igorkf / gen-papers

List of papers related to Agricultural Statistics, Plant Breeding, and Quantitative Genetics

gen-papers

These are the papers we've been reading in the Quantitative Genetics lab's journal club at the University of Arkansas.

1. Moeinizade, S., Kusmec, A., Hu, G., Wang, L., & Schnable, P. S. (2020). Multi-trait genomic selection methods for crop improvement. Genetics.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. Piepho, H. P., Büchse, A., & Emrich, K. (2003). A hitchhiker's guide to mixed models for randomized experiments. Journal of Agronomy and Crop Science.

10. 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.

11. 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.

12. 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.

13. 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.

14. 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.

15. Huang, W., & Mackay, T. F. (2016). The genetic architecture of quantitative traits cannot be inferred from variance component analysis. PLoS Genetics.

16. Wang, M., Li, R., & Xu, S. (2020). Deshrinking ridge regression for genome-wide association studies. Bioinformatics, 36(14), 4154-4162.

17. 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.

18. Hu, X., Carver, B.F., El-Kassaby, Y.A. et al. (2023) Weighted kernels improve multi-environment genomic prediction. Heredity 130, 82–91.