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作物学报 ›› 2021, Vol. 47 ›› Issue (5): 869-881.doi: 10.3724/SP.J.1006.2021.01051

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

宁麦9号/扬麦158重组自交系群体产量性状的遗传解析

姜朋1,2(), 张旭1,3, 吴磊1, 何漪1, 张平平1, 马鸿翔1,*(), 孔令让2,*()   

  1. 1江苏省农业生物学重点实验室 / 江苏省农业科学院, 江苏南京210014
    2作物生物学国家重点实验室 / 山东农业大学, 山东泰安271018
    3江苏省粮食作物现代产业技术协同创新中心 / 扬州大学, 江苏扬州 225009
  • 收稿日期:2020-06-18 接受日期:2020-10-15 出版日期:2021-05-12 网络出版日期:2020-11-18
  • 通讯作者: 马鸿翔,孔令让
  • 作者简介:E-mail: hmjp2005@163.com
  • 基金资助:
    国家重点研发计划项目(2017YFD0100801);江苏省农业科技自主创新资金(CX181001);江苏省重点研发计划项目(BE2019377);国家现代农业产业技术体系建设专项(CARS-3)

Genetic analysis for yield related traits of wheat (Triticum aestivum L.) based on a recombinant inbred line population from Ningmai 9 and Yangmai 158

JIANG Peng1,2(), ZHANG Xu1,3, WU Lei1, HE Yi1, ZHANG Ping-Ping1, MA Hong-Xiang1,*(), KONG Ling-Rang2,*()   

  1. 1Provincial Key Laboratory for Agrobiology / Jiangsu Academy of Agricultural Sciences, Nanjing 210014, Jiangsu, China
    2State Key Laboratory of Crop Biology / Shandong Agricultural University, Tai’an 271018, Shandong, China
    3Co-Innovation Center for Modern Production Technology of Grain Crops Wheat Research Center / Yangzhou University, Yangzhou 225009, Jiangsu, China
  • Received:2020-06-18 Accepted:2020-10-15 Published:2021-05-12 Published online:2020-11-18
  • Contact: MA Hong-Xiang,KONG Ling-Rang
  • Supported by:
    National Key Research and Development Program of China(2017YFD0100801);Jiangsu Agricultural Science and Technology Innovation Fund(CX181001);Key Research and Development Program of Jiangsu Province(BE2019377);China Agriculture Research System(CARS-3)

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摘要：

宁麦9号与扬麦158是我国长江中下游麦区的主栽品种和骨干亲本, 二者都为高产类型品种, 但产量结构类型不同, 研究二者产量及构成因素的遗传对产量遗传改良具重要意义。本研究以宁麦9号与扬麦158为亲本构建的包含282个家系的重组自交系群体为材料, 利用Illumina 90K芯片分析遗传群体基因型, 建立了高密度遗传图谱。连续3个生长季对产量及构成要素进行表型鉴定, 结果显示, 3年中宁麦9号穗粒数均高于扬麦158, 千粒重低于扬麦158, 穗数与产量在不同环境中表现不一致, 在产量及其构成要素中, 千粒重的遗传力最高。表型结合遗传作图对产量及构成因素进行分子定位, 获得控制穗数、穗粒数、千粒重及产量的QTL分别为9、8、10与12个。为了将检测到的QTL有效地应用于标记辅助选择, 将重复检测到的3个千粒重QTL相关标记转化成适用于高通量基因分型的KASP标记, 并在育种料中进行了验证, 3个QTL位点均具有较好的选择效果, 且具有累加效应。上述研究结果可为长江中下游麦区小麦产量性状的分子育种提供理论依据和技术支撑。

关键词: 小麦, 宁麦9号, 扬麦158, 产量, KASP标记

Abstract:

Ningmai 9 and Yangmai 158 are the main commercial wheat varieties, as well as the backbone parents in wheat breeding programs in the middle and lower reaches of the Yangtze River in China. Both of them have high yield potential, while they showed significant difference in yield components. To understand the genetic mechanism of their high yield potential, 282 recombinant inbred lines (RILs) derived from the cross between Ningmai 9 and Yangmai 158 were genotyped with the Illumina iSelect 90K wheat single nucleotide polymorphism (SNP) assay to construct a high-density genetic map. Yield (YD) and yield-related traits including spike number (SN), kernel number per spike (KN), and 1000-kernel weight (TKW) were evaluated for three consecutive growing seasons. The results indicated that the KN of Ningmai 9 was higher than that of Yangmai 158, while TKW was lower than that of Yangmai 158. In total, 9, 8, 10, and 12 QTLs associated with SN, KN, TKW and YD were identified by QTL mapping, respectively. Among these traits, TKW possessed the highest heritability, and three QTL associated with TKW were identified repetitively. The markers related to such QTLs were then transferred to Kompetitive Allele Specific PCR (KASP) markers for high throughput selection in 139 wheat accessions. It was indicated that pyramiding 2 or 3 QTLs was more effective than single QTL for TKW improvement. The results in this study could provide more information for marker assisted selection in wheat yield breeding program.

