Identification of trait-improving quantitative trait loci for grain yield components from a dent corn inbred line in an advanced backcross BC2F2 population and comparison with its F2:3 population in popcorn

Hotspot Regions of Quantitative Trait Loci and Candidate Genes for Ear-Related Traits in Maize: A Literature Review

In this study, hotspot regions, QTL clusters, and candidate genes for eight ear-related traits of maize (ear length, ear diameter, kernel row number, kernel number per row, kernel length, kernel width, kernel thickness, and 100-kernel weight) were summarized and analyzed over the past three decades. This review aims to (1) comprehensively summarize and analyze previous studies on QTLs associated with these eight ear-related traits and identify hotspot bin regions located on maize chromosomes and key candidate genes associated with the ear-related traits and (2) compile major and stable QTLs and QTL clusters from various mapping populations and mapping methods and techniques providing valuable insights for fine mapping, gene cloning, and breeding for high-yield and high-quality maize. Previous research has demonstrated that QTLs for ear-related traits are distributed across all ten chromosomes in maize, and the phenotypic variation explained by a single QTL ranged from 0.40% to 36.76%. In total, 23 QTL hotspot bins for ear-related traits were identified across all ten chromosomes. The most prominent hotspot region is bin 4.08 on chromosome 4 with 15 QTLs related to eight ear-related traits. Additionally, this study identified 48 candidate genes associated with ear-related traits. Out of these, five have been cloned and validated, while twenty-eight candidate genes located in the QTL hotspots were defined by this study. This review offers a deeper understanding of the advancements in QTL mapping and the identification of key candidates associated with eight ear-related traits. These insights will undoubtedly assist maize breeders in formulating strategies to develop higher-yield maize varieties, contributing to global food security.

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.

Genome-wide analyses reveal footprints of divergent selection and popping-related traits in CIMMYT's maize inbred lines

Popcorn (Zea mays L. var. Everta) is the most ancient type of cultivated maize. However, there is little known about the genetics of popping-related traits based on genotyping-by-sequencing (GBS) technology. Here, we characterized the phenotypic variation for seven popping-related traits in maize kernels among 526 CIMMYT inbred lines (CMLs). In total, 155 083 high-quality single nucleotide polymorphism (SNP) markers were identified by a GBS approach. Several trait-associated loci were detected by genome-wide association study for color, popping expansion volume, shape, pericarp, flotation index, floury/vitreous, and protein content, explaining a majority of the observed phenotypic variance, and these were validated by a diverse panel comprising 764 tropical landrace accessions. Sixty two of the identified loci were recognized to have undergone selection. On average, there was a 55.27% frequency for alleles that promote popping in CMLs. Our work not only pinpoints previously unknown loci for popping-related traits, but also reveals that many of these loci have undergone selection. Beyond establishing a new benchmark for the genetics of popcorn, our study provides a foundation for gene discovery and breeding. It also presents evidence to investigate the role of a gradual loss of popping ability as a by-product of diversification of culinary uses throughout the evolution of teosinte-to-modern maize.

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.

Genetic dissection of yield-related traits and mid-parent heterosis for those traits in maize (Zea mays L.)

BACKGROUND

Utilization of heterosis in maize could be critical in maize breeding for boosting grain yield. However, the genetic architecture of heterosis is not fully understood. To dissect the genetic basis of yield-related traits and heterosis in maize, 301 recombinant inbred lines derived from 08 to 641 × YE478 and 298 hybrids from the immortalized F₂ (IF₂) population were used to map quantitative trait loci (QTLs) for nine yield-related traits and mid-parent heterosis.

