Construction of genetic linkage map and identification of QTLs related to agronomic traits in DH population of maize (Zea mays L.) using SSR markers

GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize

Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield.

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.

The Era of Plant Breeding: Conventional Breeding to Genomics-assisted Breeding for Crop Improvement

Plant breeding has made a significant contribution to increasing agricultural production. Conventional breeding based on phenotypic selection is not effective for crop improvement. Because phenotype is considerably influenced by environmental factors, which will affect the selection of breeding materials for crop improvement. The past two decades have seen tremendous progress in plant breeding research. Especially the availability of high-throughput molecular markers followed by genomic-assisted approaches significantly contributed to advancing plant breeding. Integration of speed breeding with genomic and phenomic facilities allowed rapid quantitative trait loci (QTL)/gene identifications and ultimately accelerated crop improvement programs. The advances in sequencing technology helps to understand the genome organization of many crops and helped with genomic selection in crop breeding. Plant breeding has gradually changed from phenotype-to-genotype-based to genotype-to-phenotype-based selection. High-throughput phenomic platforms have played a significant role in the modern breeding program and are considered an essential part of precision breeding. In this review, we discuss the rapid advance in plant breeding technology for efficient crop improvements and provide details on various approaches/platforms that are helpful for crop improvement. This review will help researchers understand the recent developments in crop breeding and improvements.

QTL Mapping and a Transcriptome Integrative Analysis Uncover the Candidate Genes That Control the Cold Tolerance of Maize Introgression Lines at the Seedling Stage

Chilling injury owing to low temperatures severely affects the growth and development of maize (Zea mays.L) seedlings during the early and late spring seasons. The existing maize germplasm is deficient in the resources required to improve maize's ability to tolerate cold injury. Therefore, it is crucial to introduce and identify excellent gene/QTLs that confer cold tolerance to maize for sustainable crop production. Wild relatives of maize, such as Z. perennis and Tripsacum dactyloides, are strongly tolerant to cold and can be used to improve the cold tolerance of maize. In a previous study, a genetic bridge among maize that utilized Z. perennis and T. dactyloides was created and used to obtain a highly cold-tolerant maize introgression line (MIL)-IB030 by backcross breeding. In this study, two candidate genes that control relative electrical conductivity were located on MIL-IB030 by forward genetics combined with a weighted gene co-expression network analysis. The results of the phenotypic, genotypic, gene expression, and functional verification suggest that two candidate genes positively regulate cold tolerance in MIL-IB030 and could be used to improve the cold tolerance of cultivated maize. This study provides a workable route to introduce and mine excellent genes/QTLs to improve the cold tolerance of maize and also lays a theoretical and practical foundation to improve cultivated maize against low-temperature stress.

QTL and Candidate Genes: Techniques and Advancement in Abiotic Stress Resistance Breeding of Major Cereals

At least 75% of the world's grain production comes from the three most important cereal crops: rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays). However, abiotic stressors such as heavy metal toxicity, salinity, low temperatures, and drought are all significant hazards to the growth and development of these grains. Quantitative trait locus (QTL) discovery and mapping have enhanced agricultural production and output by enabling plant breeders to better comprehend abiotic stress tolerance processes in cereals. Molecular markers and stable QTL are important for molecular breeding and candidate gene discovery, which may be utilized in transgenic or molecular introgression. Researchers can now study synteny between rice, maize, and wheat to gain a better understanding of the relationships between the QTL or genes that are important for a particular stress adaptation and phenotypic improvement in these cereals from analyzing reports on QTL and candidate genes. An overview of constitutive QTL, adaptive QTL, and significant stable multi-environment and multi-trait QTL is provided in this article as a solid framework for use and knowledge in genetic enhancement. Several QTL, such as DRO1 and Saltol, and other significant success cases are discussed in this review. We have highlighted techniques and advancements for abiotic stress tolerance breeding programs in cereals, the challenges encountered in introgressing beneficial QTL using traditional breeding techniques such as mutation breeding and marker-assisted selection (MAS), and the in roads made by new breeding methods such as genome-wide association studies (GWASs), the clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system, and meta-QTL (MQTL) analysis. A combination of these conventional and modern breeding approaches can be used to apply the QTL and candidate gene information in genetic improvement of cereals against abiotic stresses.

