These are the papers we've been reading in the Quantitative Genetics lab's journal club at the University of Arkansas.
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Moeinizade, S., Kusmec, A., Hu, G., Wang, L., & Schnable, P. S. (2020). Multi-trait genomic selection methods for crop improvement. Genetics.
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Farooq, M., van Dijk, A. D., Nijveen, H., Mansoor, S., & de Ridder, D. (2023). Genomic prediction in plants: opportunities for ensemble machine learning based approaches. F1000Research.
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Lane, H. M., Murray, S. C., Montesinos‑López, O. A., Montesinos‑López, A., Crossa, J., Rooney, D. K., ... & Morgan, C. L. (2020). Phenomic selection and prediction of maize grain yield from near‐infrared reflectance spectroscopy of kernels. The Plant Phenome Journal.
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Weiß, T. M., Zhu, X., Leiser, W. L., Li, D., Liu, W., Schipprack, W., ... & Würschum, T. (2022). Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.). G3.
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Sun, H., Wei, M., Xu, Z., Bai, C., & Sun, B. (2022). PC‐DOT: Improving genomic prediction ability of principal component regression by DOT product. Animal Genetics.
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Cheng, J., Maltecca, C., VanRaden, P. M., O'Connell, J. R., Ma, L., & Jiang, J. (2023). SLEMM: million-scale genomic predictions with window-based SNP weighting. Bioinformatics.
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Robert, P., Brault, C., Rincent, R., & Segura, V. (2022). Phenomic Selection: A New and Efficient Alternative to Genomic Selection. In Genomic Prediction of Complex Traits: Methods and Protocols (pp. 397-420). New York, NY: Springer US.
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Westhues, C. C., Mahone, G. S., da Silva, S., Thorwarth, P., Schmidt, M., Richter, J. C., ... & Beissinger, T. M. (2021). Prediction of maize phenotypic traits with genomic and environmental predictors using gradient boosting frameworks. Frontiers in Plant Science.
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Piepho, H. P., Büchse, A., & Emrich, K. (2003). A hitchhiker's guide to mixed models for randomized experiments. Journal of Agronomy and Crop Science.
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Crain, J., Mondal, S., Rutkoski, J., Singh, R. P., & Poland, J. (2018). Combining high‐throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding. The Plant Genome.
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Biswas, A., Andrade, M. H. M. L., Acharya, J. P., de Souza, C. L., Lopez, Y., de Assis, G., ... & Rios, E. F. (2021). Phenomics-assisted selection for herbage accumulation in alfalfa (Medicago sativa L.). Frontiers in Plant Science.
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Qu, J., Morota, G., & Cheng, H. (2022). A Bayesian random regression method using mixture priors for genome‐enabled analysis of time‐series high‐throughput phenotyping data. The Plant Genome.
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Wilson, G., Aruliah, D. A., Brown, C. T., Chue Hong, N. P., Davis, M., Guy, R. T., ... & Wilson, P. (2014). Best practices for scientific computing. PLOS Biology.
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Khaipho-Burch, M., Cooper, M., Crossa, J., de Leon, N., Holland, J., Lewis, R., ... & Buckler, E. S. (2023). Genetic modification can improve crop yields—but stop overselling it. Nature.
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Huang, W., & Mackay, T. F. (2016). The genetic architecture of quantitative traits cannot be inferred from variance component analysis. PLoS Genetics.
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Wang, M., Li, R., & Xu, S. (2020). Deshrinking ridge regression for genome-wide association studies. Bioinformatics, 36(14), 4154-4162.
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Moreira, F. F., Oliveira, H. R., Volenec, J. J., Rainey, K. M., & Brito, L. F. (2020). Integrating high-throughput phenotyping and statistical genomic methods to genetically improve longitudinal traits in crops. Frontiers in Plant Science, 11, 681.
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Hu, X., Carver, B.F., El-Kassaby, Y.A. et al. (2023) Weighted kernels improve multi-environment genomic prediction. Heredity 130, 82–91.
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Alemu, A., Åstrand, J., Montesinos-López, O. A., y Sánchez, J. I., Fernández-Gónzalez, J., Tadesse, W., ... & Chawade, A. (2024). Genomic selection in plant breeding: key factors shaping two decades of progress. Molecular Plant.
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Mackay, T. F., & Anholt, R. R. (2024). Pleiotropy, epistasis and the genetic architecture of quantitative traits. Nature Reviews Genetics, 1-19.
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Saad, N. S. M., Neik, T. X., Thomas, W. J., Amas, J. C., Cantila, A. Y., Craig, R. J., ... & Batley, J. (2022). Advancing designer crops for climate resilience through an integrated genomics approach. Current Opinion in Plant Biology, 67, 102220.