黄莉^,*, 陈玉宁, 罗怀勇, 周小静, 刘念, 陈伟刚, 雷永, 廖伯寿, 姜慧芳中国农业科学院油料作物研究所 / 农业农村部油料作物生物学与遗传育种重点实验室, 湖北武汉 430062

Advances of QTL mapping for seed size related traits in peanut

HUANG Li^,*, CHEN Yu-Ning, LUO Huai-Yong, ZHOU Xiao-Jing, LIU Nian, CHEN Wei-Gang, LEI Yong, LIAO Bo-Shou, JIANG Hui-FangOil Crops Research Institute, Chinese Academy of Agricultural Sciences / Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan 430062, Hubei, China

通讯作者: *黄莉, E-mail:huangli5100@126.com

收稿日期:2021-01-28接受日期:2021-07-29网络出版日期:2022-08-09

基金资助:

本研究由国家现代农业产业技术体系建设专项(花生)(CARS-13) 中国农业科学院科技创新工程项目资助(CAAS-ASTIP-2013-OCRI)

Received:2021-01-28Accepted:2021-07-29Published online:2022-08-09

Fund supported:

This study was supported by the China Agriculture Research System (Peanut)(CARS-13) the Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2013-OCRI)

摘要
花生是我国重要的油料作物和经济作物, 目前国内花生的产量远远不能满足消费者的所需, 进一步提高花生单产是解决花生生产供不应求的重要途径。花生种子大小相关性状是花生的重要农艺性状, 对提高花生单产至关重要。本文综述了植物种子大小的调控途径以及近年来花生分子标记、遗传图谱构建、种子大小相关性状QTL定位研究中取得的进展, 探讨了目前花生种子大小相关性状研究中面临的挑战和机遇, 对花生产量遗传改良进行了展望。
关键词： 花生;种子大小;数量性状遗传位点;连锁分析;关联分析

Abstract
Peanut is an important oil and economic crop in China. Currently, the domestic production of peanut remains far below the needs of consumers. Thus, further improving the yield per unit area is a crucial approach to meet the rising market demand. Seed size related traits are important agronomic traits in peanut, fundamentally contributing to improving yield per unit area. This review summarized the research progress on the regulatory pathways of seed size in plants, molecular markers, genetic linkage map construction, and QTL mapping of seed size related traits in peanut. We discussed the frontline challenges and opportunities for the coming researches of peanut seed related traits and the perspectives of yield improvement in peanut.
Keywords：peanut;seed size;quantitative trait locus (QTLs);linkage mapping;association mapping


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本文引用格式
黄莉, 陈玉宁, 罗怀勇, 周小静, 刘念, 陈伟刚, 雷永, 廖伯寿, 姜慧芳. 花生种子大小相关性状QTL定位研究进展. 作物学报, 2021, 48(2): 280-291 DOI:10.3724/SP.J.1006.2022.14046
HUANG Li, CHEN Yu-Ning, LUO Huai-Yong, ZHOU Xiao-Jing, LIU Nian, CHEN Wei-Gang, LEI Yong, LIAO Bo-Shou, JIANG Hui-Fang. Advances of QTL mapping for seed size related traits in peanut. Acta Crops Sinica, 2021, 48(2): 280-291 DOI:10.3724/SP.J.1006.2022.14046


花生(Arachis hypogaea L.)是我国重要的油料作物和经济作物, 是食用植物油和蛋白质的重要来源。花生及其制品以其优良的风味、品质及保健功能, 深得消费者喜爱, 在国民经济和社会发展占有重要地位。我国是世界上最大的花生生产国, 年产量在1700万吨以上, 种植面积在国际上仅次于印度居全球第2位, 占全球花生面积约17% ^[1]。近几年来我国花生总产约52%用于榨油, 是花生最大的利用途径。随着人民生活水平的不断改善, 国内花生油的市场需求量持续上升, 国内花生产量远远不能满足持续增长的消费需求。目前, 我国人均耕地资源短缺, 我国花生种植面积难以大幅增加, 花生生产供不应求的矛盾只能依靠提高花生单产来解决, 因此, 进一步提高花生单产是满足我国植物油日益增长需求的重要途径。

