Effect of Chromosomal Localization of NGS-Based Markers on Their Applicability for Analyzing Genetic Variation and Population Structure of Hexaploid Triticale
Abstract
:1. Introduction
2. Results
3. Discussion
4. Materials and Methods
4.1. Plant Material
4.2. DNA Extraction and Genotyping
4.3. Quality Control of Markers and Genetic Diversity
4.4. Statistics
4.5. Population Structure
4.6. Linkage Disequilibrium
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Wilson, S. Wheat and rye hybrids. In Transactions of the Botanical Society of Edinburgh; Taylor & Francis: Boca Raton, FL, USA, 1873; Volume 1–4, pp. 286–288. [Google Scholar]
- Ammar, K.; Mergoum, M.; Rajaram, S. The history and evolution of triticale. In Triticale Improvement and Production; Mergoum, M., Gómez-Macpherson, H., Eds.; FAO Plant Production and Protection Paper: Rome, Italy, 2004; Volume 179, pp. 1–10. Available online: http://www.fao.org/docrep/009/y5553e/y5553e00.htm (accessed on 1 June 2024).
- Güngör, H.; Çakir, M.F.; Dumlupinar, Z. Evaluation of triticale: Genotype by environment interaction and GGE biplot analysis. J. Anim. Plant Sci. 2022, 32, 1637–1647. [Google Scholar] [CrossRef]
- Ayalew, H.; Kumssa, T.T.; Butler, T.J.; Ma, X.-F. Triticale improvement for forage and cover crop uses in the southern great plains of the United States. Front. Plant Sci. 2018, 9, 1130. [Google Scholar] [CrossRef] [PubMed]
- Derejko, A.; Studnicki, M.; Wójcik-Gront, E. Grain yield performance and stability of winter wheat and triticale cultivars in a temperate climate. Crop Sci. 2021, 61, 3962–3971. [Google Scholar] [CrossRef]
- Randhawa, H.S.; Bona, L.; Graf, R.J. Triticale Breeding-Progress and Prospect. In Triticale; Eudes, F., Ed.; Springer International Publishing: Cham, Switzerland, 2015; pp. 15–32. [Google Scholar] [CrossRef]
- Miedaner, T.; Flath, K.; Starck, N.; Weißmann, S.; Maurer, H.P. Quantitative-genetic evaluation of resistances to five fungal diseases in a large triticale diversity panel (×Triticosecale). Crops 2022, 2, 218–232. [Google Scholar] [CrossRef]
- Peña, R.J. Food uses of triticale. In Triticale Improvement and Production; Mergoum, M., Gómez-Macpherson, H., Eds.; FAO Plant Production and Protection Paper: Rome, Italy, 2004; Volume 179, pp. 37–48. [Google Scholar]
- Wang, S.; Thomas, K.C.; Ingledew, W.M.; Sosulski, K.; Sosulski, F.W. Rye and triticale as feedstock for fuel ethanol production. Cereal Chem. 1997, 74, 621–625. [Google Scholar] [CrossRef]
- Oettler, G.S. The fortune of a botanical curiosity—Triticale: Past, present and future. J. Agric. Sci. 2005, 143, 329–346. [Google Scholar] [CrossRef]
- Furman, B.J.; Qualset, C.O.; Skovmand, B.; Heaton, J.H.; Corke, H.; Wesenberg, D.M. Characterization and analysis of North American triticale genetic resources. Crop Sci. 1997, 37, 1951–1959. [Google Scholar] [CrossRef]
- Kwiatek, M.T.; Nawracała, J. Chromosome manipulations for progress of triticale (×Triticosecale) breeding. Plant Breed. 2018, 137, 823–831. [Google Scholar] [CrossRef]
- Diordiieva, I.; Riabovol, I.; Riabovol, L.; Serzhuk, O.; Novak, Z.; Cherno, O. Triticale breeding improvement by the intraspecific and remote hybridization. Ukr. J. Ecol. 2020, 10, 67–71. [Google Scholar] [CrossRef]
- Ukalska, J.; Kociuba, W. Phenotypical diversity of winter triticale genotypes collected in the Polish gene bank between 1982 and 2008 with regard to major quantitative traits. Field Crop Res. 2013, 149, 203–212. [Google Scholar] [CrossRef]
