Genome-wide association study on legendre random regression coefficients for the growth and feed intake trajectory on Duroc Boars

Howard, Jeremy; Jiao, Shihui; Tiezzi, Francesco; Huang, Yijian; Gray, Kent; Maltecca, Christian

doi:10.1186/s12863-015-0218-8

Cited by 50 publications

(38 citation statements)

References 53 publications

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“…The ssGWAS model allows all the available data, including genetic markers, phenotype records and pedigree information, to be examined simultaneously in one step. Many studies have recently validated this method and effectively implemented ssGWAS in pigs (Howard et al ., ; Wu et al ., ) and other species (Silva et al ., ), achieving a greater power and more precise estimates than other models. The ssGWAS method is currently being tested in plant species by exploring the use of raw data (individual replicates) as opposed to standard approaches that use the means (across replicates), which leads to error inflation for the association analysis (unpublished data).…”

Section: Going Beyond Conventional Mappingmentioning

confidence: 99%

Salt stress under the scalpel – dissecting the genetics of salt tolerance

et al. 2019

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Summary Salt stress limits the productivity of crops grown under saline conditions, leading to substantial losses of yield in saline soils and under brackish and saline irrigation. Salt tolerant crops could alleviate these losses while both increasing irrigation opportunities and reducing agricultural demands on dwindling freshwater resources. However, despite significant efforts, progress towards this goal has been limited, largely because of the genetic complexity of salt tolerance for agronomically important yield‐related traits. Consequently, the focus is shifting to the study of traits that contribute to overall tolerance, thus breaking down salt tolerance into components that are more genetically tractable. Greater consideration of the plasticity of salt tolerance mechanisms throughout development and across environmental conditions furthers this dissection. The demand for more sophisticated and comprehensive methodologies is being met by parallel advances in high‐throughput phenotyping and sequencing technologies that are enabling the multivariate characterisation of vast germplasm resources. Alongside steady improvements in statistical genetics models, forward genetics approaches for elucidating salt tolerance mechanisms are gaining momentum. Subsequent quantitative trait locus and gene validation has also become more accessible, most recently through advanced techniques in molecular biology and genomic analysis, facilitating the translation of findings to the field. Besides fuelling the improvement of established crop species, this progress also facilitates the domestication of naturally salt tolerant orphan crops. Taken together, these advances herald a promising era of discovery for research into the genetics of salt tolerance in plants.

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Section: Going Beyond Conventional Mappingmentioning

confidence: 99%

Salt stress under the scalpel – dissecting the genetics of salt tolerance

et al. 2019

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“…Thus, these models utilize a few parameters to describe the full covariance and are much more computationally efficient. In animal breeding, RR models have been used extensively to estimate heritabilities and perform pedigree‐based prediction of important longitudinal traits such as growth, feed intake, fat, and milk production (Bermejo et al., ; Bohmanova et al., ; Costa et al., ; Howard et al., ; Nobre et al., ; Wetten et al., ).…”

Section: Introductionmentioning

confidence: 99%

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high‐throughput phenotyping

2018

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The accessibility of high‐throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, has provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study was to demonstrate the utility of random regression ( RR ) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice ( Oryza sativa ). An estimate of shoot biomass, projected shoot area ( PSA ), was recorded over a period of 20 days for a panel of 357 diverse rice accessions using an image‐based greenhouse phenotyping platform. A RR that included a fixed second‐order Legendre polynomial, a random second‐order Legendre polynomial for the additive genetic effect, a first‐order Legendre polynomial for the environmental effect, and heterogeneous residual variances was used to model PSA trajectories. The utility of the RR model over a single time point ( TP ) approach, where PSA is fit at each time point independently, is shown through four prediction scenarios. In the first scenario, the RR and TP approaches were used to predict PSA for a set of lines lacking phenotypic data. The RR approach showed a 11.6% increase in prediction accuracy over the TP approach. Much of this improvement could be attributed to the greater additive genetic variance captured by the RR approach. The remaining scenarios focused forecasting future phenotypes using a subset of early time points for known lines with phenotypic data, as well new lines lacking phenotypic data. In all cases, PSA could be predicted with high accuracy ( r : 0.79 to 0.89 and 0.55 to 0.58 for known and unknown lines, respectively). This study provides the first application of RR models for genomic prediction of a longitudinal trait in rice and demonstrates that RR models can be effectively used to improve the accuracy of genomic prediction for complex traits compared to a TP approach.

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“…Lastly, the utility of the RRM does not preclude its use in other applications. For example, the RRM offers a new avenue for performing longitudinal GWAS (e.g., Howard et al, 2015; Campbell et al, 2019) and genotype-by-environment interactions using the reaction norm (Arnold et al, 2019). In summary, an RRM using Legendre polynomial or spline functions could be an effective option for modeling trait trajectories of HTP data and may have potential applications in characterizing phenotypic plasticity in plants.…”

Section: Discussionmentioning

confidence: 99%

Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines

Momen

Campbell

Walia

et al. 2019

Preprint

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28Recent advancements in phenomics coupled with increased output from sequencing tech-29 nologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, 30 which increasingly face productivity challenges due to climate change. In particular, the 31 high-throughput phenotyping (HTP) enables researchers to generate large-scale data with 32 temporal resolution. Recently, a random regression model (RRM) was used to model a 33 longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. 34 However, the utility of RRM is still unknown for phenotypic trajectories obtained from 35 stress environments. Here, we sought to apply RRM to forecast the rice PSA in control 36 and water-limited conditions under various longitudinal cross-validation scenarios. To this 37 end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA 38 trajectories. Prediction accuracy declined slightly for the water-limited plants compared to 39 control plants. Overall, RRM delivered reasonable prediction performance and yielded better 40 prediction than the baseline multi-trait model. The difference between the results obtained 41 using Legendre polynomials and that using B-splines was small; however, the former yielded 42 a higher prediction accuracy. Prediction accuracy for forecasting the last five time points 43 was highest when the entire trajectory from earlier growth stages was used to train the basis 44 functions. Our results suggested that it was possible to decrease phenotyping frequency by 45 only phenotyping every other day in order to reduce costs while minimizing the loss of pre-46 diction accuracy. This is the first study showing that RRM could be used to model changes 47 in growth over time under abiotic stress conditions. 48 3 Background 49 Plant biology has become a large-scale, data-rich field with the development of high-throughput 50 technologies for genomics and phenomics. This has increased the feasibility of data driven ap-51 proaches to be applied to address the challenge of developing climate-resilient crops (Tester 52 and Langridge, 2010). Crop responses to environmental changes are highly dynamic and 53 have a strong temporal component. Such responses are also known as function-valued traits, 54 for which means and covariances along the trajectory change continuously. Single time 55 point measurements of phenotypes, however, only provide a snapshot, posing a series of 56 challenges for research efforts aimed at understanding the ability of the plant to mount a 57 tolerant response to an environmental constraint. Advancements in high-throughput phe-58 notyping (HTP) technologies have enabled the automated collection of measurements at 59high temporal resolution to produce high density image data that can capture a plethora of 60 morphological and physiological measurements (Furbank and Tester, 2011). In particular, 61 image-based phenotyping has been deemed a game changer because conventional phenotyp-62 ing is laborious an...

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Genome-wide association study on legendre random regression coefficients for the growth and feed intake trajectory on Duroc Boars

Cited by 50 publications

References 53 publications

Salt stress under the scalpel – dissecting the genetics of salt tolerance

Salt stress under the scalpel – dissecting the genetics of salt tolerance

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high‐throughput phenotyping

Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines

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