Welcome to Acta Agronomica Sinica，

Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (3): 394-404.doi: 10.3724/SP.J.1006.2021.01024

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Genome-wide association study of nitrogen use efficiency related traits in common wheat (Triticum aestivum L.)

JIN Yi-Rong1(), LIU Jin-Dong2(), LIU Cai-Yun1, JIA De-Xin1, LIU Peng1,*(), WANG Ya-Mei2,*()   

  1. 1Dezhou Academy of Agricultural Sciences, Dezhou 253015, Shandong, China
    2Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, Guangdong, China
  • Received:2020-03-24 Accepted:2020-09-13 Online:2021-03-12 Published:2020-10-15
  • Contact: LIU Peng,WANG Ya-Mei E-mail:jyr2014@163.com;liujindong_1990@163.com;liup9@163.com;wangyamei@caas.cn
  • Supported by:
    Support Project for Grassroots Scientific and Technological Talents of Shandong Western Economic Swells up Belt(XB2018FW029);Key Research and Development Program of Shandong Province (2017GNC10107)(2017GNC10107);Natural Science Foundation of Shandong Province (ZR2017BC015),(ZR2017BC015);Agricultural Science and Technology Innovation Project of Shandong Academy of Agricultural Sciences(CXGC2016B01)

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Abstract:

Nitrogen application plays an important role in plant growth and development. Exploring genetic loci related to nitrogen use efficiency is of great significance for improving wheat yield and reducing environmental pollution. Root system architecture (RSA) determined the composition of plant root system, and significantly affected by nitrogen level. Under different nitrogen levels (deficiency and normal), 160 winter wheat accessions from the Huanghuai valley and Northern winter wheat region were counted for their root architecture-related traits (total root length, total root surface area, total root volume, average root diameter, and root tip number). Genotype was analyzed using 660K SNP (single nucleotide polymorphism) data. Genome-wide association study (GWAS) was employed to identify the relevant loci for nitrogen use efficiency. A total of 34 associated loci were detected, which explained 6.9%-15.4% of the phenotypic variation. These loci distributed on all chromosomes and mainly centered on chromosomes 1A, 2B, 3B, 5B, 6A, 6B, and 7A, respectively. Among the loci detected in this study, 11 loci overlapped or were close to the reported ones, while the other 23 might be novel loci. In addition, we explored a candidate gene encoding the E3 ubiquitin ligase. This study is of great significance for understanding the genetic mechanism of nitrogen utilization and breeding high-yield wheat varieties.

Key words: common wheat, root architecture, nitrogen use efficiency, GWAS, 660K SNP chip

Table s1

The details of the 160 common wheat accessions and their root system architecture (RSA) related traits"

