on average, and the post-1965 line indicated an absolute rate of increase of 0.04% content decade −1 or 0.121% decade −1 on average. The comparisons between pre-1965 and post-1965 indicated a twofold increase for white clover dry matter yield and a fourfold increase in rates of gain for white c...

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Section: Resultsmentioning

confidence: 99%

Rate of Genetic Gain for Persistence to Grazing and Dry Matter Yield in White Clover across 90 Years of Cultivar Development

et al. 2019

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“…Instead of calculating R for individual crosses via the breeder's equation, genetic gains for a breeding program as a whole can be simulated vis-à-vis the costs and time required. Simulation packages such as QU-GENE (Podlich and Cooper 1998), AlphaSim (Faux et al 2016), and DeltaGen (Jahufer and Luo 2018) have been developed to model quantitative variation and selection, and such software has been used to compare breeding schemes (Wang et al 2003;Jahufer and Luo 2018). In the future, simulation tools need to be able to incorporate existing germplasm, molecular marker data, and phenotypic data as input variables.…”

Section: Borrowed: Focus On Reliability and The Least Significant Difmentioning

confidence: 99%

Reinventing quantitative genetics for plant breeding: something old, something new, something borrowed, something BLUE

Bernardo

2020

Heredity

111

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The goals of quantitative genetics differ according to its field of application. In plant breeding, the main focus of quantitative genetics is on identifying candidates with the best genotypic value for a target population of environments. Keeping quantitative genetics current requires keeping old concepts that remain useful, letting go of what has become archaic, and introducing new concepts and methods that support contemporary breeding. The core concept of continuous variation being due to multiple Mendelian loci remains unchanged. Because the entirety of germplasm available in a breeding program is not in Hardy-Weinberg equilibrium, classical concepts that assume random mating, such as the average effect of an allele and additive variance, need to be retired in plant breeding. Doing so is feasible because with molecular markers, mixed-model approaches that require minimal genetic assumptions can be used for best linear unbiased estimation (BLUE) and prediction. Plant breeding would benefit from borrowing approaches found useful in other disciplines. Examples include reliability as a new measure of the influence of genetic versus nongenetic effects, and operations research and simulation approaches for designing breeding programs. The genetic entities in such simulations should not be generic but should be represented by the pedigrees, marker data, and phenotypic data for the actual germplasm in a breeding program. Over the years, quantitative genetics in plant breeding has become increasingly empirical and computational and less grounded in theory. This trend will continue as the amount and types of data available in a breeding program increase.Plant breeders may sometimes think that quantitative genetics was developed for the purpose of enhancing plant and animal improvement. After all, most economically

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“…Data analyses were performed across the five populations, for individual locations and across the two locations, using the restricted maximum likelihood (REML) method, by fitting a linear mixed model in GenStat (Payne et al 2009). Analyses were also performed on the five populations individually, by fitting linear mixed models in DeltaGen (Jahufer and Luo 2018). Family, family-by-location interaction, replicates, rows and columns were considered as random effects, whereas location, population and repeated checks were considered as fixed effects.…”

Section: Statistical Models and Variance Componentsmentioning

confidence: 99%

“…The genotypic correlation among traits was estimated as proposed by Falconer (1960). Multivariate analysis of variance (MANOVA) was performed in DeltaGen (Jahufer and Luo 2018), using the multilocation trait data from half-sib families, to estimate variance and covariance among traits:…”

Section: Genotypic and Phenotypic Correlationmentioning

confidence: 99%

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass

Arojju

Cao²,

Jahufer³

et al. 2020

G3 Genes|Genomes|Genetics

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Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P , 0.05) family variance was detected for all nutritive traits and genomic heritability (h 2 g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCp genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.

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DeltaGen: A Comprehensive Decision Support Tool for Plant Breeders

Cited by 43 publications

References 41 publications

Rate of Genetic Gain for Persistence to Grazing and Dry Matter Yield in White Clover across 90 Years of Cultivar Development

Rate of Genetic Gain for Persistence to Grazing and Dry Matter Yield in White Clover across 90 Years of Cultivar Development

Reinventing quantitative genetics for plant breeding: something old, something new, something borrowed, something BLUE

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass

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