中国农业科学 ›› 2021, Vol. 54 ›› Issue (21): 4487-4499.doi: 10.3864/j.issn.0578-1752.2021.21.001
张鹏霞1(),周秀文1,梁雪2,郭营1,赵岩1,李斯深1,孔凡美1,*(
)
收稿日期:
2021-02-07
接受日期:
2021-04-14
出版日期:
2021-11-01
发布日期:
2021-11-09
通讯作者:
孔凡美
作者简介:
联系方式:张鹏霞,E-mail: 基金资助:
ZHANG PengXia1(),ZHOU XiuWen1,LIANG Xue2,GUO Ying1,ZHAO Yan1,LI SiShen1,KONG FanMei1,*(
)
Received:
2021-02-07
Accepted:
2021-04-14
Online:
2021-11-01
Published:
2021-11-09
Contact:
FanMei KONG
摘要:
【目的】探究不同氮素供应环境下与小麦苗期生物量及氮效率相关性状显著关联的SNP位点,预测相关候选基因,为小麦氮效率的基因克隆及其在育种中的应用提供参考。【方法】以134个小麦品种(系)组成的群体为供试群体,设置低氮、正常氮和高氮3个处理,各处理重复4次,并在2年(2013和2014年)进行了2次完全重复的营养液培养试验。试验对小麦苗期生物量及氮效率相关的14个性状进行了表型鉴定,采用MLM+K+Q混合线性模型,利用90K SNP芯片对小麦生物量及氮效率相关性状进行全基因组关联分析(genome-wide association study,GWAS),获得显著关联的SNP位点。【结果】与正常氮处理相比,低氮处理条件下,根系、地上部及植株氮含量和氮积累量均显著下降,而根生物量和根、植株氮效率均显著增加,高氮处理下,几乎所有鉴定性状均显著增加;14个性状的广义遗传力均在40%以上,其中,植株总干重的遗传力最高(95.73%)。利用9 329个SNP标记进行关联分析,共检测到838个SNP标记位点与供试材料的14个性状存在显著关联(P≤0.001),分布在21条染色体上。有435个(51.91%)SNP标记位点仅在一个关联分析环境中被检测到;有403个位点至少在2个处理环境(包含均值环境)中被检测到与同一性状显著关联(稳定关联标记)。其中8个SNP标记位点至少在3个环境中被检测到。在4个环境下(包括均值环境)均检测到的稳定关联位点有2个:Kukri_c65481_121和tplb0025f09_1052,分别与植株总氮利用效率(total nitrogen use efficiency of plant,TNUE)和根系总氮利用效率(root nitrogen use efficiency,RNUE)显著关联;同时与至少6个性状(生物量及养分效率相关性状)显著关联的SNP标记位点共5个,分别位于1A、1B(3)和2A染色体上;根据小麦基因组注释及LD衰减水平,在同时定位了6个性状的5个SNP位点和2个多环境(4个环境)稳定关联的SNP位点的214 kb的基因组区域中共筛选候选基因84个,根据已知克隆氮效率基因的编码蛋白类型、候选基因功能注释信息及利用植物比较基因组学资源库蛋白序列同源比对分析,筛选到3个候选基因与生物量及氮效率相关。【结论】不同氮素处理显著影响小麦苗期生物量、氮效率相关性状及其相关QTL的表达,大多数SNP位点仅在1个氮素检测环境中被检测到,但也存在环境稳定性较强的位点。生物量及氮效率相关性状之间存在显著相关关系,并在一定程度上受到相同的QTL/基因控制。
张鹏霞,周秀文,梁雪,郭营,赵岩,李斯深,孔凡美. 小麦苗期生物量及氮效率相关性状的全基因组关联分析[J]. 中国农业科学, 2021, 54(21): 4487-4499.
ZHANG PengXia,ZHOU XiuWen,LIANG Xue,GUO Ying,ZHAO Yan,LI SiShen,KONG FanMei. Genome-Wide Association Analysis for Yield and Nitrogen Efficiency Related Traits of Wheat at Seedling Stage[J]. Scientia Agricultura Sinica, 2021, 54(21): 4487-4499.
