Yield-related Traits In Maize Research Articles

Genomic analysis of a new heterotic maize group reveals key loci for pedigree breeding.

Genome-wide analyses of maize populations have clarified the genetic basis of crop domestication and improvement. However, limited information is available on how breeding improvement reshaped the genome in the process of the formation of heterotic groups. In this study, we identified a new heterotic group (X group) based on an examination of 512 Chinese maize inbred lines. The X group was clearly distinct from the other non-H&L groups, implying that X × HIL is a new heterotic pattern. We selected the core inbred lines for an analysis of yield-related traits. Almost all yield-related traits were better in the X lines than those in the parental lines, indicating that the primary genetic improvement in the X group during breeding was yield-related traits. We generated whole-genome sequences of these lines with an average coverage of 17.35× to explore genome changes further. We analyzed the identity-by-descent (IBD) segments transferred from the two parents to the X lines and identified 29 and 28 IBD conserved regions (ICRs) from the parents PH4CV and PH6WC, respectively, accounting for 28.8% and 12.8% of the genome. We also identified 103, 89, and 131 selective sweeps (SSWs) using methods that involved the π, Tajima's D, and CLR values, respectively. Notably, 96.13% of the ICRs co-localized with SSWs, indicating that SSW signals concentrated in ICRs. We identified 171 annotated genes associated with yield-related traits in maize both in ICRs and SSWs. To identify the genetic factors associated with yield improvement, we conducted QTL mapping for 240 lines from a DH population (PH4CV × PH6WC, which are the parents of X1132X) for ten key yield-related traits and identified a total of 55 QTLs. Furthermore, we detected three QTL clusters both in ICRs and SSWs. Based on the genetic evidence, we finally identified three key genes contributing to yield improvement in breeding the X group. These findings reveal key loci and genes targeted during pedigree breeding and provide new insights for future genomic breeding.

Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.).

Maize serves as a crucial nutrient reservoir for a significant portion of the global population. However, to effectively address the growing world population's hidden hunger, it is essential to focus on two key aspects: biofortification of maize and improving its yield potential through advanced breeding techniques. Moreover, the coordination of multiple targets within a single breeding program poses a complex challenge. This study compiled mapping studies conducted over the past decade, identifying quantitative trait loci associated with grain quality and yield related traits in maize. Meta-QTL analysis of 2,974 QTLs for 169 component traits (associated with quality and yield related traits) revealed 68 MQTLs across different genetic backgrounds and environments. Most of these MQTLs were further validated using the data from genome-wide association studies (GWAS). Further, ten MQTLs, referred to as breeding-friendly MQTLs (BF-MQTLs), with a significant phenotypic variation explained over 10% and confidence interval less than 2Mb, were shortlisted. BF-MQTLs were further used to identify potential candidate genes, including 59 genes encoding important proteins/products involved in essential metabolic pathways. Five BF-MQTLs associated with both quality and yield traits were also recommended to be utilized in future breeding programs. Synteny analysis with wheat and rice genomes revealed conserved regions across the genomes, indicating these hotspot regions as validated targets for developing biofortified, high-yielding maize varieties in future breeding programs. After validation, the identified candidate genes can also be utilized to effectively model the plant architecture and enhance desirable quality traits through various approaches such as marker-assisted breeding, genetic engineering, and genome editing.

Identification of QTN-by-environment interactions for yield related traits in maize under multiple abiotic stresses.

