Genetic Mapping and QTL Analysis for Disease Resistance Using F 2 and 1 F 5 Generation-based Genetic Maps Derived from Tifrunner × GT-C20 in 2 Peanut (Arachis hypogaea L.) 3 4 Hui Wang†, Manish K. Pandey†, Lixian Qiao, Hongde Qin, Albert K. Culbreath, Guohao He, 5 Rajeev K. Varshney, and Baozhu Guo * 6 7 H. Wang, Fujian Agricultural and Forestry University, College of Plant Protection, Fuzhou, 8 China; H. Wang, M.K. Pandey, L. Qiao, and A.K. Culbreath, University of Georgia, 9 Department of Plant Pathology, Tifton, GA 31793; H. Wang, M.K. Pandey, L. Qiao, and B. 10 Guo, USDA-ARS, Crop Protection and Management Research Unit, Tifton, GA 31793; M.K. 11 Pandey and R.K. Varshney, International Crops Research Institute for the Semi-Arid Tropics 12 (ICRISAT), Hyderabad, India; L. Qiao, Qingdao Agricultural University, College of Life 13 Science, Qingdao, China; H. Qin, Hubei Academy of Agricultural Sciences, Wuhan, China; 14 G. He, Tuskegee University, Tuskegee, AL 36088; H. Wang, Peanut Research Institute, 15 Shandong Academy of Agricultural Sciences, Qingdao, China. Received ____________. 16 †Contributed equally. *Corresponding author ([email protected]). 17 18 Abbreviations: QTL, quantitative trait loci; PVE, phenotypic variation explained; TSWV, 19 Tomato spotted wilt virus; LS, leaf spots. 20 Running Head: QTL analysis of disease resistance in peanut 21 Key words: Genetic map, Quantitative trait loci, thrips, leaf spots, Tomato spotted wilt virus 22 The Plant Genome: Posted 31 July 2013; doi: 10.3835/plantgenome2013.05.0018
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Genetic Mapping and QTL Analysis for Disease Resistance Using F2 and 1
F5 Generation-based Genetic Maps Derived from Tifrunner × GT-C20 in 2
Peanut (Arachis hypogaea L.) 3
4
Hui Wang†, Manish K. Pandey†, Lixian Qiao, Hongde Qin, Albert K. Culbreath, Guohao He, 5
Rajeev K. Varshney, and Baozhu Guo* 6
7
H. Wang, Fujian Agricultural and Forestry University, College of Plant Protection, Fuzhou, 8
China; H. Wang, M.K. Pandey, L. Qiao, and A.K. Culbreath, University of Georgia, 9
Department of Plant Pathology, Tifton, GA 31793; H. Wang, M.K. Pandey, L. Qiao, and B. 10
Guo, USDA-ARS, Crop Protection and Management Research Unit, Tifton, GA 31793; M.K. 11
Pandey and R.K. Varshney, International Crops Research Institute for the Semi-Arid Tropics 12
(ICRISAT), Hyderabad, India; L. Qiao, Qingdao Agricultural University, College of Life 13
Science, Qingdao, China; H. Qin, Hubei Academy of Agricultural Sciences, Wuhan, China; 14
G. He, Tuskegee University, Tuskegee, AL 36088; H. Wang, Peanut Research Institute, 15
Shandong Academy of Agricultural Sciences, Qingdao, China. Received ____________. 16
AHO116). The first genomic region (GM2744 – seq5D5) harbored three QTLs for TSWV 312
(5.14 – 34.92% PV) and five QTLs for LS (7.80 – 13.11% PV) with the contribution from the 313
resistant parent, Tifrunner. Similarly, the second genomic region (TC5A07 – TC7G10) 314
The Plant Genome: Posted 31 July 2013; doi: 10.3835/plantgenome2013.05.0018
harbored a single QTL for TSWV (23.02% PV) and four QTLs for LS (10.08 – 24.19% PV) 315
with the contribution coming from the susceptible parent, GT-C20. The third genomic region 316
(seq2G4 – PM499) harbored a single QTL for TSWV (15.75% PV) and four QTLs for LS 317
(6.61 – 18.97% PV) contributed by the susceptible parent, GT-C20. The fourth genomic 318
region (GNB2 – AHO116) harbored three QTL for TSWV (6.26 – 21.18% PV) and four 319
QTLs for LS (15.30 – 21.19% PV) contributed by the resistant parent, Tifrunner. 320
In contrast to the F2 map, there was no common QTL for all three traits in the F5 map. 321
There was only one common genomic region located on LGT6 (TC11A02-300 – GNB523-322
500) harboring one QTL for TSWV (qF5TSWV2) with 7.71% PV and LS (qF5LS4) with 323
8.02% PV. 324
Common QTLs Identified Between Two Maps 325
There was one QTL controlling LS in F2 map (AhXVIII) and one QTL controlling TSWV in 326
F5 map (LGT7) flanked by same markers i.e., GNB159 – GNB335. In the other case, even 327
though the flanking markers were not same but the QTLs were found on the same linkage 328
group. Such QTLs have been observed between corresponding LGs of both genetic maps, for 329