花生种子大小是衡量花生单产的重要指标, 花生的种子长、种子宽和籽仁重直接显著地影响花生产量。花生优良品种的选育以及新品种的推广应用极大地提高了花生的单产水平。自20世纪50年代以来, 花生品种实现了5次更新, 每次品种更新均显著提高了花生的单产^[2]。研究花生种子大小的遗传基础, 能为花生优良亲本组合的选配、优良高产种质的创制以及高产分子育种提供坚实的理论基础, 有助于加快花生高产育种进程。本文主要总结了植物种子大小的调控途径以及近年来花生在分子标记、遗传图谱构建、种子大小相关性状QTL定位研究中取得的进展。探讨了目前花生种子大小相关性状研究中面临的挑战和机遇, 对花生产量遗传改良进行了展望, 以期为相关研究提供参考。

1 植物种子大小的调控途径研究进展

种子大小是影响产量的一个重要因素, 同时, 也是作物驯化过程中的一个主要农艺性状。种子的大小由植物自身的遗传机制决定^[3]。单子叶和双子叶植物的种子均从双受精开始发育, 受精后, 胚珠发育成种子, 珠被发育成种皮。双子叶植物种子的发育过程主要包括胚乳的增殖和胚的生长, 胚乳由于快速增殖, 形成多核体, 使种腔变大, 之后胚乳细胞化形成多个单细胞。当胚乳细胞化完成时, 即确定了该种子的大小^[4]。而单子叶植物的胚乳并未消失, 成为种子的重要组成部分。此外, 不论是双子叶植物还是单子叶植物, 其珠被经过细胞化以及色素和淀粉粒的积累, 形成了种皮。种皮的存在会限制种子的最终大小^[5]。因此, 胚、胚乳以及珠被共同调控种子的大小^[6]。种子生长发育的过程, 从细胞学水平看, 是由细胞增殖和细胞膨大相互协调控制, 它们分别控制着细胞的数目和大小^[7]。细胞增殖或细胞膨大均会导致种子的大小发生改变, 其中细胞增殖, 即改变细胞数目对种子大小的影响更大^[8]。

近年来, 在植物中已经图位克隆了一批控制种子大小的基因或者调控因子, 比如水稻中的GS3^[9]、GW2^[10]、GW5^[11]、GS5^[12]、GLW7^[13]。这些基因或者调控因子通过植物激素途径、IKU途径、泛素-蛋白酶体途径等调控细胞增殖和细胞数目, 从而影响胚、胚乳和珠被的发育, 进而影响种子的大小(图1)。

植物激素途径是种子大小调控网络中一个重要的途径, 其涉及到的激素包括生长素(auxin, IAA)、油菜素内酯(brassinolide, BR)、细胞分裂素(cytokinin, CTK)以及赤霉素(gibberellin, GA)等, 这些激素在种子生长发育过程中具有重要的作用^[14]。生长素主要是通过auxin response factors (ARFs)调控生长素介导的基因来调控种子的大小。拟南芥中已经鉴定到ARF2基因通过限制珠被内的细胞增殖来控制种子的大小^[15], 并且该基因在调控种子大小性状上具有母体效应^[16]。此外, 油菜中也已经图位克隆到基因ARF18, 该基因同时调控油菜的千粒重以及角果长, 并且该基因也具有母体效应^[17]。BR是一种类固醇激素, 研究表明, BR通过改变胚和胚乳从而调控种子的大小, 也可以通过调控其他种子大小相关基因的表达从而调控种子大小^[18]。在细胞分裂素缺失突变体中, 细胞数目增多, 胚细胞增大, 种子变大, 表明CTK通过调控细胞增殖来调控种子的大小^[19]。GA通过抑制DELLA蛋白的活性而促进种子的发育^[20], GA信号途径中的部分基因调控了细胞的膨大^[21]。

IKU途径是植物种子早期发育阶段的一个重要调控机制, 涉及HAIKU1 (IKU1)、HAIKU2 (IKU2)、MINISEED3 (MINI3)和SHORT HYPOCOTYL UNDER BIUE1 (SHB1)。其中IKU1和IKU2分别编码一个含有VQ结构域的蛋白和亮氨酸受体激酶, 它们使胚乳细胞化提前, 抑制胚细胞增殖和珠被细胞的伸长, 从而导致种子变小^[4]。MINI3编码WRKY10转录因子, 可以与IKU1和IKU2相互作用^[22]。SHB1正调控细胞的大小和数目, 在种子发育过程中, 可以结合IKU2和MINI3的启动子区域, 促进IKU2和MINI3的表达, 使胚乳生长加快, 提高胚细胞增殖和扩展的速度^[23]。

