Combining Hyperspectral Techniques and Genome-Wide Association Studies to Predict Peanut Seed Vigor and Explore Associated Genetic Loci
Abstract
:1. Introduction
2. Results
2.1. Seed Vigor Variation of Peanut Exhibits Diversity
2.2. SVM and RF Models Predict Peanut Seed Vigor
2.3. Genes Regulating Seed Vigor Are Efficiently Mined Using GWAS
2.3.1. Phenotypic Data Reflects Genetic Diversity
2.3.2. Screening of Arahy.VMLN7L and Arahy.7XWF6F
2.4. VMLN7L and 7XWF6F Are Involved in the Mechanisms Regulating Seed Vigor
3. Discussion
3.1. Predictive Models Based on Vigor Phenotypes
3.2. GWAS Results Confirm High-Throughput Predictive Phenotyping Validity in Investigating Genetic Relationships for Seed Vigor
3.3. Constraints and Potential Future Research
4. Materials and Methods
4.1. Seed Materials and Experimental Design
4.2. Spectral Measurements
4.3. Seed Vigor Data Acquisition
4.4. Hyperspectral Data Pre-Processing
4.5. Modeling Approaches
4.6. Vigor Phenotype and Genetics Analysis
4.6.1. Phenotypic Statistical Analysis
4.6.2. Genotyping
4.6.3. Population Structure and Linkage Disequilibrium Analysis
4.6.4. GWAS Analysis
4.6.5. GBA and Haplotype Analysis
4.6.6. Expression Analysis of Candidate Genes
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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Vigor Index | Experiments | Max | Min | Mean | SD | CV |
---|---|---|---|---|---|---|
N1 | 1.0000 | 0.0278 | 0.8217 | 0.2501 | ||
N2 | 1.0000 | 0.0000 | 0.5105 | 0.3563 | ||
CK | 1.0000 | 0.3667 | 0.8194 | 0.2646 | ||
A3 | 1.0000 | 0.0667 | 0.7000 | 0.3618 | ||
GE | A6 | 1.0000 | 0.0333 | 0.6194 | 0.3875 | 0.5516 |
A9 | 0.8000 | 0.0000 | 0.3472 | 0.2684 | ||
A12 | 0.5000 | 0.0000 | 0.2000 | 0.1933 | ||
A15 | 0.3667 | 0.0000 | 0.0944 | 0.1294 | ||
A18 | 0.2667 | 0.0000 | 0.0611 | 0.0908 | ||
N1 | 1.0000 | 0.0000 | 0.7284 | 0.2967 | ||
N2 | 1.0000 | 0.0000 | 0.2875 | 0.2732 | ||
CK | 0.9667 | 0.2333 | 0.7667 | 0.2689 | ||
A3 | 0.9667 | 0.0000 | 0.6194 | 0.3751 | ||
GP | A6 | 0.9000 | 0.0333 | 0.4833 | 0.3103 | 0.5839 |
A9 | 0.5000 | 0.0000 | 0.1833 | 0.1709 | ||
A12 | 0.2000 | 0.0000 | 0.0667 | 0.0752 | ||
A15 | 0.1667 | 0.0000 | 0.0194 | 0.0481 | ||
A18 | 0.1000 | 0.0000 | 0.0167 | 0.0333 | ||
N1 | 25.5000 | 0.5000 | 13.1971 | 4.9501 | ||
N2 | 19.3333 | 0.0000 | 6.4408 | 4.9333 | ||
CK | 28.8333 | 6.8333 | 22.3708 | 8.5257 | ||
A3 | 26.7000 | 0.6667 | 16.4833 | 10.0378 | ||
GI | A6 | 21.9167 | 0.5000 | 12.3347 | 8.1685 | 0.5683 |
A9 | 11.2500 | 0.0000 | 4.4361 | 3.7211 | ||
A12 | 5.0333 | 0.0000 | 2.2736 | 2.2277 | ||
A15 | 4.1500 | 0.0000 | 0.9153 | 1.3953 | ||
A18 | 2.9167 | 0.0000 | 0.6056 | 0.9658 |
GE | GP | GI | |||||||
---|---|---|---|---|---|---|---|---|---|
