Fine Mapping of a QTL Associated with Kernel Row Number on Chromosome 1 of Maize

Pilot-scale genome-wide association mapping in diverse sorghum germplasms identified novel genetic loci linked to major agronomic, root and stomatal traits

This genome-wide association studies (GWAS) used a subset of 96 diverse sorghum accessions, constructed from a large collection of 219 accessions for mining novel genetic loci linked to major agronomic, root morphological and physiological traits. The subset yielded 43,452 high quality single nucleotide polymorphic (SNP) markers exhibiting high allelic diversity. Population stratification showed distinct separation between caudatum and durra races. Linkage disequilibrium (LD) decay was rapidly declining with increasing physical distance across all chromosomes. The initial 50% LD decay was ~ 5 Kb and background level was within ~ 80 Kb. This study detected 42 significant quantitative trait nucleotide (QTNs) for different traits evaluated using FarmCPU, SUPER and 3VmrMLM which were in proximity with candidate genes related and were co-localized in already reported quantitative trait loci (QTL) and phenotypic variance (R2) of these QTNs ranged from 3 to 20%. Haplotype validation of the candidate genes from this study resulted nine genes showing significant phenotypic difference between different haplotypes. Three novel candidate genes associated with agronomic traits were validated including Sobic.001G499000, a potassium channel tetramerization domain protein for plant height, Sobic.010G186600, a nucleoporin-related gene for dry biomass, and Sobic.002G022600 encoding AP2-like ethylene-responsive transcription factor for plant yield. Several other candidate genes were validated and associated with different root and physiological traits including Sobic.005G104100, peroxidase 13-related gene with root length, Sobic.010G043300, homologous to Traes_5BL_8D494D60C, encoding inhibitor of apoptosis with iWUE, and Sobic.010G125500, encoding zinc finger, C3HC4 type domain with Abaxial stomatal density. In this study, 3VmrMLM was more powerful than FarmCPU and SUPER for detecting QTNs and having more breeding value indicating its reliable output for validation. This study justified that the constructed subset of diverse sorghums can be used as a panel for mapping other key traits to accelerate molecular breeding in sorghum.

The Genetic Structures and Molecular Mechanisms Underlying Ear Traits in Maize (Zea mays L.)

Maize (Zea mays L.) is one of the world's staple food crops. In order to feed the growing world population, improving maize yield is a top priority for breeding programs. Ear traits are important determinants of maize yield, and are mostly quantitatively inherited. To date, many studies relating to the genetic and molecular dissection of ear traits have been performed; therefore, we explored the genetic loci of the ear traits that were previously discovered in the genome-wide association study (GWAS) and quantitative trait locus (QTL) mapping studies, and refined 153 QTL and 85 quantitative trait nucleotide (QTN) clusters. Next, we shortlisted 19 common intervals (CIs) that can be detected simultaneously by both QTL mapping and GWAS, and 40 CIs that have pleiotropic effects on ear traits. Further, we predicted the best possible candidate genes from 71 QTL and 25 QTN clusters that could be valuable for maize yield improvement.

Designed and validated novel allele-specific primer to differentiate Kernel Row Number (KRN) in tropical field corn

BACKGROUND

Kernel row number (KRN) is an important yield component trait with a direct impact on the productivity of maize. The variability in KRN is influenced by the inflorescence meristem size, which is determined by the CLAVATA-WUSCHEL pathway. A CLAVATA receptor-like protein, encoded by the FASCIATED EAR2 (fea2gene), enhances the growth of inflorescence meristem and is thus involved in the determination of KRN. The amplicon sequencing-based method was employed to dissect the allelic variation of the fea2 gene in tropical field corn.

