High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth

Abstract

With increasing demand for novel traits in crop breeding, the plant research community faces the challenge of quantitatively analyzing the structure and function of large numbers of plants. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In this study, we quantified 106 traits from a maize (Zea mays) recombinant inbred line population (n = 167) across 16 developmental stages using the automatic phenotyping platform. Quantitative trait locus (QTL) mapping with a high-density genetic linkage map, including 2,496 recombinant bins, was used to uncover the genetic basis of these complex agronomic traits, and 988 QTLs have been identified for all investigated traits, including three QTL hotspots. Biomass accumulation and final yield were predicted using a combination of dissected traits in the early growth stage. These results reveal the dynamic genetic architecture of maize plant growth and enhance ideotype-based maize breeding and prediction.

Figure 1.

High-throughput maize plant phenotyping. A, Maize plant sowing and maize cultivation. B, The procedure of maize inspection. C, The image-analysis pipeline. D, The extracted maize phenotypic traits and modeling.

Figure 2.

Performance evaluation of RAP-Maize. A, Scatterplot of automatic measurements versus manual measurements for plant height. B, Scatterplot of automatic measurements versus manual measurements for fresh weight. C, Scatterplot of automatic measurements versus manual measurements for dry weight.

Figure 3.

Phenotypic trait heritability during 16 growth stages. The heat map shows broad-sense heritability (H²) of the investigated phenotypic traits over 16 time points (left) as exemplified by the digital volume for natural plant height (top right) and for mean H² (bottom right). Trait identifiers are given as in the heat map and colored according to their classification as indicated: black, plant morphological traits; green, leaf architecture traits; blue, biomass-related traits; pink, histogram texture feature; purple, color trait. MEAN, H² of the mean value for all traits.

Figure 4.

Chromosomal distribution of identified QTLs. A, Network of QTLs associated with the traits obtained from RAP-Maize. Blue nodes represent the 42 traits, and pink nodes represent all identified significant loci from QTL mapping. B, Density of QTLs of all investigated phenotypic traits across the genome. The window size is 10 cM. Three QTL hotspots are marked with stars. Detailed information for all detected QTLs is shown in Supplemental Data S2. C, Chromosomal distribution of dry weight (DW) QTLs identified across 16 time points. QTL regions (represented by the confidence interval for each QTL assigned as 1 − log of odds [LOD] drop of the peak) across the maize genome responsible for dry weight are shown as black solid boxes. QTL hot regions for dry weight on the chromosome are marked with the gray hollow box. D, LOD curves of QTL mapping for SA and dry weight around the chromosome 7 QTLs at the sixth time point. E, LOD values of the bins at the peak of the QTL interval shown as a function of their genetic positions. F, Candidate genes (GRMZM2G180490, GRMZM2G010702, GRMZM2G151649, and GRMZM2G057023) located in the peak bin. Other candidates genes located in the two bins next to the peak bin are listed in Supplemental Table S9.

Figure 5.

Predication of maize digital biomass accumulation and yield. A, Heat map showing the difference of digital biomass accumulation with different maize RILs. B, Comparison of actual digital biomass (blue line) and predicted digital biomass (red line; using digital biomass of the first six time points to predict the digital biomass of the remaining 10 time points). Error bars represent the se of the dry weight of 167 samples at each time point. C, Scatterplot showing the relationship between the actual grain yield and the predicted yield with the predicted formula; a, b, c, d, e, f, g, and h represent FDIC_1, LTA_above_1, GCV_8, SDLC_8, LTA_above_9, LTA_below_9, LNL_above_16, and LSA_below_16, respectively. FDIC_1 and LNL_above_16 are leaf morphological traits; LTA_above_1, SDLC_8, LTA_above_9, LTA_below_9, and LSA_below_16 are leaf angle traits; GCV_8 is a plant color trait. The black line is the fitting line, and the standardized coefficients are shown in Supplemental Table S7. D, Predicted ideotype maize plant based on the associated traits in early stages with higher GCV, FDIC, LNL_above, and LTA_below and lower LTA_above.