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Proceeding Paper

Cost-Effective Markers and Candidate Genes Analysis at Wheat MQTL Loci †

1
Institute for Sustainable Agriculture (IAS-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo s/n, 14004 Córdoba, Spain
2
Department of Languages and Computer Science, ETSI Informática, Campus de Teatinos, Universidad de Málaga, Andalucía Tech, 29071 Málaga, Spain
3
INRAE Centre Clermont Auvergne-Rhône-Alpes, UMR INRAE/UCA 1095 Génétique, Diversité et Ecophysiologie des Céréales, Site de Crouël, 5 Chemin de Beaulieu, 63000 Clermont-Ferrand, France
4
Dep. Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, 14071 Córdoba, Spain
5
Department of Biotechnology-Plant Biology, School of Agricultural, Food and Biosystems Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain
*
Authors to whom correspondence should be addressed.
Presented at the 1st International Electronic Conference on Plant Science, 1–15 December 2020; Available online: https://iecps2020.sciforum.net/.
Biol. Life Sci. Forum 2021, 4(1), 44; https://doi.org/10.3390/IECPS2020-08571
Published: 30 November 2020
(This article belongs to the Proceedings of The 1st International Electronic Conference on Plant Science)

Abstract

:
High-resolution melting analysis (HRM) is a resolutive technique, using PCR amplification and in-tube detection, which is based on the PCR product’s melting analysis. It is a promising technique for breeding analysis, as it does not require dedicated sequencing equipment. It can be performed using QRT-PCR equipment that can be available in small-medium molecular biology laboratories or locally by the breeders, and it does not require an electrophoretic step to analyze the amplified DNA fragments. To develop effective HRM assays, the search for highly polymorphic sites amenable to PCR amplification is a prerequisite, which is not an easy task in wheat due to its genome complexity. The insertion site-based polymorphism markers (ISBPs) are PCR markers designed based on the knowledge of the sequence flanking transposable element (TE) sequences. The two PCR primers are designed with one in the transposable element and the other in the flanking DNA sequence. TEs are very abundant and nested in the wheat genome, with unique (genome-specific) insertion sites that are highly polymorphic. In this work, we analyze the available HRM-ISBP assays for wheat 3B and 4A chromosomes, and update their applications in wheat diversity at drought and heat MQTL loci.

1. Introduction

Wheat is considered one of the most important crops worldwide [1]. Its development and final yield are affected by abiotic stresses, such as drought [2,3] and heat [4,5], whose effects are increasing as a consequence of the predicted climate change [6,7]. As a result, tolerance to these abiotic stresses is an important aim in plant breeding to increase crop production [8]. In this way, molecular markers, such as ISBPs, can be developed and applied to identify genomic regions and genes of interest closely related to interesting traits, such as drought and heat tolerance [9]. ISBPs are PCR markers that re designed based on the information of sequence-flanking TE sequences [10]. They have appeared as powerful and interesting tools to apply in genomic and genetic studies in wheat [10,11,12], and have also been used in marker-assisted selection (MAS) and as selecting tools in plant breeding programs [10]. These markers have been designed for several wheat chromosomes [9,11,13,14,15,16] and applied with different aims [17,18,19,20,21]. Mérida-García et al. [9] developed HRM assays based on ISBP markers for wheat chromosomes 4A and 3B. Both chromosomes contain interesting QTLs related to biotic and abiotic stress tolerance, and important agronomic traits, and some of the ISBP markers are located within or in proximity to previously described MQTLs [22]. Meta-QTL analysis was developed to integrate results from individual QTL studies into a common dataset to identify and corroborate the interval of QTL regions [23]. MQTL analysis has been performed in wheat in several studies for root-related traits [24], grain weight [25,26], and heat and drought conditions [22,27]. In this presentation, we assessed and updated the polymorphic HRM-ISBP assays developed for wheat chromosomes 4A and 3B, regarding their applications in wheat at drought and heat stress MQTL loci.

