Mining novel kernel size‐related genes by pQTL mapping and multi‐omics integrative analysis in developing maize kernels

Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that 54% of the identified proteins were annotated and enriched in carbohydrate metabolism (13%), amino acid metabolism (16%) and genetic information processing (18%) (Figure 1a). The results reveal that the transcript level alone does not always reliably predict protein abundance at the population scale, and protein abundance variation may play an important role in orchestrating the biological functions of genes involved in the same biological pathways. pQTL analysis is therefore necessary to fully elucidate the molecular basis of kernel-related phenotypes. The x-axis indicates the physical positions (Mb) of the pQTLs across ten maize chromosomes, and the heatmap shows the density of these pQTLs across the maize genome (window size = 10 Mb). (f) Flowchart of data analysis. (g) Candidate gene-based association mapping and pairwise linkage disequilibrium analysis of the local pQTL for P1107. [...]the correlation among different levels is low because the flow of information from DNA to phenotype is a signal propagation process. [...]this integrative strategy does not apply to functionally unrelated QTLs that co-segregate, such as distant pQTLs.