METHODOLOGY/PRINCIPAL FINDING

Amplicon-based sequencing of AI 535 (Low KRN) and AI 536 (High KRN) was undertaken for the gene fea 2 gene that codes for KRN in maize. Upon multiple sequence alignment of both sequences, A to T transversion at the 1311 position was noticed between Low KRN and High KRN genotypes resulting in different allelic forms of a fea2 gene in tropical maize. An allele-specific primer 1311 fea2.1 was designed and validated that can differentiate High and Low KRN genotypes.

CONCLUSION/SIGNIFICANCE

Maize has high variability for KRN and is exemplified by the wide values ranging from 8-26 KRN in the maize germpalsm. The sequence-based approach of SNP detection through the use of a specific primer facilitated the detection of variation present in the target trait. This makes it possible to capture these variations in the early generation. In the study, the PCR-based differentiation method described for the identification of desirable high KRN genotypes would augment the breeding programs for improving the productivity of field corn.

Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population

BACKGROUND

Maize kernel row number (KRN) is one of the most important yield traits and has changed greatly during maize domestication and selection. Elucidating the genetic basis of KRN will be helpful to improve grain yield in maize.

RESULTS

Here, we measured KRN in four environments using a nested association mapping (NAM) population named HNAU-NAM1 with 1,617 recombinant inbred lines (RILs) that were derived from 12 maize inbred lines with a common parent, GEMS41. Then, five consensus quantitative trait loci (QTLs) distributing on four chromosomes were identified in at least three environments along with the best linear unbiased prediction (BLUP) values by the joint linkage mapping (JLM) method. These QTLs were further validated by the separate linkage mapping (SLM) and genome-wide association study (GWAS) methods. Three KRN genes cloned through the QTL assay were found in three of the five consensus QTLs, including qKRN1.1, qKRN2.1 and qKRN4.1. Two new QTLs of KRN, qKRN4.2 and qKRN9.1, were also identified. On the basis of public RNA-seq and genome annotation data, five genes highly expressed in ear tissue were considered candidate genes contributing to KRN.

CONCLUSIONS

This study carried out a comprehensive analysis of the genetic architecture of KRN by using a new NAM population under multiple environments. The present results provide solid information for understanding the genetic components underlying KRN and candidate genes in qKRN4.2 and qKRN9.1. Single-nucleotide polymorphisms (SNPs) closely linked to qKRN4.2 and qKRN9.1 could be used to improve inbred yield during molecular breeding in maize.

Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize

Grain size, grain number per panicle, and grain weight are crucial determinants of yield-related traits in cereals. Understanding the genetic basis of grain yield-related traits has been the main research object and nodal in crop science. Sorghum and maize, as very close C4 crops with high photosynthetic rates, stress tolerance and large biomass characteristics, are extensively used to produce food, feed, and biofuels worldwide. In this review, we comprehensively summarize a large number of quantitative trait loci (QTLs) associated with grain yield in sorghum and maize. We placed great emphasis on discussing 22 fine-mapped QTLs and 30 functionally characterized genes, which greatly hinders our deep understanding at the molecular mechanism level. This review provides a general overview of the comprehensive findings on grain yield QTLs and discusses the emerging trend in molecular marker-assisted breeding with these QTLs.

The arches and spandrels of maize domestication, adaptation, and improvement

People living in the Balsas River basin in southwest México domesticated maize from the bushy grass teosinte. Nine thousand years later, in 2021, Ms. Deb Haaland - a member of the Pueblo of Laguna tribe of New Mexico - wore a dress adorned with a cornstalk when she was sworn in as the Secretary of Interior of the United States of America. This choice of garment highlights the importance of the coevolution of maize and the farmers who, through careful selection over thousands of years, domesticated maize and adapted the physiology and shoot architecture of maize to fit local environments and growth habits. Some traits such as tillering were directly selected on (arches), and others such as tassel size are the by-products (spandrels) of maize evolution. Here, we review current knowledge of the underlying cellular, developmental, physiological, and metabolic processes that were selected by farmers and breeders, which have positioned maize as a top global staple crop.