2. Experiments

2.1. Insertion Site-Based Polymorphism Markers

ISBP markers were developed by Mérida-García et al. [9], using the IsbpFinder program [10] and the wheat chromosome 4A survey sequencing [28] to locate the ISBPs and Primer3 (http://primer3.sourceforge.net, accessed on 30 November 2013) for primer design. ISBP markers for wheat chromosome 3B were designed using BAC-end sequences as described in Paux et al. [10]. PCR setup and HRM analysis is described in Mérida-García et al. [9].

2.2. Candidate Genes

ISBP markers were mapped and blasted against the RefSeq v1 [15] as described in Mérida-García et al. [9]. Candidate genes for markers for wheat chromosomes 4A and 3B were assessed within a window of +/−20 kb and +/−300 kb (due to the reduced density of genes found for 3B), respectively, of the marker’s hit in the pseudomolecule [15] gene model annotation. ISBP marker positions were compared to the wheat MQTLs described in Kumar et al. [27] for drought tolerance. The position of MQTL was determined with flanking markers [27] and using BLAST against the RefSeq v1 [15].

2.3. Gene Expression Analysis

Gene expression analysis was performed using the information previously published by Liu et al. [29] (experimental seedling samples grown under controlled conditions (NCBI SRA ID SRP045409): control (IS), heat and PEG-induced drought stress for 1 and 6 h (PEG1 and PEG6, respectively)), Ma et al. [30] (experimental samples grown in a shelter (NCBI SRA ID SRP102636): anther stage irrigated leaf phenotype (AD_C), anther stage drought-stressed leaf phenotype (AD_S), tetrad stage irrigated developing spike phenotype (T_C), and tetrad stage drought-stressed developing spike phenotype (T_S)), and Gálvez et al. [31] (flag leaf samples from field-grown plants (NCBI SRA ID SRP119300): irrigated (IF), mild stress (MS), and severe stress (SS)). This information was applied to construct gene expression heatmaps, using data retrieved from Wheat Expression (www.wheat-expression.com/, accessed on 31 May 2020) and the R package ‘NMF 0.21.0’ [31]. Transcripts per kilobase millions (TPMs) of genes under each condition and differential gene expression analysis were performed on the raw data using the RefSeq v1 [15] gene models and two bioinformatic pipelines [9].

3. Results and Discussion

A recent wheat MQTL analysis [27] described a novel drought tolerance MQTL (MQTL3), which is mainly placed in the wheat chromosome 3B centromeric region (Figure 1). Akhunov et al. [32] and Munkvold et al. [33] highlighted a positive gradient of the gene density from the centromere to the telomeres in wheat, which is consistent with the low presence of ISBP markers found in the MQTL3 [27] proximities. Two ISBP markers (HRM3B_331497483 and HRM3B_465802537 [9]) for the wheat chromosome 3B were found surrounding this MQTL (58 and 52 Mb, respectively). Marker HRM3B_465802537 was previously located within MQTL26 (previously described by Acuña-Galindo et al. [22]) and mapped to two interesting genes (TraesCS3B01G290200 and TraesCS3B290300) by Mérida-García et al. [9], who highlighted that they encode a glycosyltransferase and ABC transporter B family protein, respectively. Both are related to plant responses to abiotic stresses [34,35] and both genes were found to be upregulated under different drought conditions [9].
Within MQTL3 [27], we found 269 HC and LC genes of which 31 and 8 genes, respectively, were found differentially expressed under different drought stress conditions (Figure 2). The gene TraesCS3B01G246000 encodes a MYB-related transcription factor, which plays a crucial role in the control of plant-specific processes as responses to abiotic and biotic stresses [36]. This gene was found to be downregulated under PEG treatments, which agrees with previous studies that indicated the expression of many MYB genes is regulated by drought [37]. The gene TraesCS3B01G246300 encodes an ATP-dependent zinc metalloprotease FtsH, some of which have been proposed as contributors to stress responses in plants [38] and also related to photosynthesis and protein stability [39]. This gene was found to be upregulated under heat and PEG-induced drought (PEG1 and PGE6) conditions, which is in agreement with previous studies in wild barley in response to heat stress conditions [39]. The gene TraesCS3B01G251100 encoding a 3′-N-debenzoyl-2′-deoxytaxol N-benzoyltransferase, which has been related to cellular biogenesis, and its overexpression promotes increased root growth in maize [40]. In this regard, this gene was found to be upregulated under PEG treatments.

