title: The genetic architecture of maize (Zea mays L.) kernel weight determination.
abstract: Individual kernel weight is an important trait for maize yield determination. We have identified genomic regions controlling this trait by using the B73xMo17 population; however, the effect of genetic background on control of this complex trait and its physiological components is not yet known. The objective of this study was to understand how genetic background affected our previous results. Two nested stable recombinant inbred line populations (N209xMo17 and R18xMo17) were designed for this purpose. A total of 408 recombinant inbred lines were genotyped and phenotyped at two environments for kernel weight and five other traits related to kernel growth and development. All traits showed very high and significant (P < 0.001) phenotypic variability and medium-to-high heritability (0.60-0.90). When N209xMo17 and R18xMo17 were analyzed separately, a total of 23 environmentally stable quantitative trait loci (QTL) and five epistatic interactions were detected for N209xMo17. For R18xMo17, 59 environmentally stable QTL and 17 epistatic interactions were detected. A joint analysis detected 14 stable QTL regardless of the genetic background. Between 57 and 83% of detected QTL were population specific, denoting medium-to-high genetic background effects. This percentage was dependent on the trait. A meta-analysis including our previous B73xMo17 results identified five relevant genomic regions deserving further characterization. In summary, our grain filling traits were dominated by small additive QTL with several epistatic and few environmental interactions and medium-to-high genetic background effects. This study demonstrates that the number of detected QTL and additive effects for different physiologically related grain filling traits need to be understood relative to the specific germplasm.
authors.affiliation: Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Zavalla S2125ZAA, Prov. de Santa Fe, Argentina saprado@unr.edu.ar.
authors.forename: Santiago
authors.initials: S
authors.lastname: Alvarez Prado
authors.affiliation: Facultad de Ciencias Agrarias, Universidad de Lomas de Zamora, Lavallol 1836, Prov. de Buenos Aires, Argentina.
authors.forename: César G
authors.initials: CG
authors.lastname: López
authors.affiliation: Syngenta Seeds Inc., Research Triangle Park, North Carolina 27709.
authors.forename: M Lynn
authors.initials: ML
authors.lastname: Senior
authors.affiliation: Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Zavalla S2125ZAA, Prov. de Santa Fe, Argentina.
authors.forename: Lucas
authors.initials: L
authors.lastname: Borrás
mesh_terms: D002874:Chromosome Mapping; D005838:Genotype; D010641:Phenotype; D040641:Quantitative Trait Loci; D012639:Seeds / Q000235:genetics / Q000254:growth & development; D003313:Zea mays / Q000235:genetics* / Q000254:growth & development*
keywords: MPP; Multiparent Advanced Generation Inter-Cross (MAGIC); Multiparental populations; complex traits; genetic background effects; grain-filling duration; kernel growth rate; kernel weight