Search

Phenotypic plasticity is the property of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant for future agriculture under changing climate. Here, we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a maize diverse population, planted at five sites with distinct environmental conditions and genotyped with ~ 6.60 million SNPs. We found that altitude-related environmental factors were main drivers for across site variation in flowering time traits but not plant architecture and yield traits. For 23 traits, we detected 109 QTLs, of which 29 was for mean, 66 was for plasticity, and 14 for both parameters, besides, 80% of the QTLs were interreacted with the environment. The effects of several QTLs changed in magnitude or sign, driving variation in phenotype plasticity, and we further experimentally validated one plastic gene ZmTPS14.1 whose effect was likely mediated by the compensation effect of ZmSPL6 which was from the downstream pathway probably. By integrating genetic diversity, environmental variation, and their interaction in a joint model, we could provide site-specific predictions with increased accuracy by as much as 15.5%, 3.8%, and 4.4% for DTT, PH, and EW, respectively. Overall, we revealed a complex genetic architecture involving multiallelic, pleiotropy, and genotype by environment interaction underlying maize complex trait mean and plasticity variation. Our study thus provided novel insights into the dynamic genetic architectures of agronomic traits in response to changing environments, paving a practical route to precision agriculture.

Additional file 1: Figure S1. Statistics of pan-Zea NGS de novo assemblies. Figure S2. Schematic diagram illustrated the filtering of assembly-to-assembly alignment. Figure S3. Sketch of the non-reference sequence anchoring and clustering pipeline. Figure S4. Pan-Zea gene annotation and functional annotation pipeline. Figure S5. Statistics of the proportions of functionally annotated genes. Figure S6. Validation of the gene present and absent variations (gPAVs). Figure S7. PCA and SNP LD rank analysis of gPAV. Figure S8. Core and dispensable genes and ortholog groups. Figure S9. KEGG pathways of each dispensable ortholog group cluster. Figure S10. The KEGG pathways enriched in maize concentrated ortholog groups when compared with teosinte concentrated ortholog groups. Figure S11. Distribution of sub-population enriched genes and ortholog groups. Figure S12. The sketch of SV calling and genotyping pipeline. Figure S13. Additional features of the maize genetic variation map. Figure S14. Distribution of the associated QTLs and causal variations along the chromosome. Figure S15. Distribution of the features of associated QTLs, genes and causal variations. Figure S16. Multiple sequence alignment of the PME genes in the Ga1 locus. Figure S17. Multiple sequence alignments of the protein sequences of pan-Zea PME genes. Figure S18. Statistics of candidate causal variants. Figure S19. Enrichment of causal variants features within different omics trait classes. Figure S20. Enrichment of cis-eQTL causal variants along different distance to TSS. Figure S21. Expression patterns of the candidate genes of the example SV-QTLs. Figure S22. Blast graphic summary of PZ00001aSV02097079INS. Figure S23. Distribution of the Zm00001d023299 expression patterns in the tenth leaf of V9 stage in the CUBIC offspring population related to the haplotypes flanking 500Kb of Zm00001d023299. Figure S24. The diagram of vectors used in the luciferase reporter assays of Zm00001d023299 promoter with (A) or without (B) the predicted ABRE.

Additional file 2. Target-Oriented Prioritization (TOP) tutorial.

Additional file 13. Review history.

Additional file 1. Supplementary figures and tables.

Additional file 3. Review history.

Additional file 12: Supplementary Materials and Methods [88–159].

Additional file 1. Figures S1-S11.

Additional file 5. Review history.

Additional file 5. Review history.

Additional file 4. Manhattan plots for GWAS of three traits across all populations.

Additional file 9: Note S1-S2. Information about the i-traits and the operation guide for the HSI, CT, and RGB data processing programs.

Additional file 1: Figures S1-S14.

Additional file 10: Figures S1-S11.

Additional file 1. Supplemental Tables S1 to S5 and Figures S1 to S9.

Additional file 7. Review history.

Additional file 2. Review history.

Additional file 11. Review history.

Additional file 2. Figures S1-S11.

Interactomes are powerful tools for encoding and decoding complex life systems. Here, we generated a maize interactome map that integrates genomic interactions, transcriptomic co-expression networks, translatomic co-expression networks, and protein–protein interactions throughout the maize lifecycle. This map, containing over 9 million interactions in more than 5,000 functional modules, reveals extensive functional divergence for duplicate genes and a progressive increase in regulatory divergence between the two maize subgenomes during the flow of genetic information. This network enables dissecting and validating gene functions, re-constructing regulatory pathways, and deciphering molecular mechanisms underlying complex traits combining big data mining technique-machine learning. By applying this map to flowering-time, we identified 1,843 high-confidence genes enriched in eight molecular pathways that are related to flowering time. The function of 30 (out of 58 tested) genes, including 27 novel genes, was verified by loss-of-function mutagenesis. Furthermore, a new pathway involving histone modification was identified and confirmed to regulate flowering time. The interactome map illustrates how coherent sets of molecular interactions connect different types of functional elements and pathway modules to map a genome-wide functional wiring landscape of maize, which will be applicable in a wide range of species.

Yield is the most important aim for maize breeding; however, fine mapping and cloning of genes for yield-related traits is relatively difficult. ABERRANT PANICLE ORGANIZATION1 ( APO1 ) controlling panicle structure enhances lodging resistance and improves grain yield in rice. In this study, two homologs of OsAPO1 , ZmAPO1-6 and ZmAPO1-9 ,were cloned in maize. Both of them share an F-box domain and have more than 83% amino acid identity with OsAPO1. RT-PCR analysis reveals that they are mainly expressed in the ears. Association mapping analysis was then conducted using about 200 testcrossing hybrids of an S type cytoplasmic male sterility line with diverse maize inbreds. A total of 14 and eight significant loci for five yield-related traits were identified, and the loci in ZmAPO1-6 were mainly related to plant height and stem diameter, while those in ZmAPO1-9 were mainly related to kernel weight. The results of this study implies that the two homologs might be related to yield-related traits in maize, and would facilitate further study of their detailed functions.