泛素-蛋白酶体途径是泛素首先被泛素活化酶E1、泛素结合酶E2和泛素连接酶E3依次催化, 形成泛素化的蛋白质, 然后被分解为短肽或氨基酸, 该途径也参与调控种子大小, 例如基因DA1、SOD2、SOD7等。拟南芥的DA1基因为种子大小的抑制子, 通过限制细胞增殖时间而调控种子大小; SOD7基因通过抑制珠被和细胞增殖来调控种子大小^[24,25]。研究发现, 过量表达SOD7基因, 会使转基因植株的种子显著变小, 而敲除SOD7基因, 会使转基因植株的种子显著变大^[26]。DA1基因和SOD7基因对种子大小性状的调控均具有母体效应^[24]。此外, SOD2基因编码泛素特异的蛋白酶UBIQUITIN-SPECIFIC PROTEASE15 (UBP15), 负调控拟南芥种子的种子大小, 并且同样具有母体效应^[27]。

除以上3种途径, 种子大小还受其他因子的调控, 比如转录因子(AP2类、MADS-box类、bHLH类)、G-蛋白、激酶、microRNA等^[28]。这些调控因子相互影响、相互制约, 构成了一个复杂的调控网络, 从而对种子的胚、胚乳和珠被进行调控, 进而调控种子大小。

图1

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图1植物种子大小的主要调控途径及关键基因

修改自Hussain等^[28]。
Fig. 1Regulatory pathways and key genes of seed size in plants

This figure is modified from that of Hussain et al. ^[28]

2 花生种子大小相关性状QTL定位研究进展

2.1 花生分子标记

早在20世纪90年代, 限制性片段长度多态性(restriction fragment length polymorphism, RFLP)、随机扩增DNA多态性(random amplified polymorphic DNA, RAPD)和扩增片段长度多态性(amplified fragments length polymorphism, AFLP)就开始应用于花生遗传多样性分析和遗传图谱构建。Kochert等^[29]利用RFLP标记对8份美国花生品种和14份野生花生资源进行了遗传多样性分析。后来, Hopkins等^[30]发现栽培种花生中存在大量CT和GT重复序列, 认为花生与其他作物一样, 基因组中也存在简单重复序列(simple sequence repeats, SSR), 能够开发大量的SSR标记进行遗传分析与研究。随后, 研究发现SSR标记在花生栽培种资源中存在较高的遗传多态性, 可以进行群体遗传学分析^[31]。目前, 国内外研究者通过SSR富集文库^[32]、BAC末端序列^[33]、cDNA文库^[34,35]、转录组序列^[36]、公共数据库^[37]、二倍体野生花生基因组^[38]和栽培种花生基因组^[39]已开发了上万个花生SSR标记, 这些SSR标记已广泛应用于花生资源遗传多样性、遗传图谱构建及QTL定位研究中。

随着高通量测序技术的发展, 单核苷酸多态性(SNP)标记由于具有基因组分布广、数量大、可实现高通量检测的优点, 被认为是最具有利用潜力的分子标记。目前, 芯片杂交技术和新一代测序技术是常用的SNP高通量检测手段。Pandey等^[40]利用30份花生栽培种材料和来源于6个不同二倍体野生种的11份野生花生材料, 构建了花生的第1张SNP芯片“Axiom_Arachis”芯片, 该芯片包含58,000个SNP位点, 目前已成功应用于分析花生资源遗传多样性^[40]、重要性状QTL定位^[41]等研究中。随着测序技术的飞速发展, 二倍体野生种Arachis duranensis^[42,43]和A. ipaensis^[42,44]、四倍体野生种A. monticola^[45]和四倍体栽培种Tifrunner^[46]、狮头企^[47]、伏花生^[48]基因组陆续发表, 使得利用全基因组重测序技术从基因组中检测SNP分子标记这种快捷有效的技术手段在花生研究中成为可能。但是, 由于花生栽培种基因组较大(约2.7 G), 群体重测序花费较高, 限制了其被大规模应用。于是, 以RAD-seq (restriction-site-associated DNA sequencing)技术和SLAF-seq (specific-locus amplified fragment sequencing)技术为代表的“简化基因组测序(reduced- representation sequencing)”便应运而生。它利用限制性核酸内切酶将基因组DNA进行酶切, 并制备一批DNA片段文库, 这些DNA片段文库可以作为全基因的简化代表, 从而降低了测序的费用。简化基因组测序技术的发展促进了SNP标记在花生遗传多样性分析^[49]、高密度遗传图谱构建^{[50,51,52,53,54,55,56]}及数量性状位点^{[51,52,53,54,55,56,57]}研究中的应用。