Quantitative trait nucleotide (QTN)-by-environment interactions (QEIs) play an increasingly essential role in the genetic dissection of complex traits in crops as global climate change accelerates. The abiotic stresses, such as drought and heat, are the major constraints on maize yields. Multi-environment joint analysis can improve statistical power in QTN and QEI detection, and further help us to understand the genetic basis and provide implications for maize improvement. In this study, 3VmrMLM was applied to identify QTNs and QEIs for three yield-related traits (grain yield, anthesis date, and anthesis-silking interval) of 300 tropical and subtropical maize inbred lines with 332,641 SNPs under well-watered and drought and heat stresses. Among the total 321 genes around 76 QTNs and 73 QEIs identified in this study, 34 known genes were reported in previous maize studies to be truly associated with these traits, such as ereb53 (GRMZM2G141638) and thx12 (GRMZM2G016649) associated with drought stress tolerance, and hsftf27 (GRMZM2G025685) and myb60 (GRMZM2G312419) associated with heat stress. In addition, among 127 homologs in Arabidopsis out of 287 unreported genes, 46 and 47 were found to be significantly and differentially expressed under drought vs well-watered treatments, and high vs. normal temperature treatments, respectively. Using functional enrichment analysis, 37 of these differentially expressed genes were involved in various biological processes. Tissue-specific expression and haplotype difference analysis further revealed 24 candidate genes with significantly phenotypic differences across gene haplotypes under different environments, of which the candidate genes GRMZM2G064159, GRMZM2G146192, and GRMZM2G114789 around QEIs may have gene-by-environment interactions for maize yield. All these findings may provide new insights for breeding in maize for yield-related traits adapted to abiotic stresses.

A Systemic Investigation of Genetic Architecture and Gene Resources Controlling Kernel Size-Related Traits in Maize.

Grain yield is the most critical and complex quantitative trait in maize. Kernel length (KL), kernel width (KW), kernel thickness (KT) and hundred-kernel weight (HKW) associated with kernel size are essential components of yield-related traits in maize. With the extensive use of quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) analyses, thousands of QTLs and quantitative trait nucleotides (QTNs) have been discovered for controlling these traits. However, only some of them have been cloned and successfully utilized in breeding programs. In this study, we exhaustively collected reported genes, QTLs and QTNs associated with the four traits, performed cluster identification of QTLs and QTNs, then combined QTL and QTN clusters to detect consensus hotspot regions. In total, 31 hotspots were identified for kernel size-related traits. Their candidate genes were predicted to be related to well-known pathways regulating the kernel developmental process. The identified hotspots can be further explored for fine mapping and candidate gene validation. Finally, we provided a strategy for high yield and quality maize. This study will not only facilitate causal genes cloning, but also guide the breeding practice for maize.

The role of transposon inverted repeats in balancing drought tolerance and yield-related traits in maize.

The genomic basis underlying the selection for environmental adaptation and yield-related traits in maize remains poorly understood. Here we carried out genome-wide profiling of the small RNA (sRNA) transcriptome (sRNAome) and transcriptome landscapes of a global maize diversity panel under dry and wet conditions and uncover dozens of environment-specific regulatory hotspots. Transgenic and molecular studies of Drought-Related Environment-specific Super eQTL Hotspot on chromosome 8 (DRESH8) and ZmMYBR38, a target of DRESH8-derived small interfering RNAs, revealed a transposable element-mediated inverted repeats (TE-IR)-derived sRNA- and gene-regulatory network that balances plant drought tolerance with yield-related traits. A genome-wide scan revealed that TE-IRs associate with drought response and yield-related traits that were positively selected and expanded during maize domestication. These results indicate that TE-IR-mediated posttranscriptional regulation is a key molecular mechanism underlying the tradeoff between crop environmental adaptation and yield-related traits, providing potential genomic targets for the breeding of crops with greater stress tolerance but uncompromised yield.

Genome wide association analysis for yield related traits in maize

BackgroundUnderstanding the genetic basis of yield related traits contributes to the improvement of grain yield in maize.ResultsUsing 291 excellent maize inbred lines as materials, six yield related traits of maize, including grain yield per plant (GYP), grain length (GL), grain width (GW), kernel number per row (KNR), 100 kernel weight (HKW) and tassel branch number (TBN) were investigated in Jinan, in 2017, 2018 and 2019. The average values of three environments were taken as the phenotypic data of yield related traits, and they were statistically analyzed. Based on 38,683 high-quality SNP markers in the whole genome of the association panel, the MLM with PCA model was used for genome-wide association analysis (GWAS) to obtain 59 significantly associated SNP sites. Moreover, 59 significantly associated SNPs (P < 0.0001) referring to GYP, GL, GW, KNR, HKW and TBN, of which 14 SNPs located in yield related QTLs/QTNs previously reported. A total of 66 candidate genes were identified based on the 59 significantly associated SNPs, of which 58 had functional annotation.ConclusionsUsing genome-wide association analysis strategy to identify genetic loci related to maize yield, a total of 59 significantly associated SNP were detected. Those results aid in our understanding of the genetic architecture of maize yield and provide useful SNPs for genetic improvement of maize.

Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize.

Heterosis contributes a big proportion to hybrid performance in maize, especially for grain yield. It is attractive to explore the underlying genetic architecture of hybrid performance and heterosis. Considering its complexity, different from former mapping method, we developed a series of linear mixed models incorporating multiple polygenic covariance structures to quantify the contribution of each genetic component (additive, dominance, additive-by-additive, additive-by-dominance, and dominance-by-dominance) to hybrid performance and midparent heterosis variation and to identify significant additive and non-additive (dominance and epistatic) quantitative trait loci (QTL). Here, we developed a North Carolina II population by crossing 339 recombinant inbred lines with two elite lines (Chang7-2 and Mo17), resulting in two populations of hybrids signed as Chang7-2 × recombinant inbred lines and Mo17 × recombinant inbred lines, respectively. The results of a path analysis showed that kernel number per row and hundred grain weight contributed the most to the variation of grain yield. The heritability of midparent heterosis for 10 investigated traits ranged from 0.27 to 0.81. For the 10 traits, 21 main (additive and dominance) QTL for hybrid performance and 17 dominance QTL for midparent heterosis were identified in the pooled hybrid populations with two overlapping QTL. Several of the identified QTL showed pleiotropic effects. Significant epistatic QTL were also identified and were shown to play an important role in ear height variation. Genomic selection was used to assess the influence of QTL on prediction accuracy and to explore the strategy of heterosis utilization in maize breeding. Results showed that treating significant single nucleotide polymorphisms as fixed effects in the linear mixed model could improve the prediction accuracy under prediction schemes 2 and 3. In conclusion, the different analyses all substantiated the different genetic architecture of hybrid performance and midparent heterosis in maize. Dominance contributes the highest proportion to heterosis, especially for grain yield, however, epistasis contributes the highest proportion to hybrid performance of grain yield.

Enhancing grain-yield-related traits by CRISPR-Cas9 promoter editing of maize CLE genes.

Several yield-related traits selected during crop domestication and improvement1,2 are associated with increases in meristem size3, which is controlled by CLE peptide signals in the CLAVATA-WUSCHEL pathway4-13. Here, we engineered quantitative variation for yield-related traits in maize by making weak promoter alleles of CLE genes, and a null allele of a newly identified partially redundant compensating CLE gene, using CRISPR-Cas9 genome editing. These strategies increased multiple maize grain-yield-related traits, supporting the enormous potential for genomic editing in crop enhancement.

QTL analysis across multiple environments reveals promising chromosome regions associated with yield-related traits in maize under drought conditions

Drought is one of the most critical abiotic stresses influencing maize yield. Improving maize cultivars with drought tolerance using marker-assisted selection requires a better understanding of its genetic basis. In this study, a doubled haploid (DH) population consisting of 217 lines was created by crossing the inbred lines Han 21 (drought-tolerant) and Ye 478 (drought-sensitive). The population was genotyped with a 6 K SNP assay and 756 SNP (single nucleotide polymorphism) markers were used to construct a linkage map with a length of 1344 cM. Grain yield (GY), ear setting percentage (ESP), and anthesis–silking interval (ASI) were recorded in seven environments under well-watered (WW) and water-stressed (WS) regimes. High phenotypic variation was observed for all traits under both water regimes. Using the LSMEAN (least-squares mean) values from all environments for each trait, 18 QTL were detected, with 9 associated with the WW and 9 with the WS regime. Four chromosome regions, Chr. 3: 219.8–223.7 Mb, Chr. 5: 191.5–194.7 Mb, Chr. 7: 132.2–135.6 Mb, and Chr. 10: 88.2–89.4 Mb, harbored at least 2 QTL in each region, and QTL co-located in a region inherited favorable alleles from the same parent. A set of 64 drought-tolerant BC3F6 lines showed preferential accumulation of the favorable alleles in these four regions, supporting an association between the four regions and maize drought tolerance. QTL-by-environment interaction analysis revealed 28 edQTL (environment-dependent QTL) associated with the WS regime and 22 associated with the WW regime for GY, ESP, and ASI. All WS QTL and 55.6% of WW QTL were located in the edQTL regions. The hotspot genomic regions identified in this work will support further fine mapping and marker-assisted breeding of drought-tolerant maize.

