Journals / Turkish Journal of Biology / 2021 / Cilt: 45 - Sayı: 1

Detection of grain yield QTLs in the durum population Lahn/Chaml tested in contrasting environments

Pages
65–78
Publication Date
2021-02-09
DOI
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

The results showed that the detected QTLs can be used as a potential candidate for marker-assisted selection in durum breeding programs.

📌 3 Key Takeaways

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Objective & Hypothesis:Durum wheat (Triticum turgidum L. var durum) is tetraploid wheat (AABB); it is the main source of semolina and other pasta products.
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Key Findings & Data:Grain yield in wheat is quantitatively inherited and influenced by the environment.
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Conclusion & Impact:The results showed that the detected QTLs can be used as a potential candidate for marker-assisted selection in durum breeding programs.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Molecular markers · grain yield · genetic linkage map

Abstract

Durum wheat (Triticum turgidum L. var durum) is tetraploid wheat (AABB); it is the main source of semolina and other pasta products. Grain yield in wheat is quantitatively inherited and influenced by the environment. The genetic map construction constitutes the essential step in identifying quantitative trait loci (QTLs) linked to complex traits, such as grain yield. The study aimed to construct a genetic linkage map of two parents that are widely grown durum cultivars (Lahn and Chaml) in the Mediterranean basin, which is characterized by varying climate changes. The genetic linkage map of Lahn/Chaml population consisted of 112 recombinant inbred lines (RILs) and was used to determine QTLs linked to the grain yield in 11 contrasting environments (favorable, cold, dry, and hot). Simple sequence repeat (SSR) molecular markers were used to construct an anchor map, which was later enriched with single nucleotide polymorphisms (SNPs). The map was constructed with 247 SSRs and enriched with 1425 SNPs. The map covered 6122.22 cM. One hundred and twenty-six QTLs were detected on different chromosomes. Chromosomes 2A and 4B harbored the most significant grain yield QTLs. Furthermore, by comparison with several wheat mapping populations, all the A and B chromosomes of Lahn/Cham 1 QTLs contributed to grain yield. The results showed that the detected QTLs can be used as a potential candidate for marker-assisted selection in durum breeding programs.

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Cite This Article

FAROUK, I., ALSALEH, A., MOTOWAJ, J., GABOUN, F., BELKADİ, B., MALTOUF, A. F., KEHEL, Z., ELOUAFİ, I., NSARELLAH, N., NACHİT, M. M. (2021). Detection of grain yield QTLs in the durum population Lahn/Chaml tested in contrasting environments. Turkish Journal of Biology, Cilt: 45 - Sayı: 1, 65–78. https://ajindex.com/articles/197893

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References (4)

  1. Abiola O, 2003, NAT REV GENET, V4, P911, DOI 10.1038/nrg1206 Akbari M, 2006, THEOR APPL GENET, V113, P1409, DOI 10.1007/s00122-006-0365-4 Almeida GD, 2013, THEOR APPL GENET, V126, P583, DOI 10.1007/s00122-012-2003-7 Alsaleh A, 2015, PLANT MOL BIOL REP, V33, P209, DOI 10.1007/s11105-014-0749-6 Bennett D, 2012, THEOR APPL GENET, V125, P255, DOI 10.1007/s00122-012-1831-9 Blanco A, 1998, THEOR APPL GENET, V97, P721, DOI 10.1007/s001220050948 Blanco A, 1996, PLANT BREEDING, V115, P310, DOI 10.1111/j.1439-0523.1996.tb00925.x Bogard M, 2011, J EXP BOT, V62, P3621, DOI 10.1093/jxb/err061 Bort J, 2014, J AGR SCI-CAMBRIDGE, V152, P408, DOI 10.1017/S0021859613000269 Dura S, 2013, CROP PASTURE SCI, V64, P957, DOI 10.1071/CP13287 Edwards D, 2012, PLANT BIOTECHNOL J, V10, P703, DOI 10.1111/j.1467-7652.2012.00717.x El Hassouni K, 2019, AGRONOMY-BASEL, V9, DOI 10.3390/agronomy9080414 Elouafi I, 2004, THEOR APPL GENET, V108, P401, DOI 10.1007/s00122-003-1440-8 Farre A, 2016, BMC PLANT BIOL, V16, DOI 10.1186/s12870-016-0849-6 FEDERER W. T., 1956, Hawaiian Planters' Record, V55, P191 Habash DZ, 2009, J EXP BOT, V60, P2805, DOI 10.1093/jxb/erp211 Habash DZ, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0108431 Haile JK, 2012, MOL BREEDING, V30, P1479, DOI 10.1007/s11032-012-9734-7 Heidari B, 2011,
  2. GENOME, V54, P517, DOI [10.1139/g11-017, 10.1139/G11-017] Kato K, 2000, THEOR APPL GENET, V101, P1114, DOI 10.1007/s001220051587 Kehel Z, 2010, AGRON J, V102, P1542, DOI 10.2134/agronj2010.0175 Kirigwi FM, 2007, MOL BREEDING, V20, P401, DOI 10.1007/s11032-007-9100-3 Korzun V, 1999, THEOR APPL GENET, V98, P1202, DOI 10.1007/s001220051185 Kosambi DD, 1943, ANN EUGENIC, V12, P172, DOI 10.1111/j.1469-1809.1943.tb02321.x Kuchel H, 2007, THEOR APPL GENET, V115, P1029, DOI 10.1007/s00122-007-0629-7 Li HH, 2007,
  3. GENETICS, V175, P361, DOI 10.1534/genetics.106.066811 Li SS, 2007, MOL BREEDING, V20, P167, DOI 10.1007/s11032-007-9080-3 Li ZK, 2014, GENET MOL RES, V13, P1412, DOI 10.4238/2014.February.28.14 Maccaferri M, 2008,
  4. GENETICS, V178, P489, DOI 10.1534/genetics.107.077297 Maccaferri M, 2015, PLANT BIOTECHNOL J, V13, P648, DOI 10.1111/pbi.12288 Maccaferri M, 2011, J EXP BOT, V62, P409, DOI 10.1093/jxb/erq287 Marza F, 2006, THEOR APPL GENET, V112, P688, DOI 10.1007/s00122-005-0172-3 McIntyre CL, 2010, THEOR APPL GENET, V120, P527, DOI 10.1007/s00122-009-1173-4 Nachit M., 2004, CROP SCI, V32, P203. Nachit MM, 2001, THEOR APPL GENET, V102, P177, DOI 10.1007/s001220051633 Nachit MM, 2016, APP