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Establishment and evaluation of a peanut association panel and analysis of key nutritional traits
Author(s) -
Zhang Xiurong,
Zhu Suqing,
Zhang Kun,
Wan Yongshan,
Liu Fengzhen,
Sun Qingfang,
Li Yingjie
Publication year - 2018
Publication title -
journal of integrative plant biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.734
H-Index - 83
eISSN - 1744-7909
pISSN - 1672-9072
DOI - 10.1111/jipb.12601
Subject(s) - linkage disequilibrium , association mapping , biology , genetic association , arachis hypogaea , false positive paradox , selection (genetic algorithm) , peanut oil , genetic analysis , genetics , linoleic acid , population , microbiology and biotechnology , allele , haplotype , genotype , agronomy , statistics , fatty acid , mathematics , gene , medicine , single nucleotide polymorphism , biochemistry , computer science , raw material , ecology , environmental health , artificial intelligence
Breeding programs aim to improve the yield and quality of peanut ( Arachis hypogaea L.); using association mapping to identify genetic markers linked to these quantitative traits could facilitate selection efficiency. A peanut association panel was established consisting of 268 lines with extensive phenotypic and genetic variation, meeting the requirements for association analysis. These lines were grown over 3 years and the key agronomic traits, including protein and oil content were examined. Population structure (Q) analysis showed two subpopulations and clustering analysis was consistent with Q‐based membership assignment and closely related to botanical type. Relative Kinship (K) indicated that most of the panel members have no or weak familial relatedness, with 52.78% of lines showing K = 0. Linkage disequilibrium (LD) analysis showed a high level of LD occurs in the panel. Model comparisons indicated false positives can be effectively controlled by taking Q and K into consideration and more false positives were generated by K than Q. A preliminary association analysis using a Q + K model found markers significantly associated with oil, protein, oleic acid, and linoleic acid, and identified a set of alleles with positive and negative effects. These results show that this panel is suitable for association analysis, providing a resource for marker‐assisted selection for peanut improvement.