Genetic variation of seed oil characteristics in native Korean germplasm of Perilla crop (Perilla frutescens L.) using SSR markers

BACKGROUND

In order to maximize the use of valuable native Perilla germplasm in South Korea, knowledge of the Perilla seed oil content and genetic variation among native Perilla germplasm resources is very important for the conservation and development of new Perilla seed oil varieties using the native Perilla germplasm accessions preserved from the Rural Development Administration Genebank (RDA-Genebank) collection from South Korea.

OBJECTIVES

In this study, we studied population structure and association mapping to identify Perilla SSR markers (PSMs) associated with the five fatty acid contents and two seed characteristics of the native Korean Perilla germplasm accessions of cultivated var. frutescens of the RDA-Genebank collected in South Korea.

METHODS

For an association mapping analysis to find PSMs associated with the five fatty acid contents and two seed characteristics of the Perilla germplasm accessions of cultivated var. frutescens, we evaluated the content of five fatty acids of 280 native Korean Perilla germplasm accessions and used 29 Perilla SSR primer sets to measure the genetic diversity and relationships, population structure, and association mapping of the native Korean Perilla germplasm accessions of the RDA-Genebank collected in South Korea.

RESULTS

Five fatty acids of 280 native Korean Perilla accessions were identified as follows: palmitic acid (PA) (5.30-8.66%), stearic acid (SA) (1.60-4.19%), oleic acid (OA) (9.60-22.5%), linoleic acid (LA) (8.38-25.4%), and linolenic acid (LNA) (52.7-76.4%). In a correlation analysis among the five fatty acids and two seed characteristics of the 280 Perilla accessions, the combinations of PA and SA (0.794**) and SA and OA (0.724**) showed a particularly high positive correlation coefficients compare to other combinations. By using an association analysis of the 29 PSMs and the five fatty acids in the 280 Perilla accessions, we found 17 PSMs (KNUPF1, KNUPF2, KNUPF4, KNUPF10, KNUPF16, KNUPF25, KNUPF26, KNUPF28, KNUPF37, KNUPF55, KNUPF62, KNUPF71, KNUPF74, KNUPF77, KNUPF85, KNUPF89, and KNUPF118) associated with the content of the five fatty acid components and two seed characteristics.

CONCLUSIONS

These PSMs are considered to be useful molecular markers related to five fatty acid components and two seed characteristics for selecting accessions from the germplasm accessions of the Perilla crop and their related weedy types through association mapping analysis and marker-assisted selection (MAS) breeding programs.

Mapping of QTL for agronomic traits using high-density SNPs with an RIL population in maize

BACKGROUND

Genome wide association studies (GWAS) have been widely used to identify QTLs underlying quantitative traits in humans and animals, and they have also become a popular method of mapping QTLs in many crops, including maize. Advances in high-throughput genotyping technologies enable construction of high-density linkage maps using SNP markers.

OBJECTIVES

High-density genetic mapping must precede to find molecular markers associated with a particular trait. The objectives of this study were to (1) construct a high-density linkage map using SNP markers and (2) detect the QTLs for grain yield and quality related traits of the Mo17/KW7 RIL population.

METHODS

In this study, two parental lines, Mo17 (normal maize inbred line) and KW7 (waxy inbred line) and 80 F_7:8 lines in the Mo17/KW7 RIL population were genotyped using the MaizeSNP50 BeadChip, an Illumina BeadChip array of 56,110 maize SNPs. Marker integration and detection of QTLs was performed using the inclusive composite interval mapping (ICIM) method within the QTL IciMapping software.

