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United States Department of Agriculture

Agricultural Research Service

Research Project: ENHANCING CORN WITH RESISTANCE TO AFLATOXIN CONTAMINATION AND INSECT DAMAGE

Location: Corn Host Plant Resistance Research

Title: Meta-analyses of QTL for grain yield and anthesis silking interval in 18 maize populations evaluated under water-stressed and well-watered environments

Authors
item Samagn, Kassa -
item Beyene, Yoseph -
item WARBURTON, MARILYN
item Tarkegne, Amsal -
item Mugo, Stephen -
item Meisel, Barbara -
item Schabiague, Pierre -
item Prasanna, B. -

Submitted to: Biomed Central (BMC) Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 3, 2013
Publication Date: May 10, 2013
Citation: Samagn, K., Beyene, Y., Warburton, M.L., Tarkegne, A., Mugo, S., Meisel, B., Schabiague, P., Prasanna, B.M. 2013. Meta-analyses of QTL for grain yield and anthesis silking interval in 18 maize populations evaluated under water-stressed and well-watered environments. BMC Genomics. 14:313. http://www.biomedcentral.com/1471-2164/14/313.

Interpretive Summary: Quantitative trait locus (QTL) mapping has been a powerful tool to determine the number of genes that positively affect traits of agronomic importance in crop plants, as well as the magnitude of the effect and the genetic action of these genes. The QTL mapping of different individuals often uncovers different genes that are important to the trait, and the precise locations of the genes on the chromosomes are usually impossible to identify. In fact, very large chromosomal regions may be identified, which are not suitable for marker assisted selection (MAS). The meta-analysis of several QTL mapping data sets simultaneously allows the confirmation of important genomic regions from many different individuals, and can narrow down the region associated with trait improvement, which is precisely the information needed for successful MAS. One trait of great importance in maize is resistance of plants to drought, especially during flowering time. Drought tolerance and one related trait, the anthesis-silking interval (ASI) were used in a meta-analysis of 18 mapping populations. Genomic regions of QTL found following meta-analysis were reduced by 12-fold compared to the QTL found in individual populations, and the smaller number of QTL found in more than one population will make this information particularly relevant for MAS of this difficult but urgently important trait.

Technical Abstract: Identification of QTL with large phenotypic effects conserved across genetic backgrounds and environments and with small genomic size is one of the prerequisites for crop improvement using Marker Assisted Selection (MAS). The objectives of this study were to identify meta-QTL (mQTL) for grain yield (GY) and anthesis silking interval (ASI) across 18 bi-parental maize populations evaluated in the same manner across 2-4 managed water stressed and 3-4 well watered environments. The meta-analyses identified 68 mQTL (9 QTL specific to ASI, 15 specific to GY, and 44 for both GY and ASI). Correlation between GY and ASI was significant but moderately low (-0.10 to -0.51; p < 0.05). Mean phenotypic variance explained by each mQTL varied from 1.2 to 13.1% and the overall average was 6.5%. Few QTL were detected under both environmental treatments and/or multiple (>4 populations) genetic backgrounds. The number and 95% genetic and physical confidence intervals of the mQTL were highly reduced compared to the QTL identified in the original studies. Each physical interval of the mQTL consisted of 5 to 926 candidate genes. Meta-analyses reduced the number of QTL by 68% and narrowed the confidence intervals up to 12-fold. At least the 4 mQTL associated with GY under both stressed and optimum environments and detected up to 6 populations may be considered for fine mapping to confirm effects in different genetic backgrounds and pyramid them into new drought resistant breeding lines. As far as we are aware, this is the first extensive report using data from over 3100 individuals genotyped using the same SNP platform and evaluated in the same manner across a wide range of managed water-stressed and well-watered environments.

Last Modified: 9/10/2014
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