品种（系）
名称		 类别 来源 氮+磷+钾 (N+P+K) 磷+钾 (P+K)
总根长 总根表面积 平均根直径 总根体积 根尖数 总根长 总根表面积 平均根直径 总根体积 根尖数
TRL (cm) TRS (cm2) ARD (mm) TRV (cm3) NRT TRL (cm) TRS (cm2) ARD (mm) TRV (cm3) NRT
石特14 育成种 196.3 20.2 0.691 0.429 58.0 461.3 25.9 0.387 0.800 590.0
复壮30 育成种 274.2 19.1 0.533 0.424 141.0 252.4 21.7 0.441 0.512 411.5
平阳27 育成种 102.5 11.4 0.923 0.397 38.0 282.9 20.7 0.483 0.618 320.0
蚰包 地方品种 104.1 13.7 0.656 0.273 48.5 439.6 26.0 0.386 0.513 226.5
郑州6号 育成种 河南 208.0 19.8 0.578 0.432 152.5 406.8 25.5 0.395 0.773 702.0
豫麦49 育成种 河南 233.0 19.8 0.666 0.484 68.5 350.8 22.6 0.385 0.436 565.5
白芒麦1 地方品种 246.3 22.0 0.652 0.473 112.5 396.1 25.3 0.398 0.633 514.5
半截芒 地方品种 158.5 17.6 0.744 0.344 86.0 263.9 21.8 0.403 0.376 278.0
老来瞎 地方品种 110.5 13.6 0.981 0.380 26.5 265.4 22.4 0.390 0.429 272.0
西山扁穗 地方品种 141.4 15.3 0.581 0.293 72.5 473.3 23.7 0.420 0.528 316.5
红狗豆 地方品种 128.4 15.2 0.729 0.277 40.0 255.6 18.8 0.478 0.497 254.5
蚰子麦 育成种 271.6 21.5 0.739 0.593 121.5 433.8 25.0 0.428 0.534 375.0
白扁穗 地方品种 127.5 14.7 0.880 0.458 47.0 367.3 26.3 0.420 0.705 428.5
白齐麦 地方品种 150.4 14.2 0.732 0.310 82.5 285.5 20.7 0.396 0.395 315.0
白秃子头 地方品种 168.1 17.2 0.585 0.328 81.5 363.8 23.4 0.407 0.531 539.0
有芒扫谷旦 地方品种 254.6 19.5 0.671 0.538 142.5 401.0 23.7 0.427 0.493 364.5
阜阳红 地方品种 安徽 276.2 24.2 0.743 0.727 116.0 347.3 24.5 0.398 0.566 487.0
蚂蚱麦 地方品种 219.3 22.3 0.540 0.345 133.0 283.2 23.8 0.421 0.366 270.0
抢场麦 地方品种 101.5 12.9 0.769 0.250 24.5 306.2 19.3 0.402 0.390 357.5
火麦 地方品种 179.7 16.6 0.498 0.358 112.5 442.8 23.0 0.434 0.519 338.0
碱麦 地方品种 190.3 19.2 0.642 0.321 75.5 404.0 25.7 0.380 0.539 375.0
大口麦 地方品种 138.0 19.6 0.781 0.294 39.0 189.5 14.6 0.506 0.486 208.0
白条鱼 地方品种 155.5 18.0 0.655 0.383 75.0 436.2 19.6 0.419 0.475 450.0
老齐麦 地方品种 228.8 20.4 0.418 0.574 276.5 360.5 19.8 0.390 0.464 514.0
出山豹 地方品种 191.8 20.0 0.560 0.572 105.5 409.2 23.3 0.462 0.652 253.0
大粒半芒 地方品种 231.6 23.7 0.494 0.434 260.0 342.4 20.0 0.392 0.527 285.0
扁穗麦 育成种 214.1 19.0 0.678 0.506 92.5 353.2 24.5 0.416 0.643 375.0
齐大195 育成种 159.4 17.9 0.886 0.724 117.5 587.5 25.5 0.403 0.543 376.0
黄县大粒半芒 育成种 156.5 18.0 0.517 0.289 95.0 490.1 22.7 0.426 0.601 357.0
泗水三八麦 育成种 山东 350.5 25.7 0.475 0.548 177.0 631.3 25.8 0.392 0.965 593.5
蚰包麦 育成种 197.9 16.4 0.802 0.564 116.0 292.8 20.8 0.436 0.403 220.0
碧蚂1号 育成种 209.8 22.1 0.481 0.363 124.0 426.5 22.8 0.390 0.624 488.0
烟农78 育成种 山东 182.1 17.3 0.498 0.357 174.0 522.4 25.7 0.434 0.878 475.5
济宁3号 育成种 山东 192.2 19.8 0.816 0.624 92.5 358.7 22.9 0.432 0.504 483.0
鲁麦8号 育成种 山东 121.8 16.4 0.951 0.478 30.0 244.3 20.2 0.510 0.678 362.0
鲁麦12号 育成种 山东 231.3 21.6 0.710 0.523 102.5 529.9 25.9 0.404 0.570 390.5
山农辐63 育成种 山东 271.2 22.8 0.609 0.645 142.0 358.7 24.7 0.414 0.665 431.0
山农587 育成种 山东 194.1 18.4 0.570 0.440 83.5 518.8 25.7 0.387 0.398 551.0
刑麦9号 高代品系 268.8 21.9 0.604 0.566 214.5 353.8 25.1 0.440 0.538 616.0
鲁麦1号 育成种 山东 406.2 18.8 0.404 0.716 299.0 406.2 18.8 0.404 0.716 299.0
鲁麦22 育成种 山东 222.8 19.4 0.719 0.653 97.0 427.4 26.9 0.397 0.663 521.0
鲁麦23 育成种 山东 116.2 12.6 0.771 0.371 42.0 215.0 18.9 0.370 0.615 543.0
鲁麦16 育成种 山东 259.3 20.3 0.710 0.706 128.5 558.4 25.8 0.398 0.666 349.5
鲁麦19 育成种 山东 203.6 20.1 0.611 0.375 97.0 330.1 21.1 0.374 0.608 627.0
鲁麦20 育成种 山东 86.3 11.8 0.971 0.424 28.0 347.8 24.5 0.420 0.651 422.5
淄麦12 育成种 河南 307.5 23.7 0.595 0.746 171.0 288.2 22.8 0.412 0.392 393.0
烟辐188 育成种 山东 264.3 23.3 0.561 0.507 171.5 525.4 24.3 0.421 0.785 483.5
山农664 育成种 山东 223.5 19.7 0.608 0.429 71.0 373.1 22.1 0.432 0.560 375.0
汶农5号 育成种 山东 179.7 16.1 0.763 0.428 98.5 368.4 24.5 0.431 0.867 551.0
莱州137 育成种 山东 267.7 19.4 0.714 0.602 141.0 459.2 24.0 0.387 0.615 569.5
烟2415 育成种 山东 238.0 21.0 0.573 0.734 153.5 443.0 24.7 0.435 0.434 323.5
山农11 育成种 山东 161.8 16.1 0.607 0.352 103.0 276.9 21.8 0.408 0.534 682.5
淄麦7号 育成种 河南 223.8 20.1 0.579 0.531 171.5 392.9 22.6 0.418 0.650 578.0
