Background: Higher seed yield is one of the objectives of jatropha breeding. However, genetic analysis of the yield traits has not been done in jatropha. Quantitative trait loci (QTL) mapping was conducted to identify genetic factors controlling growth and seed yield in jatropha, a promising biofuel crop.
Results: A linkage map was constructed consisting of 105 SSR (simple sequence repeat) markers converged into 11 linkage groups. With this map, we identified a total of 28 QTLs for 11 growth and seed traits using a population of 296 backcrossing jatropha trees. Two QTLs qTSW-5 and qTSW-7 controlling seed yield were mapped on LGs 5 and 7 respectively, where two QTL clusters controlling yield related traits were detected harboring five and four QTLs respectively. These two QTL clusters were critical with pleiotropic roles in regulating plant growth and seed yield. Positive additive effects of the two QTLs indicated higher values for the traits conferred by the alleles from J. curcas, while negative additive effects of the five QTLs on LG6, controlling plant height, branch number (in the 4th and 10th months post seed germination), female flower number and fruit number respectively, indicated higher values conferred by the alleles from J. integerrima. Therefore favored alleles from both the parents could be expected to be integrated into elite jatropha plant by further backcrossing and marker assisted selection. Efficient ways to improve the seed yield by applying the two QTL clusters are discussed.
Conclusion: This study is the first report on genetic analysis of growth and seed traits with molecular markers in jatropha. An approach for jatropha improvement is discussed using pleiotropic QTLs, which will be likely to lead to initiation of molecular breeding in jatropha by integrating more markers in the QTL regions.
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http://dx.doi.org/10.1186/1754-6834-5-42 | DOI Listing |
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Department of Agriculture, Agribusiness, and Environmental Sciences, Texas A&M University, 700 University Blvd, MSC 228, Kingsville, TX 78363, USA.
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View Article and Find Full Text PDFYing Yong Sheng Tai Xue Bao
October 2024
College of Plant Protection, Shandong Agricultural University, Tai'an 271018, Shandong, China.
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View Article and Find Full Text PDFFront Artif Intell
December 2024
School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, United States.
The ability to accurately predict the yields of different crop genotypes in response to weather variability is crucial for developing climate resilient crop cultivars. Genotype-environment interactions introduce large variations in crop-climate responses, and are hard to factor in to breeding programs. Data-driven approaches, particularly those based on machine learning, can help guide breeding efforts by factoring in genotype-environment interactions when making yield predictions.
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Laboratory of Plant Molecular Biology and Biotechnology, Department of Biology, The University of North Carolina at Greensboro, Greensboro, NC, United States.
Tef [ (Zucc.) Trotter] is the major staple crop for millions of people in Ethiopia and Eritrea and is believed to have been domesticated several thousand years ago. Tef has the smallest grains of all the cereals, which directly impacts its productivity and presents numerous challenges to its cultivation.
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