QTL Mapping of Yield and Fiber Quality Traits in an Upland Cotton RIL Population

HUANG Sha; ZHANG Zhengsheng; WANG Wenwen; LIU Dajun

doi:10.13718/j.cnki.xdzk.2023.01.006

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Cotton is the leading natural fiber crop, and also an important protein crop and oil crop in the world. A RIL population consisting of 184 lines (Yumian 1 × Chaozao 3) was constructed in this study. The SNP markers obtained by SLAF-seq and polymorphic SSR markers were used to construct a high-density genetic map. The phenotypic data of yield and fiber quality traits detected in multiple environments were used to locate QTL for yield and fiber quality traits. The results are as follows: ① Linkage analysis was conducted on 8 020 markers, and a genetic map containing 2 945 (41 SSR and 2 904 SNP) loci were finally constructed. The genetic length of the constructed map was 4 650.71 cM, and covered 98.30% of the upland cotton genome. ② Totally, 35 QTLs for yield traits and 41 QTLs for fiber quality traits were detected, which explained 6.4% to 23.4% of the phenotypic variation, and their LOD value ranged from 2.50 to 7.76. ③ Ten QTLs were detected in two or more environments, which were environment-stable QTL.

Corresponding author: LIU Dajun

Received Date: 25/03/2022

Available Online: 20/01/2023

Key words:

Abstract: Cotton is the leading natural fiber crop, and also an important protein crop and oil crop in the world. A RIL population consisting of 184 lines (Yumian 1 × Chaozao 3) was constructed in this study. The SNP markers obtained by SLAF-seq and polymorphic SSR markers were used to construct a high-density genetic map. The phenotypic data of yield and fiber quality traits detected in multiple environments were used to locate QTL for yield and fiber quality traits. The results are as follows: ① Linkage analysis was conducted on 8 020 markers, and a genetic map containing 2 945 (41 SSR and 2 904 SNP) loci were finally constructed. The genetic length of the constructed map was 4 650.71 cM, and covered 98.30% of the upland cotton genome. ② Totally, 35 QTLs for yield traits and 41 QTLs for fiber quality traits were detected, which explained 6.4% to 23.4% of the phenotypic variation, and their LOD value ranged from 2.50 to 7.76. ③ Ten QTLs were detected in two or more environments, which were environment-stable QTL.

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棉花作为全球重要的经济农作物，是最多的天然纤维来源，被广泛应用于织造行业. 栽培棉花多为异源四倍体，由两个异源二倍体棉属经A基因组与D基因组杂交后染色体加倍形成^[1]. 陆地棉(Gossypium hirsutum)作为最重要的异源四倍体棉种占领了超过全球95%的棉花市场. 产量和纤维品质是棉花研究中最受关注的部分^[2]，但棉花产量与纤维品质呈负相关，难以同时改良^[3]，是培育优异棉种的巨大挑战. 棉花产量及纤维品质作为数量性状，受多个基因控制. 探究棉花产量与纤维品质的遗传决定因素，利用分子标记检测来为目的基因定位提供依据，对棉花品质改良具有重要意义. 由于陆地棉种内杂交多态性低，因此构建高密度的陆地棉种内遗传图谱是其分子标记辅助选择育种的关键.

构建高密度遗传图谱需要寻找众多的分子标记. 简单序列重复(Simple sequence repeat，SSR)标记分布多且广，实验重复性好且结果可靠，可以在等位基因间显示多个差异^[4-5]. 简化基因组测序(Specific locus amplified fragment sequencing，SLAF-seq)是在高通量测序的背景下发展起来的^[6-7]. SLAF-seq的3个显著优势是：深度测序保证了基因分型准确；测序成本低；正式测序前利用预测系统提升标记效率. SLAF-seq近年来已被广泛用于单核苷酸多样性(Single nucleotide polymorphism，SNP)检测^[8-11].