RESULTS

We observed 156 QTLs, 28 pairs of loci with epistatic interaction, and 10 significant QTL × environment interactions in the inbred and hybrid mapping populations. The high heterosis in F₁ and IF₂ populations for kernel weight per ear (KWPE), ear weight per ear (EWPE), and kernel number per row (KNPR) matched the high percentages of QTLs (over 50%) for those traits exhibiting overdominance, whereas a notable predominance of loci with dominance effects (more than 70%) was observed for traits that show low heterosis such as cob weight per ear (CWPE), rate of kernel production (RKP), ear length (EL), ear diameter (ED), cob diameter, and row number (RN). The environmentally stable QTL qRKP3-2 was identified across two mapping populations, while qKWPE9, affecting the trait mean and the mid-parent heterosis (MPH) level, explained over 18% of phenotypic variations. Nine QTLs, qEWPE9-1, qEWPE10-1, qCWPE6, qEL8, qED2-2, qRN10-1, qKWPE9, qKWPE10-1, and qRKP4-3, accounted for over 10% of phenotypic variation. In addition, QTL mapping identified 95 QTLs that were gathered together and integrated into 33 QTL clusters on 10 chromosomes.

CONCLUSIONS

The results revealed that (1) the inheritance of yield-related traits and MPH in the heterotic pattern improved Reid (PA) × Tem-tropic I (PB) is trait-dependent; (2) a large proportion of loci showed dominance effects, whereas overdominance also contributed to MPH for KNPR, EWPE, and KWPE; (3) marker-assisted selection for markers at genomic regions 1.09-1.11, 2.04, 3.08-3.09, and 10.04-10.05 contributed to hybrid performance per se and heterosis and were repeatedly reported in previous studies using different heterotic patterns is recommended.

Fine mapping and candidate gene analysis of qhkw5-3, a major QTL for kernel weight in maize

KEY MESSAGE: qhkw5-3, a major QTL for kernel weight in maize, was mapped to an interval of 125.3 kb between the InDel markers InYM20 and InYM36, and the candidate genes were analysed. Yield, of which kernel weight is a major component, is the primary trait of interest in maize breeding programmes. In our previous study, a major QTL (named qhkw5-3), which controls hundred-kernel weight, was identified and mapped to the interval between simple sequence repeat (SSR) markers SYM033 and SYM108 on chromosome 5, using an F_2:3 population derived from a cross between the maize inbred line Zheng58 and the single-segment substitution line Z22. In order to fine map qhkw5-3, a larger BC₁F₁ segregating population of 14,759 seeds, derived from a (Z22 × Zheng58) × Z22 cross, was screened using the SSR markers SYM036 and SYM119. Forty genotypes with donor chromosomal fragments of different lengths were obtained. Progeny testing results indicated that qhkw5-3 was mapped to an interval of 442.6 kb between the SSR markers SYM077 and SYM084. Overlap mapping results, based on seven homozygous recombinant lines, showed that qhkw5-3 was narrowed down to an interval of 125.3 kb between the InDel markers InYM20 and InYM36. Within this interval, six candidate genes were analysed using qRT-PCR. The results of this study lay the foundations for cloning and functional analysis of qhkw5-3 and will contribute to advancing our knowledge of the genetic basis of yield traits in maize.

Genetic Diversity and Genome-Wide Association Study of Major Ear Quantitative Traits Using High-Density SNPs in Maize

Kernel and ear traits are key components of grain yield in maize (Zea mays L.). Investigation of these traits would help to develop high-yield varieties in maize. Genome-wide association study (GWAS) uses the linkage disequilibrium (LD) in the whole genome to determine the genes affecting certain phenotype. In this study, five ear traits (kernel length and width, ear length and diameter, cob diameter) were investigated across multi-environments for 2 years. Combining with the genotype obtained from Maize SNP50 chip, genetic diversity and association mapping in a set of 292 inbred lines were performed. Results showed that maize lines were clustered into seven subgroups and a total of 20 SNPs were found to be associated with ear traits significantly (P < 3.95E-05). The candidate genes identified by GWAS mainly encoded ubiquitin-activation enzymes (GRMZM2G015287), carotenoid cleavage dioxygenase (GRMZM2G446858), MYB-CC type transfactor, and phosphate starvation response protein 3, and they were associated with kernel length (KL) and ear diameter (ED), respectively. Moreover, two novel genes corresponding to RNA processing and fructose metabolism were found. Further, the SNPs detected by GWAS were confirmed by meta-QTL analysis. These genes and SNPs identified in the study would offer essential information for yield-related genes clone and breeding program in maize.