金铎1号 育成种 237.5 17.9 0.646 0.322 87.5 304.5 20.4 0.482 0.787 284.5
济南18 育成种 山东 135.8 13.5 0.785 0.336 40.0 269.5 21.6 0.396 0.523 380.0
山融3号 育成种 山东 179.5 18.5 0.688 0.391 118.0 388.6 23.4 0.406 0.644 490.5
偃麦4110 育成种 河南 347.0 23.9 0.495 0.570 377.0 532.3 23.8 0.364 0.652 678.5
良星99 育成种 山东 282.3 25.1 0.485 0.645 248.5 300.6 21.2 0.405 0.575 528.5
济麦33 高代品系 山东 270.2 22.9 0.499 0.587 268.0 421.8 24.4 0.407 0.639 442.5
济麦37 高代品系 山东 177.3 18.0 0.741 0.573 133.0 515.3 24.9 0.435 0.554 430.0
济麦0419 高代品系 山东 172.4 16.7 0.711 0.498 47.5 266.1 19.9 0.471 0.428 367.5
济麦0536 高代品系 山东 204.7 20.1 0.770 0.528 396.5 420.6 26.0 0.459 0.688 232.0
济麦08101 高代品系 山东 368.9 17.4 0.627 0.463 123.0 416.3 24.0 0.375 0.516 673.5
济麦36 高代品系 山东 286.4 21.3 0.579 0.569 76.0 541.9 25.6 0.407 0.796 441.0
济麦47 高代品系 山东 296.7 24.6 0.482 0.612 153.0 412.2 25.7 0.391 0.372 344.0
济麦49 高代品系 山东 222.8 20.7 0.560 0.488 123.0 411.5 19.5 0.378 0.613 593.0
济麦23 育成种 山东 213.6 20.1 0.598 0.521 127.0 286.1 23.7 0.433 0.483 326.5
山农15（B245) 育成种 山东 205.5 21.4 0.715 0.591 95.5 336.9 22.0 0.372 0.592 749.0
良星77 育成种 山东 239.8 21.4 0.698 0.493 80.0 472.1 24.9 0.459 0.747 789.5
鑫麦296 育成种 95.6 11.6 0.835 0.359 26.5 557.1 25.1 0.401 0.758 650.5
郯麦98 育成种 山东 240.7 23.0 0.508 0.431 138.0 433.3 19.0 0.397 0.536 736.5
泰农19 育成种 山东 163.4 14.5 0.644 0.394 71.0 345.4 20.1 0.485 0.684 263.5
汶农14 育成种 山东 123.1 17.5 0.856 0.556 52.0 448.8 22.0 0.434 0.511 358.5
山农0431 高代品系 山东 157.7 14.8 0.787 0.479 54.5 450.3 23.7 0.414 0.613 381.0
山农11J1565 高代品系 山东 190.0 20.0 0.505 0.250 74.0 334.5 22.8 0.393 0.738 594.5
山农21 育成种 山东 249.1 22.5 0.596 0.599 87.5 471.0 24.5 0.393 0.630 427.5
山农22 育成种 山东 171.0 18.9 0.578 0.406 118.0 493.1 22.8 0.399 0.630 672.0
山农670 高代品系 山东 196.9 21.1 0.584 0.592 142.5 429.9 22.1 0.424 0.521 288.0
山农711 高代品系 山东 324.1 26.0 0.497 0.554 200.5 518.1 24.4 0.429 0.757 450.5
山农737 高代品系 山东 277.9 26.3 0.464 0.573 252.5 391.1 21.7 0.466 0.516 290.0
山农0713-2 高代品系 山东 277.4 26.3 0.499 0.548 247.5 568.8 25.4 0.408 1.043 709.5
山农11J0635 高代品系 山东 191.6 17.0 0.623 0.435 204.5 464.1 23.0 0.413 0.757 621.5
山农12J1740 高代品系 山东 171.2 15.8 0.563 0.529 129.0 360.4 20.3 0.414 0.668 299.0
烟农173 育成种 山东 314.3 26.2 0.498 0.459 149.0 384.8 23.2 0.418 0.560 463.5
存麦1号 育成种 287.6 23.4 0.441 0.562 222.0 457.2 23.5 0.397 0.956 555.0
花5 高代品系 235.2 23.8 0.512 0.461 146.0 457.5 23.5 0.379 0.749 730.0
花8 高代品系 194.3 23.4 0.662 0.443 76.0 479.4 25.1 0.415 0.679 505.0
济创15 高代品系 山东 163.6 17.7 0.816 0.414 56.5 330.4 19.4 0.411 0.494 340.5
济创23 高代品系 山东 218.7 23.2 0.452 0.367 151.0 523.3 24.7 0.386 0.610 564.0
济创28 高代品系 山东 186.4 20.0 0.692 0.494 104.5 432.2 24.0 0.407 0.529 363.0
龙麦1号 高代品系 山东 175.4 18.9 0.633 0.333 97.0 413.4 22.0 0.376 0.306 401.5
洛麦21 育成种 河南 192.9 19.7 0.677 0.456 83.5 416.4 22.4 0.441 0.825 535.0
神麦1号 育成种 160.3 15.2 0.522 0.332 93.0 415.9 21.0 0.392 0.565 349.0
郑麦366 育成种 河南 224.1 17.2 0.556 0.390 93.0 554.4 25.7 0.412 0.812 534.5
周黑麦1号 育成种 河南 191.8 22.6 0.631 0.391 94.0 488.6 23.6 0.437 0.645 315.0
周麦30 育成种 河南 270.5 21.6 0.598 0.663 131.5 449.9 24.3 0.421 0.816 441.0
周麦31 高代品系 河南 210.0 20.6 0.616 0.430 80.0 371.7 23.5 0.455 0.583 418.0
BN10F(002)加1-特1 高代品系 101.1 11.2 0.595 0.231 48.0 388.4 25.6 0.412 0.880 403.5
兰材282 高代品系 河南 495.7 25.9 0.419 0.532 290.0 547.8 22.5 0.408 0.702 465.0
兰材633 高代品系 河南 259.3 25.0 0.503 0.395 126.0 527.5 24.1 0.378 0.732 562.0
兰材693 高代品系 河南 318.7 23.9 0.457 0.475 227.0 474.3 23.2 0.408 0.657 574.5
兰考198 高代品系 河南 188.8 17.3 0.667 0.368 108.0 432.1 22.0 0.439 0.598 563.0
兰考298 高代品系 河南 183.9 19.8 0.642 0.401 84.0 264.5 18.4 0.423 0.536 486.5
山农1565 高代品系 山东 150.2 18.5 0.810 0.461 51.0 485.1 23.8 0.377 0.737 444.0
兰考323 高代品系 河南 180.8 16.5 0.554 0.355 108.0 324.5 25.6 0.397 0.702 471.0
兰考358 高代品系 河南 140.7 17.3 0.660 0.391 73.0 370.1 23.1 0.430 0.667 359.0