Key words: wheat (Triticum aestivum L.), Ningmai 9, Yangmai 158, yield, KASP marker

表1

产量及其三要素的表型统计"

性状
Trait		 年份
Year		 宁麦9号
Ningmai 9		 扬麦158
Yangmai 158		 重组自交系群体 RIL population 遗传力
Heritability
最大值
Max.		 最小值
Min.		 平均值
Mean		 标准差
SD		 变异系数
CV (%)
穗数
Spike number (×104 hm-2)		 2016 197.30 292.21 729.27 137.36 343.77 84.10 24.46 0.13
2017 549.45 412.09 779.22 252.25 497.33 96.53 19.41
2018 479.52 534.47 816.68 249.75 495.31 94.39 19.06
穗粒数
Kernel number per spike		 2016 69.00 53.33 75.14 32.00 52.53 7.18 13.68 0.37
2017 65.00 58.40 82.80 37.60 63.31 7.98 12.60
2018 52.20 46.60 74.00 31.00 44.17 6.36 14.40
千粒重
1000-kernel weight (g)		 2016 32.55 35.48 47.63 21.75 34.98 3.90 11.16 0.60
2017 30.98 32.94 51.96 25.37 35.55 4.50 12.67
2018 40.40 47.01 50.62 31.51 41.79 3.27 7.82
产量
Yield (kg hm-2)		 2016 3238.43 3887.78 7251.08 1506.83 3903.92 940.63 24.09 0.18
2017 6018.98 4920.08 11,163.83 2006.33 6492.86 1752.69 26.99
2018 6993.00 8658.00 12,321.00 3330.00 7128.55 1420.14 19.92

表2

产量及其三要素的方差分析(F值)"

项目
Item		 穗数
Spike number (×104 hm-2)		 穗粒数
Kernel number per spike		 千粒重
1000-kernel weight (g)		 产量
Yield (kg hm-2)
基因型 Genotype 0.78 1.92** 2.64** 0.86
年份 Year 214.54** 754.62** 415.05** 320.18**
基因型×年份 Genotype×Year 0.64 1.21 1.06 0.64

表3

产量及其三要素的相关分析"

性状
Trait		 环境
Environment		 穗数
Spike number
(×104 hm-2)		 穗粒数
Kernel number per spike		 千粒重
1000-kernel weight
(g)		 产量
Yield
(kg hm-2)
穗数
Spike number (×104 hm-2)		 2016 (0.136*)
2017 (0.128*)
2018 (-0.042)
穗粒数
Kernel number per spike		 2016 -0.154** (0.081)
2017 -0.157** (0.344**)
2018 -0.054 (-0.014)
千粒重
1000-kernel weight (g)		 2016 -0.004 -0.131* (0.316**)
2017 -0.025 -0.284** (0.364**)
2018 -0.171** -0.146** (0.288**)
产量
Yield (kg hm-2)		 2016 0.169** -0.077 0.289** (0.242**)
2017 0.418** -0.201** 0.439** (0.052)
2018 0.312** 0.306** 0.085 (0.120*)

表4

产量及其三要素的QTL定位"