RESULTS

This study was genotyped using the Illumina MaizeSNP50 BeadChip for maize Mo17/KW7 recombinant inbred line (RIL) population. The 2904 SNP markers were distributed along all 10 maize chromosomes. The total length of the linkage map was 3553.7 cm, with an average interval of 1.22 cm between SNPs. A total of 18 QTLs controlling eight traits were detected in the Mo17/KW7 RIL population. Three QTLs for plant height (PH) were detected on chromosomes 4 and 8 and showed from 16.01% (qPH8) to 19.85% (qPH4a) of phenotypic variance. Five QTLs related to ear height (EH) were identified on chromosomes 3, 4, and 6 and accounted for 3.79% (qEH6) to 27.57% (qEH4b) of phenotypic variance. Five QTLs related to water content (WC) on chromosomes 1, 4, 8, and 9 accounted for 9.55% (qWC8b) to 23.30% (qWC4) of phenotypic variance. One QTL (qAC9) relating to amylose content (AC) on chromosome 9 showed 82.10% of phenotypic variance.

CONCLUSIONS

The high-density linkage map and putative QTLs of the maize RIL population detected in this study can be effectively utilized in waxy and normal maize breeding programs to facilitate the selection process through marker-assisted selection (MAS) breeding programs.

Identifying SSR Markers Related to Seed Fatty Acid Content in Perilla Crop (Perilla frutescens L.)

Perilla seed oil has been attracting attention in South Korea as a health food. Five fatty acids of 100 Perilla accessions were identified as follows: palmitic acid (PA) (5.10-9.13%), stearic acid (SA) (1.70-3.99%), oleic acid (OA) (11.1-21.9%), linoleic acid (LA) (10.2-23.4%), and linolenic acid (LNA) (54.3-75.4%). Additionally, the 100 Perilla accessions were divided into two groups (high or low) based on the total fatty acid content (TFAC). By using an association analysis of 40 simple sequence repeat (SSR) markers and the six Perilla seed oil traits in the 100 Perilla accessions, we detected four SSR markers associated with TFAC, five SSR markers associated with LNA, one SSR marker associated with LA, two SSR markers each associated with OA and PA, and four SSR markers associated with SA. Among these SSR markers, four SSR markers (KNUPF14, KNUPF62, KNUPF72, KNUPF85) were all associated with TFAC and LNA. Moreover, two SSR markers (KNUPF62, KNUPF85) were both associated with TFAC, LNA, and OA. Therefore, these SSR markers are considered to be useful molecular markers for selecting useful accessions related to fatty acid contents in Perilla germplasm and for improving the seed oil quality of Perilla crop through marker-assisted selection (MAS) breeding programs.

Discrimination of corn variety using Terahertz spectroscopy combined with chemometrics methods

High-oil corn is a high-quality variety of corn possessing higher oil content with greater caloric energy than normal corn. Hence, controlling the purity and authenticity of high-oil corn is of great importance in current crop research. The aim of this study is to develop a novel method for corn variety discrimination using Terahertz (THz) spectroscopy and signal classification analysis. In brief, the method involves feature extraction and variable selection of raw signals from Terahertz time-domain waveforms (THz-TDW) and absorption spectrum (THz-AS), and the use of classifiers on those treated signals to establish the discrimination models. Principle component analysis (PCA) were used for feature extraction with THz-TDW, while three different methods of variable selection were implemented with THz-AS, including uninformative variables elimination (UVE), uninformative variables elimination-successive projections algorithm (UVE-SPA) and competitive adaptive reweighted sampling (CARS). Then, two classification algorithms, Linear discriminant analysis (LDA) and support vector machine (SVM), were employed and compared in the discrimination models. Bootstrapped Latin partitions (BLP) method with 10 bootstraps and 5 Latin-partitions was applied to validate these models. Our modeling results suggest SVM as the better classification algorithm achieving higher identifying accuracy, such that the PCA-SVM model for THz-TDW has achieved 94.7% accuracy. The results also indicate variable selection as an important step to create an accurate and robust discrimination model for THZ-AS. The CARS-SVM model with radial basic function (RBF) has achieved 100% average accuracy in prediction set, while the UVE-SVM and UVE-SPA-SVM have achieved 91.2% and 99.1% accuracy, respectively. These results demonstrate that high-oil corn and normal corn can be identified successfully by using THz spectroscopy with discriminant analysis, suggesting our techniques to provide an efficient and practical reference for classifying crop varieties in agriculture research, while expanding the application of THz spectroscopy in the related field.