数量性状位点(Quantitative trait loci，QTL)是在调控生物体数量性状中起重要作用的基因片段在染色体上的位置，数量性状不仅受多个QTL的影响，也与环境互作相关，部分数量性状存在一个主效基因对该性状的控制起主导作用，农作物的产量性状一般为数量性状. 对棉花而言，产量性状、纤维品质和抗逆性等都是数量性状. CottonQTLdb搜集了大量来自全球棉花纤维品质、产量性状、抗病性、耐盐等各类性状的QTL，在release 2.3版本中囊括了截至2018年1月来自156个刊物的4 892个QTL^[12-13]；Ijaz等^[14]总结了2017-2019年多个研究者定位的、与纤维品质相关的稳定QTL.

本研究运用SLAF-seq SNP技术结合SSR分子标记构建高密度遗传图谱，帮助增加遗传图谱的准确性，缩小了QTL长度区间，使QTL定位更加准确高效，可有效推进后续精细定位群体选择和候选基因筛选，为陆地棉产量和纤维品质的分子育种提供参考.

1. 材料与方法

1.1. 材料

杂交母本为渝棉1号，由西南大学棉花研究室自主培育而成，产量高、纤维品质好. 父本为超早3号，由中国农业科学院棉花研究所提供，具有早熟、无腺体、株型紧凑等特点. 重组近交系(RIL)F_2：6群体(包含184系)及两亲本材料分别于2019年夏种植于重庆歇马棉花种植基地，2019年冬种植于海南三亚，2020年夏种植于新疆库尔勒和新疆奎屯.

1.2. DNA提取与SLAF-seq测序

提取2019年夏重庆歇马棉花种植基地的棉花植株嫩叶DNA，提取过程参照Zhang等^[15]改良的CTAB(Cetyltrimethylammonium Bromide)法. 将提取的184株棉花全套DNA送至北京百迈客生物科技有限公司进行SLAF-seq测序. 参考棉花基因组来自http://ibi.zju.edu.cn/cotton/，利用北京百迈客生物科技有限公司自主研发的基因组酶切软件对DNA进行酶切预测，选择HaeIII+Hpy166II作为最适内切酶组合，经酶切得到的片段范围长度在414~464 bp之间，预测有254 089个SLAF(Specific-Locus Amplified Fragment)标签，经日本晴酶切显示，Control数据双端比对效率为88.74%，酶切效率为84.23%，构建的SLAF库合格.

1.3. SSR分子标记

本研究筛选引物包括CCRI，SWU，JESPR，Gh，NAU，BNL，CIR，CGR，HAU，DOW，DPL^[16]. 引物CCRI的设计参照亚洲棉基因组^[17]，引物SWU的设计参照雷蒙德氏棉基因组^[18]，其他来源于棉花引物数据库http://www.cottonmarker.org. PCR反应体系：1.5 μL 10x Easy Taq PCR Buffer(Mg²⁺)、0.2 μL 2 mmol/L dNTP、0.3 μL 25 mmol/L Mg²⁺、0.2 μL 10 mmol/L前引物(pF)、0.2 μL 10 mmol/L后引物、再用ddH2O定容至10 μL. PCR反应流程：预变性94 ℃ 5 min、35个循环(变性94 ℃ 30 sec、退火53 ℃ 30 sec、延伸72 ℃ 30 sec)、延伸72 ℃ 7 min、保温16 ℃ 30 sec. 采用聚丙烯酰胺凝胶电泳法筛选多态性SSR引物，并利用筛选得到的具多态性引物对群体进行基因型检测.

1.4. 遗传图谱构建与QTL定位

将测序结果进行筛选，并将基因型缺失大于80%的标记去除，利用JoinMap 4^[19]软件对经SSR分子标记和简化基因组测序得到的SNP标记进行联合分析，作图映射函数为Kosambi函数^[20]，去除共分离后的位点用于构建遗传图谱，参照Li等^[21]测序得到的陆地棉基因组长度计算各染色体的基因组覆盖率.