Harnessing Knowledge from Maize and Rice Domestication for New Crop Breeding

Crop domestication has fundamentally altered the course of human history, causing a shift from hunter-gatherer to agricultural societies and stimulating the rise of modern civilization. A greater understanding of crop domestication would provide a theoretical basis for how we could improve current crops and develop new crops to deal with environmental challenges in a sustainable manner. Here, we provide a comprehensive summary of the similarities and differences in the domestication processes of maize and rice, two major staple food crops that feed the world. We propose that maize and rice might have evolved distinct genetic solutions toward domestication. Maize and rice domestication appears to be associated with distinct regulatory and evolutionary mechanisms. Rice domestication tended to select de novo, loss-of-function, coding variation, while maize domestication more frequently favored standing, gain-of-function, regulatory variation. At the gene network level, distinct genetic paths were used to acquire convergent phenotypes in maize and rice domestication, during which different central genes were utilized, orthologous genes played different evolutionary roles, and unique genes or regulatory modules were acquired for establishing new traits. Finally, we discuss how the knowledge gained from past domestication processes, together with emerging technologies, could be exploited to improve modern crop breeding and domesticate new crops to meet increasing human demands.

Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture

Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.

Identification of a candidate gene underlying qKRN5b for kernel row number in Zea mays L

A quantitative trait locus for kernel row number, qKRN5, was dissected into two tightly linked loci, qKRN5a and qKRN5b. Fine mapping, comparative analysis of nucleotide sequences and gene expression established the endonuclease/exonuclease/phosphatase family protein-encoding gene Zm00001d013603 as a causal gene of qKRN5b. Maize grain yield is determined by agronomically important traits that are controlled by interactions among and between genes and environmental factors. Considerable efforts have been made to identify major quantitative trait loci (QTLs) for yield-related traits; however, few were previously isolated and characterized in maize. In this study, we divided a QTL for kernel row number (KRN), qKRN5, into two tightly linked loci, qKRN5a and qKRN5b, using advanced backcross populations derived from near-isogenic lines. KRN was greater in individuals that were homozygous for the NX531 allele, which showed coupling-phase linkage. The major QTL qKRN5b had an additive effect of approximately one kernel row. Furthermore, fine mapping narrowed qKRN5b within a 147.2-kb region. The upstream sequence Zm00001d013603 and its expression in the ear inflorescence showed obvious differences between qKRN5b near-isogenic lines. In situ hybridization located Zm00001d013603 on the primordia of the spikelet pair meristems and spikelet meristems, but not in the inflorescence meristem, which indicates a role in regulating the initiation of reproductive axillary meristems of ear inflorescences. Expression analysis and nucleotide sequence alignment revealed that Zm00001d013603, which encodes an endonuclease/exonuclease/phosphatase family protein that hydrolyzes phosphatidyl inositol diphosphates, is the causal gene of qKRN5b. These results provide insight into the genetic basis of KRN and have potential value for enhancing maize grain yield.

TeoNAM: A Nested Association Mapping Population for Domestication and Agronomic Trait Analysis in Maize

Recombinant inbred lines (RILs) are an important resource for mapping genes controlling complex traits in many species. While RIL populations have been developed for maize, a maize RIL population with multiple teosinte inbred lines as parents has been lacking. Here, we report a teosinte nested association mapping (TeoNAM) population, derived from crossing five teosinte inbreds to the maize inbred line W22. The resulting 1257 BC1S4 RILs were genotyped with 51,544 SNPs, providing a high-density genetic map with a length of 1540 cM. On average, each RIL is 15% homozygous teosinte and 8% heterozygous. We performed joint linkage mapping (JLM) and a genome-wide association study (GWAS) for 22 domestication and agronomic traits. A total of 255 QTL from JLM were identified, with many of these mapping near known genes or novel candidate genes. TeoNAM is a useful resource for QTL mapping for the discovery of novel allelic variation from teosinte. TeoNAM provides the first report that PROSTRATE GROWTH1, a rice domestication gene, is also a QTL associated with tillering in teosinte and maize. We detected multiple QTL for flowering time and other traits for which the teosinte allele contributes to a more maize-like phenotype. Such QTL could be valuable in maize improvement.

