Fine-mapping of qGW4.05, a major QTL for kernel weight and size in maize

Genome-wide association analysis of four yield-related traits using a maize (Zea mays L.) F1 population

Increasing the yield of maize F1 hybrid is one of the most important target for breeders. However, as a result of the genetic complexity and extremely low heritability, it is very difficult to directly dissect the genetic basis and molecular mechanisms of yield, and reports on genetic analysis of F1 hybrid yield are rare. Taking F1 hybrid as the research object and dividing the yield into different affect factors, this approach may be the best strategy for clarifying the genetic mechanism of yield. Therefore, in this study, a maize F1 population consisting of 300 hybrids with 17,652 single nucleotide polymorphisms (SNPs) markers was used for genome-wide association study (GWAS) to filtrate candidate genes associated with the four yield-related traits, i.e., kernel row number (KRN), kernel number per row (KNPR), ear tip-barrenness (ETB), and hundred kernel weight (HKW). Combined with the results of previous studies and functional annotation information of candidate genes, a total of six candidate genes were identified as being associated with the four traits, which were involved in plant growth and development, protein synthesis response, phytohormone biosynthesis and signal transduction. Our results improve the understanding of the genetic basis of the four yield-related traits and may be provide a new strategy for the genetic basis of maize yield.

Integrated IBD Analysis, GWAS Analysis and Transcriptome Analysis to Identify the Candidate Genes for White Spot Disease in Maize

Foundation parents (FPs) play an irreplaceable role in maize breeding practices. Maize white spot (MWS) is an important disease in Southwest China that always seriously reduces production. However, knowledge about the genetic mechanism of MWS resistance is limited. In this paper, a panel of 143 elite lines were collected and genotyped by using the MaizeSNP50 chip with approximately 60,000 single nucleotide polymorphisms (SNPs) and evaluated for resistance to MWS among 3 environments, and a genome-wide association study (GWAS) and transcriptome analysis were integrated to reveal the function of the identity-by-descent (IBD) segments for MWS. The results showed that (1) 225 IBD segments were identified only in the FP QB512, 192 were found only in the FP QR273 and 197 were found only in the FP HCL645. (2) The GWAS results showed that 15 common quantitative trait nucleotides (QTNs) were associated with MWS. Interestingly, SYN10137 and PZA00131.14 were in the IBD segments of QB512, and the SYN10137-PZA00131.14 region existed in more than 58% of QR273's descendants. (3) By integrating the GWAS and transcriptome analysis, Zm00001d031875 was found to located in the region of SYN10137-PZA00131.14. These results provide some new insights for the detection of MWS's genetic variation mechanisms.

Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines

Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.

Genome wide association analysis for yield related traits in maize

BACKGROUND

Understanding the genetic basis of yield related traits contributes to the improvement of grain yield in maize.

RESULTS

Using 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.

CONCLUSIONS

Using 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.

Validating a Major Quantitative Trait Locus and Predicting Candidate Genes Associated With Kernel Width Through QTL Mapping and RNA-Sequencing Technology Using Near-Isogenic Lines in Maize