利用MapQTL^©6.0^[22-24]对群体进行QTL定位，导入所有环境位点基因型数据和表型数据，选择区间作图法运算得到每个环境QTL的遗传距离，设置似然比对数LOD≥2.5的位点作为确定的QTL邻近标记，LOD值减1的区间作为置信区间，将多个环境检测到的同一性状、同一位置的QTL记为同一个QTL，QTL的命名方式为“q性状-染色体-序号”. 利用MapChart将得到的QTL定位在遗传图谱上. QTL簇的命名方式为“qClu-染色体-序号”.

1.5. 数据统计分析

铃质量(BW)、衣分(LP)、籽指(SI)、衣指(LI)按常规方法测定；纤维上半部平均长度(FL)、纤维整齐度指数(FU)、纤维断裂比强度(FS)、纤维马克隆值(FM)、纤维伸长率(FE)送至中国农业科学院棉花研究所进行纤维检测. 各性状间的相关性系数图由R语言绘制：https//www.R-project.org/.

3. 讨论

3.1. 遗传图谱构建

分子遗传图谱的构建已经在植物的分子生物学研究中有了广泛的应用^[25-27]. 分子标记是构建遗传图谱的前提，在RFLP(Restriction fragment length polymorphism)，RAPD(Random Amplified Polymorphic DNA)，AFLP(Amplified Fragment Length Polymorphism)等众多的传统标记方式中，本研究选择了广泛应用的SSR分子标记技术，该技术准确度高，可重复利用^[28]，但工作量大且耗时长. SNP高多态性配合SLAF-seq技术高效且价格相对便宜的优势，成为分子标记的新选择. 在测序结果分析时，筛除了SNP标记中基因型缺失大于80%或者位置明显错误的标记，导致部分区域分子标记存在较大间隙. 本研究结合了SSR分子标记和SLAF-seq SNP技术，且两者相互补充，提高了遗传图谱构建的准确性.

3.2. QTL定位

高密度遗传图谱是QTL定位的有力保障，多个环境及多个性状的测量使QTL定位更加准确. 在众多QTL定位研究中，定位到的QTL数量差异很大，是因为遗传群体类型不同，亲本遗传背景不同，分子标记类型不同，遗传图谱密度不同，多数表型性状受环境影响大等各类差异所致^[29-31]. 研究定位到的QTL增效基因来源不同且QTL加性效应来源也不一致，表明在子代表型统计时出现了超亲分离现象. 本研究结合高密度遗传图谱与多环境性状定位到的76个产量和纤维品质性状QTL，可以为棉花品质精细定位群体的构建提供依据. 在41个纤维品质QTL中，关于纤维长度的QTL qFL-D11-1在4个环境中均被检测到，且其LOD值为所有QTL中的最大值7.76，可为后续棉花纤维品质的图位克隆提供重要参考.

3.3. QTL簇

QTL成簇是在棉花、水稻、玉米等许多农作物的QTL定位中都存在的现象^[32-34]. 关于棉花QTL成簇现象，置信区间内存在“一因多效”现象的解释被广泛认可，Ulloa等^[35]指出棉花的重要农艺性状QTL在染色体上表现出高度重组和基因富集的现象；Rong等^[36]提出QTL簇的出现还可能是纤维相关QTL代表了一组具有新功能的小基因家族，棉花纤维发育过程的变异涉及复杂的网络或相互作用的基因. 本研究过程中发现当遗传图谱标记间隔较大，定位到的QTL置信区间较大时，QTL之间易出现区间交叉形成QTL簇，因此高密度遗传图谱的构建尤为重要. 本研究在A07染色体上的QTL簇qClu-A07-1包含了籽指、衣分、铃质量3个产量性状的QTL，且加性效应均来自渝棉1号，表明该位点可实现对陆地棉的3个性状同时改良.

