Genetic dissection of wheat panicle traits using linkage analysis and a genome-wide association study
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Coincident regions on chromosome 4B for GW, on 5A for SD and TSS, and on 3A for SL and GNS were detected through an integration of a linkage analysis and a genome-wide association study (GWAS). In addition, six stable QTL clusters on chromosomes 2D, 3A, 4B, 5A and 6A were identified with high PVE% on a composite map.
The panicle traits of wheat, such as grain number per spike and 1000-grain weight, are closely correlated with grain yield. Superior and effective alleles at loci related to panicles developments play a crucial role in the progress of molecular improvement in wheat yield breeding. Here, we revealed several notable allelic variations of seven panicle-related traits through an integration of genome-wide association mapping and a linkage analysis. The linkage analysis was performed using a recombinant inbred line (RIL) population (173 lines of F8:9) with a high-density genetic map constructed with 90K SNP arrays, Diversity Arrays Technology (DArT) and simple sequence repeat (SSR) markers in five environments. Thirty-five additive quantitative trait loci (QTL) were discovered, including eleven stable QTLs on chromosomes 1A, 2D, 4B, 5B, 6B, and 6D. The marker interval between EX_C101685 and RAC875_C27536 on chromosome 4B exhibited pleiotropic effects for GW, SL, GNS, FSN, SSN, and TSS, with the phenotypic variation explained (PVE) ranging from 5.40 to 37.70%. In addition, an association analysis was conducted using a diverse panel of 205 elite wheat lines with a composite map (24,355 SNPs) based on the Illumina Infinium assay in four environments. A total of 73 significant marker-trait associations (MTAs) were detected for panicle traits, which were distributed across all wheat chromosomes except for 4D, 5D, and 6D. Consensus regions between RAC875_C27536_611 and Tdurum_contig4974_355 on chromosome 4B for GW in multiple environments, between QTSS5A.7-43 and BS00021805_51 on 5A for SD and TSS, and between QSD3A.2-164 and RAC875_c17479_359 on 3A for SL and GNS in multiple environments were detected through linkage analysis and a genome-wide association study (GWAS). In addition, six stable QTL clusters on chromosomes 2D, 3A, 4B, 5A, and 6A were identified with high PVE% on a composite map. This study provides potentially valuable information on the dissection of yield-component traits and valuable genetic alleles for molecular-design breeding or functional gene exploration.
Recombinant inbred line
Quantitative trait locus
Phenotypic variation explained
Significant marker-trait associations
Genome-wide association study
Grain number per spike
Fertile spikelet number per spike
Sterile spikelet number per spike
Total spike number per spike
This work was supported by the Science and Technology of Shandong project GG201703200178, 2017CXGC0308 and the Shandong Major Agricultural Technology Innovation Projects 2017 and 2016LZGC023. The SNP analysis and the construction of genetic maps were kindly conducted by Dr. Mingcheng Luo from the University of California, Davis, and by Dr. Jirui Wang of Sichuan Agricultural University. Prof. Wolfgang Friedt from University Giessen provided valuable revision suggestions.
Compliance with ethical standards
Conflict of interest
We declare no conflicts of interest involving this manuscript.
We declare that these experiments comply with the ethical standards in China.