P1 * | P2 * | P3 * | P1 | P2 | P3 | P1 | P2 | P3 | |
CK | 0.4750 | 0.9833 | 0.9333 | 0.4250 | 0.9500 | 0.8667 | 27.7583 | 11.3750 | 23.0917 |
A3 | 0.2167 | 0.9500 | 0.9333 | 0.1250 | 0.8667 | 0.8667 | 22.8417 | 3.5167 | 23.0917 |
A6 | 0.1083 | 0.8333 | 0.9167 | 0.0833 | 0.6250 | 0.7417 | 17.4250 | 1.8417 | 17.7375 |
A9 | 0.0500 | 0.6250 | 0.3667 | 0.0167 | 0.3250 | 0.2083 | 7.7917 | 0.5708 | 4.9458 |
A12 | 0.0000 | 0.3833 | 0.2167 | 0.0000 | 0.1167 | 0.0833 | 4.2833 | 0.0000 | 2.5375 |
A15 | 0.0000 | 0.2250 | 0.0583 | 0.0000 | 0.0500 | 0.0083 | 2.3250 | 0.0000 | 0.4208 |
A18 | 0.0000 | 0.1583 | 0.0250 | 0.0000 | 0.0500 | 0.0000 | 1.6417 | 0.0000 | 0.1750 |
Vigor Index | Models | Max | Min | Mean | SD | CV | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|---|
GE | SVM | 0.9809 | 0.0920 | 0.6621 | 0.2046 | 0.3090 | −0.7971 | −0.1186 |
RF | 0.9138 | 0.2260 | 0.6556 | 0.1647 | 0.2512 | −0.7352 | −0.4438 | |
Line | 1.3201 | −0.1096 | 0.6078 | 0.2362 | 0.3886 | 0.1031 | 0.3570 | |
GP | SVM | 1.0066 | 0.0055 | 0.5213 | 0.2498 | 0.4792 | −0.0525 | −1.2257 |
RF | 0.7797 | 0.1476 | 0.4570 | 0.1407 | 0.3078 | 0.1131 | −0.8223 | |
Line | 1.1046 | −0.2320 | 0.4500 | 0.2304 | 0.5120 | 0.0076 | 0.0389 | |
GI | SVM | 14.5042 | 2.4231 | 9.7315 | 3.1941 | 0.3282 | −0.3235 | −0.9603 |
RF | 17.3580 | 2.2775 | 9.2745 | 2.9555 | 0.3187 | −0.0926 | −0.3725 | |
Line | 21.9095 | −2.2886 | 9.2753 | 4.3806 | 0.4723 | 0.1001 | 0.0957 |
Gene | Forward Primer (5′-3′) | Reverse Primer (5′-3′) |
---|---|---|
Arahy.VMLN7L | CCCATGATGCGCCACAAAAT | CGGTGAAATTCTTACCGCCA |
Arahy.7XWF6F | ATGGGTTTTGGTTGGAACGG | CTATGCCCTTAGCAGTGGGA |
Actin | GATTGGAATGGAAGCTGCTG | CGGTCAGCAATACCAGGGAA |
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Xiong, Z.; Liu, S.; Tan, J.; Huang, Z.; Li, X.; Zhuang, G.; Fang, Z.; Chen, T.; Zhang, L. Combining Hyperspectral Techniques and Genome-Wide Association Studies to Predict Peanut Seed Vigor and Explore Associated Genetic Loci. Int. J. Mol. Sci. 2024, 25, 8414. https://doi.org/10.3390/ijms25158414
Xiong Z, Liu S, Tan J, Huang Z, Li X, Zhuang G, Fang Z, Chen T, Zhang L. Combining Hyperspectral Techniques and Genome-Wide Association Studies to Predict Peanut Seed Vigor and Explore Associated Genetic Loci. International Journal of Molecular Sciences. 2024; 25(15):8414. https://doi.org/10.3390/ijms25158414
Chicago/Turabian StyleXiong, Zhenhui, Shiyuan Liu, Jiangtao Tan, Zijun Huang, Xi Li, Guidan Zhuang, Zewu Fang, Tingting Chen, and Lei Zhang. 2024. "Combining Hyperspectral Techniques and Genome-Wide Association Studies to Predict Peanut Seed Vigor and Explore Associated Genetic Loci" International Journal of Molecular Sciences 25, no. 15: 8414. https://doi.org/10.3390/ijms25158414