Analysis of the genetic architecture of maize ear and grain morphological traits by combined linkage and association mapping

Using combined linkage and association mapping, 26 stable QTL and six stable SNPs were detected across multiple environments for eight ear and grain morphological traits in maize. One QTL, PKS2, might play an important role in maize yield improvement. In the present study, one bi-parental population and an association panel were used to identify quantitative trait loci (QTL) for eight ear and grain morphological traits. A total of 108 QTL related to these traits were detected across four environments using an ultra-high density bin map constructed using recombinant inbred lines (RILs) derived from a cross between Ye478 and Qi319, and 26 QTL were identified in more than two environments. Furthermore, 64 single nucleotide polymorphisms (SNPs) were found to be significantly associated with the eight ear and grain morphological traits (-log₁₀(P) > 4) in an association panel of 240 maize inbred lines. Combining the two mapping populations, a total of 17 pleiotropic QTL/SNPs (pQTL/SNPs) were associated with various traits across multiple environments. PKS2, a stable locus influencing kernel shape identified on chromosome 2 in a genome-wide association study (GWAS), was within the QTL confidence interval defined by the RILs. The candidate region harbored a short 13-Kb LD block encompassing four SNPs (SYN11386, PHM14783.16, SYN11392, and SYN11378). In the association panel, 13 lines derived from the hybrid PI78599 possessed the same allele as Qi319 at the PHM14783.16 (GG) locus, with an average value of 0.21 for KS, significantly lower than that of the 34 lines derived from Ye478 that carried a different allele (0.25, P < 0.05). Therefore, further fine mapping of PKS2 will provide valuable information for understanding the genetic components of grain yield and improving molecular marker-assisted selection (MAS) in maize.

Verification and fine mapping of qGW1.05, a major QTL for grain weight in maize (Zea mays L.)

Grain weight, one of the important factors to determine corn yield, is a typical quantitative inheritance trait. However, the molecular genetic basis of grain weight still remains limited. In our previous researches, a major QTL associated with grain weight, qGW1.05, has been identified between SSR markers umc1601 and umc1754 at bin locus 1.05-1.06 in maize. Here, its genetic and environmental stabiliteis were verified using a BC₃F₂ population to identify the effect of qGW1.05 on grain weight. Further, qGW1.05-NILs were obtained by MAS successfully. Via a large BC₆F₂ segregation population, together with polymorphic microsatellite markers developed between the parents to screen the genotype of the recombinant plants, qGW1.05 was positioned to a 1.11 Mb genome interval. Furthermore, the progenies of 15 recombinants were tested to confirm the effect of qGW1.05 on grain weight. Combining collinearity among cereal crops and genome annotation, the several candidate genes taking part in grain development were identified in the qGW1.05 region. In this study, qGW1.05 was limited to a 1.11 Mb region on chromosome 1, which established the foundation for understanding the molecular basis underlying kernel development and improving grain weight through MAS using the tightly flanking molecular markers in maize.

Identification of QTL for maize grain yield and kernel-related traits

Grain yield (GY) is one of the most important and complex quantitative traits in maize (Zea mays L.) breeding practice. Quantitative trait loci (QTLs) for GY and three kernel-related traits were detected in a set of recombinant inbred lines (RILs). One hundred and seven simple sequence repeats (SSRs) and 168 insertion/deletion polymorphism markers (Indels) were used to genotype RILs. Eight QTLs were found to be associated with four yield-related traits: GY, 100-kernel weight (HKW), 10-kernel length (KL), and 10-kernel length width (KW). Each QTL explained between 5.96 (qKL2-1) and 13.05 (qKL1-1) per cent of the phenotypic variance. Notably, one common QTL, located at the marker interval between bnlg1893 and chr2- 236477 (chromosomal bin 2.09) simultaneously controlled GY and HKW; another common QTL, at bin 2.03 was simultaneously responsible for HKW and KW. Of the QTLs identified, only one pair of significant epistatic interaction involved in chromosomal region at bin 2.03 was detected for HKW; no significant QTL × environment interactions were observed. These results provide the common QTLs and for marker-assisted breeding.