4. 结论

本研究构建了一张高密度遗传图谱，包含2 945个上图位点，总遗传长度为4 650.71 cM，图谱物理总长为2 202.95 Mb，覆盖基因组总长的98.30%. 定位到76个QTL，包括产量性状QTL 35个，纤维品质性状QTL 41个，LOD值分布在2.50~7.76之间，解释表型变异率为6.4%~23.4%. 本研究可为后续精细群体构建及候选基因筛选奠定基础，为早熟棉的产量与纤维品质相关育种提供参考.

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[1]	WENDEL J F. New World Tetraploid Cotton Contain Old World Cytoplasm[J]. Proe. Natl. Aead. Sei. USA, 1989, 86(11): 4132-4136. doi: 10.1073/pnas.86.11.4132 CrossRef Google Scholar
[2]	金宇豪, 阳会兵, 高倩文, 等. 陆地棉纤维品质和农艺性状遗传多样性分析及优良材料鉴定[J]. 东北农业大学学报, 2022, 53(2): 1-12. Google Scholar
[3]	GU Q S, KE HF, LIU Z W, et al. A High-Density Genetic Map and Multiple Environmental Tests Reveal Novel Quantitative Trait Loci and Candidate Genes for Fibre Quality and Yield in Cotton[J]. Theoretical and Applied Genetics, 2020, 133(12): 3395-3408. doi: 10.1007/s00122-020-03676-z CrossRef Google Scholar
[4]	NIE G, HUANG T, MA X, et al. Genetic Variability Evaluation and Cultivar Identification of Tetraploid Annual Ryegrass Using SSR Markers[J]. PeerJ, 2019, 7: e7742. doi: 10.7717/peerj.7742 CrossRef Google Scholar
[5]	WU Y Z, HUANG L Y, ZHOU D Y, et al. Development and Application of Perfect SSR Markers in Cotton[J]. Journal of Cotton Research, 2020, 3(1): 207-214. Google Scholar
[6]	SUN X, LIU D, ZHANG X, et al. SLAF-seq: an Efficient Method of Large-Scale De Novo SNP Discovery and Genotyping Using High-Throughput Sequencing[J]. Plos One. 2013, 8(3): e58700. doi: 10.1371/journal.pone.0058700 CrossRef Google Scholar
[7]	ZHANG J, ZHANG Q X, CHENG T R, et al. High-Density Genetic Map Construction and Identification of a Locus Controlling Weeping Trait in an Ornamental Woody Plant (Prunus Mume Sieb. et Zucc)[J]. DNA Research, 2015, 22(3): 183-191. doi: 10.1093/dnares/dsv003 CrossRef Google Scholar
[8]	ZHANG K, KURAPARTHY V, FANG H, et al. High-Density Linkage Map Construction and QTL Analyses for Fiber Quality, Yield and Morphological Traits Using Cottonsnp63k Array in Upland Cotton (Gossypium hirsutum L.)[J]. BMC Genomics, 2019, 20(1): 889. doi: 10.1186/s12864-019-6214-z CrossRef Google Scholar
[9]	NIU S F, ZHAI Y, WU R X, et al. Development and Characterization of 33 Polymorphic Microsatellite Markers in Trachurus Japonicus by SLAF‐Seq Technology[J]. Journal of Applied Ichthyology, 2021, 37(2): 308-313. doi: 10.1111/jai.14158 CrossRef Google Scholar
[10]	REN H L, HAN J N, WANG X R, et al. QTL Mapping of Drought Tolerance Traits in Soybean With SLAF Sequencing[J]. The Crop Journal, 2020, 8(6): 977-989. doi: 10.1016/j.cj.2020.04.004 CrossRef Google Scholar
[11]	HUANG KY, SHI Y L, PAN G, et al. Genome-Wide Association Analysis of Fiber Fineness and Yield in Ramie (Boehmeria Nivea) Using SLAF-Seq[J]. Euphytica, 2021, 217(2): 1-16. Google Scholar