example between AhII and LGT17, AhV and LGT16, AhVI and LGT11, and AhX and 330
LGT6. 331
332
DISCUSSION 333
Due to the increased uniformity in marker nomenclature, the corresponding linkage groups 334
between these two maps have been identified. Further, a total of 9 of the 10 LGs from A-335
genome and 8 of the 10 LGs from B-genome could be assigned after comparing these two 336
genetic maps with the reference consensus genetic maps using the common marker loci 337
(Gautami et al., 2012). In general, a good co-linearity has been observed for these two genetic 338
The Plant Genome: Posted 31 July 2013; doi: 10.3835/plantgenome2013.05.0018
maps and with the reference consensus genetic map (Figure 1). This population has shown 339
great potential not only for genetic mapping but also for identification of QTLs to several 340
economically important traits such as morphological descriptors, oil quality, and disease 341
resistance. Here, successful attempt was made to make use of both the genetic maps and the 342
identified QTLs for the three resistance traits to thrips, Tomato spotted wilt virus (TSWV) 343
and leaf spots (LS). 344
RIL population is a set of genotypes of highly inbred F2 lines. RILs approach 345
complete homozygosity for all loci as the number of generations of inbreeding approaches 346
infinity. In practice, the convention is to use six to eight generations of inbreeding, resulting 347
in ~99.84 to 99.96% homozygosity respectively. A major advantage of RILs is that the 348
descendents of any one RIL are genetically identical, hence “immortal”, allowing RILs to be 349
marker-genotyped once and phenotyped repeatedly in multiple labs and experiments 350
(Elnaccash and Tonsor, 2010). It is well understood that RIL-based QTL analysis is more 351
reliable than the F2-based mapping populations for identification of QTLs. Majority of the 352
studies showed identification of large number of QTLs with overestimated phenotypic effect. 353
However, none of the study was conducted at both the stages (F2 and RIL) using the same 354
population and thus, this study was focused on using genotyping data generated at F2 and F5 355
generation and phenotyping data generated at F8 generation onwards on the same population. 356
Phenotyping data generated on this population after F8 generation was used for both the 357
genetic maps to identify QTLs for the three traits, thrips, Tomato spotted wilt virus (TSWV) 358
and leaf spots (LS). Therefore, a total of 77 QTLs were identified in these two maps, 54 359
QTLs in F2 map (Figure 2) while 23 QTLs in F5 map (Figure 3) with PV up-to 19.43% 360
(thrips), 34.92% (TSWV) and 21.45% (LS), respectively. 361
We should therefore expect that the F2 and the RIL populations might show high 362
phenotypic variance and this effect will be exaggerated in RIL compared to the F2 because 363
The Plant Genome: Posted 31 July 2013; doi: 10.3835/plantgenome2013.05.0018
all individuals are homozygous at virtually all loci, and large sample size in RIL reducing the 364
variance of the mean and transgressive segregation and homozygosity increasing the mean's 365
variance (Beavers, 1998). As expected, the phenotypic variance explained by QTLs detected 366
in F2 map showed relatively higher phenotypic variance as compared to F5 map. Occurrence 367
of more QTLs with relatively higher estimation of phenotypic effect in F2 map than the F5 368
map was due to presence of higher level of heterozygosity in F2 generation. Nevertheless, 369
this study has provided comparative QTL analysis using genotyping data generated at F2 and 370
F5 generation on the same population and confirms the assumption established based on 371
studies on different populations. Because of above two technical deficiencies (higher number 372
of QTLs and high estimation of phenotypic variance) of using F2 population for conducting 373
QTL analysis, earlier studies support the use of RIL populations such as double haploids and 374
RILs. These RIL populations have additional advantage of being useful for phenotyping the 375
population for multiple season/location in order to identify consistent (across seasons) and 376