Kernel size is an important agronomic trait for grain yield in maize. The purpose of this study was to validate a major quantitative trait locus (QTL), qKW-1, which was identified in the F₂ and F_2:3 populations from a cross between the maize inbred lines SG5/SG7 and to predict candidate genes for kernel width (KW) in maize. A major QTL, qKW-1, was mapped in multiple environments in our previous study. To validate and fine map qKW-1, near-isogenic lines (NILs) with 469 individuals were developed by continuous backcrossing between SG5 as the donor parent and SG7 as the recurrent parent. Marker-assisted selection was conducted from the BC₂F₁ generation with simple sequence repeat (SSR) markers near qKW-1. A secondary linkage map with four markers, PLK12, PLK13, PLK15, and PLK17, was developed and used for mapping the qKW-1 locus. Finally, qKW-1 was mapped between the PLK12 and PLK13 intervals, with a distance of 2.23 cM to PLK12 and 0.04 cM to PLK13, a confidence interval of 5.3 cM and a phenotypic contribution rate of 23.8%. The QTL mapping result obtained was further validated by using selected overlapping recombinant chromosomes on the target segment of maize chromosome 3. Transcriptome analysis showed that a total of 12 out of 45 protein-coding genes differentially expressed between the two parents were detected in the identified qKW-1 physical interval by blasting with the Zea_Mays_B73 v4 genome. GRMZM2G083176 encodes the Niemann-Pick disease type C, and GRMZM2G081719 encodes the nitrate transporter 1 (NRT1) protein. The two genes GRMZM2G083176 and GRMZM2G081719 were predicted to be candidate genes of qKW-1. Reverse transcription-polymerase chain reaction (RT-qPCR) validation was conducted, and the results provide further proof of the two candidate genes most likely responsible for qKW-1. The work will not only help to understand the genetic mechanisms of KW in maize but also lay a foundation for further cloning of promising loci.

TIP41L, a putative candidate gene conferring both seed size and boll weight, was fine-mapped in an introgression line of Gossypium hirsutum-Gossypium arboreum

QTLs for yield-related traits in tetraploid cotton have been widely mapped, but QTLs introduced from diploid species into tetraploid cotton background remain uninvolved. Here, a stable introgression line with the traits of small boll and seed on Chr. A12, IL197 derived from Gossypium hirsutum (2n = AADD = 52) × Gossypium arboreum (2n = AA = 26), was employed to construct the F₂ and F₃ secondary populations for fine-mapping QTLs of yield-related traits. QTL analysis showed eight QTLs were detected for three traits, boll weight (BW), seed index (SI, one-hundred-seed weight in g), and lint percentage, with 3.94-28.13 % of the phenotypic variance explained. Of them, a stable major QTL, q(BW + SI)-A12-1 controlling both BW and SI and covering the shortest region in Chr. A12, was further narrowed into a 60.09 kb-interval through substitution mapping. Finally, five candidate genes were detected in the interval. The qRT-PCR analysis revealed only TIP41-like family protein (TIP41L) kept up-regulated expression and significantly lower in TM-1 than that in IL197 from -1 DPA to 15 DPA during cotton boll rapid developmental stage. Therefore, TIP41L gene is speculated as the most likely candidate gene. Comparative analysis with the other four allotetraploid species showed TIP41L gene was probably diverged after the formation of allotetraploid cotton, which may be selected and swept during domestication of modern upland cotton because small boll and seed are detrimental to fibre yield of cotton. This research would lay a solid foundation for further elucidating the molecular mechanism of cotton boll and seed development.

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.

Fine mapping of a kernel length-related gene with potential value for maize breeding

A key candidate gene for maize kernel length was fine mapped to an interval of 942 kb; the locus significantly increases kernel length (KL) and hundred-kernel weight (HKW). Kernel size is a major determinant of yield in cereals. Kernel length, one of the determining factors of kernel size, is a target trait for both domestication and artificial breeding. However, there are few reports of fine mapping and quantitative trait loci (QTLs)/cloned genes for kernel length in maize. In this project, a novel major QTL, named qKL9, controlling maize kernel length was identified. We verified the authenticity and stability of qKL9 via BC₂F₂ and BC₃F₁ populations, respectively, and ultimately mapped qKL9 to an ~ 942-kb genomic interval by testing the progenies of recombination events derived from BC₃F₂ and BC₄F₂ populations in multiple environments. Additionally, one new line (Mc^qKL9-A) containing the ~ 942-kb segment was screened from the BC₄F₂ population. Combining transcriptome analysis between Mc^qKL9-A and Mc at 6, 9 and 14 days after pollination and candidate regional association mapping, Zm00001d046723 was preliminarily identified as the key candidate gene for qKL9. Importantly, the replacement in the Mc line of the Mc's alleles by the V671's alleles in the qKL9 region improved the performances of single-cross hybrids obtained with elite lines, illustrating the potential value of this QTL for the genetic improvement in maize kernel-related traits. These findings facilitate molecular breeding for kernel size and cloning of the gene underlying qKL9, shedding light on the genetic basis of kernel size in maize.