[12]	SAID J I, KNAPKA J A, SONG M Z, et al. Cotton QTLdb: A Cotton QTL Database for QTL Analysis, Visualization, and Comparison Between Gossypium hirsutum and G. hirsutum×G. barbadense Populations[J]. Molecular Genetics and Genomics, 2015, 290(4): 1615-1625. doi: 10.1007/s00438-015-1021-y CrossRef Google Scholar
[13]	SAID J I, LIN Z X, ZHANG X L, et al. A Comprehensive Meta QTL Analysis for Fiber Quality, Yield, Yield Related and Morphological Traits, Drought Tolerance, and Disease Resistance in Tetraploid Cotton[J]. BMC Genomics, 2013, 14: 776. doi: 10.1186/1471-2164-14-776 CrossRef Google Scholar
[14]	IJAZ B, ZHAO N, KONG J, et al. Fiber Quality Improvement in Upland Cotton (Gossypium hirsutum L.): Quantitative Trait Loci Mapping and Marker Assisted Selection Application[J]. Front Plant Sci. 2019, 10: 1585. doi: 10.3389/fpls.2019.01585 CrossRef Google Scholar
[15]	ZHANG Z S, XIAO Y H, LUO M, et al. Construction of a Genetic Linkage Map and QTL Analysis of Fiber-Related Traits in Upland Cotton(Gossypium hirsutum L.)[J]. Euphytica, 2005, 144(1/2): 91-99. Google Scholar
[16]	王文文. 陆海种间图谱加密与染色体结构变异分析[D]. 重庆: 西南大学, 2017. Google Scholar
[17]	WANG B H, ZHUANG Z M, ZHANG Z S, et al. Advanced Backcross QTL Analysis of Fiber Strength and Fineness in a Cross Between Gossypium hirsutum and G. mustelinum Frontiters In Plant[J]. Frontiers in Plant Science, 2017, 8: 1848. doi: 10.3389/fpls.2017.01848 CrossRef Google Scholar
[18]	PATERSON A H, WENDEL J F, GUNDLACH H, et al. Repeated Polyploidization of Gossypium Genomes and the Evolution of Spinnable Cotton Fibres[J]. Nature, 2012, 492(7429): 423-427. doi: 10.1038/nature11798 CrossRef Google Scholar
[19]	SOULARD L, MOURNET P, GUITTON B, et al. Construction of Two Genetic Linkage Maps of Taro Using Single Nucleotide Polymorphism and Microsatellite Markers[J]. Molecular Breeding, 2017, 37: 1-15. doi: 10.1007/s11032-016-0586-4 CrossRef Google Scholar
[20]	KOSAMBI D D. The Estimation of Map Distances From Recombination Values[M]. New Delhi: Springer India, 2016. Google Scholar
[21]	LI F, FAN G, LU C, et al. Genome Sequence of Cultivated Upland Cotton (Gossypium hirsutum TM-1) Provides Insights into Genome Evolution[J]. Nat Biotechnol, 2015, 33(5): 524-530. doi: 10.1038/nbt.3208 CrossRef Google Scholar
[22]	ZHANG T Z, HU Y, JIANG W K, et al. Sequencing of Allotetraploid Cotton (Gossypium hirsutum) Provides a Resource for Fiber Improvement[J]. Nature Biotechnology, 2015, 33(5): 531-537. doi: 10.1038/nbt.3207 CrossRef Google Scholar
[23]	潘凤英, 刘露露, 郭泽西, 等. 华东葡萄遗传连锁图谱构建及灰霉病抗性QTL定位[J]. 南方农业学报, 2020, 53(9): 2383-2391. Google Scholar
[24]	王怡悦, 刘红, 徐姚. 以2b-RAD技术构建凡纳滨对虾遗传图谱及生长性状QTL定位[J]. 南方农业学报, 2020-09-06(网络首发), http://kns.cnki.net/kcms/detail/45.1381.S.20220905.1529.008.html. Google Scholar
[25]	FAN Z, WANG K, RAO J, et al. Interactions Among Multiple Quantitative Trait Loci Underlie Rhizome Development of Perennial Rice[J]. Frontiers in Plant Science, 2020, 11: 1-10. doi: 10.3389/fpls.2020.00001 CrossRef Google Scholar
