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Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers.
Theor Appl Genet. 2018 Sep; 131(9):1903-1924.TA

Abstract

KEY MESSAGE

We identified 21 new and stable QTL, and 11 QTL clusters for yield-related traits in three bread wheat populations using the wheat 90 K SNP assay. Identification of quantitative trait loci (QTL) for yield-related traits and closely linked molecular markers is important in order to identify gene/QTL for marker-assisted selection (MAS) in wheat breeding. The objectives of the present study were to identify QTL for yield-related traits and dissect the relationships among different traits in three wheat recombinant inbred line (RIL) populations derived from crosses Doumai × Shi 4185 (D × S), Gaocheng 8901 × Zhoumai 16 (G × Z) and Linmai 2 × Zhong 892 (L × Z). Using the available high-density linkage maps previously constructed with the wheat 90 K iSelect single nucleotide polymorphism (SNP) array, 65, 46 and 53 QTL for 12 traits were identified in the three RIL populations, respectively. Among them, 34, 23 and 27 were likely to be new QTL. Eighteen common QTL were detected across two or three populations. Eleven QTL clusters harboring multiple QTL were detected in different populations, and the interval 15.5-32.3 cM around the Rht-B1 locus on chromosome 4BS harboring 20 QTL is an important region determining grain yield (GY). Thousand-kernel weight (TKW) is significantly affected by kernel width and plant height (PH), whereas flag leaf width can be used to select lines with large kernel number per spike. Eleven candidate genes were identified, including eight cloned genes for kernel, heading date (HD) and PH-related traits as well as predicted genes for TKW, spike length and HD. The closest SNP markers of stable QTL or QTL clusters can be used for MAS in wheat breeding using kompetitive allele-specific PCR or semi-thermal asymmetric reverse PCR assays for improvement of GY.

Authors+Show Affiliations

College of Agronomy, Xinjiang Agricultural University, Ürümqi, 830052, Xinjiang, China. Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China.College of Agronomy, Xinjiang Agricultural University, Ürümqi, 830052, Xinjiang, China. Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China.Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China. International Maize and Wheat Improvement Center (CIMMYT) China Office, c/o CAAS, 12 Zhongguancun South Street, Beijing, 100081, China.Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China.Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China. Sino-Russia Agricultural Scientific and Technological Cooperation Center, Heilongjiang Academy of Agricultural Sciences, 368 Xuefu Street, Harbin, 150086, Heilongjiang, China.Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China.College of Agronomy, Xinjiang Agricultural University, Ürümqi, 830052, Xinjiang, China.Institute of Cotton Research, Chinese Academy of Agricultural Sciences (CAAS), 38 Huanghe Street, Anyang, 455000, Henan, China.Crop Research Institute, Anhui Academy of Agricultural Sciences, 40 Nongke South Street, Hefei, 230001, Anhui, China.Crop Research Institute, Anhui Academy of Agricultural Sciences, 40 Nongke South Street, Hefei, 230001, Anhui, China.Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing, 100081, China. xiaxianchun@caas.cn.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29858949

Citation

Li, Faji, et al. "Genome-wide Linkage Mapping of Yield-related Traits in Three Chinese Bread Wheat Populations Using High-density SNP Markers." TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik, vol. 131, no. 9, 2018, pp. 1903-1924.
Li F, Wen W, He Z, et al. Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers. Theor Appl Genet. 2018;131(9):1903-1924.
Li, F., Wen, W., He, Z., Liu, J., Jin, H., Cao, S., Geng, H., Yan, J., Zhang, P., Wan, Y., & Xia, X. (2018). Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers. TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik, 131(9), 1903-1924. https://doi.org/10.1007/s00122-018-3122-6
Li F, et al. Genome-wide Linkage Mapping of Yield-related Traits in Three Chinese Bread Wheat Populations Using High-density SNP Markers. Theor Appl Genet. 2018;131(9):1903-1924. PubMed PMID: 29858949.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers. AU - Li,Faji, AU - Wen,Weie, AU - He,Zhonghu, AU - Liu,Jindong, AU - Jin,Hui, AU - Cao,Shuanghe, AU - Geng,Hongwei, AU - Yan,Jun, AU - Zhang,Pingzhi, AU - Wan,Yingxiu, AU - Xia,Xianchun, Y1 - 2018/06/01/ PY - 2018/01/25/received PY - 2018/05/24/accepted PY - 2018/6/3/pubmed PY - 2018/8/23/medline PY - 2018/6/3/entrez SP - 1903 EP - 1924 JF - TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik JO - Theor Appl Genet VL - 131 IS - 9 N2 - KEY MESSAGE: We identified 21 new and stable QTL, and 11 QTL clusters for yield-related traits in three bread wheat populations using the wheat 90 K SNP assay. Identification of quantitative trait loci (QTL) for yield-related traits and closely linked molecular markers is important in order to identify gene/QTL for marker-assisted selection (MAS) in wheat breeding. The objectives of the present study were to identify QTL for yield-related traits and dissect the relationships among different traits in three wheat recombinant inbred line (RIL) populations derived from crosses Doumai × Shi 4185 (D × S), Gaocheng 8901 × Zhoumai 16 (G × Z) and Linmai 2 × Zhong 892 (L × Z). Using the available high-density linkage maps previously constructed with the wheat 90 K iSelect single nucleotide polymorphism (SNP) array, 65, 46 and 53 QTL for 12 traits were identified in the three RIL populations, respectively. Among them, 34, 23 and 27 were likely to be new QTL. Eighteen common QTL were detected across two or three populations. Eleven QTL clusters harboring multiple QTL were detected in different populations, and the interval 15.5-32.3 cM around the Rht-B1 locus on chromosome 4BS harboring 20 QTL is an important region determining grain yield (GY). Thousand-kernel weight (TKW) is significantly affected by kernel width and plant height (PH), whereas flag leaf width can be used to select lines with large kernel number per spike. Eleven candidate genes were identified, including eight cloned genes for kernel, heading date (HD) and PH-related traits as well as predicted genes for TKW, spike length and HD. The closest SNP markers of stable QTL or QTL clusters can be used for MAS in wheat breeding using kompetitive allele-specific PCR or semi-thermal asymmetric reverse PCR assays for improvement of GY. SN - 1432-2242 UR - https://www.unboundmedicine.com/medline/citation/29858949/Genome_wide_linkage_mapping_of_yield_related_traits_in_three_Chinese_bread_wheat_populations_using_high_density_SNP_markers_ L2 - https://dx.doi.org/10.1007/s00122-018-3122-6 DB - PRIME DP - Unbound Medicine ER -