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ARS Home » Southeast Area » Tifton, Georgia » Crop Protection and Management Research » Research » Publications at this Location » Publication #311799
Title: Towards deploying genomic selection for improving complex traits in peanut

Author
item PANDEY, MANISH - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item UPADHYAYA, HARI - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item JANILA, PASUPULETI - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item RATHORE, ABHISHEK - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item KHERA, PAWAN - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India
item HONG, YANBIN - Guangdong Academy Of Agricultural Sciences
item LIANG, XUANQIANG - Guangdong Academy Of Agricultural Sciences
item Guo, Baozhu
item VARSHNEY, RAJEEV - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - India

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 11/7/2014
Publication Date: 11/9/2014
Citation: Pandey, M., Upadhyaya, H.D., Janila, P., Rathore, A., Khera, P., Hong, Y., Liang, X., Guo, B., Varshney, R.K. 2014. Towards deploying genomic selection for improving complex traits in peanut. Meeting Abstract. Advances in Arachis through Genomics and Biotechnology (AAGB), November 10-14, 2014, Savannah, Georgia.

Interpretive Summary:

Technical Abstract: Marker-assisted backcrossing (MABC) is an effective approach for improving qualitative traits and has been successfully used to develop improved lines for rust resistance and high oleate trait in peanut. Further efforts are underway to pyramid genomic regions for multiple qualitative traits (rust resistance, late leaf spot resistance and high oleate trait) through MABC in existing cultivars. However, genomic selection (GS) has emerged as the most promising breeding approach for improving complex traits. GS can capture small-effect QTLs and develop superior lines with multiple traits. With an objective to improve complex traits like yield under drought stress, the GS approach has been initiated in peanut. In this context, the ‘minicore collection’ with 184 genotypes has already been evaluated for three important agronomic traits (days to flowering, seed weight and pod yield). Therefore this collection has been genotyped with 15,360 diversity array technology (DArT) features. A total of six GS models (Ridge Regression-BLUP, Bayesian LASSO, Random Forest Regression, Kinship GAUSS, BayesCp and BayesB) were tested on the phenotypic and genotypic data for estimation of correlation and cross-validation values. As a result, two best performing GS models namely Ridge Regression-BLUP and Bayesian LASSO have been identified for predicting genomic estimated breeding values (GEBVs). Furthermore, a training population comprising of 310 elite genotypes has also been constituted and genotyped with 15,360 DArT/DArTseq features. The population is being phenotyped for several agronomically important traits. The genotypic and phenotypic data will be used to define the appropriate GS model for deploying GS in peanut breeding.