krn1, a major quantitative trait locus for kernel row number in maize

Kernel row number is a fundamental component of maize (Zea mays) yield and an important target for maize breeding. The revolutionary transition from the two-rowed teosinte to maize with increased kernel row numbers dramatically enhanced yields during domestication. Kernel row number is controlled by many quantitative trait loci (QTLs), however most genes responsible for these QTLs remain uncharacterised and the molecular genetic mechanisms are unknown. Here, we combined map-based cloning and association mapping to identify a major QTL for kernel row number, krn1, which is likely to correspond to an existing gene (ids1/Ts6) encoding an AP2 domain protein homologous to the product of the wheat key domestication gene Q. The increased expression of ids1/Ts6 in two maize mutants increased spikelet pair meristem numbers and then enhanced kernel row numbers. Nucleotide diversity analysis further revealed that ids1/Ts6 and Q were under strong parallel selection in maize and wheat that increased their yields during domestication or improvement. RNA-seq revealed that ids1/Ts6 is involved in multiple pathways regulating spikelet pair meristem development, involving several key genes such as fea3, fea4 and ra3. The cloning of the krn1 gene will pave a new way to efficiently improve maize yield in the near future.

β-composite Interval Mapping for robust QTL analysis

Interval mapping approaches have been playing significant role for quantitative trait locus (QTL) mapping to discover genetic architecture of diseases or traits with molecular markers. Composite interval mapping (CIM) is one of the superior approaches of the interval mapping for discovering both linked and unlinked putative QTL positions. However, estimators of this approach are not robust against phenotypic outliers. As a result, it fails to detect true QTL positions in presence of outliers. In this study, we investigated the performance of β-Composite Interval Mapping (BetaCIM) for detecting both linked and unlinked important QTLs positions from the robustness points of views. Performance of this approach depends on the value of tuning parameter β. It reduces to the classical CIM approach for β →0. We described and formulated the cross-validation procedure for selecting trait specific optimum value of β. It was observed that the optimum value of β depends on both amount of contaminated observations and their scatteredness. BetaCIM approach discover similar QTL positions as classical IM/CIM in absence of phenotypic outliers, but gives better results in presence of phenotypic outliers in terms of detecting true QTLs and effects estimation. We formulated the generalized forms of robust QTL analysis and developed an R-package named "BetaCIM" by implementing this robust approach. Left and right kidney weight data sets of mouse intercross population (129 S1/SvlmJ × A/J) were analyzed by using BetaCIM, CIM, and IM approaches. For right kidney weight (RKW) CIM and BetaCIM provided similar LOD score profile, and both approaches identified 3 QTL positions. IM approach also identified 3 QTL positions. For left kidney weight (LKW), there was evidence of one outlying observation; and in this case the BetaCIM approach identified 2 QTL positions. However, none of the QTLs were significant by CIM and IM approaches at 5% level of significance. Gene expression ontology (GEO) search showed that the candidate genes (Otof and A330033J07Rik) of the identified QTLs for LKW were expressed in kidney. Both simulation and real data analysis results showed that BetaCIM approach improves the performance over the existing methods in presence of phenotypic outliers. Otherwise, it keeps almost equal performance.

