Table 2.
Applications of deep learning to genomic prediction.
| Study | Species | Approx. N | Approx. No. SNPs | Performance * |
|---|---|---|---|---|
| Mcdowell [51] | Arabidopsis, Maize, wheat | 270–400 | 70–1k | MLP ≥ PL |
| Liu and Wang [55] | Soybean | 5k | 4k | CNN > RR-BLUP, Lasso-Bayes, Bayes A |
| Rachmatia et al. [56] | Maize | 300 | 1k | PL > DBN |
| Bellot et al. [38] | Human | 100k | 10k–50k | PL ≥ CNN > MLP |
| Ma et al. [57] | Wheat | 2k | 33k | CNN ~ PL ~ GBLUP > MLP |
| Montesinos-López et al. [52] | Maize, wheat | 250–2k | 12k–160k | GBLUP > MLP |
| Montesinos-López et al. [53] | Wheat | 800–4k | 2k | GBLUP > MLP |
| Khaki and Wang [54] | Maize | 2k genotypes (150k samples) | 20k | DL > PL |
| Waldmann [37] | pig | 3226 (simulated) 3534 (real) |
10k, 50k | DL > GBLUP/BayesLasso |
* PL, penalized linear method; DBN, deep belief network.