表2
小麦苗期生物量和氮效率相关性状的统计参数和遗传力"
性状 Trait | 处理 Treatment | 均值 Average | 变异系数 CV (%) | 遗传力 hB2 (%) | 性状 Trait | 处理 Treatment | 均值 Average | 变异系数 CV (%) | 遗传力 hB2 (%) | |
---|---|---|---|---|---|---|---|---|---|---|
地上部干重 SDW (mg) | T1E1 | 107.95cd | 21.40 | 94.95 | 地上部氮累积量 SNA (mg/plant) | T1E1 | 5.00d | 12.77 | 93.53 | |
T2E1 | 104.08d | 23.99 | T2E1 | 5.98c | 6.93 | |||||
T3E1 | 124.71b | 23.51 | T3E1 | 7.64b | 10.36 | |||||
T1E2 | 112.33c | 19.16 | T1E2 | 4.32e | 8.64 | |||||
T2E2 | 129.49b | 22.28 | T2E2 | 7.79b | 10.23 | |||||
T3E2 | 148.77a | 22.62 | T3E2 | 9.68a | 4.76 | |||||
根系干重 RDW (mg) | T1E1 | 27.80c | 35.87 | 92.76 | 根系氮累积量 RNA(mg/plant) | T1E1 | 0.99c | 22.54 | 93.49 | |
T2E1 | 19.50e | 21.49 | T2E1 | 0.77d | 24.18 | |||||
T3E1 | 22.40d | 22.99 | T3E1 | 0.98c | 24.21 | |||||
T1E2 | 50.10a | 27.03 | T1E2 | 1.14b | 19.78 | |||||
T2E2 | 27.60c | 22.30 | T2E2 | 1.00c | 23.44 | |||||
T3E2 | 32.20b | 22.89 | T3E2 | 1.27a | 23.85 | |||||
植株总干重 TDW (mg) | T1E1 | 136.06d | 21.00 | 95.37 | 植株总氮累积量 TNA (mg/plant) | T1E1 | 5.97d | 26.93 | 94.31 | |
T2E1 | 123.08e | 21.99 | T2E1 | 6.75c | 24.20 | |||||
T3E1 | 147.10c | 22.81 | T3E1 | 8.62b | 25.48 | |||||
T1E2 | 162.52b | 20.53 | T1E2 | 5.46d | 21.53 | |||||
T2E2 | 157.18b | 21.77 | T2E2 | 8.79b | 25.94 | |||||
T3E2 | 180.66a | 22.03 | T3E2 | 10.95a | 23.66 | |||||
生物量根冠比 RSDW | T1E1 | 0.27b | 32.68 | 89.72 | 氮累积量根冠比 RSNA | T1E1 | 0.20b | 37.13 | 84.25 | |
T2E1 | 0.19d | 16.80 | T2E1 | 0.13c | 23.66 | |||||
T3E1 | 0.18d | 17.48 | T3E1 | 0.13c | 26.83 | |||||
T1E2 | 0.44a | 16.12 | T1E2 | 0.27a | 28.16 | |||||
T2E2 | 0.22c | 13.16 | T2E2 | 0.13c | 26.20 | |||||
T3E2 | 0.22c | 14.00 | T3E2 | 0.13c | 27.63 | |||||
地上部氮含量 SNC (g·kg-1) | T1E1 | 46.31d | 6.42 | 86.39 | 地上部氮利用效率 SNUE (g·g-1kg-1) | T1E1 | 2.35bc | 26.93 | 94.79 | |
T2E1 | 57.55c | 3.86 | T2E1 | 1.82e | 24.20 | |||||
T3E1 | 61.21b | 3.85 | T3E1 | 2.04d | 25.48 | |||||
T1E2 | 38.66e | 10.41 | T1E2 | 3.00a | 21.53 | |||||
T2E2 | 59.79b | 4.75 | T2E2 | 2.18cd | 25.94 | |||||
T3E2 | 64.96a | 4.47 | T3E2 | 2.29b | 23.66 | |||||
根系氮含量 RNC (g·kg-1) | T1E1 | 35.32c | 8.21 | 85.71 | 根系氮利用效率 RNUE (g·g-1kg-1) | T1E1 | 0.80b | 27.23 | 89.44 | |
T2E1 | 40.48b | 8.09 | T2E1 | 0.47c | 22.32 | |||||