2.2 连锁分析

2.2.1 遗传连锁图谱的构建 连锁分析(linkage mapping)是基于重组交换, 利用覆盖全基因组的分子标记遗传连锁图和人工构建的分离群体进行分析的一种方法。目前, 利用该方法在不同物种中定位了大量的QTL, 并利用近等基因系和图位克隆技术成功克隆了一批控制作物重要性状的基因^[58]。遗传连锁图的构建是连锁分析过程中极为重要的一个环节, 是定位和克隆数量性状基因的重要基础。由于花生分子标记开发研究工作起步较晚, 且分子标记类型和数量有限, 花生遗传图谱研究进展较为缓慢。2001年Burow等^[59]利用RFLP标记构建了一张包含307个位点和23个连锁群的遗传连锁图, 该图谱遗传距离为2210 cM。2009年Varshney等^[60]利用144个多态性SSR标记在TAG24和ICGV86031杂交构建的包含318份家系的RIL群体中进行扩增, 绘制了花生栽培种第1张基于SSR标记的遗传连锁图。由于当时能够使用的SSR标记数量较少, 此张遗传图谱上图标记只有135个, 图谱覆盖的遗传长度仅为1270.5 cM, 且SSR标记缺少基因组信息, 22个连锁群无法定位于基因组20条染色体上。此后, 伴随着大量SSR标记的开发, 研究者利用不同的单个分离群体绘制了多张基于SSR标记的花生遗传连锁图谱(表1), 上图标记数量由最初的100个增加到上千个, 遗传图谱密度也有所提高。但是, 由于花生亚基因组之间同源性较高, 遗传图谱上的SSR标记无法准确获得基因组上的位置, 不利于后续的QTL精细定位及基因图位克隆工作。近年来, 新一代测序技术的发展极大地推动了多态性更高的SNP标记在花生高密度遗传图谱构建研究中的应用。2014年, Zhou等^[50]首次对一个包含166份家系的中花5号×ICGV 86699的F₉代RIL群体进行简化基因组测序, 利用RAD-seq技术构建了一张含有1621个SNPs和64个SSR标记的花生遗传连锁图。随着花生二倍体和四倍体基因组序列的公布, 各国研究者利用RAD-seq技术、SLAF-seq技术、GBS (genotyping by sequencing)技术等, 已构建了多张基于SNP标记的高密度遗传连锁图谱(表1), 为花生重要性状QTL定位提供了有力的技术支撑。

