Location: Plant Genetics Research
Title: Genetic Architecture of Maize Kernel Quality in the Nested Association Mapping (NAM) Population Author
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: April 22, 2010
Publication Date: N/A
Technical Abstract: Many studies have been conducted to identify genes (quantitative trait loci; QTL) underlying kernel quality traits. However, these studies were limited to analyzing two parents at once and often resulted in low resolution mapping of QTL. The maize nested association mapping (NAM) population is a resource for conducting genome-wide high-resolution mapping. NAM was constructed by crossing 25 diverse inbred lines (DL) to B73 and deriving 200 recombinant inbred lines (RILs) from each cross for a total of 5000 RILs. The NAM design simultaneously exploits the strengths of both linkage analysis and association mapping, and integrates natural diversity and genomics technologies. The NAM population was grown in seven locations during 2006 to 2007. Plants from each plot were self-pollinated to control for xenia effects, and seed from each location was analyzed by near infra-red (NIR) spectroscopy. Estimates were obtained for kernel starch, protein, and oil content. The best linear unbiased prediction (BLUP) of genetic effect for each RIL was derived and used to map QTLs using SAS Procedure GLMSELECT. With the non-B73 marker alleles representing a specific genetic background for each population, a marker effect nested within population was used for detecting the association. We found 21 to 26 QTL for each trait, accounting for 60-70% of the phenotypic variation. No epistasis was observed for any of the three traits. Preliminary results indicate that NAM kernel quality QTL overlap with regions identified in previous QTL studies. Exploration of one candidate gene, DGAT1-2 controlling oil content, revealed that the four strongest alleles in NAM contain the same genetic lesion previously reported controlling the high oil phenotype of a line derived from the Illinois Long Term Selection project. The identification of the genes underlying kernel quality will enable better manipulation of these genes for maize improvement for food, feed, fuel, and industrial purposes.