T3E1 | 43.74a | 12.5 | T3E1 | 0.51c | 18.75 | |||||
T1E2 | 22.64d | 7.25 | T1E2 | 2.26a | 19.18 | |||||
T2E2 | 35.51c | 10.1 | T2E2 | 0.78b | 16.92 | |||||
T3E2 | 39.44b | 9.37 | T3E2 | 0.81b | 17.52 | |||||
植株总氮含量 TNC (g·kg-1) | T1E1 | 44.07d | 5.26 | 23.68 | 植株总氮利用效率 TNUE (g·g-1kg-1) | T1E1 | 3.09b | 22.66 | 41.34 | |
T2E1 | 54.87c | 6.12 | T2E1 | 2.25e | 24.62 | |||||
T3E1 | 58.56b | 4.25 | T3E1 | 2.51d | 23.62 | |||||
T1E2 | 33.75e | 5.10 | T1E2 | 4.92a | 22.15 | |||||
T2E2 | 55.50c | 11.20 | T2E2 | 2.84c | 21.40 | |||||
T3E2 | 60.44a | 5.26 | T3E2 | 2.99b | 22.11 |
表3
与苗期生物量及氮效率相关性状显著关联(P≤0.001)的环境稳定SNP标记"
性状 Trait | 环境 Environment | 标记 Marker | 染色体 Chr. | 位置 Position (bp) | P | 贡献率 R2(%) | |
---|---|---|---|---|---|---|---|
最大值 Max | 最小值 Min | ||||||
RNUE | T1E1, T3AV, T3E2 | RAC875_c29540_391 | 1A | 8290703 | 9.95E-04 | 1.39E-05 | 11.05—18.34 |
根系干重RDW | T2E1, T3AV, T3E2 | BobWhite_c9881_1312 | 1B | 10256166 | 3.58E-04 | 3.49E-04 | 10.09—10.13 |
根系氮利用效率RNUE | T2AV, T2E1, T3E1 | BobWhite_c9881_1312 | 1B | 10256166 | 7.45E-04 | 3.75E-05 | 9.04—13.69 |
地上部氮含量SNC | T1E1, T3AV, T3E2 | GENE-0427_442 | 1B | 5688119 | 5.79E-04 | 8.62E-05 | 9.35—12.35 |
植株总干重TDW | T1E1, T3AV, T3E2 | GENE-0427_442 | 1B | 5688119 | 6.54E-04 | 1.88E-04 | 9.17—11.11 |
植株总氮含量TNC | T1E1, T3AV, T3E2 | GENE-0427_442 | 1B | 5688119 | 6.25E-04 | 1.08E-04 | 9.23—11.98 |
地上部氮含量SNC | T2AV, T2E1, T1E2 | wsnp_RFL_Contig3060_2967139 | 2A | 9928033 | 8.05E-04 | 1.87E-04 | 11.41—13.91 |
植株总氮含量TNC | T2AV, T2E1, T1E2 | wsnp_RFL_Contig3060_2967139 | 2A | 9928033 | 2.95E-04 | 2.05E-04 | 12.57—13.74 |
植株总氮利用效率TNUE | T2AV, T2E1, T1AV, T1E2 | Kukri_c65481_121 | 3B | 13483435 | 7.19E-04 | 2.08E-04 | 9.09—11.40 |
根系氮利用效率RNUE | T1AV, T1E1, T3AV, T3E2 | tplb0025f09_1052 | 4B | 1591267 | 7.25E-04 | 1.32E-09 | 11.49—36.09 |
地上部干重SDW | T2E2, T1AV, T1E1 | wsnp_Ku_c1765_3452586 | 6A | 14086572 | 6.09E-04 | 1.39E-04 | 9.07—11.58 |
生物量根冠比RSDW | T2AV, T2E2, T3E2 | BS00022498_51 | 7B | 6251008 | 5.32E-04 | 3.25E-04 | 12.01—12.85 |
表4
小麦苗期同时与6个性状显著关联的SNP位点"
标记和染色体 Marker and Chr. | 性状 Trait | 分析环境 Analysis environment | 贡献率 R2 (%) |
---|---|---|---|
BS00077492_51 1A | 地上部干重SDW | T1E1 | 12.63 |
植株总干重TDW | T1E1 | 11.31 | |
地上部氮含量SNC | T1E1 | 11.73 | |
植株总氮含量TNC | T1E1 | 11.76 | |
地上部氮利用效率SNUE | T1E1 | 12.42 | |