Genetic Dissection of the General Combining Ability of Yield-Related Traits in Maize.

Maize yield components including row number, kernel number per row, kernel thickness, kernel width, kernel length, 100-kernel weight, and volume weight affect grain yield directly. Previous studies mainly focused on dissecting the genetic basis of per se performances for yield-related traits, but the genetic basis of general combining ability (GCA) for these traits is still unclear. In the present study, 328 RILs were crossed as males to two testers according to the NCII mating design, resulting in a hybrid panel composed of 656 hybrids. Both the hybrids and parental lines were evaluated in four environments in 2015 and 2016. Correlation analysis showed the performances of GCA effects were significantly correlated to the per se performances of RILs for all yield-related traits (0.17 ≤ r ≤ 0.64, P > 0.01). Only 17 of 95 QTL could be detected for both per se performances of RILs and GCA effects for eight yield-related traits. The QTL qKN7-1 and qHKW1-3, which could explain more than 10% of the variation in the GCA effects of KN and HKW, were also detected for per se performances for the traits. The pleiotropic loci qRN3-1 and qRN6, which together explained 14.92% of the observed variation in GCA effects for RN, were associated with the GCA effects of KW and HKW, but not with per se performances for these traits. In contrast, Incw1, which was related to seed weight in maize, was mapped to the region surrounding MK2567 at the qHKW5-2 locus, but no GCA effect was detected. The QTL identified in present study for per se performances and corresponding GCA effects for yield-related traits might be useful for maize hybrid breeding.

A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments.

Using GWAS and QTL mapping identified 100 QTL and 138 SNPs, which control yield-related traits in maize. The candidate gene GRMZM2G098557 was further validated to regulate ear row number by using a segregation population. Understanding the genetic basis of yield-related traits contributes to the improvement of grain yield in maize. This study used an inter-mated B73 × Mo17 (IBM) Syn10 doubled-haploid (DH) population and an association panel to identify the genetic loci responsible for nine yield-related traits in maize. Using quantitative trait loci (QTL) mapping, 100 QTL influencing these traits were detected across different environments in the IBM Syn10 DH population, with 25 co-detected in multiple environments. Using a genome-wide association study (GWAS), 138 single-nucleotide polymorphisms (SNPs) were identified as correlated with these traits (P < 2.04E-06) in the association panel. Twenty-one pleiotropic QTL/SNPs were identified to control different traits in both populations. A combination of QTL mapping and GWAS uncovered eight significant SNPs (PZE-101097575, PZE-103169263, ZM011204-0763, PZE-104044017, PZE-104123110, SYN8062, PZE-108060911, and PZE-102043341) that were co-located within seven QTL confidence intervals. According to the eight co-localized SNPs by the two populations, 52 candidate genes were identified, among which the ear row number (ERN)-associated SNP SYN8062 was closely linked to SBP-transcription factor 7 (GRMZM2G098557). Several SBP-transcription factors were previously demonstrated to modulate maize ERN. We then validated the phenotypic effects of SYN8062 in the IBM Syn10 DH population, indicating that the ERN of the lines with the A-allele in SYN8062 was significantly (P < 0.05) larger than that of the lines with the G-allele in SYN8062 in each environment. These findings provide valuable information for understanding the genetic mechanisms of maize grain yield formation and for improving molecular marker-assisted selection for the high-yield breeding of maize.