- Royo, C.; Soler, C.; Romagosa, I. Agronomical and morphological differentiation among winter and spring triticales. Plant Breed. 1995, 114, 413–416. [Google Scholar] [CrossRef]
- van Beuningen, L.T.; Busch, R.H. Genetic diversity among North American spring wheat cultivars: III. Cluster analysis based on quantitative morphological traits. Crop Sci. 1997, 37, 981–988. [Google Scholar] [CrossRef]
- Kuleung, C.; Baenziger, P.S.; Kachman, S.D.; Dweikat, I. Evaluating the genetic diversity of triticale with wheat and rye SSR markers. Crop Sci. 2006, 46, 1692–1700. [Google Scholar] [CrossRef]
- Niedziela, A.; Orłowska, R.; Machczyńska, J.; Bednarek, P.T. The genetic diversity of triticale genotypes involved in Polish breeding programs. SpringerPlus 2016, 5, 355. [Google Scholar] [CrossRef]
- Alheit, K.V.; Maurer, H.P.; Reif, J.C.; Tucker, M.R.; Hahn, V.; Weissmann, E.A.; Würschum, T. Genome-wide evaluation of genetic diversity and linkage disequilibrium in winter and spring triticale (× Triticosecale Wittmack). BMC Genom. 2012, 13, 235. [Google Scholar] [CrossRef] [PubMed]
- Badea, A.; Eudes, F.; Salmon, D.; Tuvesson, S.; Vrolijk, A.; Larsson, C.-T.; Caig, V.; Huttner, E.; Kilian, A.; Laroche, A. Development and assessment of DArT markers in triticale. Theor. Appl. Genet. 2011, 122, 1547–1560. [Google Scholar] [CrossRef] [PubMed]
- Costa, C.T.D.; Albuquerque, A.C.S.; Nascimento Junior, A.D.; Marcelino, F.C.; Pereira, J.F. Genetic diversity of Brazilian triticales evaluated with genomic wheat microsatellites. Pesqui. Agropecu. Bras. 2007, 42, 1577–1586. [Google Scholar] [CrossRef]
- Chao, S.; Dubcovsky, J.; Dvorak, J.; Luo, M.-C.; Baenziger, S.P.; Matnyazov, R.; Clark, D.R.; Talbert, L.E.; Anderson, J.A.; Dreisigacker, S.; et al. Population- and genome-specific patterns of linkage disequilibrium and SNP variation in spring and winter wheat (Triticum aestivum L.). BMC Genom. 2010, 11, 727. [Google Scholar] [CrossRef]
- Rabieyan, E.; Bihamta, M.R.; Moghaddam, M.E.; Mohammadi, V.; Alipour, H. Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions. BMC Genom. 2022, 23, 831. [Google Scholar] [CrossRef]
- Sthapit, S.R.; Ruff, T.M.; Hooker, M.A.; See, D.R. Population structure and genetic diversity of U.S. wheat varieties. Plant Genome US 2022, 15, e20196. [Google Scholar] [CrossRef]
- Wang, M.; Jiang, N.; Jia, T.; Leach, L.; Cockram, J.; Waugh, R.; Ramsay, L.; Thomas, B.; Luo, Z. Genome-wide association mapping of agronomic and morphologic traits in highly structured populations of barley cultivars. Theor. Appl. Genet. 2012, 124, 233–246. [Google Scholar] [CrossRef]
- Bus, A.; Körber, N.; Snowdon, R.J.; Stich, B. Patterns of molecular variation in a species-wide germplasm set of Brassica napus. Theor. Appl. Genet. 2011, 123, 1413–1423. [Google Scholar] [CrossRef]
- Kiss, T.; Balla, K.; Cseh, A.; Berki, Z.; Horváth, Á.; Vida, G.; Veisz, O.; Karsai, I. Assessment of the genetic diversity, population structure and allele distribution of major plant development genes in bread wheat cultivars using DArT and gene-specific markers. Cereal Res. Commun. 2021, 49, 549–557. [Google Scholar] [CrossRef]
- Lehnert, H.; Berner, T.; Lang, D.; Beier, S.; Stein, N.; Himmelbach, A.; Kilian, B.; Keilwagen, J. Insights into breeding history, hotspot regions of selection, and untapped allelic diversity for bread wheat breeding. Plant J. 2022, 112, 897–918. [Google Scholar] [CrossRef] [PubMed]
