Browsing by Author "Arojju SK"

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    Estimation of quantitative genetic parameters for dry matter yield and vegetative persistence-related traits in a white clover training population
    (Wiley Periodicals LLC on behalf of Crop Science Society of America, 2022-11-21) Ehoche OG; Arojju SK; Cousins G; O'Connor JR; Maw B; Tate JA; Lockhart PJ; Jahufer MZZ; Griffiths AG; Resende Jr M
    White clover (Trifolium repens L.), an economically important forage legume in temperate pastures, provides quality herbage and plant-available nitrogen. Enhancing breeding efforts to improve dry matter (DM) yield and vegetative persistence will increase on-farm value of this forage. To increase genetic gain for such traits, breeding tools like genomic selection have proven to be highly valuable in other crops. However, its success relies on a sufficiently large training population and key fundamentals of selective breeding, that is, presence of additive variation. We investigated quantitative genetic parameters for spring DM yield and vegetative persistence in a white clover training population comprising 200 half-sibling (HS) families. This population was established in a replicated cattle-grazed, mixed-sward field trial at two contrasting locations and assessed for spring DM yield and stolon-related vegetative persistence traits over a 3-yr period. The additive variation and genotype × environment interactions, comprising the effects from year, season, and location were significant (P <.05) for most traits. Narrow-sense heritability for all traits ranged from low (.13; post-summer stolon branches) to high (.73; leaf size) and there was a positive phenotypic correlation (.28) between spring DM yield and stolon number. These results indicate that both spring DM yield and persistence can be concurrently improved through selective breeding in the current population. We also demonstrated that applying a high selection pressure produces the highest predicted genetic gain. There is, however, a trade-off between genetic gain and diversity in the population for the long-term success of a breeding program.
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    Genomic selection shows improved expected genetic gain over phenotypic selection of agronomic traits in allotetraploid white clover.
    (Springer Nature, 2025-01-23) Ehoche OG; Arojju SK; Jahufer MZZ; Jauregui R; Larking AC; Cousins G; Tate JA; Lockhart PJ; Griffiths AG
    Genomic selection using white clover multi-year-multi-site data showed predicted genetic gains through integrating among-half-sibling-family phenotypic selection and within-family genomic selection were up to 89% greater than half-sibling-family phenotypic selection alone. Genomic selection, an effective breeding tool used widely in plants and animals for improving low-heritability traits, has only recently been applied to forages. We explored the feasibility of implementing genomic selection in white clover (Trifolium repens L.), a key forage legume which has shown limited genetic improvement in dry matter yield (DMY) and persistence traits. We used data from a training population comprising 200 half-sibling (HS) families evaluated in a cattle-grazed field trial across three years and two locations. Combining phenotype and genotyping-by-sequencing (GBS) data, we assessed different two-stage genomic prediction models, including KGD-GBLUP developed for low-depth GBS data, on DMY, growth score, leaf size and stolon traits. Predictive abilities were similar among the models, ranging from -0.17 to 0.44 across traits, and remained stable for most traits when reducing model input to 100-120 HS families and 5500 markers, suggesting genomic selection is viable with fewer resources. Incorporating a correlated trait with a primary trait in multi-trait prediction models increased predictive ability by 28-124%. Deterministic modelling showed integrating among-HS-family phenotypic selection and within-family genomic selection at different selection pressures estimated up to 89% DMY genetic gain compared to phenotypic selection alone, despite a modest predictive ability of 0.3. This study demonstrates the potential benefits of combining genomic and phenotypic selection to boost genetic gains in white clover. Using cost-effective GBS paired with a prediction model optimized for low read-depth data, the approach can achieve prediction accuracies comparable to traditional models, providing a viable path for implementing genomic selection in white clover.