- Akbari M, Wenzl P, Caig V, Carling J, Xia L, Yang SY, Uszynski G, Mohler V, Lehmensiek A, Kuchel H, Hayden MJ, Howes N, Sharp P, Vaughan P, Rathmell B, Huttner E, Kilian A (2006) Diversity arrays technology (DArT) for high-throughput profiling of the hexaploid wheat genome. Theor Appl Genet 113:1409–1420CrossRefPubMedGoogle Scholar
- Benjamin B, Nathalie F, Matt H, Emilie F, Adeline V, Magnus N, Joy B, Joel C, Fabrice R (2010) Linkage and association mapping of Arabidopsis thaliana flowering time in nature. PLoS Genet 6:155–160Google Scholar
- Chen JS, Chen GF, Li QF, Zhang H, Shi CL, Sun CL, Deng ZY, Liu K, Gu ZQ, Tian JC (2014) Construction of genetic map using genotyping chips and QTL analysis of grain weight. Sci Agric Sin 47:4769–4779Google Scholar
- Ding AM, Li J, Cui F, Zhao CH, Ma HY, Wang HG (2011) QTL mapping for yield related traits using two associated RIL populations of wheat. Acta Agron Sin 37:1511–1524Google Scholar
- Huang XQ, Cloutier S, Lycar L, Radovanovic N, Humphreys DG, Noll JS, Somers DJ, Brown PD (2006) Molecular detection of QTLs for agronomic and quality traits in a doubled haploid population derived from two Canadian wheat (Triticum aestivum L.). Theor Appl Genet 113:753–766CrossRefPubMedGoogle Scholar
- Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z, Buckler ES, Qian Q, Zhang QF, Li J, Han B (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961CrossRefPubMedGoogle Scholar
- Lai YP, Li J, Liu XC, Peng ZS, Hu XR, Yang WY (2011) Association analysis of main agronomic traits in wheat of Nanda2419 and its derivatives. Mol Plant Breeding 19:85–86Google Scholar
- Li WC, Li T, Zhao FT, Li XF, Wang HG (2005) QTL of wheat yield traits in D genome. Acta Agric Boreali Sin 20:23–26Google Scholar
- Lu YL, Zhang SH, Sha T, Xie CX, Hao ZF, Li XH, Farkhari M, Ribaut JM, Cao MJ, Rong TZ, Xu YB, Zhang QF (2010) Joint linkage–linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize. PNAS 107:19585–19590CrossRefPubMedPubMedCentralGoogle Scholar
- Quarrie SA, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele N, Pljevljakusic D, Waterman E, Weyen J, Schondelmaier J, Habash DZ, Farmer P, Saker L, Clarkson DT, Abugalieva A, Yessimbekova M, Turuspekov Y, Abugalieva S, Tuberosa R, Sanguineti MC, Hollington PA, Aragués R, Royo A, Dodig D (2005) A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring × SQ1 and its use to compare QTLs for grain yield across a range of environments. Theor Appl Genet 110:865–880CrossRefPubMedGoogle Scholar
- Qurat A, AwaisRasheed A, Tariq M, Muhammad I, Tariq M, Xia XCH, He ZHH, Umar MQ (2015) Genome-wide association for grain yield under rainfed conditions in historical wheat cultivars from Pakistan. Front Plant Sci 6:743Google Scholar
- Rao MVP (1977) Mapping of the Sphaerococcum gene ‘S’ on chromosome 3D of wheat. Cereal Res Commun 5:15–17Google Scholar
- Song YX, Jing RL, Huo NX, Ren ZL, Jia JZ (2006) Detection of QTL for heading in common wheat (Triticum aestivum L.) using different populations. Sci Agric Sin 39:2186–2193Google Scholar
- Wang RX, Zhang XY, Wu L, Wang R, Hai L, You GX, Yan CS, Xiao SH (2009) QTL analysis of grain size and related traits in winter wheat under different ecological environments. Sci Agric Sin 42:398–407Google Scholar
- Wu QH, Chen YX, Fu L, Zhou SH, Chen JJ, Zhao XJ, Zhang D, Ouyang SH, Wang ZZ, Li D, Wang GX, Zhang DY, Yua CG, Wang LX, You MS, Han J, Liu ZY (2016) QTL mapping of flag leaf traits in common wheat using an integrated high-density SSR and SNP genetic linkage map. Euphytica 208:337–351CrossRefGoogle Scholar
- Yao Q, Zhou RH, Pan YM, Fu TH, Jia JZ (2010) Construction of genetic linkage map and QTL analysis of agronomic important traits based on a RIL population derived from common wheat variety Yanzhan 1 and Zaosui 30. Sci Agric Sin 43:4130–4139Google Scholar