- Losert, D.; Maurer, H.P.; Marulanda, J.J.; Würschum, T. Phenotypic and genotypic analyses of diversity and breeding progress in European triticale (×Triticosecale Wittmack). Plant Breed. 2017, 136, 18–27. [Google Scholar] [CrossRef]
- Tams, S.H.; Bauer, E.; Oettler, G.; Melchinger, A.E. Genetic diversity in European winter triticale determined with SSR markers and coancestry coefficient. Theor. Appl. Genet. 2004, 108, 1385–1391. [Google Scholar] [CrossRef] [PubMed]
- Ayalew, H.; Anderson, J.D.; Krom, N.; Tang, Y.; Butler, T.J.; Rawat, N.; Tiwari, V.; Ma, X.-F. Genotyping-by-sequencing and genomic selection applications in hexaploid triticale. G3 Genes Genom. Genet. 2022, 12, jkab413. [Google Scholar] [CrossRef] [PubMed]
- Zhu, C.; Gore, M.; Buckler, E.S.; Yu, J. Status and prospects of association mapping in plants. Plant Genome US 2008, 1, 5–20. [Google Scholar] [CrossRef]
- Cao, D.; Wang, D.; Li, S.; Li, Y.; Hao, M.; Liu, B. Genotyping-by-sequencing and genome-wide association study reveal genetic diversity and loci controlling agronomic traits in triticale. Theor. Appl. Genet. 2022, 135, 1705–1715. [Google Scholar] [CrossRef]
- Niedziela, A.; Bednarek, P.T. Population structure and genetic diversity of a germplasm for hybrid breeding in rye (Secale cereale L.) using high-density DArTseq-based silicoDArT and SNP markers. J. Appl. Genet. 2023, 64, 217–229. [Google Scholar] [CrossRef]
- Serrote, C.M.L.; Reiniger, L.R.S.; Silva, K.B.; Rabaiolli, S.M.D.S.; Stefanel, C.M. Determining the Polymorphism Information Content of a molecular marker. Gene 2020, 726, 144175. [Google Scholar] [CrossRef] [PubMed]
- Targońska-Karasek, M.; Bolibok-Brągoszewska, H.; Rakoczy-Trojanowska, M. DArTseq Genotyping Reveals High Genetic Diversity of Polish Rye Inbred Lines. Crop Sci. 2017, 57, 1906–1915. [Google Scholar] [CrossRef]
- Barilli, E.; Cobos, M.J.; Carrillo, E.; Kilian, A.; Carling, J.; Rubiales, D. A High-Density Integrated DArTseq SNP-Based Genetic Map of Pisum fulvum and Identification of QTLs Controlling Rust Resistance. Front. Plant Sci. 2018, 9, 167. [Google Scholar] [CrossRef] [PubMed]
- Josia, C.; Mashingaidze, K.; Amelework, A.B.; Kondwakwenda, A.; Musvosvi, C.; Sibiya, J. SNP-based assessment of genetic purity and diversity in maize hybrid breeding. PLoS ONE 2021, 16, e0249505. [Google Scholar] [CrossRef]
- Tomar, V.; Dhillon, G.S.; Singh, D.; Singh, R.P.; Poland, J.; Joshi, A.K.; Tiwari, B.S.; Kumar, U. Elucidating SNP-based genetic diversity and population structure of advanced breeding lines of bread wheat (Triticum aestivum L.). PeerJ 2021, 9, e11593. [Google Scholar] [CrossRef]
- Dziurdziak, J.; Podyma, W.; Bujak, H.; Boczkowska, M. Tracking changes in the spring barley gene pool in Poland during 120 years of breeding. Int. J. Mol. Sci. 2022, 23, 4553. [Google Scholar] [CrossRef]
- Novoselović, D.; Bentley, A.R.; Šimek, R.; Dvojković, K.; Sorrells, M.E.; Gosman, N.; Horsnell, R.; Drezner, G.; Šatović, Z. Characterizing Croatian wheat germplasm diversity and structure in a European context by DArT markers. Front. Plant Sci. 2016, 7, 184. [Google Scholar] [CrossRef]
- Frisch, M.; Thiemann, A.; Fu, J.; Schrag, T.A.; Scholten, S.; Melchinger, A.E. Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize. Theor. Appl. Genet. 2010, 120, 441–450. [Google Scholar] [CrossRef]