兰考380 高代品系 河南 196.0 20.0 0.462 0.506 192.5 501.6 24.7 0.401 0.644 524.5
兰考381 高代品系 河南 179.1 24.0 0.504 0.455 165.0 512.4 23.7 0.416 0.682 640.5
兰考396 高代品系 河南 162.2 17.3 0.596 0.325 140.5 340.8 22.6 0.446 0.541 480.5
百农160 育成种 208.5 19.7 0.673 0.286 136.5 399.8 26.2 0.430 0.562 365.5
河农7069 育成种 河南 156.9 18.1 0.671 0.317 54.5 460.2 22.3 0.407 0.672 518.5
河农9206 育成种 河南 274.3 22.2 0.556 0.575 91.5 606.2 25.6 0.388 1.073 532.0
冀矮1号 高代品系 90.0 13.9 0.656 0.229 32.0 382.3 22.7 0.436 0.655 506.0
冀麦325 育成种 214.1 21.3 0.551 0.491 117.5 397.3 20.6 0.432 0.437 334.0
金博士731 高代品系 140.3 18.4 0.523 0.302 126.0 547.2 24.0 0.438 0.766 720.5
石矮1号 高代品系 220.7 18.6 0.659 0.424 105.5 527.5 24.6 0.383 0.583 881.0
石新733 育成种 224.9 23.4 0.473 0.485 177.5 499.0 24.8 0.433 0.677 543.0
刑麦13号 育成种 237.5 21.8 0.512 0.428 128.5 299.0 21.9 0.542 0.534 256.0
舜麦1718 育成种 262.1 23.0 0.640 0.470 116.5 617.6 26.2 0.398 0.767 576.0
徐麦31 育成种 江苏 206.8 22.0 0.608 0.627 118.5 539.2 26.1 0.427 0.767 720.0
徐麦27 育成种 江苏 353.7 26.8 0.426 0.702 234.0 483.2 23.8 0.447 0.728 519.5
徐麦856 育成种 江苏 237.0 21.6 0.557 0.644 163.5 558.5 24.9 0.417 0.911 793.0
西农979 育成种 346.1 25.0 0.491 0.743 189.5 463.8 21.8 0.406 0.683 549.5
普冰2011 高代品系 226.6 18.3 0.484 0.420 115.0 361.8 16.0 0.454 0.445 285.5
中植9号 高代品系 河南 190.5 21.1 0.578 0.339 71.5 508.5 23.0 0.466 0.599 629.0
宿2013 高代品系 195.9 17.3 0.813 0.389 49.5 425.3 21.8 0.449 0.532 352.0
安农1206 高代品系 261.5 21.8 0.470 0.538 194.5 459.8 24.6 0.385 0.702 546.0
PI27012 高代品系 191.3 20.4 0.562 0.383 89.0 365.8 20.7 0.414 0.584 528.0
宿8802（皖麦19） 育成种 188.7 17.6 0.577 0.469 52.5 564.4 23.7 0.437 0.521 456.0
济宁114076 高代品系 山东 173.7 20.5 0.657 0.438 118.0 437.7 25.0 0.418 0.563 474.5
泰科麦6338（1） 高代品系 山东 251.3 22.3 0.639 0.477 170.5 524.6 24.8 0.404 0.450 495.0
泰科麦50206（3） 高代品系 山东 219.7 18.9 0.722 0.367 92.5 429.5 23.5 0.421 0.574 315.5
济南4号 育成种 山东 120.8 17.0 0.569 0.448 111.5 301.3 21.0 0.411 0.442 440.5
济麦25 高代品系 山东 249.7 24.0 0.492 0.505 106.5 286.2 24.4 0.426 0.770 623.5
济麦31 高代品系 山东 248.8 25.1 0.522 0.692 263.0 419.3 26.2 0.400 0.638 655.0
鲁原502 育成种 山东 170.0 17.0 0.533 0.259 45.0 529.5 25.8 0.388 0.745 697.5
临麦6号 高代品系 山东 114.9 15.2 0.645 0.303 72.5 292.9 17.9 0.377 0.567 614.5
烟农999 育成种 山东 181.4 18.6 0.670 0.399 73.5 325.6 20.6 0.412 0.471 355.0
周麦16 育成种 河南 181.5 18.3 0.608 0.364 91.0 400.1 22.9 0.459 0.547 391.0
周麦22 育成种 河南 146.6 14.4 0.576 0.300 99.5 282.5 24.2 0.408 0.726 493.0
石H83-366 高代品系 166.6 19.5 0.680 0.358 75.5 199.0 17.0 0.439 0.338 269.5
淮麦05155 高代品系 245.6 21.8 0.656 0.427 80.0 479.5 24.4 0.424 0.698 870.0
峰川9号 育成种 239.1 23.4 0.571 0.432 118.0 258.1 18.2 0.425 0.309 273.0
烟农187 高代品系 山东 226.7 21.4 0.479 0.488 204.5 353.0 22.2 0.377 0.562 490.0
LA15P315 高代品系 185.8 18.9 0.666 0.471 88.5 330.9 24.2 0.443 0.752 571.0
LA15P330 高代品系 141.8 15.6 0.598 0.310 72.5 349.0 22.0 0.478 0.613 345.5
济麦51 高代品系 山东 197.9 16.7 0.595 0.458 93.0 363.5 22.7 0.421 0.486 466.5
济麦52 高代品系 山东 120.0 16.2 0.779 0.325 53.5 322.5 23.2 0.414 0.646 638.0
济麦229 育成种 山东 254.2 24.1 0.476 0.420 221.5 375.6 22.2 0.386 0.646 524.5
济麦262 育成种 山东 196.5 21.7 0.545 0.500 101.5 326.2 20.8 0.429 0.622 468.0
济麦66 高代品系 山东 198.5 20.3 0.569 0.537 137.5 312.6 21.8 0.412 0.760 402.5
LJ154052 高代品系 168.9 18.3 0.562 0.268 104.5 406.7 24.8 0.363 0.500 510.5
LJ15鉴010 高代品系 322.7 24.9 0.519 0.584 209.5 481.4 24.0 0.388 0.703 604.0
LJ15鉴16 高代品系 104.6 12.2 0.682 0.342 91.5 463.1 23.8 0.382 0.917 801.5
LJ15鉴71 高代品系 203.5 19.0 0.607 0.526 111.5 284.3 21.4 0.433 0.531 248.5
济麦21 育成种 山东 161.3 15.4 0.615 0.408 105.5 271.3 18.8 0.379 0.523 490.0
济麦22 育成种 山东 233.1 18.6 0.524 0.457 118.5 488.8 26.1 0.372 0.637 490.0
济麦44 高代品系 山东 256.2 25.0 0.565 0.364 36.0 325.3 19.6 0.426 0.679 597.5
德抗961 育成种 133.4 16.2 0.570 0.322 96.5 248.6 20.0 0.482 0.723 315.0
LJ15210 高代品系 237.6 20.3 0.452 0.421 119.5 322.9 22.2 0.412 0.596 501.0