植株总氮利用效率TNUE | T1E1 | 11.80 | |
BS00065737_51 1B | 地上部干重SDW | T1E1, T3E2 | 8.93—11.37 |
植株总干重TDW | T1E1 | 8.60 | |
地上部氮含量SNC | T1E1 | 9.80 | |
植株总氮含量TNC | T1E1 | 8.87 | |
地上部氮利用效率SNUE | T1E1, T3E2 | 10.11—10.75 | |
植株总氮利用效率TNUE | T3E2 | 9.01 | |
GENE-0427_442 1B | 地上部干重SDW | T1E1, T3E2 | 10.86—11.65 |
植株总干重TDW | T1E1, T3AV, T3E2 | 9.17—11.11 | |
地上部氮含量SNC | T1E1, T3AV, T3E2 | 9.35—12.35 | |
植株总氮含量TNC | T1E1, T3AV, T3E2 | 9.23—11.98 | |
地上部氮利用效率SNUE | T3E2 | 10.44 | |
植株总氮利用效率TNUE | T3AV, T3E2 | 9.65—11.33 | |
IACX16577 1B | 地上部干重SDW | T1E1, T3E2 | 9.49—11.83 |
植株总干重TDW | T1E1, T3E2 | 8.82—8.97 | |
地上部氮含量SNC | T1E1, T3E2 | 8.92—9.96 | |
植株总氮含量TNC | T1E1, T3E2 | 8.72—9.30 | |
地上部氮利用效率SNUE | T1E1, T3E2 | 10.82—11.02 | |
植株总氮利用效率TNUE | T1E1, T3E2 | 9.19—9.81 | |
wsnp_RFL_Contig3060_2967139 2A | 地上部干重SDW | T2AV, T2E1 | 13.39—13.77 |
植株总干重TDW | T2AV, T2E1 | 12.85—13.57 | |
地上部氮含量SNC | T2AV, T2E1, T1E2 | 13.25—13.91 | |
植株总氮含量TNC | T2AV, T2E1, T1E2 | 13.33—13.74 | |
地上部氮利用效率SNUE | T2AV, T2E1 | 12.99—13.69 | |
植株总氮利用效率TNUE | T2AV, T2E1 | 12.04—12.73 |
表5
候选基因预测"
染色体 Chr. | 基因 Gene | 位置 Position (Mb) | 标记 Marker | 基因注释或编码蛋白 Gene annotation or coding protein |
---|---|---|---|---|
1B | TraesCS1B02G058300 | 183.39 | BS00065737_51 | 无性的MADS盒子转录因子 Agamous MADS-box transcription factor |
2A | TraesCS2A02G122000 | 332.13 | wsnp_RFL_Contig3060_2967139 | 与膜相关的30 kD蛋白 Membrane-associated 30 kD protein |
4B | TraesCS4B02G007100 | 46.43 | tplb0025f09_1052 | 含R3h结构域的蛋白质 R3h domain containing protein |
[1] |
GUTTIERI M J, FRELS K, REGASSA T, WATERS B M, BAENZIGER P S. Variation for nitrogen use efficiency traits in current and historical great plains hard winter wheat. Euphytica, 2017, 213(4):87.
doi: 10.1007/s10681-017-1869-5 |
[2] |
GU B, JU X, CHANG S X, GE Y, CHANG J. Nitrogen use efficiencies in Chinese agricultural systems and implications for food security and environmental protection. Regional Environmental Change, 2017, 17(4):1217-1227.
doi: 10.1007/s10113-016-1101-5 |
[3] |
LIANG S, LI Y, ZHANG X, SUN Z, SUN N, DUAN Y, XU M, WU L. Response of crop yield and nitrogen use efficiency for wheat-maize cropping system to future climate change in northern China. Agricultural and Forest Meteorology, 2018, 262:310-321.
