数字农科院2.0

Linkage Disequilibrium In North China A.nd Xingjiang Apricot Cultivars ( Prunus Armeniaca L.)

文献类型： 外文期刊

作者： Zhang, Q. P.;Zhang, A. M.;Zhang, A. M.;Liu, W. S.;Liu, S.;Liu, D. C.;Liu, D. C.;Liu, N.

作者机构：

关键词： apricot; linkage disequilibrium; PCoA; Prunus armeniaca L.; SSR

期刊名称： XVI INTERNATIONAL SYMPOSIUM ON APRICOT BREEDING AND CULTURE

ISSN： 0567-7572

年卷期： 2018 年 1214 卷

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摘要： Linkage disequilibrium (LD) in the genome of the Chinese apricot (Prunus armeniaca L.) was analyzed using 56 simple sequence repeat (SSR) markers in 52 Chinese cultivars from two main eco-geographical populations from the North China and Xinjiang regions (northwest of China). All 56 SSR markers were detected in 621 alleles in the North China population and 391 alleles in the Xinjiang population. The average gene diversity of the North China population was 0.71, while it was 0.647 for the Xinjiang population. Polymorphism information content ranged from 0.114 (marker BPPCT006) to 0.911 (CPPCT017) in the North China population, with a mean of 0.686, and ranged from 0.09 (CPPCT029) to 0.891 (CPPCT030) in the Xinjiang population, with a mean of 0.622. Across the entire genome, the average LD coefficient r2 value was 0.0854 for the North China population and 0.0972 for the Xinjiang population. LD decayed rapidly with a genetic distance of 0.19 cM in the North China population and 4.02 cM in the Xinjiang population, with r(2)=0.1. For linkage groups, average r2 values ranged from 0.0575 for LG8 to 0.01001 for LG7, with a mean genetic distance of 9.22 cM, in the North China population; average r2 values ranged from 0.0704 for LG3 to 0.1243 for LG8, with a mean genetic distance of 8.36 cM, in the Xinjiang population. The low LD may be attributable to 1) a high recombination rate, 2) the rich genetic diversity of the Chinese apricot center of origin, or 3) low allele frequency caused by numerous rare alleles. Based on the LD in Chinese apricot, a high density of markers for genome-wide association analysis or a candidate gene approach using markers targeted to only a few genes are the preferred approaches for association mapping.

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