Genetic characterization and association mapping in near-isogenic lines of waxy maize using seed characteristics and SSR markers

BACKGROUND

Association mapping has been advocated as the method of choice for identifying loci involved in the inheritance of complex traits in crop species. This method involves identifying markers with significant differences in allele frequency between individuals with a phenotype of interest and a set of unrelated control individuals.

OBJECTIVE

The purpose of our study is not only to investigate the genetic diversity and relationships of the basic molecular markers of near-isogenic lines (NILs) of waxy maize, but it is also to identify molecular markers related to interesting seed characteristics including 4 seed quantity traits and 4 seed phenotypic traits using association analysis with population structure.

METHODS

We performed association mapping of 200 SSR markers and 8 seed characteristics among 10 NILs of waxy maize and two parental lines (HW3, HW9) (recurrent parent) of "Mibaek 2" variety.

RESULTS

In population structure and cluster analysis, the 10 NILs and two parental lines were divided into two groups. Seven inbred lines, including HW3, were assigned to Group I. Group II contained 5 inbred lines, including HW9. In addition, we found that 32 SSR markers associate with 8 seed characteristics in 10 NILs. In particular, five SSR markers (umc1986, umc1747, umc2275, phi078, umc1366) were together associated with more than one seed characteristics such as EL, 100 KW, SCC, R, L*, and V.

CONCLUSION

This study demonstrated the utility of SSR analysis for studying GD, population structure, and association mapping in 10 NILs and two parental lines (HW3, HW9) of "Mibaek 2" variety.

The development of SSR markers based on RNA-sequencing and its validation between and within Carex L. species

BACKGROUND

Carex L. is one of the largest genera in the Cyperaceae family and an important vascular plant in the ecosystem. However, the genetic background of Carex is complex and the classification is not clear. In order to investigate the gene function annotation of Carex, RNA-sequencing analysis was performed. Simple sequence repeats (SSRs) were generated based on the Illumina data and then were utilized to investigate the genetic characteristics of the 79 Carex germplasms.

RESULTS

In this study, 36,403 unigenes with a total length of 41,724,615 bp were obtained and annotated based on GO, KOG, KEGG, NR databases. The results provide a theoretical basis for gene function exploration. Out of 8776 SSRs, 96 pairs of primers were randomly selected. One hundred eighty polymorphic bands were amplified with a polymorphism rate of 100% based on 42 pairs of primers with higher polymorphism levels. The average band number was 4.3 per primer, the average distance value was 0.548, and the polymorphic information content was ranged from 0.133 to 0.494. The number of observed alleles (Na), effective alleles (Ne), Nei's (1973) gene diversity (H), and the Shannon information index (I) were 2.000, 1.376, 0.243, and 0.391, respectively. NJ clustering divided into three groups and the accessions from New Zealand showed a similar genetic attribute and clustered into one group. UPGMA and PCoA analysis also revealed the same result. The analysis of molecular variance (AMOVA) revealed a superior genetic diversity within accessions than between accessions based on geographic origin cluster and NJ cluster. What's more, the fingerprints of 79 Carex species are established in this study. Different combinations of primer pairs can be used to identify multiple Carex at one time, which overcomes the difficulties of traditional identification methods.

CONCLUSIONS

The transcriptomic analysis shed new light on the function categories from the annotated genes and will facilitate future gene functional studies. The genetic characteristics analysis indicated that gene flow was extensive among 79 Carex species. These markers can be used to investigate the evolutionary history of Carex and related species, as well as to serve as a guide in future breeding projects.