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, 7 (11), e1002383

Distinct Genetic Architectures for Male and Female Inflorescence Traits of Maize


Distinct Genetic Architectures for Male and Female Inflorescence Traits of Maize

Patrick J Brown et al. PLoS Genet.


We compared the genetic architecture of thirteen maize morphological traits in a large population of recombinant inbred lines. Four traits from the male inflorescence (tassel) and three traits from the female inflorescence (ear) were measured and studied using linkage and genome-wide association analyses and compared to three flowering and three leaf traits previously studied in the same population. Inflorescence loci have larger effects than flowering and leaf loci, and ear effects are larger than tassel effects. Ear trait models also have lower predictive ability than tassel, flowering, or leaf trait models. Pleiotropic loci were identified that control elongation of ear and tassel, consistent with their common developmental origin. For these pleiotropic loci, the ear effects are larger than tassel effects even though the same causal polymorphisms are likely involved. This implies that the observed differences in genetic architecture are not due to distinct features of the underlying polymorphisms. Our results support the hypothesis that genetic architecture is a function of trait stability over evolutionary time, since the traits that changed most during the relatively recent domestication of maize have the largest effects.

Conflict of interest statement

The authors have declared that no competing interests exist.


Figure 1
Figure 1. Evolution of plant and inflorescence architecture during maize domestication.
Male inflorescences are depicted in dark blue, and female inflorescences in light blue. The seven inflorescence traits measured in this study are also depicted. Note the change in placement of the female inflorescence during maize domestication: it occupies multiple lateral positions on the primary lateral branch of teosinte, and occupies a single, apical position on the primary lateral branch of maize.
Figure 2
Figure 2. Frequency distributions of QTL effects.
Effects from joint linkage (top) and GWAS (bottom) analyses are grouped by trait category (tassel, ear, flowering, and leaf). Effects for each phenotype are scaled by the total heritable variance (Vp * H2) in a panel of 282 diverse maize lines. Insets show the largest effects. Tassel traits include tassel length, spike length, branch zone, and branch number; ear traits include cob length, cob diameter, and ear row number; flowering traits include days to anthesis, days to silking, and anthesis-silking interval; leaf traits include leaf length, leaf width, and leaf angle.
Figure 3
Figure 3. Relationship between QTL frequencies and effects.
Effects from joint linkage (top) and GWAS (bottom) analyses are grouped by trait categories and scaled as in Figure 2.
Figure 4
Figure 4. Predictive ability of GWAS models.
The proportion of the phenotypic variance explained by GWAS models for 26 parental phenotypes (solid bars) and 4892 RIL phenotypes (hatched bars) in models without (top) and with (bottom) a family term. Trait categories are colored as in previous figures. TL = tassel length; SL = spike length; BZ = branch zone; BN = branch number; CL = cob length; CD = cob diameter; ERN = ear row number; DA = days to anthesis; DS = days to silking; ASI = anthesis-silking interval; LL = leaf length; LW = leaf width; LA = leaf angle.
Figure 5
Figure 5. Pleiotropy for maize morphological traits.
Green, red, and purple lines between traits indicate positive, negative, and both positive and negative correlations between QTL effects, respectively, with line width proportional to the degree of pleiotropy. A significance cutoff of p<0.01 (r>0.495 in a two-tailed test with 24 d.f.) was used for effect correlations. To eliminate spurious correlations, lines are only displayed for trait pairs with at least 10% pleiotropy. A. Pleiotropy assessed using joint linkage analysis. B. Pleiotropy assessed using GWAS. 5 cM sliding windows with a 2.5 cM step were used (see Materials and Methods), and only windows for which both traits had RMIP sums of at least 0.1 were considered. Trait abbreviations are the same as in Figure 4.
Figure 6
Figure 6. Relationship between QTL pleiotropy and effects.
Mean effects (in bold) and SNP number (in parentheses) are shown for GWAS SNPs in sliding windows that show significant pleiotropy within and between trait categories. For simplicity, flowering and leaf traits are combined into a single category. Only pleiotropic SNPs are considered. Non-overlapping areas within circles represent instances of pleiotropy within a trait category (for example, pleiotropy between two tassel traits). In cases of pleiotropy between trait categories (overlap between colored circles), color is used to distinguish the mean effects and SNP numbers for the different trait categories. For example, in sliding windows in which significant pleiotropy is observed between tassel and ear traits, the 48 relevant tassel SNPs have a mean effect size of 0.103, whereas the 48 relevant ear SNPs have a mean effect size of 0.138. Superscripted letters indicate which effect distributions differ significantly from each other (Kolmogorov-Smirnov test p-value<0.05). Only the top 200 GWAS SNPs for each trait were included in the analysis, ordered by decreasing RMIP.

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