- Tomkowiak, A.; Bocianowski, J.; Kwiatek, M.; Kowalczewski, P. Dependence of the heterosis effect on genetic distance, determined using various molecular markers. Open Life Sci. 2020, 15, 1–11. [Google Scholar] [CrossRef]
- Korzun, V.; Börner, A.; Melz, G. RFLP mapping of the dwarfing (Ddw1) and hairy peduncle (Hp) genes on chromosome of rye (Secale cereale L.). Theor. Appl. Genet. 1996, 92, 1073–1077. [Google Scholar] [CrossRef]
- Sun, L.; Yang, W.; Li, Y.; Shan, Q.; Ye, X.; Wang, D.; Yu, K.; Lu, W.; Xin, P.; Pei, Z.; et al. A wheat dominant dwarfing line with Rht12, which reduces stem cell length and affects gibberellic acid synthesis, is a 5AL terminal deletion line. Plant J. 2019, 97, 887–900. [Google Scholar] [CrossRef]
- Trini, J.; Maurer, H.P.; Neuweiler, J.E.; Würschum, T. Identification and fine-mapping of quantitative trait loci controlling plant height in Central European winter triticale (×Triticosecale Wittmack). Plants 2021, 10, 1592. [Google Scholar] [CrossRef] [PubMed]
- Wajdzik, K.; Gołębiowska, G.; Dyda, M.; Gawrońska, K.; Rapacz, M.; Wędzony, M. The QTL mapping of the important breeding traits in winter triticale (×Triticosecale Wittm.). Cereal Res. Commun. 2019, 47, 395–408. [Google Scholar] [CrossRef]
- Dyda, M.; Tyrka, M.; Gołębiowska, G.; Rapacz, M.; Wędzony, M. Mapping of QTL and candidate genes associated with powdery mildew resistance in triticale (×Triticosecale Wittm.). Plant Growth Regul. 2022, 98, 103–115. [Google Scholar] [CrossRef]
- Wąsek, I.; Dyda, M.; Gołębiowska, G.; Tyrka, M.; Rapacz, M.; Szechyńska-Hebda, M.; Wędzony, M. Quantitative trait loci and candidate genes associated with freezing tolerance of winter triticale (×Triticosecale Wittmack). J. Appl. Genet. 2022, 63, 15–33. [Google Scholar] [CrossRef]
- Gołębiowska-Paluch, G.; Dyda, M. The genome regions associated with abiotic and biotic stress tolerance, as well as other important breeding traits in triticale. Plants 2023, 12, 619. [Google Scholar] [CrossRef] [PubMed]
- Naranjo, T.; Fernández-Rueda, P. Homoeology of rye chromosome arms to wheat. Theor. Appl. Genet. 1991, 82, 577–586. [Google Scholar] [CrossRef] [PubMed]
- Liu, C.J.; Atkinson, M.D.; Chinoy, C.N.; Devos, K.M.; Gale, M.D. Nonhomoeologous translocations between group 4, 5 and 7 chromosomes within wheat and rye. Theor. Appl. Genet. 1992, 83, 305–312. [Google Scholar] [CrossRef]
- Devos, K.M.; Atkinson, M.D.; Chinoy, C.N.; Francis, H.A.; Harcourt, R.L.; Koebner, R.M.D.; Liu, C.J.; Masojć, P.; Xie, D.X.; Gale, M.D. Chromosomal rearrangements in the rye genome relative to that of wheat. Theor. Appl. Genet. 1993, 85, 673–680. [Google Scholar] [CrossRef]
- Carter, A.; Chen, X.; Garland-Campbell, K.; Kidwell, K. Identifying QTL for high-temperature adult-plant resistance to stripe rust (Puccinia striiformis f. sp. tritici) in the spring wheat (Triticum aestivum L.) cultivar ‘Louise’. Theor. Appl. Genet. 2009, 119, 1119–1128. [Google Scholar] [CrossRef]
- Losert, D.; Maurer, H.P.; Leiser, W.L.; Würschum, T. Defeating the Warrior: Genetic architecture of triticale resistance against a novel aggressive yellow rust race. Theor. Appl. Genet. 2017, 130, 685–696. [Google Scholar] [CrossRef] [PubMed]
- Mohler, V.; Stadlmeier, M. Dynamic QTL for adult plant resistance to powdery mildew in common wheat (Triticum aestivum L.). J. Appl. Genet. 2019, 60, 291–300. [Google Scholar] [CrossRef] [PubMed]