stable (across locations) QTLs. 377
It was interesting to note that not only alleles of the resistant parent have contributed 378
towards the total phenotypic variance but the susceptible parent also made significant 379
contribution through favorable alleles. For thrips no study so far has been conducted while 380
for TSWV, earlier using the same population, Qin et al. (2012) reported one QTL with 12.9% 381
PV (qtswv1). Beside above QTL, no other QTL for TSWV has been reported so far in peanut. 382
Therefore, all the QTLs identified in current study for thrips and TSWV are novel in nature 383
and are of great importance for further study and their deployment in molecular breeding. 384
The highest PV explained by any QTL for leaf spot was 27.35% (qF2LS11) in present 385
study, while earlier QTL analysis using extensive phenotyping data on two RIL populations 386
(TAG 24 × GPBD 4 and TG 26 × GPBD 4) for 7–8 seasons and genotyping data (207 marker 387
loci each) resulted in identification of a total of 28 QTLs for late leaf spot (LLS; 10.1 to 388
The Plant Genome: Posted 31 July 2013; doi: 10.3835/plantgenome2013.05.0018
67.8% PV) (Khedikar et al., 2010; Sujay et al., 2012). These QTLs include a major QTL for 389
LLS with upto 62.34% PV flanked by GM1573/GM1009 and seq8D09. 390
Plants possess a strong immune system and defense mechanism to prevent themselves 391
from the pathogens. Thus common genomic regions controlling more than one disease may 392
be even more important in order to improve plant resilience. Considering the above 393
hypothesis, two common genomic regions (GM2337 – TC42A02 and IPAHM108-2 – 394
AHGS0347) were identified in F2 map for all the three diseases, while four common genomic 395
regions (GM2744 – seq5D5, TC5A07 – TC7G10, seq2G4 – PM499 and GNB2 – AHO116) 396
in F2 map and one common genomic region (TC11A02-300 – GNB523-500) in F5 map were 397
identified for LS and TSWV. The presence of common QTLs has also been reported by Sujay 398
et al., (2012) where in three genomic regions harbored QTLs from two populations for both 399
leaf rust and late leaf spot. Thus, these common genomic regions may harbor genes which 400
play major role in plant defense against several pathogens and hence can be used for 401
improving resistance for more than one disease through increasing resistance. 402
In summary, through screening more than 5000 markers, genetic maps upto 329 403
marker loci have been developed. High DNA polymorphism and high phenotypic variability 404
between parental genotypes have made the T-population a very good genetic material for 405
identification of linked markers through QTL analysis to thrips, TSWV and LS. Common 406
genomic regions controlling more than one disease has also been identified with significant 407
contribution towards disease resistance. Thus, this population has shown great potential for 408
dense genetic mapping and identification of QTLs controlling several disease and agronomic 409
traits in peanut. In addition it was evident that the number of QTLs and the estimates of 410
phenotypic variance were reduced in F5 map. The identified QTLs, consistent or not, will be 411
studied further through fine mapping for potential use in breeding for genetic improvement of 412
disease resistance in peanut. 413
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414
Acknowledgements 415
We thank Billy Wilson, Jake Fountain, Stephanie Lee, Lucero Gutierrez and Sara Beth 416
Pelham for technical assistance in the field and the laboratory. This research was partially 417
supported by funds provided by the USDA Agricultural Research Service, the Georgia 418
Agricultural Commodity Commission for Peanuts, Peanut Foundation and National Peanut 419
Board. We are also thankful to National Science Foundation to DRC (NSF, DBI-0605251) 420
and USDA/CSREES/Capacity Building Program to GH (#2006-38814-17489) for supporting 421
this research. Mention of trade names or commercial products in this publication is solely for 422
the purpose of providing specific information and does not imply recommendation or 423
endorsement by the U.S. Department of Agriculture. 424
425
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