Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components
Abstract
:1. Introduction
2. Results
2.1. Phenotyping Evaluations
2.2. Population Structure and Kinship
2.3. GWAS Analysis
2.4. Extracting Candidate Genes Undelaying Detected QTL
3. Discussion
4. Materials and Methods
4.1. Population and Experimental Design
4.2. Phenotyping
4.3. Genotyping
4.4. Statistical Analyses
4.5. Analysis of Population Structure
4.6. Association Studies
4.7. Mixed Linear Model (MLM)
4.8. Fixed and Random Model Circulating Probability Unification (FarmCPU)
4.9. Random Forest (RF)
4.10. Support-Vector Regression (SVR)
4.11. Implementation of ML Algorithms in GWAS
4.12. Variable Importance Measurement
4.13. Extracting Candidate Genes Undelaying Detected QTL
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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GWAS Method | Chromosome | Peak SNP Position | Co-Located QTL | Reference |
---|---|---|---|---|
MLM | 2 | 2212910 | Sclero 3-g31 | [38] |
8233782 | Seed Weight 6-g1 | [39] | ||
FarmCPU | 2 | 2212910 | Sclero 3-g31 | [38] |
8233766 | Seed Weight 6-g1 | [39] | ||
20 | 37765851 | WUE 2-g53 | [40] | |
RF | 3 | 2978272 | Leaflet area 1-g2.1 | [41] |
Leaflet width 1-g4.1 | [41] | |||
Leaflet area 1-g2.2 | [41] | |||
Leaflet width 1-g4.2 | [41] | |||
Salt tolerance 1-g12 | [42] | |||
16 | 5730281 | Plant height 6-g17 | [43] | |
Plant height 1-g17 | [43] | |||
First flower 4-g63 | [44] | |||
17 | 34757372 | SDS root retention 1-g6 | [45] | |
SVR | 2 | 695362 | Seed linolenic 2-g1 | [46] |
Seed linolenic 2-g2 | [46] | |||
720134 | SDS 1-g12.1 | [47] | ||
SDS 1-g12.2 | [47] | |||
Ureide content 1-g2 | [48] | |||
827374 | SDS 1-g12.3 | [47] | ||
10 | 1595239 | Shoot Cu 1-g8 | [49] | |
1689395 | Seed oil 5-g3 | [39] | ||
16 | 2438652 | Reproductive period 4-g16 | [43] | |
R8 full maturity 9-g2 | [43] | |||
2460921 | Reproductive period 2-g16 | [43] | ||
R8 full maturity 2-g2 | [43] | |||
19 | 47513536 | R8 full maturity 4-g1 | [39] | |
47513572 | First flower 4-g81 | [44] |
GWAS Method | Chromosome | Peak SNP Position | Co-Located QTL | Reference |
---|---|---|---|---|
MLM | 5 | 34391386 | Ureide content 1-g16.1 | [48] |
Ureide content 1-g16.2 | [48] | |||
FarmCPU | 5 | 34391386 | Ureide content 1-g16.1 | [48] |
Ureide content 1-g16.2 | [48] | |||
RF | 7 | 1032587 | WUE 2-g18 | [40] |
SVR | 3 | 36309302 | First flower 4-g10 | [44] |
First flower 3-g2 | [50] | |||
Seed weight 4-g3 | [50] | |||
Seed yield 4-g2 | [50] | |||
R8 full maturity 3-g3 | [50] | |||
37617293 | Plant height 3-g17 | [51] | ||
Leaflet shape 1-g1.1 | [41] | |||
Leaflet shape 1-g1.2 | [41] | |||
Leaflet shape 1-g1.3 | [41] | |||
Seed set 1-g32.1 | [41] | |||
Seed set 1-g32.2 | [41] | |||
7 | 44488152 | Seed yield 4-g4 | [50] | |