Table 1
表1
表1花生遗传连锁图谱汇总
Table 1Summary of genetic linkage map in peanut

杂交组合 Cross combination	群体 Population size	标记类型 Marker type	位点数 Loci mapped	LG数目 LGs number	图谱长度 Total map distance (cM)	参考文献 Reference
TAG 24 × ICGV 86031	318 RILs	SSR	135	22	1270.5	Varshney et al. 2009 [60]
	318 RILs	SSR	191	22	1785.4	Ravi et al. 2011 [61]
TAG 24 × GPBD 4	268 RILs	SSR	56	14	462.2	Khedikar et al. 2010 [62]
	266 RILs	SSR	188	20	1922.4	Sujay et al. 2012 [63]
TG 26 × GPBD 4	146 RILs	SSR	181	21	1963.0	Sujay et al. 2012 [63]
ICGS 44 × ICGS 76	188 RILs	SSR	82	15	831.4	Gautami et al. 2012 [64]
ICGS 76 × GSMG 84-1	177 RILs	SSR	119	20	2208.2	Gautami et al. 2012 [64]
Satonoka × Kintoki	94 F2	SSR, TE	1114	21	2166.4	Shirasawa et al. 2012 [65]
Nakateyutaka × YI-0311	186 F2	SSR, TE	326	20	1332.9	Shirasawa et al. 2012 [65]
SunOleic 97R × NC94022	352 RILs	SSR, CAPs	172	22	920.7	Qin et al. 2012 [66]
	352 RILs	SSR, CAPs	206	20	1780.6	Pandey et al. 2014 [67]
	352 RILs	SSR	248	21	1425.9	Khera et al. 2016 [68]
Tifrunner × GT-C20	94 F2	SSR	318	21	1674.4	Wang et al. 2012 [33]
	248 RILs	SSR, CAPs	239	26	1213.4	Qin et al. 2012 [66]
	248 RILs	SSR	418	20	1935.4	Pandey et al. 2017 [69]
	91 RILs	SNP	2156	20	3120	Agarwal et al. 2018 [70]
中花5号 × ICGV 86699 Zhonghua 5 × ICGV 86699	166 RILs	SNP, SSR	1685	20	1446.7	Zhou et al. 2014 [50]
中花10号 × ICG 12625 Zhonghua 10 × ICG 12625	232 F2	SSR	470	20	1877.3	Huang et al. 2015 [71]
	140 RILs	SSR, TE	1219	20	2038.8	Huang et al. 2016 [72]
富川大花生× ICG 6375 Fuchuandahuasheng × ICG 6375	218 F2	SSR	347	22	1675.6	Chen et al. 2016 [73]
	188 RILs	SSR	609	20	1557.5	Chen et al. 2017 [74]
徐花13 ×中花6号 Xuhua 13 ×Zhonghua 6	282 F2	SSR	228	22	1337.7	Chen et al. 2016 [73]
	186 RILs	SNP	2595	20	2465.6	Liu et al. 2020 [55]
ICGV 07368 × ICGV 06420	184 F2	SSR, DArT	854	20	3525.8	Shasidhar et al. 2017 [75]
ICGV 06420 × SunOleic 95R	179 F2	DArT, DArTseq	1435	20	1869.2	Shasidhar et al. 2017 [75]
远杂9102 ×徐州68-4 Yuanza 9102 × Xuzhou 68-4	195 RILs	SSR	830	20	1386.2	Luo et al. 2017 [38]
	188 RILs	SNP	2187	20	1566.1	Wang et al. 2018 [52]
Huayu 28 × P76	146 RILs	SNP, SSR	2334	20	2586.4	Hu et al. 2018 [53]
Florida-07 × GP-NC WS 16	192 RILs	SNP	2753	20	3695.4	Han et al. 2018 [76]
ZH16 × sd-H1	242 RILs	SNP	2636	20	2098.1	Wang et al. 2018 [51]
Huayu 36 × 6-13	181 RILs	SNP, SSR	3866	20	1266.8	Zhang et al. 2019 [54]
79266 × D893	151 RILs	SSR	231	23	905.2	Li et al. 2019 [77]
TG37A × NRCG-CS85	270 RILs	SNP	585	20	2430.0	Modia et al. 2019 [78]
Xinhuixiaoli × Yueyou 92	314 RILs	SNP	5022	20	2231.3	Khan et al. 2020 [56]
Zheng 8903 × Yuhua 4	212 RILs	SNP	3634	20	1817.9	Liu et al. 2020 [79]