doi: 10.1016/j.agrformet.2018.07.019 |
[4] | SCHACHTMAN D P, SHIN R. Nutrientsensing and signaling: NPKS. Annual Review Plant Biology, 2007, 58: 47/69. |
[5] |
DAVIDSON E. A. The contribution of manure and fertilizer nitrogen to atmospheric nitrous oxide since 1860. Nature Geoscience, 2009, 2(9):659-662.
doi: 10.1038/ngeo608 |
[6] | PALME K, LI X, TEALE W D. Towards second green revolution: engineering nitrogen use efficiency. Genet Genomics, 2014, 41(6):315-326. |
[7] | YIN L, DAI X, HE M. Delayed sowing improves nitrogen utilization efficiency in winter wheat without impacting yield. Field Crops Research, 2018, 221: 90/97. |
[8] |
CEOTTO E. The issues of energy and carbon cycle: New perspectives for assessing the environmental impact of animal waste utilization. Bioresour Technology, 2005, 96(2):191-196.
doi: 10.1016/j.biortech.2004.05.007 |
[9] |
HITZ K, CLARK A J, VAN SANFORD D A. Identifying nitrogen-use efficient soft red winter wheat lines in high and low nitrogen environments. Field Crops Research, 2017, 200:1-9.
doi: 10.1016/j.fcr.2016.10.001 |
[10] |
MAHJOURIMAJD S, KUCHEL H, LANGRIDGE P, OKAMOTO M. Evaluation of Australian wheat genotypes for response to variable nitrogen application. Plant and Soil, 2015, 399(1/2):247-255.
doi: 10.1007/s11104-015-2694-z |
[11] |
WANG Y Y, SUN X Y, ZHAO Y, KONG F M, GUO Y, ZHANG G Z, PU Y Y, WU K, LI S S. Enrichment of a common wheat genetic map and QTL mapping for fatty acid content in grain. Plant Science, 2011, 181(1):65-75.
doi: 10.1016/j.plantsci.2011.03.020 |
[12] |
FRELS K, GUTTIERI M, JOYCE B, LEAVITT B, BAENZIGER P S. Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat. Field Crops Research, 2018, 217:82-92.
doi: 10.1016/j.fcr.2017.12.004 |
[13] |
JIN D, GAO J, JIANG P, LV X, WANG Y, ZHANG W. Nitrogen use efficiency and rice yield of different locations in Northeast China. National Academy Science Letters, 2017, 40(4):227-232.
doi: 10.1007/s40009-017-0553-6 |
[14] |
RENGEL Z, MARSCHNER P. Nutrient availability and management in the rhizosphere: Exploiting genotypic differences. New Phytology, 2005, 168(2):305-312.
doi: 10.1111/nph.2005.168.issue-2 |
[15] |
QUARRIE S A, STEED A, CALESTANI C, SEMIKHODSKII A, LEBRETON C, CHINOY C, STEELE N, PLJEVLJAKUSIC D, WATERMAN E, WEYEN J, SCHONDELMAIER J, HABASH D Z, FARMER P, SAKER L, CLARKSON D T, ABUGALIEVA A, YESSIMBEKOVA M, TURUSPEKOV Y, ABUGALIEVA S, TUBEROSA R, SANGUINETI M C, HOLLINGTON P A, ARAGUES R, ROYO A, DODIG D. A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring× SQ1 and its use to compare QTLs for grain yield across a range of environments. Theoretical and Applied Genetics, 2005, 110(5):865-880.
doi: 10.1007/s00122-004-1902-7 |
[16] |
LÉTIZIA C K, JEAN-BAPTISTE V, DELPHINE M, VALÉRIE C, MARIE F, STÉPHANIE B, PIERRE D, BRIGITTE G, DOMENICA M, ALAIN C. Maize adaptation to temperate climate: Relationship between population structure and polymorphism in the Dwarf8 gene. Genetics, 172(4):2449-2463.
doi: 10.1534/genetics.105.048603 |
[17] |
THORNSBERRY J M, GOODMAN M M, DOEBLEY J, KRESOVICH S, NIELSEN D, BUCKLER E S. Dwarf8 polymorphisms associate with variation in flowering time. Nature Genetics, 2001, 28(3):286-289
doi: 10.1038/90135 |
[18] |
DONG Y, LIU J, ZHANG Y, GENG H HE Z J P O, . Genome-wide association of stem water soluble carbohydrates in bread wheat. PLoS ONE, 2016, 11(11):e0164293.
