首页> 美国卫生研究院文献>PLoS Clinical Trials >Construction of an SNP-based high-density linkage map for flax (Linum usitatissimum L.) using specific length amplified fragment sequencing (SLAF-seq) technology
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Construction of an SNP-based high-density linkage map for flax (Linum usitatissimum L.) using specific length amplified fragment sequencing (SLAF-seq) technology

机译:使用特定长度扩增片段测序(SLAF-seq)技术构建亚麻(Linum usitatissimum L.)的基于SNP的高密度连锁图谱

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摘要

Flax is an important crop for oil and fiber, however, no high-density genetic maps have been reported for this species. Specific length amplified fragment sequencing (SLAF-seq) is a high-resolution strategy for large scale de novo discovery and genotyping of single nucleotide polymorphisms. In this study, SLAF-seq was employed to develop SNP markers in an F2 population to construct a high-density genetic map for flax. In total, 196.29 million paired-end reads were obtained. The average sequencing depth was 25.08 in male parent, 32.17 in the female parent, and 9.64 in each F2 progeny. In total, 389,288 polymorphic SLAFs were detected, from which 260,380 polymorphic SNPs were developed. After filtering, 4,638 SNPs were found suitable for genetic map construction. The final genetic map included 4,145 SNP markers on 15 linkage groups and was 2,632.94 cM in length, with an average distance of 0.64 cM between adjacent markers. To our knowledge, this map is the densest SNP-based genetic map for flax. The SNP markers and genetic map reported in here will serve as a foundation for the fine mapping of quantitative trait loci (QTLs), map-based gene cloning and marker assisted selection (MAS) for flax.
机译:亚麻是油和纤维的重要农作物,但是,尚未报道该物种的高密度遗传图谱。特定长度扩增片段测序(SLAF-seq)是用于大规模从头发现和单核苷酸多态性基因分型的高分辨率策略。在这项研究中,SLAF-seq用于开发F2群体中的SNP标记,以构建亚麻的高密度遗传图谱。总共获得了1.629亿对配对读物。雄性亲本的平均测序深度为25.08,雌性亲本的平均测序深度为32.17,每个F2后代的平均测序深度为9.64。总共检测到389,288个多态性SLAF,由此开发了260,380个多态性SNP。过滤后,发现适合于遗传图谱构建的4,638个SNP。最终的遗传图谱包括15个连锁组上的4,145个SNP标记,长度为2,632.94 cM,相邻标记之间的平均距离为0.64 cM。就我们所知,该图是亚麻中最密集的基于SNP的遗传图。此处报道的SNP标记和遗传图谱将为亚麻定量性状位点(QTL),基于图谱的基因克隆和标记物辅助选择(MAS)的精细定位奠定基础。

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