Table 1

Summary of root system architecture (RSA) traits under different nitrogen level in 160 common wheat accessions"

处理
Treatment		 性状
Trait		 极小值
Min.		 极大值
Max.		 均值
Average		 标准差
Standard deviation		 变异系数
CV (%)
完全营养液
Absolute nutrient solution		 TRL (cm) 86.33 495.67 210.23 64.44 30.7
TRS (cm2) 11.25 26.85 19.62 3.48 17.7
TRV (cm3) 0.40 0.98 0.42 0.12 18.8
ARD (mm) 0.23 0.75 0.46 0.12 26.2
NRT 24.50 396.50 119.97 65.50 54.6
缺氮营养液
N-deficiency
nutrient solution		 TRL (cm) 189.52 631.29 406.85 94.21 23.2
TRS (cm2) 14.62 26.87 22.97 2.27 9.9
TRV (cm3) 0.36 0.54 0.62 0.03 7.1
ARD (mm) 0.31 1.07 0.62 0.14 22.6
NRT 208.00 881.00 476.83 145.20 30.5
相对比值
Relative ratio		 RTRL 0.92 5.83 2.10 0.75 35.7
RTRS 0.74 2.27 1.21 0.26 21.2
RTRV 0.95 2.52 1.49 0.28 19.2
RARD 0.53 3.82 1.44 0.50 34.6
RNRT 0.59 16.60 5.15 3.06 54.7

Fig. S1

Distribution of root system architecture (RSA) related traits in 160 common wheat accessions (N+P+K)"

Fig. s2

Distribution of root system architecture (RSA) related traits in 160 common wheat accessions (P+K)"

Fig. s3

Distribution of relative root system architecture (RSA) related traits under different nitrogen level in 160 common wheat accessions"

Table 2

Genotype data statistics of 160 common wheat lines"

染色体
Chr.		 标记数目
Number of markers		 长度 a
Length a
(Mb)		 密度
Density
(Mb/marker)		 最小等位基因频率
Minimum allele frequency (MAF)		 基因多态性
Genetic
diversity		 多态性信息含量
Polymorphic information content (PIC)
均值
Average		 范围
Range		 均值
Average		 范围
Range		 均值
Average		 范围
Range
1A 18,975 594.00 0.031 0.26 0.05-0.50 0.37 0.10-0.50 0.28 0.09-0.38
1B 13,810 689.40 0.050 0.31 0.05-0.50 0.39 0.10-0.50 0.29 0.09-0.38
1D 4464 495.40 0.111 0.18 0.05-0.49 0.34 0.10-0.50 0.28 0.09-0.38
2A 2710 780.70 0.288 0.27 0.05-0.50 0.36 0.10-0.50 0.30 0.09-0.38
2B 14,287 801.20 0.056 0.25 0.05-0.50 0.38 0.10-0.50 0.30 0.09-0.38
2D 3604 651.60 0.181 0.32 0.05-0.50 0.36 0.10-0.50 0.30 0.09-0.38
3A 13,177 750.80 0.057 0.26 0.05-0.50 0.36 0.10-0.50 0.27 0.09-0.38
3B 20,116 830.30 0.041 0.27 0.05-0.50 0.40 0.10-0.50 0.31 0.09-0.38
3D 2417 615.40 0.255 0.20 0.05-0.50 0.31 0.10-0.50 0.26 0.09-0.38
4A 12,438 744.50 0.060 0.25 0.05-0.50 0.38 0.10-0.50 0.30 0.09-0.38
4B 10,680 673.40 0.063 0.23 0.05-0.50 0.36 0.10-0.50 0.28 0.09-0.38
4D 1055 509.60 0.483 0.26 0.05-0.49 0.38 0.10-0.50 0.31 0.09-0.38
5A 11,972 709.70 0.059 0.27 0.05-0.50 0.37 0.10-0.50 0.29 0.09-0.38
5B 23,584 713.00 0.030 0.29 0.05-0.50 0.37 0.10-0.50 0.29 0.09-0.38
5D 2512 566.00 0.225 0.29 0.05-0.50 0.33 0.10-0.50 0.27 0.09-0.38
6A 14,250 618.00 0.043 0.25 0.05-0.50 0.40 0.10-0.50 0.32 0.09-0.38
6B 14,434 720.90 0.050 0.27 0.05-0.50 0.33 0.10-0.50 0.27 0.09-0.38
6D 2544 473.50 0.186 0.25 0.05-0.50 0.36 0.10-0.50 0.27 0.09-0.38
7A 20,807 736.60 0.035 0.27 0.05-0.50 0.32 0.10-0.50 0.29 0.09-0.38
7B 12,025 750.60 0.062 0.28 0.05-0.50 0.36 0.10-0.50 0.28 0.09-0.38
7D 4537 638.60 0.141 0.22 0.05-0.50 0.29 0.10-0.50 0.26 0.09-0.38
A genome 93,664 4934.50 0.052 0.27 0.05-0.50 0.38 0.10-0.50 0.29 0.09-0.38
B genome 108,936 5179.00 0.048 0.29 0.05-0.50 0.36 0.10-0.50 0.28 0.09-0.38
D genome 20,568 3950.40 0.193 0.24 0.05-0.50 0.33 0.10-0.50 0.28 0.09-0.38

Fig. 1

Principal component analysis of 160 common wheat lines"

Fig. 2

LD decay analysis of 160 common wheat lines"

Table 3

Marker-trait associations for nitrogen use efficiency related traits (P ≤ 0.001)"

