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Updated: Apr 14, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
Published on: July 1, 2020
Genome-wide association of carbon and nitrogen metabolism in the maize nested association mapping population
Nengyi Zhang1, Yves Gibon2, Jason G Wallace1
1Institute for Genomic Diversity (N.Z., J.G.W., K.K., E.S.B.), Boyce Thompson Institute for Plant Research (P.L., L.D., T.B.), Department of Plant Breeding and Genetics (C.C., K.K., E.S.B.), and Department of Plant Biology (T.B.), Cornell University, Ithaca, New York 14853;Max Planck Institute of Molecular Plant Physiology, 14476 Golm-Potsdam, Germany (Y.G., M.S.);United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853 (N.L., P.J.B., E.S.B.); andDepartment of Crop Science, North Carolina State University, Raleigh, North Carolina 27695 (Y.-S.S.).
Abstract:
Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C4 metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.
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