Natural Variation and Domestication Selection of ZmPGP1 Affects Plant Architecture and Yield-Related Traits in Maize

ZmPGP1, involved in the polar auxin transport, has been shown to be associated with plant height, leaf angle, yield traits, and root development in maize. To explore natural variation and domestication selection of ZmPGP1, we re-sequenced the ZmPGP1 gene in 349 inbred lines, 68 landraces, and 32 teosintes. Sequence polymorphisms, nucleotide diversity, and neutral tests revealed that ZmPGP1 might be selected during domestication and improvement processes. Marker–trait association analysis in inbred lines identified 11 variants significantly associated with 4 plant architecture and 5 ear traits. SNP1473 was the most significant variant for kernel length and ear grain weight. The frequency of an increased allele T was 40.6% in teosintes, and it was enriched to 60.3% and 89.1% during maize domestication and improvement. This result revealed that ZmPGP1 may be selected in the domestication and improvement process, and significant variants could be used to develop functional markers to improve plant architecture and ear traits in maize.

Population structure and association mapping studies for yield-related traits in Maize (Zea mays L.)

Association mapping is an efficient approach for dissecting the genetic architecture of yield-related traits using single nucleotide polymorphism (SNP) markers in maize. The present study was carried out with a set of 93 diverse maize accessions with the objectives of analysing the population structure and conducting association mapping between SNP markers and yield-related traits viz. number of kernel rows per ear (NKR), number of kernel columns per ear (NKC), 100 grain weight (HGW) and grain yield (GY). Illumina golden gate assay containing 1536 SNPs distributed in all the chromosomes were used for genotyping 93 maize accessions. The population structure studies depicted the presence of five subpopulations, named ZM1, ZM2, ZM3, ZM4 and ZM5, contained 21, 16, 19, 12 and 25 accessions, respectively. Association mapping analysis carried out using mixed linear model (MLM), which is identified 36 marker-trait associations (MTAs) in the two years. Of these, 26 MTAs were detected either nearby or flanking the regions where the genomic regions have been reported. While, remaining10 MTAs were detected in the novel genomic region. The number of markers associated with each trait ranged from 6 (NKC) to 13 (HGW). We also found that one trait was associated with many markers (e.g., HGW with 13 markers), and single marker was also associated with multiple traits (eg. PZB00114_1 associated with HGW and GY). Our results provide good information for genetic architecture of yield-related traits and associated SNP markers could be valuable in implementing marker assisted selection (MAS) in maize breeding programs.

Assessment of combining ability effects using quality protein maize donors as testers for yield and yield traits in maize

The present study was accomplished to assess the general combining ability effects of parents and specific combining ability effects of hybrids for yield and yield related traits in maize. Sixty three F1 hybrids were developed and by crossing nine productive maize inbred lines with seven quality protein maize inbred testers in Line x Tester mating design and evaluated for twelve yield and yield related characters at CCSHAU, Regional Research Station, Karnal, Haryana. The combining ability analysis revealed the presence of higher magnitude of SCA than GCA variance for all characters under study except for number of grains per cob. The preponderance ratio of sca/gca variance revealed presence of non additive gene action in the expression of all the characters under study. Among the sixteen parents inbred lines viz., HKI 1040-4, HKI 323, HKI 161 and HKI 163 were found to be the best parent for grain yield and among the hybrids, HKI 1128 x HKI 163, HKI 659-3 x HKI 194-6, HKI 1126 x HKI 161, HKI 536YN x HKI 193-1 and HKI 488 x HKI 170(1+2) exhibited highest significant and favourable sca effects for yield and yield attributing characters.

Candidate Loci for Yield-Related Traits in Maize Revealed by a Combination of MetaQTL Analysis and Regional Association Mapping.