性状
Trait		 标记
Marker		 染色体
Chromosome		 位置
Position (Mb)		 优异等位基因
Favorable allele		 P值
P-value		 表型解释变异
R2 (%)		 参考文献
Reference
RNRT AX_110370425 1B 577.6 G 5.38E-04 9.7
RNRT AX_109497462 2B 756.7 T 9.75E-04 8.9
RNRT AX_108811964 2D 612.3 C 4.81E-04 10.0
RNRT AX_109933410 3A 22.6 G 3.45E-04 8.4
RNRT AX_109931146 3B 117.6 T 3.78E-04 10.2 Ren et al.[29]
RNRT AX_110438119 3B 778.3 T 2.68E-04 11.2
RNRT AX_109838302 4B 540.3 T 2.84E-05 13.8
RARD AX_108964719 2B 207.1 G 9.08E-04 9.2
RARD AX_110968453 3B 386.4 G 4.06E-04 10.4
RTRL AX_108913559 1A 515.8 C 9.88E-04 7.0
RTRL AX_110617713 1B 633.3 T 6.67E-04 9.6
RTRL AX_109031690 2B 25.5 T 6.79E-04 9.6
RTRL AX_108858890 2B 26.0 T 5.03E-04 10.4
RTRL AX_109958492 2B 26.0 G 7.36E-04 9.4
RTRL AX_108989039 2B 26.1 G 7.36E-04 9.4
RTRL AX_110583038 2B 26.1 G 8.66E-04 9.2
RTRL AX_110536069 2B 26.1 G 4.07E-04 10.2
RTRL AX_109855057 2B 26.1 A 7.73E-04 9.3
RTRL AX_108758132 2B 26.1 C 8.12E-04 9.6
RTRL AX_109974238 2B 785.8 T 9.99E-04 9.5
RTRL AX_108811964 2D 612.3 C 1.26E-04 11.9
RTRL AX_109892051 3B 821.0 T 6.39E-04 10.3
RTRL AX_110045465 5B 707.1 C 5.19E-05 13.0
RTRS AX_110543784 1A 496.9 A 3.82E-04 10.9
RTRS AX_109973556 1A 520.9 C 3.62E-04 10.3
RTRS AX_110111289 1B 384.3 G 3.24E-04 10.4
RTRS AX_108774755 2A 125.9 G 4.60E-04 9.9
RTRS AX_110075829 3B 250.6 G 3.91E-04 10.2
RTRS AX_110612422 3B 403.5 G 2.72E-04 10.6
RTRS AX_110411187 3B 410.1 G 7.86E-04 9.2
RTRS AX_110388860 3B 466.1 G 4.02E-04 10.1
RTRS AX_108977570 3B 780.4 G 3.87E-04 10.2
RTRS AX_109915316 4B 554.8 G 4.40E-04 10.0
RTRS AX_110522770 5A 8.2 T 4.58E-04 10.1 Ren et al. [29]
RTRS AX_110044622 5B 59.2 T 3.82E-04 10.2
RTRS AX_110366842 5B 60.2 G 4.52E-04 10.0
RTRS AX_109440517 5B 504.0 T 4.23E-04 10.0
RTRS AX_108813077 5B 517.9 C 4.56E-04 9.9
RTRS AX_110045465 5B 707.1 C 1.34E-05 14.8
RTRS AX_108740494 6A 564.9 G 4.87E-04 9.9
RTRS AX_109907271 6B 54.4 T 2.13E-04 11.2 Ren et al. [29]
RTRS AX_110402455 6D 469.2 G 6.43E-04 9.6
RTRS AX_110447828 6D 472.3 T 4.04E-04 10.3
RTRS AX_110444174 7A 722.6 T 1.21E-04 11.8
性状
Trait		 标记
Marker		 染色体
Chromosome		 位置
Position (Mb)		 优异等位基因
Favorable allele		 P值
P-value		 表型解释变异
R2 (%)		 参考文献
Reference
RTRS AX_109983408 7B 23.3 G 3.50E-04 10.5
RTRV AX_110003555 1A 486.7 T 5.71E-04 9.6
RTRV AX_110543784 1A 496.9 A 3.80E-06 17.9
RTRV AX_109973556 1A 520.9 C 3.38E-06 16.9
RTRV AX_110111289 1B 384.3 G 3.01E-06 17.0
RTRV AX_110492902 1D 382.2 T 2.82E-05 16.7
RTRV AX_108774755 2A 125.9 G 2.48E-06 17.4
RTRV AX_110536069 2B 26.1 C 7.23E-04 9.3
RTRV AX_109974238 2B 785.8 T 8.56E-04 9.6
RTRV AX_109586550 2D 632.0 G 6.33E-04 9.5
RTRV AX_109933410 3A 22.6 G 8.64E-04 7.2
RTRV AX_110935379 3A 491.9 T 1.29E-05 14.9
RTRV AX_109931146 3B 117.6 T 8.08E-04 9.4
RTRV AX_110075829 3B 250.6 G 2.85E-06 17.0
RTRV AX_110612422 3B 403.5 G 3.16E-06 16.9 Shen et al. [30]
RTRV AX_110411187 3B 410.1 G 6.65E-05 12.6
RTRV AX_110388860 3B 466.1 G 2.83E-06 17.1
RTRV AX_108977570 3B 780.4 G 2.61E-06 17.2 Shen et al. [30]
RTRV AX_110531944 3B 780.4 T 9.04E-04 9.0
RTRV AX_109827784 3B 780.7 G 8.90E-04 9.0
RTRV AX_109279719 3B 780.8 G 8.87E-04 9.0
RTRV AX_108848657 3B 781.3 T 4.03E-04 10.2
RTRV AX_108783900 3B 781.5 G 3.93E-04 10.1
RTRV AX_109892051 3B 821.0 T 1.54E-04 12.0
RTRV AX_109915316 4B 554.8 G 3.27E-06 16.8 Ren et al. [29]
RTRV AX_109364182 4B 655.2 T 8.98E-05 13.2 Ren et al. [29]
RTRV AX_110522770 5A 8.2 T 2.32E-06 17.5
RTRV AX_110044622 5B 59.2 T 2.11E-06 17.5 Ren et al. [29]
RTRV AX_110366842 5B 60.2 G 3.18E-06 16.9
RTRV AX_109440517 5B 504.0 T 2.51E-06 17.2
RTRV AX_108813077 5B 517.9 C 2.88E-06 17.0
RTRV AX_110045465 5B 707.1 C 2.21E-05 14.1 Ren et al. [29]
RTRV AX_108882877 6A 557.9 G 5.68E-04 9.9 Shen et al. [30]
RTRV AX_108740494 6A 564.9 G 3.75E-06 16.8
RTRV AX_110504351 6A 596.0 G 8.66E-05 12.7
RTRV AX_109907271 6B 54.4 T 3.12E-06 17.0
RTRV AX_109580957 6B 149.9 T 1.18E-06 19.6
RTRV AX_109308595 6B 485.5 T 1.34E-05 15.2 Ren et al. [29]
RTRV AX_109364747 7A 136.3 T 7.92E-05 12.3
RTRV AX_109024988 7A 689.3 T 9.92E-04 8.9
RTRV AX_110444174 7A 722.6 T 3.97E-04 10.2
RTRV AX_109983408 7B 23.3 G 3.46E-06 17.3

