芝麻资源群体结构及含油量关联分析

摘要

[目的]推测芝麻资源群体材料的遗传变异、群体结构和分子亲缘关系,通过关联分析检测群体中影响含油量表型的位点,为芝麻高油育种及开展其它性状关联分析等提供科学依据.[方法]利用20对SSR引物、43对SRAP引物及16对AFLP引物,对来自芝麻核心品的216份芝麻资源进行遗传多样性分析、群体结构分析和含油量性状关联分析.[结果]共检测到338个等位变异,群体遗传多样性为0.2493、多态性信息含量为0.2090.群体结构分析将216份芝麻资源分为2个亚群(P0P1和P0P2)；其中,174份资源(80.56％)归属P0P1亚群,42份资源(19.44％)归属P0P2亚群.P0P1亚群的遗传多样性(0.2180)、多态性信息指数(0.1840)等指标值都低于P0P2亚群(分别为0.3190和0.2561),表明P0P2亚群内种质遗传变异更为丰富.AM0VA分析表明,亚群内的遗传变异(86.83％)极显著高于亚群间的遗传变异(13.17％) (P＜0.001).关联分析重复检测出2年环境下与含油量性状极显著关联的分子标记8个(P＜0.01).这8个标记的性状变异解释率变幅为0.0343(标记M20E16-8)-0.0587(标记SSR12-2),总的变异解释率为0.2846(2008年)和0.3801(2009年).[结论]以216份来自中国芝麻核心品的资源构成自然群体,群体结构简单、遗传变异较为丰富,可以用于开展芝麻重要目标性状的关联作图.应用关联分析方法在2个年度环境下重复检出8个标记与含油量性状极显著关联(P＜0.01),这些标记可能与含油量性状存在稳定、可靠相关联.%[ Objective ] Germplasm diversity is the mainstay for crop improvement and genetic dissection of complex traits. In this study, we estimated the genetic diversity and population structure in a nature population of Chinese sesame accessions, which would be of great importance for effective utilization of these germplasms for sesame improvement and association mapping of target traits in sesame. [ Method ] Totally 79 polymorphic SSR, SRAP, and AFLP primer combinations were used in amplification of 216 sesame accessions from Chinese sesame core collections. Analysis of genetic diversity, population structure, and trait association was conducted. [Result] A total of 338 polymorphic bands were generated. The genetic diversity and polymorphic information content (PIC) of nature population are 0.2493 and 0.2090, respectively. The nature population of 216 accessions was divided into two distinguishable subpopulations, named POP1 and POP2, by population structure analysis. There were 174 accessions, 80.56% in proportion, assigned into POP1 and 42 ones, 19.44% in proportion, into POP2. The genetic diversity (0.2180) and PIC (0.1840) of POP1 were lower than those of POP2, 0.3190 and 0.2561, respectively. AMOVA unraveled that substantially more genetic variation within subpopulations (86.83%) was observed than between subpopulations (13.17%) at P<0.001 level. Trait association analysis showed eight markers could be detected in two years repeatedly at highly significant level (P<0.01) with total explanation of variation of 0.2846 in Year 2008 and 0.3801 in Year 2009, which indicated these markers might be stably and affirmatively associated with QTLs controlling oil content in sesame. [ Conclusion ] An abundant genetic variation but week population structure was detected in 216 Chinese sesame accessions which indicated the nature population to be representative for further association analysis of quantitative traits in sesame. Population structure analysis showed that the nature population could be divided into two subpopulations and AMOVA revealed that the genetic variation within subpopulations was substantially more than that between subpopulations (P< 0.001). Association analysis indicated that eight markers might be associated with oil content for they could be detected associated stably with oil content at significant level (P<0.01) in Year 2008 and 2009, repeatedly. The results suggested that the population is useful for the marker-trait association mapping. This new association population has a potential to identify quantitative trait loci (QTL) with small effects, which will aid in dissecting complex traits and in exploiting diversity present in sesame germplasms.