Quantitative Trait Locus Analysis of Late Leaf Spot Resistance and Plant-Type-Related Traits in Cultivated Peanut (Arachis hypogaea L.) under Multi-Environments

Late leaf spot (LLS) is one of the most serious foliar diseases affecting peanut worldwide leading to huge yield loss. To understand the genetic basis of LLS and assist breeding in the future, we conducted quantitative trait locus (QTL) analysis for LLS and three plant-type-related traits including height of main stem (HMS), length of the longest branch (LLB) and total number of branches (TNB). Significant negative correlations were observed between LLS and the plant-type-related traits in multi-environments of a RIL population from the cross Zhonghua 5 and ICGV 86699. A total of 20 QTLs were identified for LLS, of which two QTLs were identified in multi-environments and six QTLs with phenotypic variation explained (PVE) more than 10%. Ten, seven, fifteen QTLs were identified for HMS, LLB and TNB, respectively. Of these, one, one, two consensus QTLs and three, two, three major QTLs were detected for HMS, LLB and TNB, respectively. Of all 52 unconditional QTLs for LLS and plant-type-related traits, 10 QTLs were clustered in five genetic regions, of which three clusters including five robust major QTLs overlapped between LLS and one of the plant-type-related traits, providing evidence that the correlation could be genetically constrained. On the other hand, conditional mapping revealed different numbers and different extent of additive effects of QTLs for LLS conditioned on three plant-type-related traits (HMS, LLB and TNB), which improved our understanding of interrelationship between LLS and plant-type-related traits at the QTL level. Furthermore, two QTLs, qLLSB6-7 and qLLSB1 for LLS resistance, were identified residing in two clusters of NB-LRR—encoding genes. This study not only provided new favorable QTLs for fine-mapping, but also suggested that the relationship between LLS and plant-type-related traits of HMS, LLB and TNB should be considered while breeding for improved LLS resistance in peanut.

A comprehensive meta-analysis of plant morphology, yield, stay-green, and virus disease resistance QTL in maize (Zea mays L.)

The meta-QTL and candidate genes will facilitate the elucidation of molecular bases underlying agriculturally important traits and open new avenues for functional markers development and elite alleles introgression in maize breeding program. A large number of QTLs attributed to grain productivity and other agriculturally important traits have been identified and deposited in public repositories. The integration of fruitful QTL becomes a major issue in current plant genomics. To this end, we first collected QTL for six agriculturally important traits in maize, including yield, plant height, ear height, leaf angle, stay-green, and maize rough dwarf disease resistance. The meta-analysis method was then employed to retrieve 113 meta-QTL. Additionally, we also isolated candidate genes for target traits by the bioinformatic technique. Several candidates, including some well-characterized genes, GA3ox2 for plant height, lg1 and lg4 for leaf angle, zfl1 and zfl2 for flowering time, were co-localized with established meta-QTL intervals. Intriguingly, in a relatively narrow meta-QTL region, the maize ortholog of rice yield-related gene GW8/OsSPL16 was believed to be a candidate for yield. Leveraging results presented in this study will provide further insights into the genetic architecture of maize agronomic traits. Moreover, the meta-QTL and candidate genes reported here could be harnessed for the enhancement of stress tolerance and yield performance in maize and translation to other crops.

Characterization of Genome-Wide Variation in Four-Row Wax, a Waxy Maize Landrace with a Reduced Kernel Row Phenotype

In southwest China, some maize landraces have long been isolated geographically, and have phenotypes that differ from those of widely grown cultivars. These landraces may harbor rich genetic variation responsible for those phenotypes. Four-row Wax is one such landrace, with four rows of kernels on the cob. We resequenced the genome of Four-row Wax, obtaining 50.46 Gb sequence at 21.87× coverage, then identified and characterized 3,252,194 SNPs, 213,181 short InDels (1-5 bp) and 39,631 structural variations (greater than 5 bp). Of those, 312,511 (9.6%) SNPs were novel compared to the most detailed haplotype map (HapMap) SNP database of maize. Characterization of variations in reported kernel row number (KRN) related genes and KRN QTL regions revealed potential causal mutations in fea2, td1, kn1, and te1. Genome-wide comparisons revealed abundant genetic variations in Four-row Wax, which may be associated with environmental adaptation. The sequence and SNP variations described here enrich genetic resources of maize, and provide guidance into study of seed numbers for crop yield improvement.

