Enriching an intraspecific genetic map and identifying QTL for fiber quality and yield component traits across multiple environments in Upland cotton (Gossypium hirsutum L.)
Enriching an intraspecific genetic map and identifying QTL for fiber quality and yield component traits across multiple environments in Upland cotton (Gossypium hirsutum L.)
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丰富陆地棉（Gossypium hirsutum L.）多种环境下的种内遗传图谱并鉴定纤维质量和产量组成性状的 QTL
DOI：
10.1007/s00438-017-1347-8
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发表时间：
2017-12-01
影响因子：
3.1
通讯作者：
Zhang, Zhengsheng
中科院分区：
文献类型：
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作者：
Liu, Xueying;Teng, Zhonghua;Zhang, Zhengsheng
棉花是一种重要的经济作物，在许多领域都发挥着不可或缺的作用。构建高密度遗传图谱和鉴定控制农艺性状的稳定QTL是分子标记辅助选择的必要前提。根据G.以中棉所35和豫棉1号为作图亲本，对180个材料进行了SSR标记分析，结果表明，在中棉所35 ×豫棉1号的重组自交系群体中，有712个SSR标记表现多态性。对712个多态性SSR标记中的726个位点进行了遗传连锁分析，沿着对前期获得的1379个SSR位点进行了连锁分析，构建了一张包含2051个位点的高密度遗传图谱，图谱跨度为3508.29 cM，相邻标记间的平均距离为1.71 cM。连锁图上的标记顺序与aG上相应的物理顺序高度一致。多毛物基因组序列根据6个环境中纤维品质和产量构成性状的数据，通过2种分析方法共定位到113个QTL。在这113个QTL中，有50个被认为是稳定的（在多个环境中检测到或由加性效应解释的表型方差大于环境效应），这50个QTL中有18个被两种方法鉴定为稳定的。这18个QTLs（其中11个与纤维品质相关，7个与产量构成性状相关）可作为MAS的首选QTL。
Cotton is a significant commercial crop that plays an indispensable role in many domains. Constructing high-density genetic maps and identifying stable quantitative trait locus (QTL) controlling agronomic traits are necessary prerequisites for marker-assisted selection (MAS). A total of 14,899 SSR primer pairs designed from the genome sequence ofG. raimondiiwere screened for polymorphic markers between mapping parents CCRI 35 and Yumian 1, and 712 SSR markers showing polymorphism were used to genotype 180 lines from a (CCRI 35 × Yumian 1) recombinant inbred line (RIL) population. Genetic linkage analysis was conducted on 726 loci obtained from the 712 polymorphic SSR markers, along with 1379 SSR loci obtained in our previous study, and a high-density genetic map with 2051 loci was constructed, which spanned 3508.29 cM with an average distance of 1.71 cM between adjacent markers. Marker orders on the linkage map are highly consistent with the corresponding physical orders on aG. hirsutumgenome sequence. Based on fiber quality and yield component trait data collected from six environments, 113 QTLs were identified through two analytical methods. Among these 113 QTLs, 50 were considered stable (detected in multiple environments or for which phenotypic variance explained by additive effect was greater than environment effect), and 18 of these 50 were identified with stability by both methods. These 18 QTLs, including eleven for fiber quality and seven for yield component traits, could be priorities for MAS.