Enhancing the prediction accuracy of groundnut yield by integrating significant markers and modeling genotype × environment interaction

Lubanga, N and Okaron, V and Gimode, D M and Persa, R and Mwololo, J and Okello, D K and Ssemakula, M O and Odong, T L and Abincha, W and Odeny, D A and Jarquin, D (2025) Enhancing the prediction accuracy of groundnut yield by integrating significant markers and modeling genotype × environment interaction. The Plant Genome (TSI), 18 (3). pp. 1-14. ISSN 1940-3372

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Abstract

Multi-environment trials are routinely conducted in plant breeding to capture the genotype-by-environment interaction (G × E) effects. Significant G × E could alter the response pattern of genotypes (the change in rankings of genotypes), subsequently complicating the selection process. Four genomic prediction (GP) models were assessed in three groundnut yield-related traits: pod yield (PY), seed weight (SW), and 100 seed weight (SW100), across four environments. The models, M1 (environment + line), M2 (environment + line + genomic), M3 (environment + line + genomic + genomic × environment interaction), and M4 (environment + line + genomic + genomic × environment interaction + significant markers), were tested using four cross-validation (CV) schemes (CV2, CV1, CV0, and CV00), each simulating different practical breeding scenarios. The results revealed that models incorporating marker data (M2, M3, and M4) consistently improved predictive ability in comparison to the phenotypic model (M1). Incorporating G × E (M3 and M4) further improved predictive ability and reduced residual and environmental variances. The inclusion of significant markers and G × E was more advantageous in CV1 and CV00 scenarios, demonstrating that this strategy is especially useful when phenotypic data for the target genotypes is limited or unavailable. Across the CV schemes, predictive ability was higher in CV2, suggesting that including additional information on the performance of genotypes in known environments can increase the accuracy of selecting superior genotypes in breeding programs. Integrating significant markers and modeling G × E in GP models could be an effective approach in groundnut breeding programs to accelerate genetic gains.

Item Type:	Article
Divisions:	Research Program : East & Southern Africa
CRP:	UNSPECIFIED
Uncontrolled Keywords:	genotype-by-environment interaction (G × E), genotypes, plant breeding, high-yielding, groundnut
Subjects:	Others > Crop Yield Mandate crops > Groundnut Others > Genetics and Genomics
Depositing User:	Mr Nagaraju T
Date Deposited:	17 Mar 2026 04:54
Last Modified:	17 Mar 2026 04:54
URI:	http://oar.icrisat.org/id/eprint/13547
Official URL:	https://acsess.onlinelibrary.wiley.com/doi/full/10...
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	The first two authors are thankful to the staff of the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in Malawi and the national staff in Uganda who helped in the data collection. This work was supported by the Food and Agriculture Organization (FAO) through the benefit sharing fund of the International Treaty on Plant Genetics for Food and Agriculture. Supplemental funding was provided by the Scholarship Advert for Short Term Academic Mobility (SCIFSA) and the Regional Universities’ Forum for Capacity Building in Agriculture (RUFORUM) for Velma Okaron Ph.D fellowship. The testing and cross-validation of GP models was made possible through the analytical resources provided by the Institute of Biological, Environmental and Rural Sciences (IBERS) High-Performance Computer Cluster.
Links:	Google Scholar

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