Details

Autor(en) / Beteiligte

• Peiffer, Jason A
• Romay, Maria C
• Gore, Michael A
• Flint-Garcia, Sherry A
• Zhang, Zhiwu
• Millard, Mark J
• Gardner, Candice A. C
• McCullen, Michael D
• Holland, James B
• Bradbury, Peter J
• Buckler, Edward S

Titel

genetic architecture of maize height

Ist Teil von

• Genetics (Austin), 2014-04, Vol.196 (4), p.1337-1356

Ort / Verlag

United States: Genetics Society of America

Erscheinungsjahr

2014

Link zum Volltext

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• Height is one of the most heritable and easily measured traits in maize (Zea mays L.). Given a pedigree or estimates of the genomic identity-by-state (IBS) among related plants, height is also accurately predictable. But, mapping alleles explaining natural variation in maize height remains a formidable challenge. To address this challenge, we measured the plant height, ear height, flowering time, and node counts of plants grown in >64,500 plots across 13 environments. These plots contained >7,300 inbreds representing most publically available maize inbreds in the U.S.A. as well as families of the maize Nested Association Mapping (NAM) panel. Joint-linkage mapping of quantitative trait loci (QTL), fine mapping in near isogenic lines (NILs), genome wide association studies (GWAS), and genomic best linear unbiased prediction (GBLUP) were performed. The heritability of plant height was estimated to be over 90%. Mapping of NAM family-nested QTL revealed the largest explained about 2.1 ± 0.9% of height variation. The effects of two tropical alleles at this QTL were independently validated by fine mapping. Several significant associations found by GWAS co-localized with established height loci including brassinosteroid-deficient dwarf1, dwarf plant1, and semi-dwarf2. GBLUP explained >80% of plant height variation in the observed panels and outperformed bootstrap aggregation of family-nested QTL models in evaluations of prediction accuracy. These results revealed maize height was under strong genetic control and had a highly polygenic genetic architecture. They also showed that multiple models of genetic architecture differing in polygenicity and effect sizes can plausibly explain a population’s variation in maize height, but they may vary in predictive efficacy.