Author Contributions

S.G. performed bioinformatics analyses. R.M.-G., S.G., E.P., G.D., L.P., P.G., and P.H. analyzed the results. R.M.-G. and P.H. drafted the manuscript. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

This research was funded by project P18-RT-992 from Junta de Andalucía (Andalusian Regional Government), Spain (Co-funded by FEDER), and by the Spanish Ministry of Science and Innovation project PID2019-109089RB-C32. The marker Xgwm685 sequence was kindly provided by Marion Roeder, IPK Gatersleben.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
HRMHigh Resolution Melting
MQTLMeta-Quantitative Trait Loci
TETransposable Element
ISBPInsertion Site-Based Polymorphism
bpbase pairs
Mbmegabase

References

  1. Briggle, L.W.; Curtis, B.C. Wheat Worldwide. In Wheat and Wheat Improvement, 2nd ed.; Heyne, E.G., Ed.; American Socierty of Agronomy: Madison, WI, USA, 1988; Volume 146, pp. 292–293. [Google Scholar] [CrossRef]
  2. Rampino, P.; Pataleo, S.; Gerardi, C.; Mita, G.; Perrotta, C. Drought stress response in wheat: Physiological and molecular analysis of resistant and sensitive genotypes. Plant Cell Environ. 2006, 29, 2143–2152. [Google Scholar] [CrossRef]
  3. Sallam, A.; Alqudah, A.M.; Dawood, M.F.A.; Baenziger, P.S.; Börner, A. Drought stress tolerance in wheat and barley: Advances in physiology, breeding and genetics research. Int. J. Mol. Sci. 2019, 20, 3137. [Google Scholar] [CrossRef] [Green Version]
  4. Wang, W.; Vinocur, B.; Altman, A. Plant responses to drought, salinity and extreme temperatures: Towards genetic engineering for stress tolerance. Planta 2003, 218, 1–14. [Google Scholar] [CrossRef]
  5. Wang, W.; Vinocur, B.; Shoseyov, O.; Altman, A. Role of plant heat-shock proteins and molecular chaperones in the abiotic stress response. Trends Plant Sci. 2004, 9, 244–252. [Google Scholar] [CrossRef] [PubMed]
  6. IPCC Assessment Report. 2020. Available online: https//www.ipccch/srccl/ (accessed on 31 March 2020).
  7. Lesk, C.; Rowhani, P.; Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 2016, 529, 84–87. [Google Scholar] [CrossRef] [PubMed]
  8. Cattivelli, L.; Rizza, F.; Badeck, F.W.; Mazzucotelli, E.; Mastrangelo, A.M.; Francia, E.; Marè, C.; Tondelli, A.; Stanca, A.M. Drought tolerance improvement in crop plants: An integrated view from breeding to genomics. Field Crops Res. 2008, 105, 1–14. [Google Scholar] [CrossRef]
  9. Mérida-García, R.; Gálvez, S.; Paux, E.; Dorado, G.; Pascual, L.; Giraldo, P.; Hernandez, P. High resolution melting and insertion site-based polymorphism markers for wheat variability analysis and candidate genes selection at drought and heat MQTL loci. Agronomy 2020, 10, 1294. [Google Scholar] [CrossRef]
  10. Paux, E.; Faure, S.; Choulet, F.; Roger, D.; Gauthier, V.; Martinant, J.P.; Sourdille, P.; Balfourier, F.; Le Paslier, M.C.; Chauveau, A.; et al. Insertion site-based polymorphism markers open new perspectives for genome saturation and marker-assisted selection in wheat. Plant Biotechnol. J. 2010, 8, 196–210. [Google Scholar] [CrossRef]
  11. Paux, E.; Gao, L.; Faure, S.; Choulet, F.; Roger, D.; Chevalier, K.; Saintenac, C.; Balfourier, F.; Paux, K.; Cakir, M.; et al. Insertion Site-Based Polymorphism: A Swiss Army Knife for Wheat Genomics; Sydney University Press: Sydney, Australia, 2008; pp. 4–6. [Google Scholar]