Genetic and molecular control of grain yield in maize

Understanding the genetic and molecular basis of grain yield is important for maize improvement. Here, we identified 49 consensus quantitative trait loci (cQTL) controlling maize yield-related traits using QTL meta-analysis. Then, we collected yield-related traits associated SNPs detected by association mapping and identified 17 consensus significant loci. Comparing the physical positions of cQTL with those of significant SNPs revealed that 47 significant SNPs were located within 20 cQTL regions. Furthermore, intensive reviews of 31 genes regulating maize yield-related traits found that the functions of many genes were conservative in maize and other plant species. The functional conservation indicated that some of the 575 maize genes (orthologous to 247 genes controlling yield or seed traits in other plant species) might be functionally related to maize yield-related traits, especially the 49 maize orthologous genes in cQTL regions, and 41 orthologous genes close to the physical positions of significant SNPs. In the end, we prospected on the integration of the public sources for exploring the genetic and molecular mechanisms of maize yield-related traits, and on the utilization of genetic and molecular mechanisms for maize improvement.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01214-3.

Maize kernel development

Maize (Zea mays) is a leading cereal crop in the world. The maize kernel is the storage organ and the harvest portion of this crop and is closely related to its yield and quality. The development of maize kernel is initiated by the double fertilization event, leading to the formation of a diploid embryo and a triploid endosperm. The embryo and endosperm are then undergone independent developmental programs, resulting in a mature maize kernel which is comprised of a persistent endosperm, a large embryo, and a maternal pericarp. Due to the well-characterized morphogenesis and powerful genetics, maize kernel has long been an excellent model for the study of cereal kernel development. In recent years, with the release of the maize reference genome and the development of new genomic technologies, there has been an explosive expansion of new knowledge for maize kernel development. In this review, we overviewed recent progress in the study of maize kernel development, with an emphasis on genetic mapping of kernel traits, transcriptome analysis during kernel development, functional gene cloning of kernel mutants, and genetic engineering of kernel traits.

QTL Analysis and Fine Mapping of a Major QTL Conferring Kernel Size in Maize (Zea mays)

Kernel size is an important agronomic trait for grain yield in maize. The purpose of this study is to map QTLs and predict candidate genes for kernel size in maize. A total of 199 F2 and its F2 : 3 lines from the cross between SG5/SG7 were developed. A composite interval mapping (CIM) method was used to detect QTLs in three environments of F2 and F2 : 3 populations. The result showed that a total of 10 QTLs for kernel size were detected, among which were five QTLs for kernel length (KL) and five QTLs for kernel width (KW). Two stable QTLs, qKW-1, and qKL-2, were mapped in all three environments. Three QTLs, qKL-1, qKW-1, and qKW-2, were overlapped with the QTLs identified from previous studies. In order to validate and fine map qKL-2, near-isogenic lines (NILs) were developed by continuous backcrossing between SG5 as the donor parent and SG7 as the recurrent parent. Marker-assisted selection was conducted from BC2F1 generation with molecular markers near qKL-2. A secondary linkage map with six markers around the qKL-2 region was developed and used for fine mapping of qKL-2. Finally, qKL-2 was confirmed in a 1.95 Mb physical interval with selected overlapping recombinant chromosomes on maize chromosome 9 by blasting with the Zea_Mays_B73 v4 genome. Transcriptome analysis showed that a total of 11 out of 40 protein-coding genes differently expressed between the two parents were detected in the identified qKL-2 interval. GRMZM2G006080 encoding a receptor-like protein kinase FERONIA, was predicted as a candidate gene to control kernel size. The work will not only help to understand the genetic mechanisms of kernel size of maize but also lay a foundation for further fine mapping and even cloning of the promising loci.