- Karbarz, M.; Tyrka, M.; Woźniak-Strzembicka, A.; Czajowski, G.; Wardyńska, A.; Tyrka, D.; Pojmaj, M.; Wędzony, M. Quantitative trait loci mapping of adult-plant resistance to powdery mildew in triticale. Ann. Appl. Biol. 2020, 177, 223–231. [Google Scholar] [CrossRef]
- Gupta, P.K.; Rustgi, S.; Kulwal, P.L. Linkage disequilibrium and association studies in higher plants: Present status and future prospects. Plant Mol. Biol. 2005, 57, 461–485. [Google Scholar] [CrossRef] [PubMed]
- Flint-Garcia, S.A.; Thornsberry, J.M.; Buckler, E.S. Structure of linkage disequilibrium in plants. Annu. Rev. Plant Biol. 2003, 54, 357–374. [Google Scholar] [CrossRef] [PubMed]
- Money, D.; Migicovsky, Z.; Gardner, K.; Myles, S. LinkImputeR: User-guided genotype calling and imputation for non-model organisms. BMC Genom. 2017, 18, 523. [Google Scholar] [CrossRef]
- Dadshani, S.; Mathew, B.; Ballvora, A.; Mason, A.S.; Léon, J. Detection of breeding signatures in wheat using a linkage disequilibrium-corrected mapping approach. Sci. Rep. 2021, 11, 5527. [Google Scholar] [CrossRef]
- Rufo, R.; Alvaro, F.; Royo, C.; Soriano, J.M. From landraces to improved cultivars assessment of genetic diversity and population structure of Mediterranean wheat using SNP markers. PLoS ONE 2019, 14, e0219867. [Google Scholar] [CrossRef]
- Roncallo, P.F.; Larsen, A.O.; Achilli, A.L.; Pierre, C.S.; Gallo, C.A.; Dreisigacker, S.; Echenique, V. Linkage disequilibrium patterns, population structure and diversity analysis in a worldwide durum wheat collection including Argentinian genotypes. BMC Genom. 2021, 22, 233. [Google Scholar] [CrossRef]
- Vendelbo, N.M.; Sarup, P.; Orabi, J.; Kristensen, P.S.; Jahoor, A. Genetic structure of a germplasm for hybrid breeding in rye (Secale cereale L.). PLoS ONE 2020, 15, e0239541. [Google Scholar] [CrossRef]
- Vos, P.G.; Paulo, M.J.; Voorrips, R.E.; Visser, R.G.; van Eck, H.J.; van Eeuwijk, F.A. Evaluation of LD decay and various LD-decay estimators in simulated and SNP-array data of tetraploid potato. Theor. Appl. Genet. 2017, 130, 123–135. [Google Scholar] [CrossRef]
- Oettler, G.S.; Tams, H.; Utz, H.F.; Bauer, E.; Melchinger, A.E. Prospects for hybrid breeding in winter triticale: I. Heterosis and combining ability for agronomic traits in European elite germplasm. Crop Sci. 2005, 45, 1476–1482. [Google Scholar] [CrossRef]
- Alam, M.; Neal, J.; O’Connor, K.; Kilian, A.; Topp, B. Ultra-high-throughput DArTseq-based SilicoDArT and SNP markers for genomic studies in Macadamia. PLoS ONE 2018, 13, e0203465. [Google Scholar] [CrossRef] [PubMed]
- Peakall, R.; Smouse, P.E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 2012, 28, 2537–2539. [Google Scholar] [CrossRef] [PubMed]
- Pagnotta, M.A. Comparison among methods and statistical software packages to analyze germplasm genetic diversity by means of codominant markers. J 2018, 1, 197–215. [Google Scholar] [CrossRef]
- Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 1978, 89, 583–590. [Google Scholar] [CrossRef]
- TIBCO Statistica; Version 13.3.0.; TIBCO Software Inc.: Palo Alto, CA, USA, 2017.
- Hammer, O.; Harper, D.A.T.; Ryan, P.D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 2001, 4, e9. Available online: http://palaeo-electronica.org/2001_1/past/issue1_01.htm (accessed on 1 June 2024).
- Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res. 1967, 27, 209–220. [Google Scholar] [PubMed]
- Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Genetics 2000, 155, 574–578. [Google Scholar] [CrossRef] [PubMed]
