Table 1 DL application to genomic selection
From: A review of deep learning applications for genomic selection
Obs | Year | Authors | Crop | Topology | Response variable(s) | Comparison with |
|---|---|---|---|---|---|---|
1 | 2011 | Gianola et al. [61] | Wheat and Jersey cows | MLP | Grain yield (GY), fat yield, milk yield, protein yield, fat yield | Bayesian Ridge regression (BRR) |
2 | 2012 | Pérez-RodrÃguez et al. [62] | Wheat | MLP | GY and days to heading (DTHD) | BL, BayesA, BayesB, BRR, Reproducing Kernel Hilbert Spaces (RKHS) regression |
3 | 2012 | Gonzalez-Camacho et al. [6] | Maize | MLP | GY, female flowering (FFL) or days to silking, male flowering time (MFL) or days to anthesis, and anthesis-silking interval (ASI) | RKHS regression, BL |
4 | 2015 | Ehret et al. [63] | Holstein-Friesian and German Fleckvih cattle | MLP | Milk yield, protein yield, and fat yield | GBLUP |
5 | 2016 | Gonzalez-Camacho et al. [64] | Maize and wheat | MLP | GY | Probabilistic neural network (PNN) |
6 | 2016 | McDowell [65] | Arabidopsis, maize and wheat | MLP | Days to flowering, dry matter, grain yield (GY), spike grain, time to young microspore. | OLS, RR, LR, ER, BRR |
7 | 2017 | Rachmatia et al. [66] | Maize | DBN | GY, female flowering (FFL) (or days to silking), male flowering (MFL) (or days to anthesis), and the anthesis-silking interval (ASI) | RKHS, BL and GBLUP |
8 | 2018 | Ma et al. [67] | Wheat | CNN and MLP | Grain length (GL), grain width (GW), thousand-kernel weight (TW), grain protein (GP), and plant height (PH) | RR-BLUP, GBLUP |
9 | 2018 | Waldmann [68] | Pig data and TLMAS2010 data | MLP | Trait number of live born piglets | GBLUP, BL |
10 | 2018 | Montesinos-López et al. [70] | Maize and wheat | MLP | Grain yield | GBLUP |
11 | 2018 | Montesinos-López et al. [71] | Maize and wheat | MLP | Grain yield (GY), anthesis-silking interval (ASI), PH, days to heading (DTHD), days to maturity (DTMT) | BMTME |
12 | 2018 | Bellot et al. [72] | Human traits | MLP and CNN | Height and bone heel mineral density | BayesB, BRR |
13 | 2019 | Montesinos-López et al. [73] | Wheat | MLP | GY, DTHD, DTMT, PH, lodging, grain color (GC), leaf rust and stripe rust | SVM, TGBLUP |
14 | 2019 | Montesinos-López et al. [74] | Wheat | MLP | GY, DH, PH | GBLUP |
15 | 2019 | Khaki and Wang [75] | Maize | MLP | GY, check yield, yield difference | LR, regression tree |
16 | 2019 | Azodi et al. [77] | 6 species | MLP | 18 traits | rrBLUP, BRR, BA, BB, BL, SVM, GTB |
17 | 2019 | Liu et al. [78] | Soybean | CNN | GY, protein, oil, moisture, PH | rrBLUP, BRR, BayesA, BL |
18 | 2020 | Abdollahi-Arpanahi et al. [79] | Holstein bulls | MLP and CNN | Sire conception rate | GBLUP, BayesB and RF |
19 | 2020 | Zingaretti et al. [80] | Strawberry and blueberry | MLP and CNN | Average fruit weight, early marketable yield, total marketable weight, soluble solid content, percentage of culled fruit | RKHS, BRR, BL, |
22 | 2020 | Montesinos-López et al. [81] | Wheat | MLP | Fusarium head blight | BRR and GP |
20 | 2020 | Waldmann et al. [43] | Pig data | CNN | Trait number of live born piglets | GBLUP, BL |
21 | 2020 | Pook et al. [82] | Arabidopsis | MLP and CNN | Arabidopsis traits | GBLUP, EGBLUP, BayesA |
23 | 2020 | Pérez-RodrÃguez et al. [83] | Maize and wheat | MLP | Leaf spot diseases, Gray Leaf Spot | Bayesian ordered probit linear model |