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2.2.2 花生种子大小连锁分析QTL定位 在遗传图谱的基础上, 国内外研究者已通过连锁分析获得了一批花生重要性状的QTL, 但是这些研究主要涉及耐旱、锈病抗性、叶斑病、抗性、油酸等性状, 对于花生种子大小相关性状, 则报道较少, 鉴定到的QTL信息见表2。Shirasawa等^[65]和Huang等^[71]分别利用SSR标记和F₂群体鉴定到1个和10个种子大小相关性状的QTL, 但是由于利用的分离群体为F₂群体, 花生单株结果数较少, 表型很难重复鉴定, 所以检测到的QTL无法多环境重复鉴定。Chen等^[74]利用RIL群体和609个SSR位点, 在3个环境下, 共检测到40个种子大小相关性状的QTL, 其中种子宽QTL qSWA10.1a、百仁重QTL q100SWA7.1a、q100SWA7.1b、q100SWB6.1a、q100SWB6.1b、q100SWB8.1a在多环境下均能重复检测到, 为稳定的QTL。由于当时缺少花生基因组信息, 无法获得这些稳定QTL在基因组上的位置, 难于精细定位。新一代测序技术的发展和花生野生种二倍体、四倍体基因组序列的公开发表, 促使研究者能够准确获得QTL在基因组上的位置。Wang等^[51]基于SLAF-seq技术和野生种二倍体基因组序列, 共检测到22个种子大小相关性状的QTL, 其中连锁群B7上区间130.9~143.9 cM能够多环境重复检测到种子长、种子宽和百仁重的主效QTL, 通过比对区间两端SNP标记的位置, 该主效QTL位于野生种二倍体B基因组上123.8~124.9 Mb。曾新颖等^[80]利用一个RIL群体和基于二倍体基因组序列开发的SSR标记, 在2个环境下共检测到66个种子大小QTL, 其中种子长主效QTL和百仁重QTL共定位于连锁群A5上区间A05A1500~A05A1530; 长宽比主效QTL和百仁重QTL共定位于连锁群A5上区间A05A1053~ A05A1150; 种子宽主效QTL和百仁重QTL共定位于连锁群B6上区间A06B135~A06B113。通过比对标记序列, Zhang等^[54]基于SLAF-seq技术和四倍体花生基因组, 在4个环境下检测到27个QTL, 其中种子长和种子长宽比共定位于染色体A2上92.75~ 99.81 Mb, 百仁重主效QTL位于染色体B6上8.84~ 11.61 Mb。Mondal等^[81]在连锁群B7上检测到1个百仁重主效QTL, 将区间两侧标记序列与四倍体基因组序列比对, 将该QTL定位于B7上1.98~4.65 Mb。

综上, 目前通过连锁分析获得的稳定的种子长主效QTL主要位于染色体A2^[54]、A5^[80]、B6^[51,80]、B7^[51]上, 种子宽主效QTL位于染色体B6^[80]和B7^[51]上, 种子长宽比主效QTL位于染色体A2^[54]和A5^[80]上, 百仁重主效QTL位于染色体B6^[80]和B7^[51,81]上。这些主效QTL不仅调控种子大小相关性状, 还调控着荚果长、荚果宽、百果重等性状^[51,80], 这表明荚果和种子性状具有协同调控关系。虽然目前已报道的研究借助四倍体花生基因组信息已获得了种子大小性状QTL的物理位置, 但是有的QTL物理区间仍然较大, 所以目前研究结果仍然只是QTL初定位, 后续还需要通过构建次级分离群体来进一步精细定位, 从而将目标QTL缩小至某几个候选基因。

Table 2
表2
表2花生种子大小相关性状QTL的汇总
Table 2Summary of QTL for seed size related traits in peanut

杂交组合 Cross combination	群体 Population	标记 Marker	性状 Trait	QTL数目 QTLs number	分布连锁群 LGs	表型变异解释率 R2 (%)	参考文献 Reference
Satonoka × Kintoki	94 F2	SSR	籽仁重 Seed weight	1	LG08.2	19.10	Shirasawa et al. 2012 [65]
中花10号 × ICG 12625	232 F2:3	SSR	种子长 Seed length	3	A3, B2, B3	9.86-10.48	Huang et al. 2015 [71]
Zhonghua 10 × IGG 12625			种子宽 Seed width	4	A2, A3, A7	6.39-12.20	Huang et al. 2015 [71]
			百仁重 Hundred seed weight	3	A8, B2, B3	1.69-17.88	Huang et al. 2015 [71]
富川大花生× ICG 6375	188 RILs	SSR	种子长 Seed length	12	A5, A7, A10, B4, B6, B7	5.44-13.20	Chen et al. 2017 [74]
Fuchuandahuasheng × ICG 6375			种子宽 Seed width	10	A3, A8, A10, B1, B2, B6	6.00-12.80	Chen et al. 2017 [74]
			长宽比 Length to width ratio	9	A2, A3, A7, B2, B4, B6, B7	5.50-11.80	Chen et al. 2017 [74]
			百仁重 Hundred seed weight	9	A2, A7, B4, B6, B8	5.20-10.80	Chen et al. 2017 [74]
ZH16 × sd-H1	242 RILs	SNP	种子长 Seed length	12	A3, A4, A6, B6, B7, B8	4.03-18.21	Wang et al. 2018 [51]
			种子宽 Seed width	4	B7	15.06-18.22	Wang et al. 2018 [51]
			长宽比 Length to width ratio	3	B2, B6	4.50-15.41	Wang et al. 2018 [51]
			百仁重 Hundred seed weight	6	A3, B6, B7, B8	5.17-17.95	Wang et al. 2018 [51]
中花16 × J11	188 RILs	SSR	种子长 Seed length	18	A5, B6	3.3-33.0	曾新颖等2019 [80] Zeng et al. 2019 [80]
Zhonghua 16× J11			种子宽 Seed width	16	A9 B6	4.08-14.27	曾新颖等2019 [80] Zeng et al. 2019 [80]
			长宽比 Length to width ratio	18	A2, A5	3.23-20.30	曾新颖等2019 [80] Zeng et al. 2019 [80]
			百仁重 Hundred seed weight	14	A5, B6	3.43-23.54	曾新颖等2019 [80] Zeng et al. 2019 [80]
Huayu 36 × 6-13	181 RILs	SNP	种子长 Seed length	10	A2, A5, A7, A9, A10	0.74-61.74	Zhang et al. 2019 [54]
			种子宽 Seed width	5	A5, B3, B6	12.12-21.58	Zhang et al. 2019 [54]
			长宽比 Length to width ratio	8	A2, A3, A5, A9	4.47-83.23	Zhang et al. 2019 [54]
			百仁重 Hundred seed weight	4	A2, B6	24.69-35.39	Zhang et al. 2019 [54]
VG 9514 × TAG 24	164 RILs	SSR	百仁重 Hundred seed weight	9	B3, B7, B8	6.71-23.88	Mondal et al. 2019 [81]

