Dengfeng Zhang

Chinese Academy of Agricultural Sciences, Peping, Beijing, China

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Publications (3)13.78 Total impact

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    ABSTRACT: Background: A genome-wide association study (GWAS) is the foremost strategy used for finding genes that control human diseases and agriculturally important traits, but it often reports false positives. In contrast, its complementary method, linkage analysis, provides direct genetic confirmation, but with limited resolution. A joint approach, using multiple linkage populations, dramatically improves resolution and statistical power. For example, this approach has been used to confirm that many complex traits, such as flowering time controlling adaptation in maize, are controlled by multiple genes with small effects. In addition, genotyping by sequencing (GBS) at low coverage not only produces genotyping errors, but also results in large datasets, making the use of high-throughput sequencing technologies computationally inefficient or unfeasible. Results: In this study, we converted raw SNPs into effective recombination bins. The reduced bins not only retain the original information, but also correct sequencing errors from low-coverage genomic sequencing. To further increase the statistical power and resolution, we merged a new temperate maize nested association mapping (NAM) population derived in China (CN-NAM) with the existing maize NAM population developed in the US (US-NAM). Together, the two populations contain 36 families and 7,000 recombinant inbred lines (RILs). One million SNPs were generated for all the RILs with GBS at low coverage. We developed high-quality recombination maps for each NAM population to correct genotyping errors and improve the computational efficiency of the joint linkage analysis. The original one million SNPs were reduced to 4,932 and 5,296 recombination bins with average interval distances of 0.34 cM and 0.28 cM for CN-NAM and US-NAM, respectively. The quantitative trait locus (QTL) mapping for flowering time (days to tasseling) indicated that the high-density, recombination bin map improved resolution of QTL mapping by 50 % compared with that using a medium-density map. We also demonstrated that combining the CN-NAM and US-NAM populations improves the power to detect QTL by 50 % compared to single NAM population mapping. Among the QTLs mapped by joint usage of the US-NAM and CN-NAM maps, 25 % of the QTLs overlapped with known flowering-time genes in maize. Conclusion: This study provides directions and resources for the research community, especially maize researchers, for future studies using the recombination bin strategy for joint linkage analysis. Available resources include efficient usage of low-coverage genomic sequencing, detailed positions for genes controlling maize flowering, and recombination bin maps and flowering- time data for both CN and US NAMs. Maize researchers even have the opportunity to grow both CN and US NAM populations to study the traits of their interest, as the seeds of both NAM populations are available from the seed repository in China and the US.
    BMC Biology 09/2015; 13(1):78. DOI:10.1186/s12915-015-0187-4 · 7.98 Impact Factor
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    ABSTRACT: Plant architecture is a key factor for high productivity maize because ideal plant architecture with an erect leaf angle and optimum leaf orientation value allow for more efficient light capture during photosynthesis and better wind circulation under dense planting conditions. To extend our understanding of the genetic mechanisms involved in leaf-related traits, three connected recombination inbred line (RIL) populations including 538 RILs were genotyped by genotyping-by-sequencing (GBS) method and phenotyped for the leaf angle and related traits in six environments. We conducted single population quantitative trait locus (QTL) mapping and joint linkage analysis based on high-density recombination bin maps constructed from GBS genotype data. A total of 45 QTLs with phenotypic effects ranging from 1.2% to 29.2% were detected for four leaf architecture traits by using joint linkage mapping across the three populations. All the QTLs identified for each trait could explain approximately 60% of the phenotypic variance. Four QTLs were located on small genomic regions where candidate genes were found. Genomic predictions from a genomic best linear unbiased prediction (GBLUP) model explained 45±9% to 68±8% of the variation in the remaining RILs for the four traits. These results extend our understanding of the genetics of leaf traits and can be used in genomic prediction to accelerate plant architecture improvement.
    PLoS ONE 03/2015; 10(3):e0121624. DOI:10.1371/journal.pone.0121624 · 3.23 Impact Factor
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    ABSTRACT: MADS-box genes encode a family of transcription factors, which control diverse developmental processes in flowering plants, with organs ranging from roots, flowers and fruits. In this study, six maize cDNAs encoding MADS-box proteins were isolated. BLASTX searches and phylogenetic analysis indicated that the six MADS-box genes belonging to the AGL2-like clade. qRT-PCR analysis revealed that these genes had differential expression patterns in different organs in maize. The results of yeast one-hybrid system indicated that the protein ZMM3-1, ZMM3-2, ZMM6, ZMM7-L, ZMM8-L and ZMM14-L had transcriptional activation activity. Subcellular localization of ZMM7-L demonstrated that the fluorescence of ZMM7-L-GFP was mainly detected in the nuclei of onion epidermal cells. qRT-PCR analysis for expression pattern of ZMM7-L showed that the gene was up-regulated by abiotic stresses and down-regulated by exogenous ABA. The germination rates of over-expression transgenic lines were lower than that of the wild type on medium with 150 mM NaCl, 350 mM mannitol. These results indicated that ZMM7-L might be a negative transcription factor responsive to abiotic stresses.
    Journal of plant physiology 03/2012; 169(8):797-806. DOI:10.1016/j.jplph.2011.12.020 · 2.56 Impact Factor