Maize grain yield and related traits are complex and are controlled by a large number of genes of small effect or quantitative trait loci (QTL). Over the years, a large number of yield-related QTLs have been identified in maize and deposited in public databases. However, integrating and re-analyzing these data and mining candidate loci for yield-related traits has become a major issue in maize. In this study, we collected information on QTLs conferring maize yield-related traits from 33 published studies. Then, 999 of these QTLs were iteratively projected and subjected to meta-analysis to obtain metaQTLs (MQTLs). A total of 76 MQTLs were found across the maize genome. Based on a comparative genomics strategy, several maize orthologs of rice yield-related genes were identified in these MQTL regions. Furthermore, three potential candidate genes (Gene ID: GRMZM2G359974, GRMZM2G301884, and GRMZM2G083894) associated with kernel size and weight within three MQTL regions were identified using regional association mapping, based on the results of the meta-analysis. This strategy, combining MQTL analysis and regional association mapping, is helpful for functional marker development and rapid identification of candidate genes or loci.

Inheritance of yield and yield-related traits in highland maize hybrids of Uganda

Although many studies have been conducted on gene action of grain yield and yield related traits in maize, none of them focused on highland maize in Uganda. This study was conducted to establish the gene action controlling inheritance of yield and its related traits in highland maize hybrids. Thirty-six F1 hybrids generated from a 9 x 9 half diallel mating design, were planted with two local checks in three highland locations; Kalengyere, Kachwekano, and Buginyanya with two replications using a 2 x 19 alpha (0, 1) lattice design. Results showed that inheritance of ear length and anthesis-silking interval was controlled by both additive and non-additive gene action while the inheritance of days to anthesis, days to silking was mainly controlled by additive gene action. The inheritance of grain yield and other yield related traits was greatly influenced by environment and genotype x environment interaction. Considering the great influence of the environment and genotype x environment interaction on most of the traits including grain yield, further testing in additional locations over more seasons and broadening the genetic base of the parents is encouraged.

ZmWAK, a quantitative resistance gene to head smut in maize, improves yield performance by reducing the endophytic pathogen Sporisorium reiliana

Head smut is a soil-borne systemic maize disease caused by the causal pathogen Sporisorium reiliana. ZmWAK is the gene within the major QTL qHSR1 conferring resistance to head smut disease. ZmWAK has been transferred by marker-assisted backcrossing into elite inbred lines with high-yield potential but susceptible to head smut. The converted inbred lines, together with their hybrids, were grown in 2012 and 2013 to investigate resistance performance, yield per plot, and yield-related traits. ZmWAK-mediated resistance to maize head smut follows a dominant genetic model, and a single ZmWAK allele could achieve high disease resistance to prevent yield loss from S. reiliana infection. Genetically, ZmWAK itself had no detectable negative impacts on yield-related traits. Instead, ZmWAK could indirectly improve most yield components, particularly ear-related traits, in symptomless plants by reducing the amount of endophytic S. reiliana. Overall, ZmWAK is beneficial to both head smut resistance and yield-related traits in maize.

Cloning of the Homologs of &amp;lt;italic&amp;gt;OsAPO1&amp;lt;/italic&amp;gt; and Their Association with Yield-Related Traits in Maize

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.

Identification of combining ability loci for five yield-related traits in maize using a set of testcrosses with introgression lines

Combining ability is essential for hybrid breeding in crops. However, the genetic basis of combining ability remains unclear and has been seldom investigated. Identifying molecular markers associated with this complex trait would help to understand its genetic basis and provide useful information for hybrid breeding in maize. In this study, we identified genetic loci of general combining ability (GCA) and specific combining ability (SCA) for five yield-related traits under three environments using a set of testcrosses with introgression lines (ILs). GCA or SCA of the five yield-related traits of the ILs was estimated by the performance of testcrosses with four testers from different heterotic groups. Genetic correlations between GCA of the traits and the corresponding traits per se were not significant or not strong, suggesting that the genetic basis between them is different. A total of 56 significant loci for GCA and 21 loci for SCA were commonly identified in at least two environments, and only 5 loci were simultaneously controlling GCA and SCA, indicating that the genetic basis of GCA and SCA is different. For all of the traits investigated, positive and significant correlations between the number of GCA loci in the ILs and the performance of the corresponding GCA of the ILs were detected, implying that pyramiding GCA loci would have positive effect on the performance of GCA. Results in this study would be useful for maize hybrid breeding.