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.

Mapping of QTL for Grain Yield Components Based on a DH Population in Maize

The elite maize hybrid Zhengdan 958 (ZD958), which has high and stable yield and extensive adaptability, is widely grown in China. To elucidate the genetic basis of yield and its related traits in this elite hybrid, a set of doubled haploid (DH) lines derived from ZD958 were evaluated in four different environments at two locations over two years, and a total of 49 quantitative trait loci (QTL) and 24 pairs of epistatic interactions related to yield and yield components were detected. Furthermore, 21 QTL for six investigated phenotypic traits were detected across two different sites. Combining the results of these QTL in each environment and across both sites, three main QTL hotspots were found in chromosomal bins 2.02, 2.05–2.06, and 6.05 between the simple sequence repeat (SSR) markers umc1165-bnlg1017, umc1065-umc1637, and nc012-bnlg345, respectively. The existence of three QTL hotspots associated with various traits across multiple environments could be explained by pleiotropic QTL or multiple tightly linked QTL. These genetic regions could provide targets for genetic improvement, fine mapping, and marker-assisted selection in future studies.

Temporal profiling of essential amino acids in developing maize kernel of normal, opaque-2 and QPM germplasm

Maize, an important cereal crop, has a poor quality of endosperm protein due to the deficiency of essential amino acids, especially lysine and tryptophan. Discovery of mutants such as opaque-2 led to the development of nutritionally improved maize with a higher concentration of lysine and tryptophan. However, the pleiotropic effects associated with opaque-2 mutants necessitated the development of nutritionally improved hard kernel genotype, the present-day quality protein maize (QPM). The aim of present study was to analyze and compare the temporal profile of lysine and tryptophan in the developing maize kernel of normal, opaque-2 and QPM lines. A declining trend in protein along with tryptophan and lysine content was observed with increasing kernel maturity in the experimental genotypes. However, opaque-2 retained the maximum concentration of lysine (3.43) and tryptophan (1.09) at maturity as compared to QPM (lysine-3.05, tryptophan-0.99) and normal (lysine-1.99, tryptophan-0.45) lines. Opaque-2 mutation affects protein quality but has no effect on protein quantity. All maize types are nutritionally rich at early stages of kernel development indicating that early harvest for cattle feed would ensure a higher intake of lysine and tryptophan. Two promising lines (CML44 and HKI 1105) can be used for breeding high value corn for cattle feed or human food in order to fill the protein inadequacy gap. Variation in lysine and tryptophan content within QPM lines revealed that differential expression of endosperm modifiers with varying genetic background significantly affects nutritional quality, indicating that identification of alleles affecting amino acid composition can further facilitate QPM breeding program.

Map-based cloning of qBWT-c12 discovered brassinosteroid-mediated control of organ size in cotton

Assuring fiber yield stability is the primary objective for cotton breeders since the world population is on the rise, and the demand for cotton fiber is increasing every year. Thus, enhancing average cotton boll weight (BWT) could improve seed cotton production, and ultimately to increase cotton fiber yield. This study accomplished the map-based cloning of a novel boll weight regulating locus, qBWT-c12, in cotton. Bulk segregation analysis detected linked markers, aided in the detection of a stable BWT regulating locus, qBWT-c12, on Chr12 in a novel boll size mutant, BS41. Progeny evaluation confined the qBWT-c12 to a 0.89 cM interval between the AD-A12_07 and AD-FM_44 markers in recombinant derived F₃ and F₄ populations. Homology mapping detected a 40 bp insertion-deletion (InDel) site in the AD-FM_44 clone sequence situated +341 downstream of GhBRH1_A12, which showed complete linkage to the BWT phenotype. The suppressed expression of GhBRH1_A12 suggested its putative involvement during early boll development events in BS41. Although brassinosteroid (BR) biosynthesis and signaling pathway genes were up regulated in different tissues, but the organ growth was suppressed leading to dwarf plants, smaller leaves, and de-morphed smaller bolls in BS41. Thus, a disruption in the BR signal cascade is anticipated and could be related to lower GhBRH1_A12 expression in BS41.This study firstly reported the genetic dissection of boll size regulation of G. barbadense in G. hirsutum background using map-based cloning of a BWT regulating locus, qBWT-c12. Moreover, it also emphasized the putative role GhBRH1_A12 in regulating BR homeostasis and its potential to modulate plant growth and boll development in cotton.

