基于 SSR 标记的苦瓜(Momordica charantia L.)产量和产量性状 QTL 图谱绘制与遗传分析

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Ipsita Panigrahi , Tusar Kanti Behera , A.D. Munshi , S.S. Dey , A.B. Gaikwad , Manoranjan Senapati
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引用次数: 0

摘要

本研究使用简单序列重复(SSR)标记对苦瓜的产量和其他产量性状进行 QTL 测绘。在两个对比亲本(DBGS-2 和 Pusa Purvi)中共筛选出 630 个 SSR 标记,其中只有 35 个具有多态性。利用这些多态性标记确定了杂交的 F1 植株(10 个个体),并进一步利用这些植株建立了由 120 株植株组成的制图群体(F2)。从更宽的特征值范围可以看出,制图群体中存在更高的变异。连续的频率分布等级与钟形对称正态分布曲线相结合,揭示了所研究性状的定量遗传性质。所有性状的 PCV 均高于 GCV,而且大多数性状的 PCV 与 GCV 之间的差异很大,这表明在制图群体中,环境对这些性状的表达有更大的影响。大多数性状还存在转性分离。在不同的连接组(LG)中,LG4 的标记数最多,覆盖了 171.07 cM 的图距。LG4 也拥有最多的 QTL(9 个),而 LG1 有 6 个。利用多态性 SSR 进行 QTL 绘图,共检测到 14 个性状的 28 个 QTL,即分别是单株产量(公斤)、早熟性或与花相关的性状(节间至第一朵雌花、节间至第一朵雄花、至第一朵雄花的天数、雌雄花比)、果实性状(单株果实数、果实直径(厘米)、果实长度/直径比、果皮厚度(毫米)和每粒果实的种子数)和无性系性状(节间长度(厘米)、主枝数、叶片宽度(厘米)、长度和宽度比)。这些 QTL 的 LOD 值从 3.01 到 64.47 不等,总表型变异(PVE)从 1.52 到 34.57 % 不等,加性效应从 -3.69 到 17.07 不等。其中,有 19 个主要 QTL,其 PVE 为 10%。单株产量检测到 3 个 QTL,而早熟性状(即第一朵雄花、第一朵雌花和第一朵雄花的节数)共检测到 7 个 QTL。在所有检测到的 QTLs 中,qFrtLDR-4-1(K)的 LOD(64.47)和 PVE(34.57 %)值最大。第一个热点有四个 QTL [qLfW-1-1、qMFR-1-1 (K)、qSPF-1-1、qNFmlF-1-1 (K)],第二个热点有两个与单株产量有关的 QTL [qYldpl-1-1 (K) 和 qYldpl-1-1]。其中许多 QTL 也是首次在苦瓜中报道。本研究的结果可用于 MAS、DNA 指纹、遗传图谱、基因组学分析等,从而加快苦瓜改良的步伐。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SSR markers based QTL mapping and genetic analysis for yield and yield-attributing traits in bitter gourd (Momordica charantia L.)
The present study was conducted for QTL mapping for yield and other yield attributing traits in bitter gourd using Simple Sequence Repeat (SSR) markers. A total of 630 SSR markers were screened for polymorphism in two contrasting parents (DBGS-2 and Pusa Purvi), out of which only 35 were polymorphic. F1 plants (10 individuals) in which hybridity was ascertained (using these polymorphic markers); were further employed for development of mapping population (F2) consisting of 120 plants. Higher variation was present in the mapping population as evident from the wider range value of the characters. Continuous frequency distribution classes combined with bell-shaped, symmetrical normal distribution curve, revealed the quantitative inheritance nature of the traits studied. Higher PCV than the GCV for all the traits, along with high difference between the PCV and GCV for majority of traits indicated higher influence of environment in expression of these traits in the mapping population. Presence of transgressive segregation was also noted for majority of the traits. Amongst the various linkage groups (LG), LG 4 had the maximum number of markers, covering 171.07 cM map distance. LG4 also possessed the maximum number (nine) of QTLs while LG1 had six. QTL mapping using polymorphic SSRs resulted in detection of a total of 28 QTLs for fourteen traits viz. yield per plant (kg), earliness or flower related traits (node to first female flower, node to first male flower, days to first male flower, male to female flower ratio), fruit traits (fruit number per plant, fruit diameter (cm), fruit length/ diameter ratio, pericarp thickness (mm) and number of seed per fruit) and vegetative traits (internodal length (cm), number of primary branches, leaf width (cm), length and width ratio). The LOD score of these QTLs ranged from 3.01 to 64.47, the total phenotypic variances (PVE) ranged from 1.52 to 34.57 % and additive effects ranged from –3.69 to 17.07. Of the total, nineteen were major QTLs, having PVE >10 %. Three QTLs were detected for yield per plant while a total of seven for the traits imparting earliness viz. days to first male flower, node to first female flower and node to first male flower. Amongst all the QTLs detected, qFrtLDR-4-1 (K) had the maximum LOD (64.47) and PVE (34.57 %) value. Two hotspots were detected with multiple QTLs clustered in the LG 1. The first hotspot possessed four QTLs [qLfW-1-1, qMFR-1-1 (K), qSPF-1-1, qNFmlF-1-1 (K)] while the second had two related to yield per plant [qYldpl-1-1 (K) and qYldpl-1-1]. Many of these QTLs are also being reported for the first time in bitter gourd. The findings of the present study can be used to fasten the bitter gourd improvement by utilizing these in MAS, DNA fingerprinting, genetic mapping, genomics analysis etc.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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