doi: 10.1371/journal.pone.0164293 |
[19] |
LIU J, HE Z, RASHEED A, WEN W, YAN J, ZHANG P, WAN Y, ZHANG Y, XIE C, XIA X J B P B. Genome-wide association mapping of black point reaction in common wheat (Triticum aestivum L.). BMC Plant Biology, 2017, 17(1):220.
doi: 10.1186/s12870-017-1167-3 |
[20] |
GUO J, SHI W, ZHANG Z, CHENG J, SUN D, YU J, LI X, GUO P, HAO C J B P B. Association of yield-related traits in founder genotypes and derivatives of common wheat (Triticum aestivum L.). BMC Plant Biology, 2018, 18(1):38.
doi: 10.1186/s12870-018-1234-4 |
[21] |
RASHEED A, XIA X, OGBONNAYA F, MAHMOOD T, ZHANG Z, MUJEEB-KAZI A, HE Z J B P B. Genome-wide association for grain morphology in synthetic hexaploid wheats using digital imaging analysis. BMC Plant Biology, 2014, 14(1):128.
doi: 10.1186/1471-2229-14-128 |
[22] | BULL P, ZHANG J, CHAO S, CHEN X, PUMPHREY M. Genetic architecture of resistance to stripe rust in a global winter wheat germplasm collection. Genetics, 2016, 6(8):2237-2253. |
[23] | MACCAFERRI M, RICCI A, SALVI S, MILNER S G, JOURNAL R T J P B. A high-density, SNP-based consensus map of tetraploid wheat as a bridge to integrate durum and bread wheat genomics and breeding. Biotechnology, 2015, 13(5):648-663. |
[24] |
SIDDIQI M Y, GLASS A D M. Utilization index: A modified approach to the estimation and comparison of nutrient utilization efficiency in plants. Journal of Plant Nutrition, 1981, 4(3):289-302.
doi: 10.1080/01904168109362919 |
[25] | KNAPP S J, STROUP W W, ROSS W M. Exact confidence intervals for heritability on a progeny mean basis. Theoretical and Applied Genetics, 1985, 25(1):192-204. |
[26] | 屈春艳. 水旱条件下小麦产量性状和抗旱性的全基因组关联分析[D]. 泰安: 山东农业大学, 2018. |
QU C Y. Genome-wide association study on yield traits and drought tolerance under irrigation and drought conditions in wheat[D]. Taian: Shandong Agricultural University, 2018. (in Chinese) | |
[27] | MARCO M, WALID E F, GHASEMALI N, SILVIO S, ANGELA C M, CHIARA C M, SANDRA S, ROBERTO T. Prioritizing quantitative trait loci for root system architecture in tetraploid wheat. Journal of Experimental Botany, 2016, (4):1161-1178 |
[28] |
BISHOPP A, LYNCH J P. The hidden half of crop yields. Nature Plants, 2015, 1(8):15117.
doi: 10.1038/nplants.2015.117 |
[29] |
CHENYANG H, YUQUAN W, SHIAOMAN C, TIAN L, HONGXIA L, LANFEN W, XUEYONG Z. The iSelect 9 K SNP analysis revealed polyploidization induced revolutionary changes and intense human selection causing strong haplotype blocks in wheat. Scientific Reports, 2017, 7:41247.
doi: 10.1038/srep41247 |
[30] |
CUI F, ZHAO C, DING A, LI J, WANG L, LI X, BAO Y, LI J, WANG H. Construction of an integrative linkage map and QTL mapping of grain yield-related traits using three related wheat RIL populations. Theoretical and Applied Genetics, 2014, 127(3):659-675.
doi: 10.1007/s00122-013-2249-8 |
[31] |
FONTAINE J X, RAVEL C, PAGEAU K, HEUMEZ E, DUBOIS F, HIREL B, LE GOUIS J. A quantitative genetic study for elucidating the contribution of glutamine synthetase, glutamate dehydrogenase and other nitrogen-related physiological traits to the agronomic performance of common wheat. Theoretical and Applied Genetics, 2009, 119(4):645-662.
