close
Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Nov 26;8(11):e11873.
doi: 10.1016/j.heliyon.2022.e11873. eCollection 2022 Nov.

Optimizing genomic selection in soybean: An important improvement in agricultural genomics

Mohsen Yoosefzadeh-Najafabadi  1 , Istvan Rajcan  1 , Milad Eskandari  1
Affiliations

Optimizing genomic selection in soybean: An important improvement in agricultural genomics

Mohsen Yoosefzadeh-Najafabadi et al. Heliyon. .

Abstract

Fast-paced yield improvement in strategic crops such as soybean is pivotal for achieving sustainable global food security. Precise genomic selection (GS), as one of the most effective genomic tools for recognizing superior genotypes, can accelerate the efficiency of breeding programs through shortening the breeding cycle, resulting in significant increases in annual yield improvement. In this study, we investigated the possible use of haplotype-based GS to increase the prediction accuracy of soybean yield and its component traits through augmenting the models by using sophisticated machine learning algorithms and optimized genetic information. The results demonstrated up to a 7% increase in the prediction accuracy when using haplotype-based GS over the full single nucleotide polymorphisms-based GS methods. In addition, we discover an auspicious haplotype block on chromosome 19 with significant impacts on yield and its components, which can be used for screening climate-resilient soybean genotypes with improved yield in large breeding populations.

Keywords: Food security; Haplotype block; Machine learning algorithms; Soybean breeding; Soybean yield.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Estimated correlation coefficient values of the tested algorithms for (A) yield, (B) NP, (C) NRNP, (D) RNP, and (E) PP using full SNP (Full-) and haplotype block (Haplotype-) dataset. Ensemble-Bagging (E-B), Radial basis function (RBF), Random forest (RF), support vector regression (SVR), Ridge regression best linear unbiased prediction (rrBLUP).
Figure 2
Figure 2
Estimated coefficient of determination (R2) values of the tested algorithms for (A) yield, (B) NP, (C) NRNP, (D) RNP, and (E) PP using haplotype block (Haplotype-) dataset. Ensemble-Bagging (E-B), Radial basis function (RBF), random forest (RF), support vector regression (SVR), ridge regression best linear unbiased prediction (rrBLUP).
Figure 3
Figure 3
The Mean Absolute Error (MAE, Top) and Root Mean Square Error (RMSE, Bottom) values of the tested algorithms for (A) yield, (B) NP, (C) NRNP, (D) RNP, and (E) PP using haplotype blocks (Haplotype-) dataset. Ensemble-Bagging (E-B), radial basis function (RBF), random forest (RF), Support vector regression (SVR), ridge regression best linear unbiased prediction (rrBLUP).
Figure 4
Figure 4
The haplotype block 16, on chromosome 19, with its 12 putative candidate genes and 18 GO profiles, underlying soybean yield improvement.
See this image and copyright information in PMC

References

    1. IFAD, UNICEF, WFP, WHO . FAO Rome; Italy: 2021. The State of Food Security and Nutrition in the World 2021. Transforming Food Systems for Food Security, Improved Nutrition and Affordable Healthy Diets for All.
    1. Liu Z., Ying H., Chen M., Bai J., Xue Y., Yin Y., Batchelor W.D., Yang Y., Bai Z., Du M. Optimization of China’s maize and soy production can ensure feed sufficiency at lower nitrogen and carbon footprints. Nat. Food. 2021:1–8. - PubMed
    1. Ray D.K., Mueller N.D., West P.C., Foley J.A. Yield trends are insufficient to double global crop production by 2050. PLoS One. 2013;8 - PMC - PubMed
    1. Ray D.K., Ramankutty N., Mueller N.D., West P.C., Foley J.A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 2012;3:1293. - PubMed
    1. Yoosefzadeh-Najafabadi M., Tulpan D., Eskandari M. Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. PLoS One. 2021;16 - PMC - PubMed

LinkOut - more resources