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F_2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F₂ population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F₂ population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.

Identification of quantitative trait loci for kernel-related traits and the heterosis for these traits in maize (Zea mays L.)

Heterosis has been extensively applied for many traits during maize breeding, but there has been relatively little attention paid to the heterosis for kernel size. In this study, we evaluated a population of 301 recombinant inbred lines derived from a cross between 08-641 and YE478, as well as 298 hybrids from an immortalized F₂ (IF₂) population to detect quantitative trait loci (QTLs) for six kernel-related traits and the mid-parent heterosis (MPH) for these traits. A total of 100 QTLs, six pairs of loci with epistatic interactions, and five significant QTL × environment interactions were identified in both mapping populations. Seven QTLs accounted for over 10% of the phenotypic variation. Only four QTLs affected both the trait means and the MPH, suggesting the genetic mechanisms for kernel-related traits and the heterosis for kernel size are not completely independent. Moreover, more than half of the QTLs for each trait in the IF₂ population exhibited dominance, implying that dominance is more important than other genetic effects for the heterosis for kernel-related traits. Additionally, 20 QTL clusters comprising 46 QTLs were detected across ten chromosomes. Specific chromosomal regions (bins 2.03, 6.04-6.05, and 9.01-9.02) exhibited pleiotropy and congruency across diverse heterotic patterns in previous studies. These results may provide additional insights into the genetic basis for the MPH for kernel-related traits.

Combined linkage and association mapping reveal candidate loci for kernel size and weight in maize

Yield improvement is a top priority for maize breeding. Kernel size and weight are important determinants of maize grain yield. In this study, a recombinant inbred line (RIL) population and an association panel were used to identify quantitative trait loci (QTLs) for four maize kernel-related traits: kernel length, width, thickness and 100-kernel weight. Twenty-seven QTLs were identified for kernel-related traits across three environments and the best linear unbiased predictions (BLUPs) of each trait by linkage analysis, and four QTLs were stably detected in more than two environments. Additionally, 29 single nucleotide polymorphisms (SNPs) were identified as significantly associated with the four kernel-related traits and BLUPs by genome-wide association study, and two loci could be stably detected in both environments. In total, four QTLs/SNPs were co-associated with various traits in both populations. Using combined-linkage analysis and association mapping, PZE-101066560 on chromosome 1, associated with kernel width and with 100-kernel weight in the association panel, was co-localized within the QTL interval of qKW1-3 for kernel width in the RILs. Two annotated genes in the candidate region were considered as potential candidate genes. The QTLs and candidate genes identified here will facilitate molecular breeding for grain yield improvement in maize.

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.

Candidate loci for the kernel row number in maize revealed by a combination of transcriptome analysis and regional association mapping

BACKGROUND

The kernel row number (KRN) of an ear is an important trait related to yield and domestication in maize. Exploring the underlying genetic mechanisms of KRN has great research significance and application potential.

RESULTS

In the present study, N531 with a KRN of 18-22 and SLN with a KRN of 4-6 were used as the recurrent parent and the donor parent, respectively, to develop two introgression lines (ILs), IL_A and IL_B, both of which have common negative-effect alleles from SLN on chromosomes 1, 5 and 10 and significantly reduced inflorescence meristem (IM) diameter and KRN compared with those of N531. We used RNA-Seq to investigate the transcriptome profiles of 5-mm immature ears of N531, IL_A and IL_B. We identified a total of 2872 differentially expressed genes (DEGs) between N531 and IL_A, 2428 DEGs between N531 and IL_B and 1811 DEGs between IL_A and IL_B. A total of 1252 DEGs were detected as overlapping DEGs, and 89 DEGs were located on the common introgression fragments. Furthermore, three DEGs (Zm00001d013277, Zm00001d015310 and Zm00001d015377) containing three SNPs associated with KRN were identified using regional association mapping.