Yield QTLome distribution correlates with gene density in maize

The genetic control of yield and related traits in maize has been addressed by many quantitative trait locus (QTL) studies, which have produced a wealth of QTL information, also known as QTLome. In this study, we assembled a yield QTLome database and carried out QTL meta-analysis based on 44 published studies, representing 32 independent mapping populations and 49 parental lines. A total of 808 unique QTLs were condensed to 84 meta-QTLs and were projected on the 10 maize chromosomes. Seventy-four percent of QTLs showed a proportion of phenotypic variance explained (PVE) smaller than 10% confirming the high genetic complexity of grain yield. Yield QTLome projection on the genetic map suggested pericentromeric enrichment of QTLs. Conversely, pericentromeric depletion of QTLs was observed when the physical map was considered, suggesting gene density as the main driver of yield QTL distribution on chromosomes. Dominant and overdominant yield QTLs did not distribute differently from additive effect QTLs.

Analysis of heterosis and quantitative trait loci for kernel shape related traits using triple testcross population in maize

Kernel shape related traits (KSRTs) have been shown to have important influences on grain yield. The previous studies that emphasize kernel length (KL) and kernel width (KW) lack a comprehensive evaluation of characters affecting kernel shape. In this study, materials of the basic generations (B73, Mo17, and B73 × Mo17), 82 intermated B73 × Mo17 (IBM) individuals, and the corresponding triple testcross (TTC) populations were used to evaluate heterosis, investigate correlations, and characterize the quantitative trait loci (QTL) for six KSRTs: KL, KW, length to width ratio (LWR), perimeter length (PL), kernel area (KA), and circularity (CS). The results showed that the mid-parent heterosis (MPH) for most of the KSRTs was moderate. The performance of KL, KW, PL, and KA exhibited significant positive correlation with heterozygosity but their Pearson’s R values were low. Among KSRTs, the strongest significant correlation was found between PL and KA with R values was up to 0.964. In addition, KW, PL, KA, and CS were shown to be significant positive correlation with 100-kernel weight (HKW). 28 QTLs were detected for KSRTs in which nine were augmented additive, 13 were augmented dominant, and six were dominance × additive epistatic. The contribution of a single QTL to total phenotypic variation ranged from 2.1% to 32.9%. Furthermore, 19 additive × additive digenic epistatic interactions were detected for all KSRTs with the highest total R² for KW (78.8%), and nine dominance × dominance digenic epistatic interactions detected for KL, LWR, and CS with the highest total R² (55.3%). Among significant digenic interactions, most occurred between genomic regions not mapped with main-effect QTLs. These findings display the complexity of the genetic basis for KSRTs and enhance our understanding on heterosis of KSRTs from the quantitative genetic perspective.

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement. Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02-1.03, 1.04-1.06, 2.05-2.07, 4.07-4.08 and 9.03-9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.

Identification of bioconversion quantitative trait loci in the interspecific cross Sorghum bicolor × Sorghum propinquum

