Kernel-related Traits Research Articles

SNP-based association study of kernel architecture in a worldwide collection of durum wheat germplasm.

Durum wheat, genetic resource with favorable alleles is considered as natural gene pool for wheat breeding. Kernel size and weight are important factors affecting grain yield in crops. Here, association analysis was performed to dissect the genetic constitution of kernel-related traits in 150 lines collected from 46 countries and regions using a set of EST-derived and genome-wide SNP markers with five consecutive years of data. Total 109 significant associations for eight kernel-related traits were detected under a mix linear model, generating 54 unique SNP markers distributed on 13 of 14 chromosomes. Of which, 19 marker-trait associations were identified in two or more environments, including one stable and pleiotropic SNP BE500291_5_A_37 on chromosome 5A correlated with six kernel traits. Although most of our SNP loci were overlapped with the previously known kernel weight QTLs, several novel loci for kernel traits in durum were reported. Correlation analysis implied that the moderate climatic variables during growth and development of durum are needed for the large grain size and high grain weight. Combined with our previous studies, we found that chromosome 5A might play an important role in durum growth and development.

QTL Mapping of Kernel Traits and Validation of a Major QTL for Kernel Length-Width Ratio Using SNP and Bulked Segregant Analysis in Wheat

One RIL population derived from the cross between Dalibao and BYL8 was used to examine the phenotypes of kernel-related traits in four different environments. Six important kernel traits, kernel length (KL), kernel width (KW), kernel perimeter (KP), kernel area (KA), kernel length/width ratio (KLW), and thousand-kernel weight (TKW) were evaluated in Yangling, Shaanxi Province, China (2016 and 2017), Nanyang, Henan Province, China (2017) and Suqian, Jiangsu Province, China (2017). A genetic linkage map was constructed using 205 SSR markers, and a total of 21 significant QTLs for KL, KW, KP, KA, KLW and TKW were located on 10 of the 21 wheat chromosomes, including 1A, 1B, 2A, 2B, 2D, 3D, 4D, 5A, 5B, and 7D, with a single QTL in different environments explaining 3.495–30.130% of the phenotypic variation. There were four loci for KLW, five for KA, five for KL, three for KP, two for KW, and two for TKW among the detected QTLs. We used BSA + 660 K gene chip technology to reveal the positions of major novel QTLs for KLW. A total of 670 out of 5285 polymorphic SNPs were detected on chromosome 2A. The SNPs in 2A are most likely related to the major QTL, and there may be minor QTLs on 5B, 7A, 3A and 4B. SSR markers were developed to verify the chromosome region associated with KLW. A linkage map was constructed with 7 SSR markers, and a major effect QTL was identified within a 21.55 cM interval, corresponding to a physical interval of 10.8 Mb in the Chinese Spring RefSeq v1.0 sequence. This study can provide useful information for subsequent construction of fine mapping and marker-assisted selection breeding.

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.

Genome-wide patterns of population structure and association mapping of nut-related traits in Persian walnut populations from Iran using the Axiom J. regia 700K SNP array

Persian plateau (including Iran) is considered as one of the primary centers of origin of walnut. Sampling walnut trees originating from this arena and exploiting the capabilities of next-generation sequencing (NGS) can provide new insights into the degree of genetic variation across the walnut genome. The present study aimed to explore the population structure and genomic variation of an Iranian collection of Persian walnut (Juglans regia L.) and identify loci underlying the variation in nut and kernel related traits using the new Axiom J. regia 700K SNP genotyping array. We genotyped a diversity panel including 95 walnut genotypes from eight Iranian provinces with a variety of climate zones. A majority of the SNPs (323,273, 53.03%) fell into the “Poly High Resolution” class of polymorphisms, which includes the highest quality variants. Genetic structure assessment, using several approaches, divided the Iranian walnut panel into four principal clusters, reflecting their geographic partitioning. We observed high genetic variation across all of the populations (HO = 0.34 and HE = 0.38). The overall level of genetic differentiation among populations was moderate (FST = 0.07). However, the Semnan population showed high divergence from the other Iranian populations (on average FST = 0.12), most likely due to its geographical isolation. Based on parentage analysis, the level of relatedness was very low among the Iranian walnuts examined, reflecting the geographical distance between the Iranian provinces considered in our study. Finally, we performed a genome-wide association study (GWAS), identifying 55 SNPs significantly associated with nut and kernel-related traits. In conclusion, by applying the novel Axiom J. regia 700K SNP array we uncovered new unexplored genetic diversity and identified significant marker-trait associations for nut-related traits in Persian walnut that will be useful for future breeding programs in Iran and other countries.