1032587 | WUE 2-g18 | [40] | ||
15 | 34958361 | SCN 5-g35 | [52] | |
19 | 41385139 | Seed weight 5-g20 | [53] | |
Seed weight 4-g18 | [50] | |||
Seed yield 4-g5 | [50] | |||
Shoot Zn 1-g28.1 | [49] | |||
Shoot Zn 1-g28.2 | [49] | |||
Shoot Zn 1-g29.1 | [49] | |||
Shoot Zn 1-g29.2 | [49] | |||
Shoot Zn 1-g29.3 | [49] |
GWAS Method | Chromosome | Peak SNP Position | Co-Located QTL | Reference |
---|---|---|---|---|
FarmCPU | 19 | 40131952 | Pubescence density 1-g17 | [54] |
Seed weight 9-g5.1 | [55] | |||
RF | 4 | 1205787 | Shoot Ca 1-g10 | [49] |
6 | 50570624 | Seed set 1-g51.1 | [41] | |
Seed set 1-g43.1 | [41] | |||
Seed set 1-g25.1 | [41] | |||
Seed set 1-g43.2 | [41] | |||
Seed set 1-g25.2 | [41] | |||
Seed set 1-g51.2 | [41] | |||
50570473 | Seed set 1-g43.3 | [41] | ||
Seed set 1-g51.3 | [41] | |||
Seed set 1-g25.3 | [41] | |||
Pod number 1-g3 | [41] | |||
Seed palmitic 2-g2 | [41] | |||
Seed long-chain faty acid 1-g22 | [41] | |||
SVR | 6 | 50570624 | Seed set 1-g51.1 | [41] |
Seed set 1-g43.1 | [41] | |||
Seed set 1-g25.1 | [41] | |||
Seed set 1-g43.2 | [41] | |||
Seed set 1-g25.2 | [41] | |||
Seed set 1-g51.2 | [41] | |||
50570473 | Seed set 1-g43.3 | [41] | ||
Seed set 1-g51.3 | [41] | |||
Seed set 1-g25.3 | [41] | |||
Pod number 1-g3 | [41] | |||
Seed palmitic 2-g2 | [41] | |||
Seed long-chain faty acid 1-g22 | [41] | |||
7 | 1032587 | WUE 2-g18 | [40] | |
1092403 | WUE 2-g18 | [40] | ||
First flower 3-g4 | [41] | |||
18 | 55645699 | Leaflet shape 1-g4.1 | [41] | |
Leaflet shape 1-g4.2 | [41] | |||
Leaflet shape 1-g4.3 | [41] | |||
Seed stearic 4-g5 | [56] | |||
Node number 1-g6.1 | [41] | |||
Node number 1-g6.2 | [41] | |||
Pod number 1-g1.1 | [41] | |||
Pod number 1-g1.2 | [41] | |||
Pode number 1-g1.3 | [41] | |||
WUE 3-g31 | [40] | |||
Seed weight, SoyNAM 14-g28 | [57] | |||
Lodging, SoyNAM 4-g15 | [58] | |||
Branching 1-g1.1 | [41] | |||
Plant height 5-g4.2 | [41] | |||
Plant height 5-g4.3 | [41] | |||
Shoot p 1-g30 | [49] | |||
19 | 47350110 | Node number 1-g2.3 | [41] |
GWAS Method | Chromosome | Peak SNP Position | Co-Located QTL | Reference |
---|---|---|---|---|
MLM | 15 | 10193796 | Seed protein 6-g2 | [59] |
Seed Arg 1-g4 | [59] | |||
Seed coat luster 1-g1.3 | [41] | |||
FarmCPU | 15 | 10193796 | Seed protein 6-g2 | [59] |
Seed Arg 1-g4 | [59] | |||
Seed coat luster 1-g1.3 | [41] | |||
RF | 1 | 54647498 | First flower 4-g2 | [44] |
7 | 329800 | Phytoph 2-g32 | [60] | |
Phytoph 2-g7 | [60] | |||
18 | 12945778 | SCN 4-g14 | [61] | |
19 | 40218800 | Seed weight 9-g5.1 | [55] | |
SVR | 7 | 1032587 | WUE 2-g18 | [40] |
19 | 40218800 | Seed weight 9-g5.1 | [55] |
GWAS Method | Chromosome | Peak SNP Position | Co-Located QTL | Reference |
---|---|---|---|---|
RF | 9 | 40285014 | Shoot Fe 1-g8.1 | [49] |