For lignocellulosic bioenergy to be economically viable, genetic improvements must be made in feedstock quality including both biomass total yield and conversion efficiency. Toward this goal, multiple studies have considered candidate genes and discovered quantitative trait loci (QTL) associated with total biomass accumulation and/or grain production in bioenergy grass species including maize and sorghum. However, very little research has been focused on genes associated with increased biomass conversion efficiency. In this study, Trichoderma viride fungal cellulase hydrolysis activity was measured for lignocellulosic biomass (leaf and stem tissue) obtained from individuals in a F5 recombinant inbred Sorghum bicolor × Sorghum propinquum mapping population. A total of 49 QTLs (20 leaf, 29 stem) were associated with enzymatic conversion efficiency. Interestingly, six high-density QTL regions were identified in which four or more QTLs overlapped. In addition to enzymatic conversion efficiency QTLs, two QTLs were identified for biomass crystallinity index, a trait which has been shown to be inversely correlated with conversion efficiency in bioenergy grasses. The identification of these QTLs provides an important step toward identifying specific genes relevant to increasing conversion efficiency of bioenergy feedstocks. DNA markers linked to these QTLs could be useful in marker-assisted breeding programs aimed at increasing overall bioenergy yields concomitant with selection of high total biomass genotypes.

Bioenergy grass feedstock: current options and prospects for trait improvement using emerging genetic, genomic, and systems biology toolkits

For lignocellulosic bioenergy to become a viable alternative to traditional energy production methods, rapid increases in conversion efficiency and biomass yield must be achieved. Increased productivity in bioenergy production can be achieved through concomitant gains in processing efficiency as well as genetic improvement of feedstock that have the potential for bioenergy production at an industrial scale. The purpose of this review is to explore the genetic and genomic resource landscape for the improvement of a specific bioenergy feedstock group, the C4 bioenergy grasses. First, bioenergy grass feedstock traits relevant to biochemical conversion are examined. Then we outline genetic resources available bioenergy grasses for mapping bioenergy traits to DNA markers and genes. This is followed by a discussion of genomic tools and how they can be applied to understanding bioenergy grass feedstock trait genetic mechanisms leading to further improvement opportunities.

QTL underlying the resistance to soybean aphid (Aphis glycines Matsumura) through isoflavone-mediated antibiosis in soybean cultivar 'Zhongdou 27'

Soybean aphid (Aphis glycines Matsumura) results in severe yield loss of soybean in many soybean-growing countries of the world. A few loci have been previously identified to be associated with the aphid resistance in soybean. However, none of them was via isoflavone-mediated antibiosis process. The aim of the present study was to conduct genetic analysis of aphid resistance and to identify quantitative trait loci (QTL) underlying aphid resistance in a Chinese soybean cultivar with high isoflavone content. One hundred and thirty F(5:6) derived recombinant inbred lines from the 'Zhongdou 27' × 'Jiunong 20' cross were used. Two QTL were directly associated with resistance to aphid as measured by aphid damage index. qRa_1, close to Satt470 on soybean linkage group (LG) A2 (chromosome 8), was consistently detected for 3- and 4-week ratings and explained a large portion of phenotypic variations ranging from 25 to 35%. qRa_2, close to Satt144 of LG F (chromosome 13), was detected for 3- and 4-week ratings and could explain 7 and 11% of the phenotypic variation, respectively. These two QTL were highly associated with high isoflavone content and both positive alleles were derived from 'Zhongdou 27', a cultivar with higher isoflavone content. The results revealed that higher individual or total isoflavones contents in soybean lines could protect soybean against aphid attack. These two QTL detected jointly provide potential for marker-assisted selection to improve the resistance of soybean cultivars to aphid along with the increase of isoflavone content.

QTL consistency and meta-analysis for grain yield components in three generations in maize

