Genotyping-by-Sequencing (GBS) is a low-cost, high-throughput genotyping method that relies on restriction enzymes to reduce genome complexity. GBS is being widely used for various genetic and breeding applications. In the present study, 2240 individuals from eight maize populations, including two association populations (AM), backcross first generation (BC1), BC1F2, F2, double haploid (DH), intermated B73 × Mo17 (IBM), and a recombinant inbred line (RIL) population, were genotyped using GBS. A total of 955,120 of raw data for SNPs was obtained for each individual, with an average genotyping error of 0.70%. The rate of missing genotypic data for these SNPs was related to the level of multiplex sequencing: ~ 25% missing data for 96-plex and ~ 55% for 384-plex. Imputation can greatly reduce the rate of missing genotypes to 12.65% and 3.72% for AM populations and bi-parental populations, respectively, although it increases total genotyping error. For analysis of genetic diversity and linkage mapping, unimputed data with a low rate of genotyping error is beneficial, whereas, for association mapping, imputed data would result in higher marker density and would improve map resolution. Because imputation does not influence the prediction accuracy, both unimputed and imputed data can be used for genomic prediction. In summary, GBS is a versatile and efficient SNP discovery approach for homozygous materials and can be effectively applied for various purposes in maize genetics and breeding.

Plant architecture attributes, such as plant height, ear height, and internode number, have played an important role in the historical increases in grain yield, lodging resistance, and biomass in maize (Zea mays L.). Analyzing the genetic basis of variation in plant architecture using high density QTL mapping will be of benefit for the breeding of maize for many traits. However, the low density of molecular markers in existing genetic maps has limited the efficiency and accuracy of QTL mapping. Genotyping by sequencing (GBS) is an improved strategy for addressing a complex genome via next-generation sequencing technology. GBS has been a powerful tool for SNP discovery and high-density genetic map construction. The creation of ultra-high density genetic maps using large populations of advanced recombinant inbred lines (RILs) is an efficient way to identify QTL for complex agronomic traits. A set of 314 RILs derived from inbreds Ye478 and Qi319 were generated and subjected to GBS. A total of 137,699,000 reads with an average of 357,376 reads per individual RIL were generated, which is equivalent to approximately 0.07-fold coverage of the maize B73 RefGen_V3 genome for each individual RIL. A high-density genetic map was constructed using 4183 bin markers (100-Kb intervals with no recombination events). The total genetic distance covered by the linkage map was 1545.65 cM and the average distance between adjacent markers was 0.37 cM with a physical distance of about 0.51 Mb. Our results demonstrated a relatively high degree of collinearity between the genetic map and the B73 reference genome. The quality and accuracy of the bin map for QTL detection was verified by the mapping of a known gene, pericarp color 1 (P1), which controls the color of the cob, with a high LOD value of 80.78 on chromosome 1. Using this high-density bin map, 35 QTL affecting plant architecture, including 14 for plant height, 14 for ear height, and seven for internode number were detected across three environments. Interestingly, pQTL10, which influences all three of these traits, was stably detected in three environments on chromosome 10 within an interval of 14.6 Mb. Two MYB transcription factor genes, GRMZM2G325907 and GRMZM2G108892, which might regulate plant cell wall metabolism are the candidate genes for qPH10. Here, an ultra-high density accurate linkage map for a set of maize RILs was constructed using a GBS strategy. This map will facilitate identification of genes and exploration of QTL for plant architecture in maize. It will also be helpful for further research into the mechanisms that control plant architecture while also providing a basis for marker-assisted selection.

Maize rough dwarf disease (MRDD) is a viral infection that results in heavy yield losses in maize worldwide, particularly in the summer maize-growing regions of China. MRDD is caused by the Rice black-streaked dwarf virus (RBSDV). In the present study, analyses of microRNAs (miRNAs), the degradome, and transcriptome sequences were used to elucidate the RBSDV-responsive pathway(s) in maize. Genomic analysis indicated that the expression of three non-conserved and 28 conserved miRNAs, representing 17 known miRNA families and 14 novel miRNAs, were significantly altered in response to RBSDV when maize was inoculated at the V3 (third leaf) stage. A total of 99 target transcripts from 48 genes of 10 known miRNAs were found to be responsive to RBSDV infection. The annotations of these target genes include a SQUAMOSA promoter binding (SPB) protein, a P450 reductase, an oxidoreductase, and a ubiquitin-related gene, among others. Characterization of the entire transcriptome suggested that a total of 28 and 1085 differentially expressed genes (DEGs) were detected at 1.5 and 3.0 d, respectively, after artificial inoculation with RBSDV. The expression patterns of cell wall- and chloroplast-related genes, and disease resistance- and stress-related genes changed significantly in response to RBSDV infection. The negatively regulated genes GRMZM2G069316 and GRMZM2G031169, which are the target genes for miR169i-p5 and miR8155, were identified as a nucleolin and a NAD(P)-binding Rossmann-fold superfamily protein in maize, respectively. The gene ontology term GO:0003824, including GRMZM2G031169 and other 51 DEGs, was designated as responsive to RBSDV.

The maize kernel row number (KRN) is a key component that contributes to grain yield and has high broad-sense heritability (H 2 ). Quantitative trait locus/loci (QTL) mapping using a high-density genetic map is a powerful approach to detecting loci that are responsible for traits of interest. Bulked segregant ribonucleic acid (RNA) sequencing (BSR-seq) is another rapid and cost-effective strategy to identify QTL. Combining QTL mapping using a high-density genetic map and BSR-seq may dissect comprehensively the genetic architecture underlying the maize KRN. A panel of 300 F2 individuals derived from inbred lines abe2 and B73 were genotyped using the specific-locus amplified fragment sequencing (SLAF-seq) method. A total of 4,579 high-quality polymorphic SLAF markers were obtained and used to construct a high-density genetic map with a total length of 2,123 centimorgan (cM) and an average distance between adjacent markers of 0.46 cM. Combining the genetic map and KRN of F2 individuals, four QTL (qKRN1, qKRN2, qKRN5, and qKRN8-1) were identified on chromosomes 1, 2, 5, and 8, respectively. The physical intervals of these four QTL ranged from 4.36 Mb for qKRN8-1 to 7.11 Mb for qKRN1 with an average value of 6.08 Mb. Based on high-throughput sequencing of two RNA pools bulked from leaves of plants with extremely high and low KRNs, two QTL were detected on chromosome 8 in the 10–25 Mb (BSR_QTL1) and 60–150 Mb (BSR_QTL2) intervals. According to the physical positions of these QTL, qKRN8-1 was included by BSR_QTL2. In addition, qKRN8-1 was validated using QTL mapping with a recombinant inbred lines population that was derived from inbred lines abe2 and B73. In this study, we proved that combining QTL mapping using a high-density genetic map and BSR-seq is a powerful and cost-effective approach to comprehensively revealing genetic architecture underlying traits of interest. The QTL for the KRN detected in this study, especially qKRN8-1, can be used for performing fine mapping experiments and marker-assisted selection in maize breeding.