- Evanno, G.; Regnaut, S.; Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 2005, 14, 2611–2620. [Google Scholar] [CrossRef]
- Kopelman, N.M.; Mayzel, J.; Jakobsson, M.; Rosenberg, N.A.; Mayrose, I. CLUMPAK: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 2015, 15, 1179–1191. [Google Scholar] [CrossRef] [PubMed]
- Bradbury, P.J.; Zhang, Z.; Kroon, D.E.; Casstevens, T.M.; Ramdoss, Y.; Buckler, E.S. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 2007, 23, 2633–2635. [Google Scholar] [CrossRef] [PubMed]
- Hill, W.G.; Weir, B.S. Variances and covariances of squared linkage disequilibria in finite populations. Theor. Popul. Biol. 1988, 33, 54–78. [Google Scholar] [CrossRef]
- Remington, D.L.; Thornsberry, J.M.; Matsuoka, Y.; Wilson, L.M.; Whitt, S.R.; Doebley, J.; Kresovich, S.; Goodman, M.M.; Buckler, E.S. Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc. Natl. Acad. Sci. USA 2001, 98, 11479–11484. [Google Scholar] [CrossRef] [PubMed]
Chromosome | Correlation Coefficient (r) | Determination Coefficient (r2) | p |
---|---|---|---|
Total_SNP vs. Total_silicoDArT | 0.505 | 0.255 | 0.01 |
1A_SNP vs. 1A_silicoDArT | 0.027 | 0.0007 | 0.22 |
2A_SNP vs. 2A_silicoDArT | 0.120 | 0.0144 | 0.01 |
3A_SNP vs. 3A_silicoDArT | 0.036 | 0.0013 | 0.14 |
4A_SNP vs. 4A_silicoDArT | 0.028 | 0.0008 | 0.22 |
5A_SNP vs. 5A_silicoDArT | 0.109 | 0.0118 | 0.01 |
6A_SNP vs. 6A_silicoDArT | 0.096 | 0.0093 | 0.01 |
7A_SNP vs. 7A_silicoDArT | 0.169 | 0.0286 | 0.01 |
1B_SNP vs. 1B_silicoDArT | 0.021 | 0.0004 | 0.15 |
2B_SNP vs. 2B_silicoDArT | 0.238 | 0.0566 | 0.01 |
3B_SNP vs. 3B_silicoDArT | 0.215 | 0.0463 | 0.01 |
4B_SNP vs. 4B_silicoDArT | 0.110 | 0.0121 | 0.01 |
5B_SNP vs. 5B_silicoDArT | 0.307 | 0.0943 | 0.01 |
6B_SNP vs. 6B_silicoDArT | 0.178 | 0.0319 | 0.01 |
7B_SNP vs. 7B_silicoDArT | 0.145 | 0.0210 | 0.01 |
1R_SNP vs. 1R_silicoDArT | 0.335 | 0.1119 | 0.01 |
2R_SNP vs. 2R_silicoDArT | 0.286 | 0.0818 | 0.01 |
3R_SNP vs. 3R_silicoDArT | 0.387 | 0.1500 | 0.01 |
4R_SNP vs. 4R_silicoDArT | 0.074 | 0.0055 | 0.01 |
5R_SNP vs. 5R_silicoDArT | 0.041 | 0.0017 | 0.09 |
6R_SNP vs. 6R_silicoDArT | 0.075 | 0.0057 | 0.01 |
7R_SNP vs. 7R_silicoDArT | 0.114 | 0.0131 | 0.01 |
Total | 1A | 2A | 3A | 4A | 5A | 6A | 7A | 1B | 2B | 3B | 4B | 5B | 6B | 7B | 1R | 2R | 3R | 4R | 5R | 6R | 7R | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Total | 0.624 | 0.613 | 0.701 | 0.720 | 0.717 | 0.713 | 0.689 | 0.644 | 0.755 | 0.743 | 0.432 | 0.795 | 0.388 | 0.608 | 0.529 | 0.570 | 0.646 | 0.663 | 0.736 | 0.717 | 0.587 | |
1A | 0.676 | 0.377 | 0.462 | 0.470 | 0.458 | 0.460 | 0.445 | 0.435 | 0.496 | 0.484 | 0.262 | 0.489 | 0.212 | 0.334 | 0.309 | 0.304 | 0.362 | 0.363 | 0.416 | 0.386 | 0.329 | |
2A | 0.606 | 0.334 | 0.413 | 0.421 | 0.417 | 0.419 | 0.389 | 0.380 | 0.470 | 0.434 | 0.251 | 0.474 | 0.229 | 0.345 | 0.487 | 0.442 | 0.344 | 0.363 | 0.401 | 0.375 | 0.295 | |
3A | 0.634 | 0.399 | 0.404 | 0.487 | 0.488 | 0.502 | 0.538 | 0.467 | 0.531 | 0.528 | 0.275 | 0.544 | 0.266 | 0.435 | 0.311 | 0.388 | 0.479 | 0.452 | 0.468 | 0.432 | 0.346 | |
4A | 0.602 | 0.477 | 0.331 | 0.373 | 0.520 | 0.538 | 0.486 | 0.451 | 0.547 | 0.516 | 0.321 | 0.581 | 0.262 | 0.455 | 0.329 | 0.392 | 0.412 | 0.482 | 0.512 | 0.481 | 0.433 | |
5A | 0.763 | 0.555 | 0.441 | 0.456 | 0.486 | 0.509 | 0.457 | 0.423 | 0.546 | 0.530 | 0.308 | 0.582 | 0.272 | 0.449 | 0.340 | 0.398 | 0.432 | 0.458 | 0.605 | 0.481 | 0.336 | |