Fig. 3

Manhattan plots for relative root system architecture (RSA) related traits under different nitrogen level in 160 common wheat lines RNRT: relative number of root tips; RTRL: relative total root length; RTRS: relative total root surface area; RTRV: relative total root volume; RARD: relative average root diameter."

Table 4

Wheat accessions with higher nitrogen use efficiency"

品种
Cultivar		 性状Trait 优异等位基因数
Number of favorable alleles
RTRL RTRS RARD RTRV RNRT
徐麦856 Xumai 856 2.356 1.149 0.748 1.415 4.850 17
郑麦366 Zhengmai 366 2.474 1.489 0.741 2.085 5.747 18
品种
Cultivar		 性状Trait 优异等位基因数
Number of favorable alleles
RTRL RTRS RARD RTRV RNRT
济南4号 Jinan 4 2.495 1.238 0.722 0.985 3.951 16
临麦6号 Linmai 6 2.548 1.177 0.584 1.870 8.476 15
兰考358 Lankao 358 2.630 1.336 0.651 1.707 4.918 18
山农587 Shannong 587 2.674 1.401 0.678 0.903 6.599 15
济麦52 Jimai 52 2.687 1.433 0.531 1.988 11.925 16
山农0431 Shannong 0431 2.856 1.600 0.526 1.280 6.991 17
山农22 Shannong 22 2.884 1.206 0.690 1.554 5.695 19
济麦37 Jimai 37 2.907 1.385 0.587 0.968 3.233 20
鲁原502 Luyuan 502 3.114 1.520 0.729 2.882 15.500 15
山农1565 Shannong 1565 3.230 1.286 0.466 1.599 8.706 16
汶农14 Wennong 14 3.648 1.254 0.507 0.918 6.894 17
鲁麦20 Lumai 20 4.029 2.079 0.432 1.534 15.089 21
鑫麦296 Xinmai 296 5.828 2.164 0.481 2.113 24.547 20
[1] Chen J G, Zhang Y, Tan Y W, Zhang M, Zhu L L, Xu G H, Fan X R. Agronomic nitrogen-use efficiency of rice can be increased by drivingOsNRT2.1 expression with the OsNAR2.1 promoter. Plant Biotechnol J, 2016,14:1705-1715.
[2] Sandrine R, Alain G, Laurence L. Signal interactions in the regulation of root nitrate uptake. J Exp Bot, 2014,65:5509-5517.
pmid: 25165146
[3] Maccaferri M, El-Feki W, Nazemi G, Salvi S, Canè M A, Colalongo M C, Stefanelli S, Tuberosa R. Prioritizing quantitative trait loci for root system architecture in tetraploid wheat. J Exp Bot, 2016,67:1161-1178.
pmid: 26880749
[4] Kabir M R, Liu G, Guan P F, Wang F, Khan A A, Ni Z F, Yao Y Y, Hu Z R, Xin M M, Peng H R. Mapping QTLs associated with root traits using two different populations in wheat (Triticum aestivum L.). Euphytica, 2015,206:175-190.
[5] de Dorlodot S, Forster B, Pagès L, Price A, Tuberosa R, Draye X. Root system architecture: opportunities and constraints for genetic improvement of crops. Trends Plant Sci, 2007,12:474-481.
[6] Bishopp A, Lynch J P. The hidden half of crop yields. Nat Plants, 2015,1:15117.
[7] Paez-Garcia A, Motes C M, Scheible W R, Chen R, Blancaflor E B, Monteros M J. Root traits and phenotyping strategies for plant improvement. Plants, 2015,4:334-355.
doi: 10.3390/plants4020334 pmid: 27135332
[8] Osmont K S, Sibout R, Hardtke C S. Hidden branches: developments in root system architecture. Annu Rev Plant Biol, 2007,58:93-113.
pmid: 17177637
[9] Liu J D, He Z H, Rasheed A, Wen W E, Yan J, Zhang P Z, Wan Y X, Zhang Y, Xie C J, Xia X C. Genome-wide association mapping of black point reaction in common wheat (Triticum aestivum L.). BMC Plant Biol, 2017,17:220.
pmid: 29169344
[10] Dong Y, Liu J D, Zhang Y, Geng H W, Awais R, Xiao Y G, Cao S H, Fu L P, Yan J, Wen WW, Zhang Y, Jing R L, Xia X C, He Z H. Genome-wide association of stem water soluble carbohydrates in bread wheat. PLoS One, 2016,11:e0164293.
pmid: 27802269
[11] Rasheed A, Xia X C, Ogbonnaya F, Mahmood T, Zhang Z, Mujeeb-Kazi A M, He Z H. Genome-wide association for grain morphology in synthetic hexaploid wheats using digital imaging analysis. BMC Plant Biol, 2014,14:12.
pmid: 24400634
[12] Guo J, Shi W P, Zhang Z, Cheng J Y, Sun D Z, Yu J, Li X L, Guo P Y, Hao C Y. Association of yield-related traits in founder genotypes and derivatives of common wheat (Triticum aestivum L.). BMC Plant Biol, 2018,18:387.
[13] Maccaferri M, Ricci A, Salvi S, Milner S G, Noli E, Martelli P L, Casadio R, Akhunov E, Scalabrin S, Vendramin V, Ammar K, Blanco A, Desiderio F, Distelfeld A, Dubcovsky J, Fahima T, Faris J, Korol A, Massi A, Mastrangelo A M, Morgante M, Pozniak C, N’Diaye A, Xu S, Tuberosa R. A high-density, SNP-based consensus map of tetraploid wheat as a bridge to integrate durum and bread wheat genomics and breeding. Plant Biotechnol J, 2015,13:648-663.
doi: 10.1111/pbi.12288 pmid: 25424506
[14] Bulli P, Zhang J, Chao S, Chen X, Pumphrey M. Genetic architecture of resistance to stripe rust in a global winter wheat germplasm collection. G3: Genes Genom Genet, 2016,8:2237-2253.