  12. Paux, E.; Roger, D.; Badaeva, E.; Gay, G.; Bernard, M.; Sourdille, P.; Feuillet, C. Characterizing the composition and evolution of homoeologous genomes in hexaploid wheat through BAC-end sequencing on chromosome 3B. Plant J. 2006, 48, 463–474. [Google Scholar] [CrossRef] [PubMed]
  13. Lucas, S.J.; Šimková, H.; Šafář, J.; Jurman, I.; Cattonaro, F.; Vautrin, S.; Bellec, A.; Berges, H.; Doležel, J.; Budak, H. Functional features of a single chromosome arm in wheat (1AL) determined from its structure. Funct. Integr. Genom. 2012, 12, 173–182. [Google Scholar] [CrossRef]
  14. Sehgal, S.K.; Li, W.; Rabinowicz, P.D.; Chan, A.; Šimková, H.; Doležel, J.; Gill, B.S. Chromosome arm-specific BAC end sequences permit comparative analysis of homoeologous chromosomes and genomes of polyploid wheat. BMC Plant Biol. 2012, 12, 64. [Google Scholar] [CrossRef] [Green Version]
  15. IWGSC; Appels, R.; Eversole, K.; Stein, N.; Feuillet, C.; Keller, B.; Rogers, J.; Pozniak, C.J.; Choulet, F.; Distelfeld, A.; et al. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 2018, 361. [Google Scholar] [CrossRef] [Green Version]
  16. Salina, E.A.; Nesterov, M.A.; Frenkel, Z.; Kiseleva, A.A.; Timonova, E.M.; Magni, F.; Vrána, J.; Šafár, J.; Šimková, H.; Doležel, J.; et al. Features of the organization of bread wheat chromosome 5BS based on physical mapping. BMC Genom. 2018, 19. [Google Scholar] [CrossRef] [Green Version]
  17. Dong, C.; Vincent, K.; Sharp, P. Simultaneous mutation detection of three homoeologous genes in wheat by high resolution melting analysis and mutation Surveyor®. BMC Plant Biol. 2009, 9, 143. [Google Scholar] [CrossRef] [Green Version]
  18. Mondini, L.; Nachit, M.M.; Porceddu, E.; Pagnotta, M.A. HRM technology for the identification and characterization of INDEL and SNPs mutations in genes involved in drought and salt tolerance of durum wheat. Plant Genet. Resour. Characterisation Util. 2011, 9, 166–169. [Google Scholar] [CrossRef] [Green Version]
  19. Matsuda, R.; Iehisa, J.C.M.; Takumi, S. Application of real-time PCR-based SNP detection for mapping of Net2, a causal D-genome gene for hybrid necrosis in interspecific crosses between tetraploidwheat and Aegilops tauschii. Genes Genet. Syst. 2012, 87, 137–143. [Google Scholar] [CrossRef] [Green Version]
  20. Lehmensiek, A.; Sutherland, M.W.; McNamara, R.B. The use of high resolution melting (HRM) to map single nucleotide polymorphism markers linked to a covered smut resistance gene in barley. Theor. Appl. Genet. 2008, 117, 721–728. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Shatalina, M.; Messmer, M.; Feuillet, C.; Mascher, F.; Paux, E.; Choulet, F.; Wicker, T.; Keller, B. High-resolution analysis of a QTL for resistance to Stagonospora nodorum glume blotch in wheat reveals presence of two distinct resistance loci in the target interval. Theor. Appl. Genet. 2014, 127, 573–586. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Acuña-Galindo, M.A.; Mason, R.E.; Subramanian, N.K.; Hays, D.B. Meta-analysis of wheat QTL regions associated with adaptation to drought and heat stress. Crop Sci. 2015, 55, 477–492. [Google Scholar] [CrossRef]
  23. Goffinet, B.; Gerber, S. Quantitative trait loci: A meta-analysis. Genetics 2000, 155, 463–473. [Google Scholar] [CrossRef] [PubMed]
  24. Soriano, J.M.; Alvaro, F. Discovering consensus genomic regions in wheat for root-related traits by QTL meta-analysis. Sci. Rep. 2019, 9, 10537. [Google Scholar] [CrossRef] [Green Version]