6A | 0.460 | 0.313 | 0.325 | 0.327 | 0.355 | 0.389 | 0.476 | 0.445 | 0.550 | 0.537 | 0.294 | 0.560 | 0.301 | 0.389 | 0.403 | 0.364 | 0.421 | 0.401 | 0.500 | 0.555 | 0.344 | |
7A | 0.797 | 0.620 | 0.430 | 0.466 | 0.513 | 0.647 | 0.414 | 0.433 | 0.495 | 0.475 | 0.290 | 0.493 | 0.224 | 0.376 | 0.348 | 0.300 | 0.387 | 0.532 | 0.438 | 0.422 | 0.389 | |
1B | 0.780 | 0.562 | 0.411 | 0.456 | 0.454 | 0.626 | 0.329 | 0.599 | 0.471 | 0.447 | 0.256 | 0.498 | 0.253 | 0.435 | 0.575 | 0.344 | 0.373 | 0.389 | 0.424 | 0.403 | 0.320 | |
2B | 0.832 | 0.569 | 0.466 | 0.441 | 0.481 | 0.664 | 0.347 | 0.671 | 0.709 | 0.553 | 0.314 | 0.579 | 0.262 | 0.437 | 0.368 | 0.561 | 0.427 | 0.472 | 0.529 | 0.485 | 0.368 | |
3B | 0.810 | 0.487 | 0.497 | 0.598 | 0.417 | 0.588 | 0.346 | 0.617 | 0.605 | 0.628 | 0.311 | 0.572 | 0.266 | 0.420 | 0.355 | 0.368 | 0.577 | 0.445 | 0.515 | 0.501 | 0.352 | |
4B | 0.572 | 0.402 | 0.347 | 0.378 | 0.338 | 0.459 | 0.335 | 0.469 | 0.438 | 0.502 | 0.487 | 0.334 | 0.175 | 0.234 | 0.255 | 0.232 | 0.273 | 0.285 | 0.277 | 0.261 | 0.279 | |
5B | 0.838 | 0.571 | 0.453 | 0.497 | 0.510 | 0.704 | 0.411 | 0.675 | 0.700 | 0.718 | 0.689 | 0.516 | 0.288 | 0.626 | 0.370 | 0.411 | 0.451 | 0.478 | 0.642 | 0.552 | 0.400 | |
6B | 0.675 | 0.419 | 0.375 | 0.485 | 0.427 | 0.546 | 0.358 | 0.517 | 0.537 | 0.577 | 0.542 | 0.405 | 0.613 | 0.229 | 0.179 | 0.190 | 0.233 | 0.245 | 0.261 | 0.324 | 0.187 | |
7B | 0.699 | 0.470 | 0.386 | 0.434 | 0.487 | 0.577 | 0.306 | 0.612 | 0.525 | 0.564 | 0.521 | 0.357 | 0.563 | 0.467 | 0.232 | 0.328 | 0.348 | 0.428 | 0.412 | 0.374 | 0.319 | |
1R | 0.672 | 0.524 | 0.317 | 0.357 | 0.299 | 0.501 | 0.276 | 0.511 | 0.711 | 0.567 | 0.508 | 0.368 | 0.563 | 0.381 | 0.383 | 0.286 | 0.363 | 0.342 | 0.342 | 0.298 | 0.238 | |
2R | 0.690 | 0.392 | 0.449 | 0.381 | 0.409 | 0.445 | 0.221 | 0.447 | 0.574 | 0.699 | 0.465 | 0.321 | 0.513 | 0.464 | 0.445 | 0.443 | 0.358 | 0.357 | 0.398 | 0.340 | 0.240 | |
3R | 0.505 | 0.176 | 0.317 | 0.475 | 0.136 | 0.253 | 0.115 | 0.292 | 0.312 | 0.294 | 0.666 | 0.244 | 0.370 | 0.276 | 0.251 | 0.325 | 0.298 | 0.408 | 0.407 | 0.386 | 0.339 | |
4R | 0.791 | 0.527 | 0.444 | 0.502 | 0.452 | 0.590 | 0.298 | 0.643 | 0.569 | 0.594 | 0.660 | 0.489 | 0.634 | 0.480 | 0.532 | 0.522 | 0.446 | 0.492 | 0.410 | 0.390 | 0.322 | |
5R | 0.713 | 0.477 | 0.402 | 0.401 | 0.467 | 0.700 | 0.331 | 0.593 | 0.595 | 0.649 | 0.517 | 0.460 | 0.732 | 0.515 | 0.501 | 0.445 | 0.449 | 0.151 | 0.460 | 0.519 | 0.295 | |
6R | 0.758 | 0.530 | 0.436 | 0.440 | 0.452 | 0.448 | 0.626 | 0.659 | 0.591 | 0.642 | 0.590 | 0.458 | 0.671 | 0.586 | 0.516 | 0.478 | 0.415 | 0.242 | 0.547 | 0.595 | 0.307 | |
7R | 0.760 | 0.582 | 0.455 | 0.450 | 0.487 | 0.565 | 0.332 | 0.651 | 0.555 | 0.613 | 0.593 | 0.462 | 0.636 | 0.479 | 0.559 | 0.423 | 0.440 | 0.338 | 0.602 | 0.493 | 0.589 |
Total SNP | 1A | 2A | 3A | 4A | 5A | 6A | 7A | 1B | 2B | 3B | 4B | 5B | 6B | 7B | 1R | 2R | 3R | 4R | 5R | 6R | 7R | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Structure analysis | ||||||||||||||||||||||
K | 4 | 3 | 2 | 2 | 4 | 3 | 2 | 2 | 2 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 3 | 3 | 2 | 2 | 2 | 2 |
Delta K | 2460.2 | 811.3 | 2236.7 | 1625.3 | 530.4 | 508.3 | 1058.7 | 1261.5 | 2599.1 | 644.3 | 665.7 | 3429.0 | 1042.5 | 1443.3 | 555.9 | 910.9 | 525.4 | 1994.2 | 3577.7 | 2873.3 | 2881.7 | 6663.0 |
No. of accessions in Pop1 | 276 | 151 | 218 | 241 | 157 | 156 | 120 | 216 | 274 | 110 | 120 | 203 | 216 | 171 | 298 | 301 | 128 | 125 | 249 | 306 | 321 | 168 |