Grain yield is the most important and complex trait in maize. In this study, a total of 258 F(9) recombinant inbred lines (RIL), derived from a cross between dent corn inbred Dan232 and popcorn inbred N04, were evaluated for eight grain yield components under four environments. Quantitative trait loci (QTL) and their epistatic interactions were detected for all traits under each environment and in combined analysis. Meta-analysis was used to integrate genetic maps and detected QTL across three generations (RIL, F(2:3) and BC(2)F(2)) derived from the same cross. In total, 103 QTL, 42 pairs of epistatic interactions and 16 meta-QTL (mQTL) were detected. Twelve out of 13 QTL with contributions (R(2)) over 15% were consistently detected in 3-4 environments (or in combined analysis) and integrated in mQTL. Only q100GW-7-1 was detected in all four environments and in combined analysis. 100qGW-1-1 had the largest R(2) (19.3-24.6%) in three environments and in combined analysis. In contrast, 35 QTL for 6 grain yield components were detected in the BC(2)F(2) and F(2:3) generations, no common QTL across three generations were located in the same marker intervals. Only 100 grain weight (100GW) QTL on chromosome 5 were located in adjacent marker intervals. Four common QTL were detected across the RIL and F(2:3) generations, and two between the RIL and BC(2)F(2) generations. Each of five important mQTL (mQTL7-1, mQTL10-2, mQTL4-1, mQTL5-1 and mQTL1-3) included 7-12 QTL associated with 2-6 traits. In conclusion, we found evidence of strong influence of genetic structure and environment on QTL detection, high consistency of major QTL across environments and generations, and remarkable QTL co-location for grain yield components. Fine mapping for five major QTL (q100GW-1-1, q100GW-7-1, qGWP-4-1, qERN-4-1 and qKR-4-1) and construction of single chromosome segment lines for genetic regions of five mQTL merit further studies and could be put into use in marker-assisted breeding.

Identification of differentially expressed genes at two key endosperm development stages using two maize inbreds with large and small grain and integration with detected QTL for grain weight

Maize endosperm accounts for more than 80% of the grain weight. Cell division and grain filling are the two key stages for endosperm development. Previous studies showed that gene expression during differential stages in endosperm development is greatly different. However, information on systematic identification and characterization of the differentially expressed genes between the two stages are limited. In this study, suppression subtractive hybridization (SSH) was used to generate four subtracted cDNA libraries for the two stages using two maize inbreds with large and small grain. Totally, 4,784 differentially expressed sequence tags (ESTs) were sequenced and 902 were non-redundant, which consisted of 344 unique ESTs. Among them 192 had high sequence similarity to the GenBank entries and represent diverse of functional categories, such as metabolism, cell growth/division, transcription, signal transduction, protein destination/storage, protein synthesis and others. The expression patterns of 75.7% SSH-derived cDNAs were confirmed by reverse Northern blot and semi-quantitative reverse transcription polymerase chain reaction, and exhibited the similar results (75.0%). Genes differentially expressed between two key stages for the two inbreds were involved in diverse physiological process pathway, which might be responsible for the formation of grain weight. 43.8% (70 of the 160 unique ESTs) of the identified ESTs were assigned to 39 chromosome bins distributed over all ten maize chromosomes. Eleven ESTs were found to co-localize with previous detected QTLs for grain weight, which might be considered as the candidate genes of grain weight for further study.

Four rice QTL controlling number of spikelets per panicle expressed the characteristics of single Mendelian gene in near isogenic backgrounds

Development of quantitative trait loci (QTL) near isogenic lines is a crucial step to QTL isolation using the strategy of map-based cloning. In this study, a recombinant inbred line (RIL) population derived from two indica rice varieties, Zhenshan 97 and HR5, was employed to map QTL for spikelets per panicle (SPP). One major QTL (qSPP7) and three minor QTL (qSPP1, qSPP2 and qSPP3) were identified on chromosomes 7, 1, 2 and 3, respectively. Four sets of near isogenic lines (NILs) BC(4)F(2) targeted for the four QTL were developed by following a standard procedure of consecutive backcross, respectively. These QTL were not only validated in corresponding NILs, but also explained amounts of phenotypic variation with much larger LOD scores compared with those identified in RILs. SPP in the four QTL-NILs expressed bimodal or discontinuous distributions and followed the expected segregation ratio of single Mendelian factor by progeny test. Finally, qSPP1, qSPP2, qSPP3 and qSPP7 were respectively mapped to a locus, 0.5 cM from MRG2746, 0.6 cM from MRG2762, 0.8 cM from RM49 and 0.7 cM from MRG4436, as co-dominant markers on the basis of progeny tests. These results indicate no matter how small effect minor QTL is, QTL may still express the characteristics of single Mendelian factor in NILs and isolation of minor QTL will be possible using high quality NILs. Pyramiding these QTL into a variety will largely enhance rice grain yield.