Shoot Fe 1-g8.2 | [49] | |||
Shoot Fe 1-g8.3 | [49] | |||
Shoot Fe 1-g9 | [49] | |||
Shoot Fe 1-g10 | [49] | |||
Shoot Fe 1-g11 | [49] | |||
Soybean mosaic virus 2-g5 | [62] | |||
15 | 34958361 | SCN 5-g35 | [52] | |
SVR | 7 | 1032587 | WUE 2-g18 | [40] |
15 | 34958361 | SCN 5-g35 | [52] |
GWAS Method | Chromosome | Peak SNP Position | Co-Located QTL | Reference |
---|---|---|---|---|
RF | 7 | 15331676 | Seed weight, SoyNAM 14-g11 | [57] |
SVR | 9 | 39366957 | Pod number 1-g4.1 | [41] |
Pod number 1-g4.2 | [41] | |||
Pod number 1-g4.3 | [41] | |||
Seed thickness 2-g4 | [41] | |||
9 | 39372117 | Seed Thr 2-g1 | [63] | |
Seed Ser 2-g1 | [63] | |||
Seed Tyr 2-g2 | [63] | |||
Seed Lys 2-g2 | [63] | |||
Seed leu 2-g2 | [63] | |||
Seed ile 2-g2 | [63] | |||
Seed Ala 2-g2 | [63] | |||
Seed Gly 2-g2 | [63] | |||
11 | 5245870 | Ureide content 1-g29 | [48] | |
Pod number 1-g6 | [41] | |||
18 | 55645699 | Leaflet shape 1-g4.1 | [41] | |
55469601 | Leaflet shape 1-g4.2 | [41] | ||
Leaflet shape 1-g4.3 | [41] | |||
Seed stearic 4-g5 | [56] | |||
Node number 1-g6.1 | [41] | |||
Node number 1-g6.2 | [41] | |||
Pode number 1-g1.1 | [41] | |||
Pode number 1-g1.2 | [41] | |||
Pode number 1-g1.3 | [41] | |||
WUE 3-g31 | [64] | |||
Seed weight, SoyNAM 14-g28 | [57] | |||
Lodging, SoyNAM 4-g15 | [58] | |||
Branching 1-g1.1 | [41] | |||
Plant height 5-g4.2 | [41] | |||
Plant height 5-g4.3 | [41] | |||
Shoot p 1-g30 | [49] | |||
Seed yield, SoyNAM 7-g19 | [58] | |||
R8 full maturity, SoyNAM 13-g19 | [58] | |||
Plant height 5-g4.3 | [41] | |||
19 | 43077182 | Seed weight 9-g5.2 | [55] | |
Seed weight 5-g21 | [55] | |||
First flower 5-g3 | [41] | |||
First flower 5-g17 | [41] | |||
47235604 | First flower 4-g77 | [44] | ||
Seed palmitic 1-g19 | [65] | |||
47350110 | Leaf carotenoid content 1-g14 | [66] | ||
Ureide content 1-g50.3 | [48] | |||
Ureide content 1-g50.4 | [48] | |||
47224293 | Node number 1-g2.3 | [41] |
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Yoosefzadeh-Najafabadi, M.; Eskandari, M.; Torabi, S.; Torkamaneh, D.; Tulpan, D.; Rajcan, I. Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. Int. J. Mol. Sci. 2022, 23, 5538. https://doi.org/10.3390/ijms23105538
Yoosefzadeh-Najafabadi M, Eskandari M, Torabi S, Torkamaneh D, Tulpan D, Rajcan I. Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. International Journal of Molecular Sciences. 2022; 23(10):5538. https://doi.org/10.3390/ijms23105538
Chicago/Turabian StyleYoosefzadeh-Najafabadi, Mohsen, Milad Eskandari, Sepideh Torabi, Davoud Torkamaneh, Dan Tulpan, and Istvan Rajcan. 2022. "Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components" International Journal of Molecular Sciences 23, no. 10: 5538. https://doi.org/10.3390/ijms23105538