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...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 associa- tion 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 related- ness, with 52.78% of lines showing K=o. Linkagedisequilibrium （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.展开更多
基金supported by the earmarked fund for China Agriculture Research System(CARS-14)the Taishan Scholars at Seed Industry Talent ProjectPeanut Seed Industry Project in Shandong province of Chinathe earmarked fund for Agriculture Research System in Shandong province of China(SDAIT-04-03)
文摘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 associa- tion 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 related- ness, with 52.78% of lines showing K=o. Linkagedisequilibrium （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.