[15] Lian X M, Xing Y Z, Yan H, Xu C G, Li XH, Zhang Q F. QTL for low nitrogen tolerance at seedling stage identified using a recombinant inbred line population derived from an elite rice hybrid. Theor Appl Genet, 2005,112:85-96.
pmid: 16189659
[16] An D G, Su J Y, Liu Q Y, Zhu Y G, Tong Y P, Li M J, Jing R L, Lin B, Li Z S. Mapping QTL for nitrogen uptake in relation to the early growth of wheat (Triticum aestivum L.). Plant Soil, 2006,284:73-84.
[17] Quarrie S A, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele N, Pljevljakusić D, Waterman E, Weyen J, Schondelmaier J, Habash Z D, Farmer P, Saker L, Clarkson D T, Abugalieva A, Yessimbekova M, Turuspekov Y, Abugalieva S, Tuberosa R, Sanguineti M C, Hollington P A, Aragués 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 from grain yield across a range of environments. Theor Appl Genet, 2005,110:865-880.
pmid: 15719212
[18] Fontaine J X, Ravel C, Pageau K, Heumez E, Dubois F, Hirel B. 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. Theor Appl Genet, 2009,119:645-662.
doi: 10.1007/s00122-009-1076-4 pmid: 19513687
[19] 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 Genet, 2014,15:57.
[20] Hoagland D R, Arnon D I. The water-culture method for growing plants without soil. California Agric Exp Station Circular, 1950,347:1-32.
[21] Saghai-Maroof M A, Soliman K M, Jorgensen R A, Allard R W L. Ribosomal DNA spacer-length polymorphisms in barley: Mendelian inheritance, chromosomal location, and population dynamics. Proc Natl Acad Sci USA, 1984,81:8014-8018.
pmid: 6096873
[22] Breseghello F, Sorrells M E. Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics, 2006,172:1165-1177.
doi: 10.1534/genetics.105.044586 pmid: 16079235
[23] Kump K L, Bradbury P J, Wisser R J, Buckler E S, Belcher A R, Oropeza-Rosas M A, Balint-Kurti P J. Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet, 2011,43:163.
[24] Zhu C, Gore M, Buckler E S, Yu J. Status Prospects of association mapping in plants. Plant Genome, 2008,1:5-20.
[25] Poland J A, Bradbury P J, Buckler E S, Nelson R. Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci USA, 2011,108:6893-6898.
pmid: 21482771
[26] Hao C Y, Wang Y Q, Chao S M, Li T, Liu H X, Wang L F, Zhang X Y. The iSelect 9K SNP analysis revealed polyploidization induced revolutionary changes and intense human selection causing strong haplotype blocks in wheat. Sci Rep (UK), 2017,4:41247.
[27] Wang Y M, Hou J, Liu H, Li T, Wang K, Hao C Y, Liu H, Zhang X Y. TaBT1, affecting starch synthesis and thousand kernel weight, underwent strong selection during wheat improvement. J Exp Bot, 2019,70:1497-511.
doi: 10.1093/jxb/erz032 pmid: 30753656
[28] Chen X J, Min D H, Tauqeer A Y, Hu Y G. Genetic diversity, population structure and linkage disequilibrium in elite Chinese winter wheat investigated with SSR markers. PLoS One, 2012,7:e44510.
[29] Ren D, Fang X, Jiang P, Zhang G, Hu J, Wang X, Wang H. Genetic architecture of nitrogen-deficiency tolerance in wheat seedlings based on a nested association mapping (NAM) population. Front Plant Sci, 2018,9:845.
doi: 10.3389/fpls.2018.00845 pmid: 29997636
[30] 沈兴. 小麦氮营养特性的基因型差异及其遗传解析. 山东农业大学硕士学位论文, 山东泰安, 2018. pp 28-44.
Shen X. Genotypic Differences and Genetic Analysis in Nitrogen Nutritional Characteristics of Wheat. MS Thesis of Shandong Agricultural University, Tai’an, Shandong, China, 2018. pp 28-44 (in Chinese with English abstract).
[31] Craig A, Ewan R, Mesmar J, Gudipati V, Sadanandom A. E3 ubiquitin ligases and plant innate immunity. J Exp Bot, 2009,60:1123-1132.
doi: 10.1093/jxb/erp059 pmid: 19276192
[32] Park G G, Park J J, Yoon J M, Yu S N. An GH A RING finger E3 ligase gene, Oryza sativa Delayed Seed Germination 1 (OsDSG1), controls seed germination and stress responses in rice. Plant Mol Biol, 2010,74:467-478.
doi: 10.1007/s11103-010-9687-3 pmid: 20878348
[33] Semagn K, Babu R, Hearne S, Olsen M. Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement. Mol Breed, 2014,33:1-14.
doi: 10.1007/s11032-013-9917-x
[34] Rasheed A, Hao Y F, Xia X C, Khan A, Xu Y B, Varshney R K, He Z H. Crop breeding chips and genotyping platforms: progress, challenges, and perspectives. Mol Plant, 2017,10:1047-1064.
doi: 10.1016/j.molp.2017.06.008 pmid: 28669791
[35] Long Y M, Chao W S, Ma G J, Xu S S, Qi L L. An innovative SNP genotyping method adapting to multiple platforms and throughputs. Theor Appl Genet, 2016,130:597-607.
doi: 10.1007/s00122-016-2838-4 pmid: 27942775
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