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Bulletin of Botanical Research ›› 2026, Vol. 46 ›› Issue (3): 542-556.doi: 10.7525/j.issn.1673-5102.2026.03.014

• Original Paper • Previous Articles     Next Articles

Genetic Analysis and Efficient Screening of Processing Potato F₁ Population

Doudou MA1,2, Bosong LIN3, Xinjie ZHANG1, Yanhong WANG3, Guozhong JIA3, Yuanyuan XUE3, Fangming LIU3, Jianghui CUI1,2()   

  1. 1.College of Agronomy,Hebei Agricultural University,Baoding 071000
    2.Hebei Key Laboratory of Crop Germplasm Resources,Baoding 071000
    3.Potato Research Institute of Weichang Manchu and Mongolian Autonomous County,Chengde 068450
  • Received:2026-01-21 Online:2026-05-20 Published:2026-06-01
  • Contact: Jianghui CUI E-mail:cjianghui521@126.com

Abstract:

To address the breeding challenges of simultaneous selection for yield and quality, along with the high costs and low efficiency of full phenotypic identification in processing potatoes, this study established an efficient comprehensive evaluation system and a simplified early-generation prediction model. An F1 population comprising 267 individuals, derived from the cross of ‘Atlantic’בLucinda’, was utilized to conduct phenotypic measurements and genetic analysis of 24 traits over two consecutive years. The results indicated that all traits in the population exhibited extensive variation—with coefficients of variation(CV) ranging from 14.68% to 100.97%—and followed a continuous normal distribution, conforming to the quantitative genetic patterns controlled by minor polygenes. Notably, starch content and yield demonstrated significant positive heterosis and high broad-sense heritability. Through principal component analysis(PCA), nine comprehensive indicators were extracted(cumulative variance contribution rate of 84.80%). Combined with fuzzy membership function and hierarchical cluster analysis, 17 elite clones with high processing potential were successfully identified, five of which reached stringent processing quality standards. Moreover, the least absolute shrinkage and selection operator(LASSO) regression algorithm was introduced to precisely screen five core indicators, including cohesiveness and reducing sugar content, from the 24-dimensional dataset to construct a simplified model for efficient early-generation screening. This study provides reliable methodological support for the efficient breeding and germplasm evaluation of processing potato varieties.

Key words: potato, F1 population, genetic tendency, least absolute shrinkage and selection operator, simplified model

CLC Number: