低地柳枝稷多杂交种模拟草地栽培生物能源性状的数量性状位点定位

IF 4.1 3区 工程技术 Q1 AGRONOMY
Surya L. Shrestha, Christian M. Tobias, Fred Allen, Jennifer Bragg, Ken Goddard, Hem S. Bhandari
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引用次数: 0

摘要

柳枝稷(Panicum virgatum L.)是木质纤维素生物质生产生物能源的潜在来源。生物能源性状是定量遗传的。本研究利用近红外光谱定量性状位点(QTL)定位了生物能源性状的变异。本研究于2020年和2021年在田纳西州的两个环境中,采用随机完全区组设计,每个地点2个重复,对由低地栽培品种Alamo和Kanlow杂交而成的8个杂交群体(30 ~ 96个f1)进行了评价。杂交种间各性状差异均显著(p≤0.05)。通过基因型测序生成的包含17251个单核苷酸多态性(snp)的连锁图谱用于QTL定位。对不同种群和不同环境(年份和地点)的性状进行QTL分析,共检测到74个显著QTL峰,LOD分数在3.0 ~ 6.9之间。QTL解释的表型变异率(PVE)为2.1% ~ 7.4%。在4N、5K、5N、8K、8N和9N染色体上分别鉴定出10个预测乙醇产量的QTL,其中PVE值最高的QTL位于5N染色体上(7.4%)。在1K、1N、2N、5K、5N、7K和8N染色体上鉴定出4个纤维素QTL和3个半纤维素QTL, PVE范围为2.1% ~ 5.8%。1N、4K、5N和7K染色体区域具有多效性,影响多种生物能性状。与QTL相关的snp将有助于通过标记辅助育种改善生物能源性状。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mapping Quantitative Trait Loci for Bioenergy Traits in Multiple Hybrid Populations of Lowland Switchgrass in Simulated-Sward Planting

Mapping Quantitative Trait Loci for Bioenergy Traits in Multiple Hybrid Populations of Lowland Switchgrass in Simulated-Sward Planting

Switchgrass (Panicum virgatum L.) is a potential source of producing bioenergy from lignocellulosic biomass. Bioenergy traits are quantitatively inherited. This study localized variation in bioenergy traits estimated via near infrared spectroscopy using quantitative trait loci (QTL) mapping. Eight hybrid populations (30 to 96 F1s) developed by crossing lowland cultivars, Alamo and Kanlow, were evaluated in two environments in Tennessee using a randomized complete block design with two replications per location in 2020 and 2021. The hybrid populations exhibited significant variation for all the studied traits (p ≤ 0.05). A linkage map including 17,251 single nucleotide polymorphisms (SNPs) generated through genotype-by-sequencing was used for the QTL mapping. The QTL analyses were performed on the traits across populations in each and across environments (years and locations) and detected a total of 74 significant QTL peaks with the logarithm of odds (LOD) scores ranging from 3.0 to 6.9. Phenotypic variability explained (PVE) by QTL varied from 2.1% to 7.4%. Ten QTL for predicted ethanol yield were identified on chromosomes 4N, 5K, 5N, 8K, 8N, and 9N, respectively, in which the major QTL resided on chromosome 5N with the highest PVE value (7.4%). Four cellulose and three hemicellulose QTL were identified on chromosomes 1K, 1N, 2N, 5K, 5N, 7K, and 8N, with PVE ranging from 2.1% to 5.8%. The chromosomal regions of 1N, 4K, 5N, and 7K had pleiotropic effects affecting multiple bioenergy traits. SNPs linked to QTL will be useful for improving bioenergy traits through marker-assisted breeding.

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来源期刊
Global Change Biology Bioenergy
Global Change Biology Bioenergy AGRONOMY-ENERGY & FUELS
CiteScore
10.30
自引率
7.10%
发文量
96
审稿时长
1.5 months
期刊介绍: GCB Bioenergy is an international journal publishing original research papers, review articles and commentaries that promote understanding of the interface between biological and environmental sciences and the production of fuels directly from plants, algae and waste. The scope of the journal extends to areas outside of biology to policy forum, socioeconomic analyses, technoeconomic analyses and systems analysis. Papers do not need a global change component for consideration for publication, it is viewed as implicit that most bioenergy will be beneficial in avoiding at least a part of the fossil fuel energy that would otherwise be used. Key areas covered by the journal: Bioenergy feedstock and bio-oil production: energy crops and algae their management,, genomics, genetic improvements, planting, harvesting, storage, transportation, integrated logistics, production modeling, composition and its modification, pests, diseases and weeds of feedstocks. Manuscripts concerning alternative energy based on biological mimicry are also encouraged (e.g. artificial photosynthesis). Biological Residues/Co-products: from agricultural production, forestry and plantations (stover, sugar, bio-plastics, etc.), algae processing industries, and municipal sources (MSW). Bioenergy and the Environment: ecosystem services, carbon mitigation, land use change, life cycle assessment, energy and greenhouse gas balances, water use, water quality, assessment of sustainability, and biodiversity issues. Bioenergy Socioeconomics: examining the economic viability or social acceptability of crops, crops systems and their processing, including genetically modified organisms [GMOs], health impacts of bioenergy systems. Bioenergy Policy: legislative developments affecting biofuels and bioenergy. Bioenergy Systems Analysis: examining biological developments in a whole systems context.
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