Maize requires more water than most other crops; therefore, the water use efficiency of this crop must be improved for maize production under undesirable land and changing environmental conditions. To elucidate the genetic control of drought in maize, we evaluated approximately 5000 inbred lines from 30 linkage-association joint mapping populations under two contrasting water regimes for seven drought-related traits, including yield and anthesis-silking interval (ASI). The joint linkage analysis was conducted to identify 220 quantitative trait loci (QTLs) under well-watered conditions and 169 QTLs under water-stressed conditions. The genome-wide association analysis identified 365 single nucleotide polymorphisms (SNPs) associated with drought-related traits, and these SNPs were located in 354 candidate genes. Fifty-two of these genes showed significant differential expression in the inbred line B73 under the well-watered and water-stressed conditions. In addition, genomic predictions suggested that the moderate-density SNPs obtained through genotyping-by-sequencing were able to make accurate predictions in the nested association mapping population for drought-related traits with moderate-to-high heritability under the water-stressed conditions. The results of the present study provide important information that can be used to understand the genetic basis of drought stress responses and facilitate the use of beneficial alleles for the improvement of drought tolerance in maize.

The pentatricopeptide repeat (PPR) gene family is one of the largest gene families in land plants (450 PPR genes in Arabidopsis, 477 PPR genes in rice and 486 PPR genes in foxtail millet) and is important for plant development and growth. Most PPR genes are encoded by plastid and mitochondrial genomes, and the gene products regulate the expression of the related genes in higher plants. However, the functions remain largely unknown, and systematic analysis and comparison of the PPR gene family in different maize genomes have not been performed. In this study, systematic identification and comparison of PPR genes from two elite maize inbred lines, B73 and PH207, were performed. A total of 491 and 456 PPR genes were identified in the B73 and PH207 genomes, respectively. Basic bioinformatics analyses, including of the classification, gene structure, chromosomal location and conserved motifs, were conducted. Examination of PPR gene duplication showed that 12 and 15 segmental duplication gene pairs exist in the B73 and PH207 genomes, respectively, with eight duplication events being shared between the two genomes. Expression analysis suggested that 53 PPR genes exhibit qualitative variations in the different genetic backgrounds. Based on analysis of the correlation between PPR gene expression in kernels and kernel-related traits, four PPR genes are significantly negatively correlated with hundred kernel weight, 12 are significantly negatively correlated with kernel width, and eight are significantly correlated with kernel number. Eight of the 24 PPR genes are also located in metaQTL regions associated with yield and kernel-related traits in maize. Two important PPR genes (GRMZM2G353195 and GRMZM2G141202) might be regarded as important candidate genes associated with maize kernel-related traits. Our results provide a more comprehensive understanding of PPR genes in different maize inbred lines and identify important candidate genes related to kernel development for subsequent functional validation in maize.

Background: Kernel row number (KRN) is an important trait for the domestication and improvement of maize. Exploring the genetic basis of KRN has great research significance and can provide valuable information for molecular assisted selection. Results: In this study, one single-locus method (MLM) and six multilocus methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO) of genome-wide association studies (GWASs) were used to identify significant quantitative trait nucleotides (QTNs) for KRN in an association panel including 639 maize inbred lines that were genotyped by the MaizeSNP50 BeadChip. In three phenotyping environments and with best linear unbiased prediction (BLUP) values, the seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, ARF29 (Zm00001d026540, encoding auxin response factor 29) and CKO4 (Zm00001d043293, encoding cytokinin oxidase protein) were significantly related to KRN, based on expression analysis and candidate gene association mapping. Whole-genome prediction (WGP) of KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate all of the KRN-associated tagSNPs identified by all GWAS models. Conclusions: These results aid in our understanding of the genetic architecture of KRN and provide useful information for genomic selection for KRN in maize breeding.

The kernel row number (KRN) of an ear is an important trait related to yield and domestication in maize. Exploring the underlying genetic mechanisms of KRN has great research significance and application potential. In the present study, N531 with a KRN of 18–22 and SLN with a KRN of 4–6 were used as the recurrent parent and the donor parent, respectively, to develop two introgression lines (ILs), IL_A and IL_B, both of which have common negative-effect alleles from SLN on chromosomes 1, 5 and 10 and significantly reduced inflorescence meristem (IM) diameter and KRN compared with those of N531. We used RNA-Seq to investigate the transcriptome profiles of 5-mm immature ears of N531, IL_A and IL_B. We identified a total of 2872 differentially expressed genes (DEGs) between N531 and IL_A, 2428 DEGs between N531 and IL_B and 1811 DEGs between IL_A and IL_B. A total of 1252 DEGs were detected as overlapping DEGs, and 89 DEGs were located on the common introgression fragments. Furthermore, three DEGs (Zm00001d013277, Zm00001d015310 and Zm00001d015377) containing three SNPs associated with KRN were identified using regional association mapping. These results will facilitate our understanding of ear development and provide important candidate genes for further study on KRN.