  25. Avni, R.; Oren, L.; Shabtay, G.; Assili, S.; Pozniak, C.; Hale, I.; Ben-David, R.; Peleg, Z.; Distelfeld, A. Genome based meta-QTL analysis of grain weight in tetraploid wheat identifies rare alleles of GRF4 associated with larger grains. Genes 2018, 9, 636. [Google Scholar] [CrossRef] [Green Version]
  26. Swamy, B.M.; Vikram, P.; Dixit, S.; Ahmed, H.U.; Kumar, A. Meta-analysis of grain yield QTL identified during agricultural drought in grasses showed consensus. BMC Genom. 2011, 12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Kumar, A.; Saripalli, G.; Jan, I.; Kumar, K.; Sharma, P.K.; Balyan, H.S.; Gupta, P.K. Meta-QTL analysis and identification of candidate genes for drought tolerance in bread wheat (Triticum aestivum L.). Physiol. Mol. Biol. Plants 2020, 26, 1713–1725. [Google Scholar] [CrossRef] [PubMed]
  28. Hernandez, P.; Martis, M.; Dorado, G.; Pfeifer, M.; Gálvez, S.; Schaaf, S.; Jouve, N.; Šimková, H.; Valárik, M.; Doležel, J.; et al. Next-generation sequencing and syntenic integration of flow-sorted arms of wheat chromosome 4A exposes the chromosome structure and gene content. Plant J. 2012, 69, 377–386. [Google Scholar] [CrossRef] [Green Version]
  29. Liu, Z.; Xin, M.; Qin, J.; Peng, H.; Ni, Z.; Yao, Y.; Sun, Q. Temporal transcriptome profiling reveals expression partitioning of homeologous genes contributing to heat and drought acclimation in wheat (Triticum aestivum L.). BMC Plant Biol. 2015, 15, 152. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Ma, J.; Li, R.; Wang, H.; Li, D.; Wang, X.; Zhang, Y.; Zhen, W.; Duan, H.; Yan, G.; Li, Y. Transcriptomics analyses reveal wheat responses to drought stress during reproductive stages under field conditions. Front. Plant Sci. 2017, 8, 1–13. [Google Scholar] [CrossRef] [Green Version]
  31. Gálvez, S.; Mérida-García, R.; Camino, C.; Borrill, P.; Abrouk, M.; Ramírez-González, R.H.; Biyiklioglu, S.; Amil-Ruiz, F.; Dorado, G.; Budak, H.; et al. Hotspots in the genomic architecture of field drought responses in wheat as breeding targets. Funct. Integr. Genom. 2019, 19, 295–309. [Google Scholar] [CrossRef] [Green Version]
  32. Akhunov, E.D.; Goodyear, A.W.; Geng, S.; Qi, L.L.; Echalier, B.; Gill, B.S.; Miftahudin, A.; Gustafson, J.P.; Lazo, G.; Chao, S.; et al. The organization and rate of evolution of wheat genomes are correlated with recombination rates along chromosomes arms. Genome Res. 2003, 13, 753–763. [Google Scholar] [CrossRef] [Green Version]
  33. Munkvold, J.D.; Greene, R.A.; Bermudez-Kandianis, C.E.; La Rota, C.M.; Edwards, H.; Sorrells, S.F.; Dake, T.; Benscher, D.; Kantety, R.; Linkiewicz, A.M.; et al. Group 3 chromosome bin maps of wheat and their relationship to rice chromosome 1. Genetics 2004, 168, 639–650. [Google Scholar] [CrossRef] [Green Version]
  34. Vogt, T.; Jones, P. Glycosyltransferases in plant-natural product synthesis: Characterization of a supergene family. Trends Plant Sci. 2000, 5, 380–386. [Google Scholar] [CrossRef]
  35. Kang, J.; Park, J.; Choi, H.; Burla, B.; Kretzschmar, T.; Lee, Y.; Martinoia, E. Plant ABC Transporters. Am. Soc. Plant Biol. 2011, 9, e0153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Baldoni, E.; Genga, A.; Cominelli, E. Plant MYB transcription factors: Their role in drought response mechanisms. Int. J. Mol. Sci. 2015, 16, 15811–15851. [Google Scholar] [CrossRef] [Green Version]
  37. Katiyar, A.; Smita, S.; Lenka, S.K.; Rajwanshi, R.; Chinnusamy, V.; Bansal, K.C. Genome-wide classification and expression analysis of MYB transcription factor families in rice and Arabidopsis. BMC Genom. 2012, 13. [Google Scholar] [CrossRef] [Green Version]
