1 United States Department of Agriculture-Agriculture Research Service (USDA-ARS), U.S. Arid-Land Agricultural Research Center, North Cardon Lane, Maricopa, AZ 85138, USA 2 United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Robert Holley Center for Agriculture and Health, Ithaca, NY 14853, USA 3 Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA 4 Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA 5 Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA 6 Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY Alexander E. Lipka 1,2, Maria Magallanes-Lundback 3, Ruthie Angelovici 3, Sabrina Gonzalez Jorge 3, Jacabo Arango 3, Eunha Kim 3, Dean DellaPenna 3, Brenda Owens 4, Torbert Rocheford 4, Edward S. Buckler 2,5,6, and Michael Gore 1 Joint linkage analysis and GWAS in the NAM population identifies genes associated with carotenoids and tocochromanols in maize grain Summary - Vitamin A deficiency (VAD) and vitamin E deficiency (VED) are major health problems, and maize grain does not provide enough provitamin A and vitamin E - One solution is biofortification of the maize grain, where carotenoids and tocopherols are boosted through marker assisted selection (MAS) on target genes - Our analysis of carotenoid and tocochromanol levels in grain from the nested association mapping (NAM) population are identifying these genes - For more information, visit This work is supported by NSF Grant and the USDA-ARS Source: Phenotypic Data Collection Grain from the NAM population and 200 randomly selected intermated B73xMo17 (IBM) grown at Purdue University, West Lafayette, IN in 2009 > 8,500 high-pressure liquid chromatography (HPLC) ran to measure carotenoid and tocopherol levels in grain Calculate BLUPs Best linear unbiased predictors (BLUPs) of each phenotype were predicted from a random effects model that accounted for field effects Highly Heritable and Oligogenic Position of Identified QTL* NAM Family Total Carotenoids Estimated Effect Size Relative to B73 Position of Identified QTL* NAM Family α-Tocopherol Estimated Effect Size Relative to B73 Common QTL and Allelic Series Joint Linkage (JL) Analysis Stepwise regression procedure determined significant marker(family) effects with 1106 SNPs genotyped in the NAM population (Buckler et al., 2009) Genome-Wide Association Study (GWAS) Resampling procedure with 1.6 million HapMap v1 SNPs projected onto the NAM population (Valder et al., 2009; Gore et al., 2009) dxs2: Chr. 7 14,078,046 to 14,080,704 bp vte4: Chr ,527, ,531,733 bp *Average length of support interval: 3.2 Mb *Average length of support interval: 14.0 Mb Average pairwise correlation between QTL allelic effects from JL for these three traits: 0.94 Average pairwise correlation between QTL allelic effects from JL for these three traits: 0.64 Candidate Genes Found for Multiple Traits Future Work - Combine these data with grain from NAM grown during 2010 field season (currently being measured with HPLC) - Conduct linkage analysis within each NAM family to identify rare QTL - Project ~56 million HapMapv2 SNPs onto NAM to conduct GWAS with higher resolution - Conduct GWAS in the maize 282 association panel using data from the 2009 and 2010 field seasons Source: Torbert Rocheford zds1: Chr. 7 17,354,856 to 17,355,388 bp -All but two compounds have heritability > Average heritability: 0.82 | -0.6