The Genetic Architecture Of Maize Height

Abstract

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 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 >7300 inbreds representing most publically available maize inbreds in the United States and 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 maize height was estimated to be >90%. Mapping NAM family-nested QTL revealed the largest explained 2.1 ± 0.9% of height variation. The effects of two tropical alleles at this QTL were independently validated by fine mapping in NIL families. Several significant associations found by GWAS colocalized with established height loci, including brassinosteroid-deficient dwarf1, dwarf plant1, and semi-dwarf2. GBLUP explained >80% of height variation in the 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.

Association mapping for phenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits

“…Repeatability for plant height (0.87), ear height (0.86) and flowering time (0.92) were in the same range as in the US NCRPIS panel (Peiffer et al, 2014), and the Chinese panel (Yang et al, 2014). ASI8 repeatability (0.45) was in the same range as in the European panels .…”

“…Note that ZCN8 was associated with the first PCA axis, confirming a major role in the overall phenotypic variation observed in our panel. Its effect on LFNB was also found by Peiffer et al (2014), in the US diversity panel and in the US NAM (P-value o E-76). It corresponds to the Vgt2 QTL found in numerous studies (Romay et al, 2013;Bouchet et al, 2013).…”

“…However, substantial variation remains to be explained and exploited for most traits. QTL mapping experiments revealed that plant total height (PTHT) and flowering time are highly heritable polygenic traits and that their variation is controlled by many unlinked genes, each contributing a small additive effect (Buckler et al, 2009;Romay et al, 2013;Bouchet et al, 2013;Peiffer et al, 2014). Many QTL experiments for inflorescence traits showed that they are less heritable (Brown et al, 2011).…”

Novel Loci Underlie Natural Variation in Vitamin E Levels in Maize Grain

“…These 14 genes included seven of the nine intervals with largest PVEs (Figure 3; Supplemental Table 2) and in an additive model explained 56 to 93% of phenotypic variation attributed to QTL for the traits analyzed in this study (Supplemental Figure 8). This degree of gene-level resolution of JL-GWAS signals was much greater than in earlier NAM studies Brown et al, 2011;Kump et al, 2011;Poland et al, 2011;Tian et al, 2011;Cook et al, 2012;Peiffer et al, 2014;Wallace et al, 2014;Yan et al, 2015;Zhang et al, 2015) due to three main factors: clear molecular evidence of functional involvement through the incorporation of RNA-seq data, increased marker density provided by the additional 27.4 million HapMap v2 variants, and the tractable genetic architecture of tocochromanol traits (oligogenic and highly heritable). Eight of the 14 identified genes were on a list of 81 a priori maize candidate genes generated based on prior elucidation of precursor and core tocochromanol pathways, primarily in Arabidopsis, while the remaining six encode functions not previously demonstrated to affect tocochromanols in any plant species despite over two decades of molecular genetic studies (Shintani and DellaPenna, 1998;Savidge et al, 2002;Cahoon et al, 2003;Cheng et al, 2003;Sattler et al, 2004;Valentin et al, 2006;DellaPenna and Mène-Saffrané, 2011).…”

“…To screen the 10 traits for phenotypic outliers, we initially performed mixed linear model selection with custom Java code invoking ASReml-W version 3.0 (Gilmour et al, 2009) for each trait that followed the same steps of the two-stage model fitting process previously described (Peiffer et al, 2014). In brief, in the first stage, mixed linear models separately fit for each of the two environments included a fixed effect for the grand mean and random effects including the genotypic effects of family and RIL within family, a laboratory effect for HPLC autosampler plate, and spatial effects for field, set within field, and block within set within field.…”