  38. Kato, Y.; Sakamoto, W. FtsH protease in the thylakoid membrane: Physiological functions and the regulation of protease activity. Front. Plant Sci. 2018, 9, 1–8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Ashoub, A.; Baeumlisberger, M.; Neupaertl, M.; Karas, M.; Brüggemann, W. Characterization of common and distinctive adjustments of wild barley leaf proteome under drought acclimation, heat stress and their combination. Plant Mol. Biol. 2015, 87, 459–471. [Google Scholar] [CrossRef] [PubMed]
  40. Yan, H.; Li, K.; Ding, H.; Liao, C.; Li, X.; Yuan, L.; Li, C. Root morphological and proteomic responses to growth restriction in maize plants supplied with sufficient N. J. Plant Physiol. 2011, 168, 1067–1075. [Google Scholar] [CrossRef]
Figure 1. ISBP markers found in wheat chromosome 3B. MQTL3 [27] and MQTL26 [22] are indicated in the figure. MQTLs’ flanking positions are indicated in Mb (megabase); the centromere region in red color was delimited by the genome positions indicated in [15].
Figure 1. ISBP markers found in wheat chromosome 3B. MQTL3 [27] and MQTL26 [22] are indicated in the figure. MQTLs’ flanking positions are indicated in Mb (megabase); the centromere region in red color was delimited by the genome positions indicated in [15].
Blsf 04 00044 g001
Figure 2. Heatmap for gene expression analysis under several stress conditions for the candidate genes that were differentially expressed under drought conditions. As indicated in the text, they are located within the MQTL3 [27]. IF: irrigated field conditions; MS: mild stress conditions; SS: severe stress conditions [31]; IS: seedling PEG shock control; PEG1: seedling 1 h PEG stress; PEG6: seedling 6 h PEG stress [29]; AD_S: anther stage irrigated shelter phenotype; AD_S: anther stage drought stressed shelter phenotype; T_C: tetra stage irrigated shelter phenotype; and T_S: tetrad stage drought shelter phenotype [30].
Figure 2. Heatmap for gene expression analysis under several stress conditions for the candidate genes that were differentially expressed under drought conditions. As indicated in the text, they are located within the MQTL3 [27]. IF: irrigated field conditions; MS: mild stress conditions; SS: severe stress conditions [31]; IS: seedling PEG shock control; PEG1: seedling 1 h PEG stress; PEG6: seedling 6 h PEG stress [29]; AD_S: anther stage irrigated shelter phenotype; AD_S: anther stage drought stressed shelter phenotype; T_C: tetra stage irrigated shelter phenotype; and T_S: tetrad stage drought shelter phenotype [30].
Blsf 04 00044 g002
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Mérida-García, R.; Galvez, S.; Paux, E.; Dorado, G.; Pascual, L.; Giraldo, P.; Hernandez, P. Cost-Effective Markers and Candidate Genes Analysis at Wheat MQTL Loci. Biol. Life Sci. Forum 2021, 4, 44. https://doi.org/10.3390/IECPS2020-08571

AMA Style

Mérida-García R, Galvez S, Paux E, Dorado G, Pascual L, Giraldo P, Hernandez P. Cost-Effective Markers and Candidate Genes Analysis at Wheat MQTL Loci. Biology and Life Sciences Forum. 2021; 4(1):44. https://doi.org/10.3390/IECPS2020-08571

Chicago/Turabian Style

Mérida-García, Rosa, Sergio Galvez, Etienne Paux, Gabriel Dorado, Laura Pascual, Patricia Giraldo, and Pilar Hernandez. 2021. "Cost-Effective Markers and Candidate Genes Analysis at Wheat MQTL Loci" Biology and Life Sciences Forum 4, no. 1: 44. https://doi.org/10.3390/IECPS2020-08571

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