Genomic selection (GS) and phenomic selection (PS) are pivotal for accelerating plant breeding. However, the accuracy, robustness, and transferability of the two kinds of selection methods are underexplored, especially when addressing complex traits. In this study, we introduced a novel data fusion framework, termed GPS (Genomic and Phenomic Selection), aiming to improve predictive performance by integrating genomic and phenomic data using three kinds of fusion strategies: data fusion, feature fusion, and result fusion. Five widely used machine learning models (Lasso, RF, SVM, XGBoost, and LightGBM), and one advanced deep learning model (DNNGP) were selected and compared in the GPS framework.
- Version 0.1.0 -First version released on October, 12th, 2024
pheno.txt
| Phenotype1 | Phenotype2 | Phenotype3 | Phenotype4 | Phenotype5 | Phenotype6 |
|---|---|---|---|---|---|
| 36 | 91.44 | 55.3 | 3718.93 | 6.62 | 31.43 |
| 33 | 83.82 | 45.9 | 3086.78 | 7.1 | 32.99 |
| 37 | 93.98 | 43.4 | 2918.65 | 7.07 | 32.34 |
| 41 | 104.14 | 51.8 | 3483.55 | 7.1 | 32.42 |
| 37 | 93.98 | 50.9 | 3423.03 | 7.27 | 31.03 |
| 38 | 96.52 | 49.5 | 3328.88 | 7.3 | 31.75 |
geno.txt
| SNP1 | SNP2 | SNP3 | SNP4 | SNP5 | SNP6 |
|---|---|---|---|---|---|
| 1 | 0 | 2 | 1 | 2 | 1 |
| 0 | 2 | 2 | 0 | 1 | 2 |
| 0 | 0 | 1 | 2 | 0 | 2 |
| 1 | 1 | 0 | 2 | 1 | 2 |
| 2 | 1 | 1 | 0 | 2 | 1 |
| 1 | 2 | 0 | 2 | 1 | 1 |
> library("BioGPS")
> geno <- read.table("geno.txt")
> pheno_data <- read.table("pheno.txt")
> response <- pheno_data[,1]
> pheno <- pheno_data[,-1]
> prediction_D <- BioGPS_D.train(Gdata = geno, Pdata = pheno, Target = response, model = RF_500, k = 5, seed = 123)
> prediction_F <- BioGPS_F.train(Gdata = geno, Pdata = pheno, Target = response, model = Lasso_1, k = 5, seed = 123)
> prediction_R <- BioGPS_R.train(Gdata = geno, Pdata = pheno, Target = response, model = SVM, k = 5, seed = 123, nrounds = 200)If you have any bug reports or questions, please feed back 👉here👈, or send email to contact:
📧 Hongshan Wu: 843791289@qq.com