doi: 10.1007/s00122-009-1076-4 |
[32] |
GUO Y, KONG F M, XU Y F, ZHAO Y, LIANG X, WANG Y Y, AN D G, LI S S. QTL mapping for seedling traits in wheat grown under varying concentrations of N, P and K nutrients. Theoretical and Applied Genetics, 2012, 124(5):851-865.
doi: 10.1007/s00122-011-1749-7 |
[33] |
HABASH D Z, BERNARD S, SCHONDELMAIER J, WEYEN J, QUARRIE S A. The genetics of nitrogen use in hexaploid wheat: N utilisation, development and yield. Theoretical and Applied Genetics, 2007, 114(3):403-419.
doi: 10.1007/s00122-006-0429-5 |
[34] |
XU Y, WANG R, TONG Y, ZHAO H, XIE Q, LIU D, ZHANG A, LI B, XU H, AN D. Mapping QTLs for yield and nitrogen-related traits in wheat: Influence of nitrogen and phosphorus fertilization on QTL expression. Theoretical and Applied Genetics, 2014, 127(1):59-72.
doi: 10.1007/s00122-013-2201-y |
[35] |
AN D, SU J, LIU Q, ZHU Y, TONG Y, LI J, JING R, LI B, LI Z. Mapping QTLs for nitrogen uptake in relation to the early growth of wheat (Triticum aestivum L.). Plant and Soil, 2006, 284(1/2):73-84.
doi: 10.1007/s11104-006-0030-3 |
[36] |
CUI F, FAN X, ZHAO C, ZHANG W, CHEN M, JI J, LI J. A novel genetic map of wheat: Utility for mapping QTL for yield under different nitrogen treatments. BMC Genetics, 2014, 15(1):57.
doi: 10.1186/1471-2156-15-57 |
[37] |
FONTAINE J X, RAVEL C, PAGEAU K, HEUMEZ E, DUBOIS F, HIREL B, GOUIS J. A quantitative genetic study for elucidating the contribution of glutamine synthetase, glutamate dehydrogenase and other nitrogen-related physiological traits to the agronomic performance of common wheat. Theoretical and Applied Genetics, 2009, 119(4):645-662.
doi: 10.1007/s00122-009-1076-4 |
[38] |
KONG F M, GUO Y, LIANG X, WU C H, WANG Y Y, ZHAO Y, LI S S. Potassium (K) effects and QTL mapping for K efficiency traits at seedling and adult stages in wheat. Plant and Soil, 2013, 373(1/2):877-892.
doi: 10.1007/s11104-013-1844-4 |
[39] | 张国华. 黄淮麦区小麦品种(系)产量性状与分子标记的关联分析[D]. 泰安: 山东农业大学, 2013. |
ZHANG G H. Association analysis of yield traits and molecular markers of wheat varieties (lines) in Huanghuai wheat region [D]. Tai'an: Shandong Agricultural University, 2013. (in Chinese) | |
[40] |
MESSENGUY F, DUBOIS E J G. Role of MADS box proteins and their cofactors in combinatorial control of gene expression and cell development. Gene, 2003, 316(1):1-21.
doi: 10.1016/S0378-1119(03)00747-9 |
[41] | HECK G O. AGL15, a MADS domain protein expressed in developing embryos. The Plant Cell, 1995, 7(8):1271-1282. |
[42] | KUO M H, NADEAU E T, GRAYHACK E J J M, BIOLOGY C. Multiple phosphorylated forms of the Saccharomyces cerevisiae Mcm1 protein include an isoform induced in response to high salt concentrations. Molecular and Celluar Biology, 1997, 17(2):819-832. |
[43] |
LOZANO, PHYSIOLOGY R J P. Tomato flower abnormalities induced by low temperatures are associated with changes of expression of MADS-box genes. Plant Physiology, 1998, 117(1):91-100.
doi: 10.1104/pp.117.1.91 |
[44] |
LEI L, LI G, ZHANG H, POWERS C, FANG T, CHEN Y, WANG S, ZHU X and YAN L. Nitrogen use efficiency is regulated by interacting proteins relevant to development in wheat. Plant Biotechnology Journal, 2018, 16(6):1214-1226.
doi: 10.1111/pbi.2018.16.issue-6 |
[45] |
WANG M, ZHANG P, LIU Q, LI G, SHI W. TaANR1-TaBG1 and TaWabi5-TaNRT2s/NARs link ABA metabolism and nitrate acquisition in wheat roots. Plant Physiology, 2020, 182:1440-1453.