No. of accessions in Pop2 | 76 | 213 | 228 | 205 | 108 | 191 | 326 | 230 | 172 | 268 | 193 | 243 | 230 | 275 | 148 | 145 | 277 | 259 | 197 | 140 | 125 | 278 |
No. of accessions in Pop3 | 31 | 82 | 109 | 99 | 68 | 133 | 41 | 62 | ||||||||||||||
No. of accessions in Pop4 | 63 | 72 | ||||||||||||||||||||
Analysis of molecular variance | ||||||||||||||||||||||
PhiPT | 0.139 | 0.196 | 0.316 | 0.188 | 0.191 | 0.181 | 0.175 | 0.268 | 0.231 | 0.201 | 0.187 | 0.305 | 0.113 | 0.239 | 0.015 | 0.259 | 0.224 | 0.159 | 0.004 | 0.279 | 0.347 | 0.413 |
Total SNP | 1A | 4A | 5A | 2B | 3B | 2R | 3R | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Pops | Pop1 | Pop2 | Pop3 | Pop1 | Pop2 | Pop3 | Pop1 | Pop2 | Pop3 | Pop1 | Pop2 | Pop1 | Pop2 | Pop1 | Pop2 | Pop1 | Pop2 | Pop1 | Pop2 |
Pop1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||||||||||
Pop2 | 0.150 | 0 | 0.164 | 0 | 0.166 | 0 | 0.179 | 0 | 0.165 | 0 | 0.158 | 0 | 0.170 | 0 | 0.205 | 0 | |||
Pop3 | 0.237 | 0.169 | 0 | 0.204 | 0.243 | 0 | 0.211 | 0.161 | 0 | 0.179 | 0.188 | 0.253 | 0.232 | 0.215 | 0.193 | 0.263 | 0.351 | 0.225 | 0.015 |
Pop4 | 0.133 | 0.119 | 0.163 | 0.204 | 0.207 | 0.226 |
WG | 1A | 2A | 3A | 4A | 5A | 6A | 7A | 1B | 2B | 3B | 4B | 5B | 6B | 7B | 1R | 2R | 3R | 4R | 5R | 6R | 7R | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Number of markers | 4640 | 95 | 214 | 183 | 139 | 179 | 151 | 236 | 174 | 242 | 216 | 93 | 215 | 152 | 166 | 224 | 300 | 243 | 272 | 412 | 416 | 318 |
Mean distance (Mb) | 4.24 | 6.28 | 3.64 | 4.14 | 5.33 | 3.95 | 4.10 | 3.10 | 4.13 | 3.30 | 5.26 | 7.32 | 3.31 | 4.75 | 4.45 | 4.59 | 3.15 | 5.60 | 3.33 | 2.68 | 2.53 | 4.12 |
Significant LD (%) | 35.96 | 11.6 | 38.08 | 40.09 | 25.34 | 28.09 | 32.95 | 55.15 | 47.37 | 29.96 | 43.10 | 23.41 | 35.63 | 32.00 | 47.07 | 30.76 | 33.54 | 34.12 | 31.75 | 35.11 | 47.80 | 49.53 |
LD (r2) | 0.122 | 0.031 | 0.183 | 0.106 | 0.078 | 0.068 | 0.093 | 0.223 | 0.132 | 0.091 | 0.104 | 0.094 | 0.088 | 0.088 | 0.129 | 0.079 | 0.168 | 0.114 | 0.096 | 0.114 | 0.165 | 0.228 |
% r2 < 0.1 | 77.9 | 94.1 | 69.3 | 77.6 | 85.4 | 85.6 | 83.4 | 55.7 | 68.8 | 81.5 | 78.0 | 84.0 | 81.4 | 82.7 | 64.6 | 85.5 | 70.6 | 81.3 | 80.3 | 76.1 | 65.2 | 70.4 |
LD Decay (Mb) | 8.86 | 1.18 | 18.23 | 5.79 | 4.79 | 4.16 | 3.21 | 27.48 | 13.66 | 5.05 | 11.60 | 12.62 | 7.15 | 5.80 | 17.03 | 4.02 | 10.98 | 11.01 | 4.77 | 5.66 | 16.13 | 28.36 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Leśniowska-Nowak, J.; Bednarek, P.T.; Czapla, K.; Nowak, M.; Niedziela, A. Effect of Chromosomal Localization of NGS-Based Markers on Their Applicability for Analyzing Genetic Variation and Population Structure of Hexaploid Triticale. Int. J. Mol. Sci. 2024, 25, 9568. https://doi.org/10.3390/ijms25179568
Leśniowska-Nowak J, Bednarek PT, Czapla K, Nowak M, Niedziela A. Effect of Chromosomal Localization of NGS-Based Markers on Their Applicability for Analyzing Genetic Variation and Population Structure of Hexaploid Triticale. International Journal of Molecular Sciences. 2024; 25(17):9568. https://doi.org/10.3390/ijms25179568
Chicago/Turabian StyleLeśniowska-Nowak, Justyna, Piotr T. Bednarek, Karolina Czapla, Michał Nowak, and Agnieszka Niedziela. 2024. "Effect of Chromosomal Localization of NGS-Based Markers on Their Applicability for Analyzing Genetic Variation and Population Structure of Hexaploid Triticale" International Journal of Molecular Sciences 25, no. 17: 9568. https://doi.org/10.3390/ijms25179568