QTL mapping in a maize F2 population using Genotyping-by-Sequencing and a modified fine-mapping strategy

Maize possesses a tremendous genetic diversity, most of which is deposited in a large number of landraces. However, the genetic basis of local diversity from maize landrace remains largely unknown. Traditional fine mapping of the causal gene for complex trait, based on the transition from the quantitative trait locus (QTL) to a single qualitative gene through backcrossing or the construction of heterogeneous inbred family (HIF), has achieved great success in model crop rice. However, this fine-mapping strategy did not work well in maize. In this study, an F₂ population derived from a maize landrace and an elite inbred line was firstly genotyped by genotyping by sequencing (GBS). QTL analysis further revealed 29 QTLs of 12 traits, most of which individually accounted for more than 10% of phenotypic variations. Next traditional fine-mapping method successfully narrowed down a major QTL of kernel color qCOK1, but failed in the fine mapping of another major QTL of ear height EH4 because the EH4 remained quantitative feature in the HIF. Based on the quantitative feature of the EH4 in the HIF, we then performed the correlation tests between genotypes and phenotypes in the descendant populations derived from the recombination plants to enable the process of fine mapping. Our modified fine-mapping strategy successfully narrowed down the EH4 into a 3.23-Mb region on chromosome 8. The modified fine-mapping method can be applied to fast clone the QTLs originated from maize landraces.

Genetic and Molecular Mechanisms of Quantitative Trait Loci Controlling Maize Inflorescence Architecture

The establishment of inflorescence architecture is critical for the reproduction of flowering plant species. The maize plant generates two types of inflorescences, the tassel and the ear, and their architectures have a large effect on grain yield and yield-related traits that are genetically controlled by quantitative trait loci (QTLs). Since ear and tassel architecture are deeply affected by the activity of inflorescence meristems, key QTLs and genes regulating meristematic activity have important impacts on inflorescence development and show great potential for optimizing grain yield. Isolation of yield trait-related QTLs is challenging, but these QTLs have direct application in maize breeding. Additionally, characterization and functional dissection of QTLs can provide genetic and molecular knowledge of quantitative variation in inflorescence architecture. In this review, we summarize currently identified QTLs responsible for the establishment of ear and tassel architecture and discuss the potential genetic control of four ear-related and four tassel-related traits. In recent years, several inflorescence architecture-related QTLs have been characterized at the gene level. We review the mechanisms of these characterized QTLs.

Genotyping-by-sequencing highlights original diversity patterns within a European collection of 1191 maize flint lines, as compared to the maize USDA genebank

Genotyping by sequencing is suitable for analysis of global diversity in maize. We showed the distinctiveness of flint maize inbred lines of interest to enrich the diversity of breeding programs. Genotyping-by-sequencing (GBS) is a highly cost-effective procedure that permits the analysis of large collections of inbred lines. We used it to characterize diversity in 1191 maize flint inbred lines from the INRA collection, the European Cornfed association panel, and lines recently derived from landraces. We analyzed the properties of GBS data obtained with different imputation methods, through comparison with a 50 K SNP array. We identified seven ancestral groups within the Flint collection (dent, Northern flint, Italy, Pyrenees-Galicia, Argentina, Lacaune, Popcorn) in agreement with breeding knowledge. Analysis highlighted many crosses between different origins and the improvement of flint germplasm with dent germplasm. We performed association studies on different agronomic traits, revealing SNPs associated with cob color, kernel color, and male flowering time variation. We compared the diversity of both our collection and the USDA collection which has been previously analyzed by GBS. The population structure of the 4001 inbred lines confirmed the influence of the historical inbred lines (B73, A632, Oh43, Mo17, W182E, PH207, and Wf9) within the dent group. It showed distinctly different tropical and popcorn groups, a sweet-Northern flint group and a flint group sub-structured in Italian and European flint (Pyrenees-Galicia and Lacaune) groups. Interestingly, we identified several selective sweeps between dent, flint, and tropical inbred lines that co-localized with SNPs associated with flowering time variation. The joint analysis of collections by GBS offers opportunities for a global diversity analysis of maize inbred lines.

Genetic architecture of the maize kernel row number revealed by combining QTL mapping using a high-density genetic map and bulked segregant RNA sequencing

BACKGROUND

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.

RESULTS

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.

CONCLUSIONS

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.