doi: 10.1104/pp.19.01482 |
[1] | 陈吉浩, 周界光, 曲翔汝, 王素容, 唐华苹, 蒋云, 唐力为, $\boxed{\hbox{兰秀锦}}$, 魏育明, 周景忠, 马建. 四倍体小麦胚大小性状QTL定位与分析[J]. 中国农业科学, 2023, 56(2): 203-216. |
[2] | 严艳鸽, 张水勤, 李燕婷, 赵秉强, 袁亮. 葡聚糖改性尿素对冬小麦产量和肥料氮去向的影响[J]. 中国农业科学, 2023, 56(2): 287-299. |
[3] | 徐久凯, 袁亮, 温延臣, 张水勤, 李燕婷, 李海燕, 赵秉强. 畜禽有机肥氮在冬小麦季对化肥氮的相对替代当量[J]. 中国农业科学, 2023, 56(2): 300-313. |
[4] | 古丽旦,刘洋,李方向,成卫宁. 小麦吸浆虫小热激蛋白基因Hsp21.9的克隆及在滞育过程与温度胁迫下的表达特性[J]. 中国农业科学, 2023, 56(1): 79-89. |
[5] | 李周帅,董远,李婷,冯志前,段迎新,杨明羡,徐淑兔,张兴华,薛吉全. 基于杂交种群体的玉米产量及其配合力的全基因组关联分析[J]. 中国农业科学, 2022, 55(9): 1695-1709. |
[6] | 王浩琳,马悦,李永华,李超,赵明琴,苑爱静,邱炜红,何刚,石美,王朝辉. 基于小麦产量与籽粒锰含量的磷肥优化管理[J]. 中国农业科学, 2022, 55(9): 1800-1810. |
[7] | 唐华苹,陈黄鑫,李聪,苟璐璐,谭翠,牟杨,唐力为,兰秀锦,魏育明,马建. 基于55K SNP芯片的普通小麦穗长非条件和条件QTL分析[J]. 中国农业科学, 2022, 55(8): 1492-1502. |
[8] | 马小艳,杨瑜,黄冬琳,王朝辉,高亚军,李永刚,吕辉. 小麦化肥减施与不同轮作方式的周年养分平衡及经济效益分析[J]. 中国农业科学, 2022, 55(8): 1589-1603. |
[9] | 刘硕,张慧,高志源,许吉利,田汇. 437个小麦品种钾收获指数的变异特征[J]. 中国农业科学, 2022, 55(7): 1284-1300. |
[10] | 王洋洋,刘万代,贺利,任德超,段剑钊,胡新,郭天财,王永华,冯伟. 基于多元统计分析的小麦低温冻害评价及水分效应差异研究[J]. 中国农业科学, 2022, 55(7): 1301-1318. |
[11] | 职蕾,者理,孙楠楠,杨阳,Dauren Serikbay,贾汉忠,胡银岗,陈亮. 小麦苗期铅耐受性的全基因组关联分析[J]. 中国农业科学, 2022, 55(6): 1064-1081. |
[12] | 秦羽青,程宏波,柴雨葳,马建涛,李瑞,李亚伟,常磊,柴守玺. 中国北方地区小麦覆盖栽培增产效应的荟萃(Meta)分析[J]. 中国农业科学, 2022, 55(6): 1095-1109. |
[13] | 蔡苇荻,张羽,刘海燕,郑恒彪,程涛,田永超,朱艳,曹卫星,姚霞. 基于成像高光谱的小麦冠层白粉病早期监测方法[J]. 中国农业科学, 2022, 55(6): 1110-1126. |
[14] | 宗成, 吴金鑫, 朱九刚, 董志浩, 李君风, 邵涛, 刘秦华. 添加剂对农副产物和小麦秸秆混合青贮发酵品质的影响[J]. 中国农业科学, 2022, 55(5): 1037-1046. |
[15] | 马鸿翔, 王永刚, 高玉姣, 何漪, 姜朋, 吴磊, 张旭. 小麦抗赤霉病育种回顾与展望[J]. 中国农业科学, 2022, 55(5): 837-855. |
|