性状
Traits		 序号No. QTL 环境
Environment		 遗传位置
Genetic position (cM)		 物理区间
Physical interval (Mb)		 区间
Interval		 LOD 表型贡献率
PVEa (%)		 加性效应
AEb		 前人研究中的产量相关性状
Yield-related traits in previous studies
穗数
Spike number		 1 Qsn-1A 2018 101.45-101.95 543-574 Tdurum_contig96086_74-RFL_Contig3683_1784 4.85 7.54 -27.00 产量、穗粒数、单穗产量、收获指数[19]
Yield, kernel number per spike, kernel weight per spike, harvest index[19]
2 Qsn-1B.1 2016 6.35-11.15 614-616 wsnp_BE590634B_Ta_2_5-Tdurum_contig10362_555 4.03 5.11 19.22 产量[20] Yield[20]
3 Qsn-1B.2 2016 15.95-20.55 630-641 BS00069044_51-wsnp_Ex_rep_c78209_74462664 7.40 10.39 -29.21 穗长、千粒重、穗粒数[21,22]
Spike length, 1000-kernel weight, kernel number per spike[21,22]
4 Qsn-1B.3 2017 97.75-98.25 678-680 BobWhite_c4482_73-Tdurum_contig52086_342 2.97 3.45 -19.25 千粒重[23] 1000-kernel weight[23]
5 Qsn-2B 2017 78.05-79.35 781-783 Ku_c2936_1987-RAC875_c18928_455 3.21 3.68 19.78 —
6 Qsn-4B 2016 14.95-15.95 603-620 BS00039402_51-Excalibur_rep_c109675_738 3.44 3.62 14.41 —
7 Qsn-5A 2017 49.45-50.35 505-520 tplb0056b09_1000-BS00085826_51 6.27 7.56 28.32 千粒重、穗粒数[23]
1000-kernel weight, kernel number per spike[23]
8 Qsn-7A.1 2017 38.25-39.45 688-697 Tdurum_contig31699_276-wsnp_Ku_c4035_7363089 3.18 4.48 24.15 千粒重[23] 1000-kernel weight[23]
9 Qsn-7A.2 2017 108.35-111.1 730-735 BS00069019_51-BS00022284_51 3.74 4.66 -22.28 —
穗粒数
Kernel number per spike		 10 Qkn-2B 2016 68.25-69.35 768-776 Kukri_c24939_378-RAC875_rep_c83950_222 3.69 5.84 -1.75 —
12 Qkn-3B.1 2018 105.45-105.85 146-151 RAC875_c45525_131-IAAV1079 6.10 8.66 -3.41 —
11 Qkn-3B.2 2017 62.95-65.35 724-742 BobWhite_c2937_1426-BS00040739_51 3.08 4.42 -1.88 穗数、产量[5] Spike number, yield[5]
13 Qkn-4A 2017 4.05-14.95 664-683 BS00066066_51-IAAV5722 2.76 5.03 1.76 穗粒数、穗数、千粒重[23,24]
Kernel number per spike, spike number, 1000-kernel weight[23,24]
14 Qkn-5B.1 2018 91.25-93.35 615-618 Tdurum_contig85060_121-Kukri_rep_c101622_604 3.78 5.30 1.60 产量[25] Yield[25]
15 Qkn-5B.2 2017 129.35-130.05 682-698 Excalibur_c6346_266-IAAV8023 3.06 4.40 -1.77 千粒重、穗粒数[23]
1000-kernel weight, kernel number per spike[23]
16 Qkn-6B 2016 49.35-50.95 96-141 RAC875_c23812_187-wsnp_Ku_c12559_20252463 3.02 5.18 1.64 —
17 Qkn-7A 2018 0-7.05 670-680 Ku_c62742_888-wsnp_Ku_c42539_50247333 2.68 3.76 -1.40 千粒重、穗粒数、穗数[23]
1000-kernel weight, kernel number per spike, spike number[23]
千粒重
1000-kernel weight		 18 Qtkw-1A 2016 47.05-48.95 31-33 BS00084022_51-wsnp_Ex_c31983_40709866 3.08 3.60 0.76 千粒重、单穗产量、穗粒数[23,26] 1000-kernel weight, kernel weight per spike, kernel number per spike[23,26]
19 Qtkw-1B 2017/2018 35.25-39.55 640-653 IAAV1683-IAAV565 3.68 5.80 -1.15 穗长、千粒重、穗粒数[21]
Spike length, 1000-kernel weight, kernel number per spike[21]