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2.3 关联分析

2.3.1 关联分析群体 基于种质资源自然群体的关联分析是数量性状QTL定位的另一种重要有效分析方法。遗传多样性丰富的核心种质资源群体是花生数量性状关联分析的首选群体。美国农业部、印度国际半干旱地区热带作物研究所、以及中国农业科学院油料作物研究所分别构建了包含831份^[82]、1704份^[83]和576份^[84]资源的美国、印度和中国花生核心种质群体。由于构建的核心种质群体包含的资源份数较多, 不便于做大规模田间试验, 于是又相继分别构建了包含112份^[85]、184份^[86]和298份^[87]资源的微核心种质群体。此外, 中国农业科学院油料作物研究所在此基础上, 构建了一套包含99份资源的微微核心种质群体^[88], 而印度国际半干旱地区热带作物研究所构建了一套来自48个国家的300份资源的“参考集” (reference set)^[89]。此外, 山东省农业科学院花生研究所^[90]、山东农业大学^[91]和河南省农业科学院^[49]利用收集到的花生资源材料, 分别构建了包含195份、268份和320份资源材料的关联分析群体, 其中河南省农业科学院构建的群体包含了100份农家种、133份育种材料和87份美国微核心种质资源。由于自然群体的群体结构会对关联分析结果产生假阳性, 为打破性状与群体结构的相关性, 研究者通过交配设计构建了巢式关联作图(nested association mapping, NAM)群体^[92]和多亲本高世代杂交(multiple parent advanced generation intercross, MAGIC)群体^[93]进行全基因组关联分析。花生中, 美国研究者首次利用2个常用的匍匐型花生品种Tifrunner和Florida-07, 分别与8个不同的花生材料进行杂交, 获得了16个RIL群体, 构建了2个花生NAM群体^[94], 通过表型调查发现, 这些家系的产量性状、成熟期、耐盐性、晚斑病抗性等性状表型变异丰富^[95]。这些遗传多样性丰富的资源材料群体都是花生全基因组关联分析的适宜群体。