[26]	LIU W, SONGC, REN Z. Genome-wide Association Study Reveals the Genetic Basis of Fiber Quality Traits in Upland Cotton (Gossypium hirsutum L.)[J]. BMC Plant Biology, 2020, 20(1): 1-13. doi: 10.1186/s12870-019-2170-7 CrossRef Google Scholar
[27]	DENG J, XIE X L, WANG D F, et al. Paternal Origins and Migratory Episodes of Domestic Sheep[J]. Current Biology, 2020, 30(20): 4085-4095. doi: 10.1016/j.cub.2020.07.077 CrossRef Google Scholar
[28]	SHAHEEN T, ZAFAR Y, RAHMAN M, et al. QTL Mapping of Some Productivity and Fibre Traits in Gossypium arboreum[J]. Turkish Journal of Botany, 2013, 37: 802-810. doi: 10.3906/bot-1209-47 CrossRef Google Scholar
[29]	NOORMOHAMMADI Z, HASHEMINEJAD-AHANGARANI FARAHANI Y, SHEIDAI M, et al. Genetic Diversity Analysis in Opal Cotton Hybrids Based on SSR, ISSR, and RAPD Markers[J]. Genetics and Molecular Research, 2013, 12(1): 256-269. doi: 10.4238/2013.January.30.12 CrossRef Google Scholar
[30]	张正圣. 陆地棉遗传连锁图谱的构建与纤维相关性状的QTL分析[D]. 重庆: 西南农业大学, 2005. Google Scholar
[31]	ZHANG S W, WANG T, LIU Q, et al. Quantitative Trait Locus Analysis of Boll-Related Traits in an Intraspecific Population of Gossypium hirsutum[J]. Euphytica, 2015, 203(1): 121-144. doi: 10.1007/s10681-014-1281-3 CrossRef Google Scholar
[32]	ABDELRAHEEM A, FANG D D, DEVER J, et al. QTL Analysis of Agronomic, Fiber Quality, and Abiotic Stress Tolerance Traits in a Recombinant Inbred Population of Pima Cotton[J]. Crop Science, 2020, 60(4): 1823-1843. doi: 10.1002/csc2.20153 CrossRef Google Scholar
[33]	LARSON S, DEHAAN L, POLANDJ, et al. Genome Mapping of Quantitative Trait Loci (QTL) Controlling Domestication Traits of Intermediate Wheatgrass (Thinopyrum intermedium)[J]. Theoretical and Applied Genetics, 2019, 132(8): 2325-2351. doi: 10.1007/s00122-019-03357-6 CrossRef Google Scholar
[34]	YI Q, LIU Y H, HOU X B, et al. Genetic Dissection of Yield-Related Traits and Mid-Parent Heterosis for Those Traits in Maize (Zea mays L.)[J]. BMC Plant Biology, 2019, 19(1): 1-20. doi: 10.1186/s12870-018-1600-2 CrossRef Google Scholar
[35]	ULLOA M, SAHA S, JENKINS J N, et al. Chromosomal Assignment of RFLP Linkage Groups Harboring Important QTLs on an Intraspecific Cotton (Gossypium hirsutum L.) JoinMap[J]. Journal of Heredity, 2004, 96(2): 132-144. Google Scholar
[36]	RONG J K, FELTUS F A, WAGHMARE V N, et al. Meta-analysis of Polyploid Cotton QTL Shows Unequal Contributions of Subgenomes to a Complex Network of Genes and Gene Clusters Implicated in Lint Fiber Development[J]. Genetics, 2007, 176(4): 2577-2588. Google Scholar

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QTL Mapping of Yield and Fiber Quality Traits in an Upland Cotton RIL Population

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QTL Mapping of Yield and Fiber Quality Traits in an Upland Cotton RIL Population

Corresponding author: LIU Dajun