20 Qtkw-2B 2018 51.45-52.05 748-750 CAP8_c8516_542-BS00094578_51 8.09 9.43 1.06 千粒重[24] 1000-kernel weight[24]
21 Qtkw-3A 2016 0-6.15 9-10 Excalibur_c10014_307-RAC875_c36922_829 3.29 3.71 0.88 —
22 Qtkw-4A 2016/2017 3.75-19.75 661-683 BS00066066_51-BS00066891_51 5.55 6.42 -1.02 穗粒数、穗数、千粒重[23,24]
Kernel number per spike, spike number, 1000-kernel weight[23,24]
23 Qtkw-4B 2018 4.85-5.05 427-428 GENE-2847_1060-wsnp_Ex_rep_c70265_69211592 7.90 8.82 -1.02 千粒重[27] 1000-kernel weight[27]
24 Qtkw-5A 2016 55.85-56.55 537-539 BS00096758_51-RAC875_c103967_76 7.51 8.85 1.20 千粒重、穗粒数[23]
1000-kernel weight, kernel number per spike[23]
25 Qtkw-5B 2018 67.15-70.95 586-587 Kukri_c45964_273-RAC875_c43383_483 2.88 3.10 -0.65 穗粒数[28] Kernel number per spike[28]
26 Qtkw-7A 2017/2018 48.55-49.55 261-285 RFL_Contig428_290-RAC875_c16624_970 4.21 4.57 -0.79 —
27 Qtkw-7B 2016 0-4.95 15-25 wsnp_CAP7_c44_26549-Ex_c101666_634 4.74 5.99 -0.99 产量、小穗数[20,28]
Yield, the spikelet number per spike[20,28]
产量
Yield		 28 Qyd-1B.1 2017 37.55-38.65 236-433 BobWhite_c4147_1429-BS00033681_51 5.40 5.33 441.75 千粒重、单穗产量、产量[29,30]
1000-kernel weight, kernel weight per spike, yield[29,30]
29 Qyd-1B.2 2017 65.55-73.25 480-498 Ra_c68984_1882-Tdurum_contig81102_102 4.27 4.17 -611.83 —
30 Qyd-2A 2018 47.85-55.05 653-706 BS00090569_51-BS00029332_51 3.87 6.19 332.86 单株产量、小穗数、千粒重、穗粒数[31]
Kernel weight per plant, the spikelet number per spike, 1000-kernel weight, kernel number per spike[31]
31 Qyd-4B 2017 68.85-70.45 101-168 BS00071792_51-Tdurum_contig28490_463 3.26 3.23 337.82 穗粒数、单穗产量、千粒重[5,23,32]
Kernel number per spike, kernel weight per spike, 1000-kernel weight[5,23,32]
32 Qyd-5A.1 2016 13.35-14.55 389-420 BS00086963_51-BS00001322_51 4.11 7.91 242.37 千粒重、穗粒数[23]
1000-kernel weight, kernel number per spike[23]
33 Qyd-5A.2 2017 53.35-54.45 511-540 wsnp_Ex_c49211_53875575-CAP7_c1404_72 10.88 11.79 642.73 千粒重、穗粒数[23]
1000-kernel weight, kernel number per spike[23]
34 Qyd-5B.1 2018 45.15-46.15 545-547 Tdurum_contig25513_195-wsnp_Ex_c8659_14515623 3.59 4.45 -418.08 产量[27]
Yield[27]
35 Qyd-5B.2 2017 113.05-113.85 663-665 BobWhite_c36154_81-Kukri_c41_858 4.87 4.80 -285.41 —
36 Qyd-6A 2017 35.35-38.2 609-617 Excalibur_c96749_512-Excalibur_c98257_136 3.82 3.88 -408.26 产量、千粒重[20,23] Yield, 1000-kernel weight[20,23]
37 Qyd-6B 2018 53.35-53.75 144-148 BobWhite_c1905_98-wsnp_Ku_c5891_10414090 3.08 3.88 -281.40 —
38 Qyd-7A 2018 33.25-34.25 83-109 Excalibur_rep_c101407_222-BS00011072_51 2.92 3.60 -256.01 穗重、千粒重、穗粒数、穗数[23,33-34]
Ear weight per tiller, 1000-kernel weight, kernel number per spike, spike number[23,33-34]
39 Qyd-7B 2018 133.15-133.95 743-745 Tdurum_contig51105_1538-tplb0040b02_681 4.36 5.48 313.01 产量、千粒重[20,23] Yield, 1000-kernel weight[20,23]