2.3.2 花生种子大小关联分析QTL定位 与连锁分析相比, 花生关联分析研究起步较晚, 因此, 目前通过关联分析鉴定到的花生大小QTL较少。Pandey等^[89]利用154个SSR位点和4597个DArT位点对300份花生资源进行关联分析, 鉴定到17个位点与种子大小显著关联, 表型变异解释率为11.81%~30.09%。Zhao等^[96]利用554个单位点SSR标记和104份花生资源, 通过关联分析检测到30个SSR标记与种子大小显著关联, 表型变异解释率为11.22%~32.30%, 其中标记AHGA44686能够在多环境下重复检测到, 且共定位到与种子长和百仁重显著关联。由于早期研究缺少基因组信息, Pandey等^[89]和Zhao等^[96]只能鉴定到显著关联的标记位点, 无法进一步获得QTL区间以及候选基因。Wang等^[90]对195份花生资源材料进行了GBS测序, 获得了13,435个SNP, 通过关联分析, 鉴定到38个SNP与籽仁重显著关联, 这些SNP主要位于染色体B6和B9上, 将SNP位点与已报道的连锁分析结果进行比较发现, 染色体A5上54.2~82.2 Mb之间存在4个共定位区域。Gangurde等^[41]利用58K SNP芯片“Axiom_ Arachis”对2个NAM群体NAM_Tifrunner (581个家系)和NAM_Florida-07 (496个家系)进行全基因关联分析, 分别鉴定到28个和17个SNP位点与花生籽仁重显著关联, 这些SNP位点主要集中位于染色体A5、A6、B5和B6上, 依据这些SNP位点信息和基因组序列, 分别鉴定到23个和14个基因可能与花生籽仁重相关。目前花生种子大小相关性状关联分析主要定位于染色体A5、A6、B5、B6和B9上, 定位结果还仅仅局限于显著关联位点的获得, 如何快速、准确地获得候选基因成为花生关联分析后续研究的重中之重。

3 问题与展望

目前, 通过连锁分析和全基因组关联分析, 花生种子大小相关性状的主效QTL主要位于染色体A2、A5、A6、A7、B5、B6、B7和B9上, 这些主效QTL都能在多环境下同一个群体中重复检测到, 也都能在多个分离群体或者自然群体中重复检测到。种子大小是一个由多基因控制的复杂数量性状, QTL位点复杂, 除了这些已报道的主效QTL, 还需要进一步挖掘其他主效位点。利用这些主效QTL开发与种子大小紧密连锁的分子标记, 可以应用于花生产量以及粒型的分子标记辅助选择育种。此外, 还可以将种子大小分子标记与高油、高油酸的分子标记进行聚合育种, 创制高产优质的花生种质, 加快花生分子育种进程。

花生种子大小相关性状QTL定位研究早期以SSR分子标记为主, 标记密度低, 导致QTL定位精度偏低, 且缺少基因组信息, 很难进行重复验证。近几年花生二倍体基因组和四倍体基因组的陆续发表为SNP标记在群体中大规模高通量鉴定花生群体基因型提供了可行性。SNP标记具有分布广、多态性高的优点, 与其他标记相比, 在花生遗传育种中存在不可比拟的优越性。同时, 基于测序技术的SNP标记为花生QTL定位提供了准确的物理位置信息, 也能够比较不同群体获得的QTL。虽然现在已报道了一批花生种子大小相关性状的QTL, 但是这些QTL仍然还只是初定位阶段, QTL物理区间过大, 区间内包含基因较多。因此, 后续还需要进一步的精细定位、挖掘候选基因以及候选基因功能验证。

开展精细定位研究, 通常需要构建次级分离群体来完成。但是, 花生单株繁殖系数低, 如果仅仅依靠构建大规模的次级分离群体和寻找目的片段重组交换单株来完成精细定位和候选基因挖掘比较困难, 因此, 需要借助其他技术与分析方法来完成。基于构建极端表型混合池的QTL-seq分析方法目前在植物中广泛应用于QTL初定位以及精细定位^[97,98]。该方法是在分离群体中挑选极端表型材料, 构建2个极端表型的混池样品, 与亲本材料一起, 进行基因组测序或者转录组测序, 通过比较极端表型混池样品与亲本的SNP数据, 挖掘目标性状的QTL区间以及与目标性状显著关联的SNP位点。或者在已有的QTL初定位基础上, 通过极端池QTL-seq分析进行精细定位, 挖掘到与目标性状紧密连锁的SNP位点或者候选基因。与常规的连锁分析和关联分析相比, 该方法可以快速地挖掘到与目标性状紧密连锁的SNP位点, 且测序成本较低。此外, 随着高通量测序技术和质谱技术的进一步发展, 对于当前的大数据时代, 联合基因组和转录组数据, 利用先进的统计算法, 极大的推进了人类遗传疾病和复杂性状研究工作, 目前植物中已大量开展了基因组、转录组和代谢组等组学研究。对于花生复杂性状如种子大小, 开发新的高效统计模型或利用机器学习策略, 整合花生籽粒发育多组学信息, 可能是未来全面解析花生产量遗传基础的一条新的途径。

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