CONCLUSIONS

These results will facilitate our understanding of ear development and provide important candidate genes for further study on KRN.

Dissecting the Genetic Basis of Grain Size and Weight in Barley (Hordeum vulgare L.) by QTL and Comparative Genetic Analyses

Grain size and weight are crucial components of barley yield and quality and are the target characteristics of domestication and modern breeding. Despite this, little is known about the genetic and molecular mechanisms of grain size and weight in barley. Here, we evaluated nine traits determining grain size and weight, including thousand grain weight (Tgw), grain length (Gl), grain width (Gw), grain length-width ratio (Lwr), grain area (Ga), grain perimeter (Gp), grain diameter (Gd), grain roundness (Gr), and factor form density (Ffd), in a double haploid (DH) population for three consecutive years. Using five mapping methods, we successfully identified 60 reliable QTLs and 27 hotspot regions that distributed on all chromosomes except 6H which controls the nine traits of grain size and weight. Moreover, we also identified 164 barley orthologs of 112 grain size/weight genes from rice, maize, wheat and 38 barley genes that affect grain yield. A total of 45 barley genes or orthologs were identified as potential candidate genes for barley grain size and weight, including 12, 20, 9, and 4 genes or orthologs for barley, rice, maize, and wheat, respectively. Importantly, 20 of them were located in the 14 QTL hotspot regions on chromosome 1H, 2H, 3H, 5H, and 7H, which controls barley grain size and weight. These results indicated that grain size/weight genes of other cereal species might have the same or similar functions in barley. Our findings provide new insights into the understanding of the genetic basis of grain size and weight in barley, and new information to facilitate high-yield breeding in barley. The function of these potential candidate genes identified in this study are worth exploring and studying in detail.

Patterns of genomic variation in Chinese maize inbred lines and implications for genetic improvement

Genetic relationships among Chinese maize germplasms reveal historical trends in heterotic patterns from Chinese breeding programs and identify line Dan340 as a potential genome donor for elite inbred line Zheng58. The characterization of the genetic relationships, heterotic patterns and breeding history of lines in maize breeding programs allows breeders to efficiently use maize germplasm for line improvement over time. In this study, 269 temperate inbred lines, most of which have been widely used in Chinese maize breeding programs since the 1970s, were genotyped using the Illumina MaizeSNP50 BeadChip, which contains 56,110 single-nucleotide polymorphisms. The STRUCTURE analysis, cluster analysis and principal coordinate analysis results consistently revealed seven groups, of which five were consistent with known heterotic groups within the Chinese maize germplasm-Domestic Reid, Lancaster, Zi330, Tang SPT and Tem-tropic I (also known as "P"). These genetic relationships also allowed us to determine the historical trends in heterotic patterns during the three decades from 1970 to 2000, represented by Mo17 from Lancaster, HuangZaoSi (HZS) from Tang SPT, Ye478 from Domestic Reid and P178 from Tem-tropic I heterotic groups. Mo17-related commercial hybrids were widely used in the 1970s and 1980s, followed by the release of HZS- and Ye478-related commercial hybrids in the 1980s and 1990s, and the introduction of Tem-tropic I group in the 1990s and 2000s. Additionally, we identified inbred line Dan340 as a potential genome donor for Zheng58, which is the female parent of the most widely grown commercial hybrid ZhengDan958 in China. We also reconstructed the recombination events of elite line HZS and its 14 derived lines. These findings provide useful information to direct future maize breeding efforts.

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.

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.

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.