图1

产量及其三要素的QTL定位 图中数字与表4中QTL的序号一致。"

表5

千粒重QTL不同等位变异的t检测"

QTL 环境
Environment		 优势等位变异
Favorable alleles (g)		 非优势等位变异
Unfavorable alleles (g)		 差值
Difference (g)		 t值
t-value		 P值
P-value
Qtkw-1B 2016 35.21 (121) 34.56 (133) 0.65 1.36 0.18
2017 36.47 (121) 34.62 (133) 1.85 3.31 <0.01
2018 42.37 (121) 40.95 (133) 1.42 3.56 <0.01
Qtkw-4A 2016 35.68 (110) 33.71 (98) 1.97 3.86 <0.01
2017 36.48 (110) 34.41 (98) 2.07 3.41 <0.01
2018 42.38 (110) 40.68 (98) 1.7 3.94 <0.01
Qtkw-7A 2016 34.78 (168) 35.03 (78) -0.25 -0.47 0.64
2017 36.31 (168) 34.46 (78) 1.85 2.97 <0.01
2018 42.02 (168) 41.30 (78) 0.72 1.38 0.17
Qtkw-1B+Qtkw-4A 2016 36.62 (46) 33.48 (51) 3.14 4.22 <0.01
2017 37.03 (46) 33.59 (51) 3.44 4.11 <0.01
2018 43.32 (46) 40.56 (51) 2.76 4.24 <0.01
Qtkw-1B+Qtkw-7A 2016 35.42 (74) 35.48 (34) -0.06 -0.09 0.93
2017 37.51 (74) 33.58 (34) 3.93 4.26 <0.01
2018 42.82 (74) 40.79 (34) 2.03 2.98 <0.01
Qtkw-4A+Qtkw-7A 2016 35.46 (68) 33.73 (34) 1.73 2.11 0.04*
2017 37.42 (68) 34.56 (34) 2.86 3.04 <0.01
2018 42.27 (68) 40.51 (34) 1.76 2.85 <0.01
Qtkw-1B+Qtkw-4A+Qtkw-7A 2016 36.55 (26) 33.73 (12) 2.82 2.14 0.04*
2017 38.42 (26) 33.08 (12) 5.34 5.22 <0.01
2018 43.10 (26) 41.16 (12) 1.94 1.55 0.14

附表1

千粒重的KASP标记序列"

SNP QTL F1 (5°-3°) F2 (5°-3°) R (5°-3°)
Kukri_c18109_682 Qtkw-1B AACGACGCCTGGTTTTCCTCA CGACGCCTGGTTTTCCTCG TCCGAGATGGACGAGGAACTG
IAAV1683 Qtkw-1B TGTTCTTACCTGAACTTGTGTGACA GTTCTTACCTGAACTTGTGTGACG CTGAAGCTCTGATATCATCATCATCC
Kukri_c42354_263 Qtkw-1B TCGACGATACTGGCGGTGTA TCGACGATACTGGCGGTGTG TGATCGCAAAGTGCATTATGTT
BS00066891_51 Qtkw-4A TGGACGGATGGTTGATGGCTCG ATGGACGGATGGTTGATGGCTCA TATAAGCTACTACCGCTCCGGC
IAAV5722 Qtkw-4A AGCTGCAACCCATCCTCT AGCTGCAACCCATCCTCC TGTGACCTACTCGATGTTCATAAC
Excalibur_rep_c70004_275 Qtkw-7A GGTGTTCTCCCTGGTAGCTCA GGTGTTCTCCCTGGTAGCTCG CTTCTGGCACCTGCTTTCATCG
RFL_Contig428_290 Qtkw-7A GGATGTCCCGGTTGGAGAAGAC GGATGTCCCGGTTGGAGAAGAT GACAAGTACGGCCCCATCTTCT
IACX1477 Qtkw-7A CCAAAATGGCTTGTCAAATGAGATA CCAAAATGGCTTGTCAAATGAGA
TG		 GCTTGACCCTTAGCTCATGA

附图1

千粒重QTL的KASP标记开发 A表示宁麦9号等位变异类型, B表示扬麦158等位变异类型。"

表6

千粒重KASP标记的效用检测"

QTL 优势等位变异
Favorable alleles (g)		 非优势等位变异
Unfavorable alleles (g)		 差值
Difference (g)		 t值
t-value		 P值
P-value
Qtkw-1B 48.32 (62) 45.92 (77) 2.40 -3.79 <0.01
Qtkw-4A 47.67 (100) 45.26 (39) 2.41 3.40 <0.01
Qtkw-7A 47.47 (122) 43.53 (17) 3.95 -4.15 <0.01
Qtkw-1B+Qtkw-4A 48.73 (49) 44.49 (26) 4.23 4.61 <0.01
Qtkw-1B+Qtkw-7A 48.43 (60) 43.32 (15) 5.11 -4.73 <0.01
Qtkw-4A+Qtkw-7A 47.67 (98) 43.01 (15) 4.66 -4.59 <0.01
Qtkw-1B+Qtkw-4A+Qtkw-7A 48.73 (49) 42.69 (13) 6.03 -5.27 <0.01
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