Genome-wide association study revealed that the TaGW8 gene was associated with kernel size in Chinese bread wheat

Using Wheat 90 K SNP assay, kernel-related traits of Chinese bread wheat were used to perform association mapping in 14 environments by GWAS. Results indicated that 996 and 953 of 4417 and 3172 significant SNPs for kernel length and thousand-kernel weight were located on the chromosome 7B. Haplotype analysis of these SNPs on 7B generated the block containing the predicted TaGW8-B1 gene. TaGW8-B1 gene was further cloned by sequencing in bread wheat and a 276-bp InDel was found in the first intron. TaGW8-B1 without and with the 276-bp InDel were designated as TaGW8-B1a and TaGW8-B1b, respectively. Analysis of agronomic traits indicated that cultivars with TaGW8-B1a possessed significantly wider kernel width, significantly more kernel number per spike, longer kernel length, higher thousand-kernel weight and more spikelet number per spike than cultivars with TaGW8-B1b. Furthermore, cultivars with TaGW8-B1a possessed significantly higher yield than cultivars with TaGW8-B1b. Therefore, TaGW8-B1a was considered as a potentially superior allele. Meanwhile, TaGW8-B1a possessed a significantly higher expression level than TaGW8-B1b in mature seeds by qRT-PCR. It possibly suggested that the high expression of TaGW8-B1 was positively associated with kernel size in bread wheat. Distribution of TaGW8-B1 allele indicated that TaGW8-B1a has been positively selected in Chinese wheat.

The analysis of functional genes in maize molecular breeding

Molecular breeding is capable of improving important agricultural crop traits by controlling functional genes, aiming to attain high yield, stability and quality. During this process, the quantity and maneuverability of functional genes are important in determining breeding efficiency. In our research, 186 functional genes relating to maize agronomic traits, which are available for marker-assisted selection or genetic transformation strategies, were collected from the literature and projected onto an integration map. The traits corresponding to these functional genes included disease resistance, stress tolerance, tassel traits, ear traits, kernel-related traits, leaf traits and plant-type characteristics. The integration map demonstrated that these functional genes were unevenly distributed on maize chromosomes. The greatest and fewest numbers of functional genes were found on chromosomes 1 and 8, respectively. Moreover, 36, 25, 27, 23, 14, 15, 11, 6, 9 and 20 genes were found on chromosomes 1 to 10, respectively. Most of disease-resistant genes were found on chromosome 10, while the genes of kernel-related and leaf-related traits were found on chromosomes 4 and 2, respectively. Out of the 186 functional genes, 95 have been characterized using genetic mapping, 19 using map-based cloning, 53 using transposon-tagging cloning strategies and 19 using other methods. Thus, the number of functional genes identified in maize is still limited and further research on functional genes is required.

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.

Genome-wide analysis of the pentatricopeptide repeat gene family in different maize genomes and its important role in kernel development

BackgroundThe pentatricopeptide repeat (PPR) gene family is one of the largest gene families in land plants (450 PPR genes in Arabidopsis, 477 PPR genes in rice and 486 PPR genes in foxtail millet) and is important for plant development and growth. Most PPR genes are encoded by plastid and mitochondrial genomes, and the gene products regulate the expression of the related genes in higher plants. However, the functions remain largely unknown, and systematic analysis and comparison of the PPR gene family in different maize genomes have not been performed.ResultsIn this study, systematic identification and comparison of PPR genes from two elite maize inbred lines, B73 and PH207, were performed. A total of 491 and 456 PPR genes were identified in the B73 and PH207 genomes, respectively. Basic bioinformatics analyses, including of the classification, gene structure, chromosomal location and conserved motifs, were conducted. Examination of PPR gene duplication showed that 12 and 15 segmental duplication gene pairs exist in the B73 and PH207 genomes, respectively, with eight duplication events being shared between the two genomes. Expression analysis suggested that 53 PPR genes exhibit qualitative variations in the different genetic backgrounds. Based on analysis of the correlation between PPR gene expression in kernels and kernel-related traits, four PPR genes are significantly negatively correlated with hundred kernel weight, 12 are significantly negatively correlated with kernel width, and eight are significantly correlated with kernel number. Eight of the 24 PPR genes are also located in metaQTL regions associated with yield and kernel-related traits in maize. Two important PPR genes (GRMZM2G353195 and GRMZM2G141202) might be regarded as important candidate genes associated with maize kernel-related traits.ConclusionsOur results provide a more comprehensive understanding of PPR genes in different maize inbred lines and identify important candidate genes related to kernel development for subsequent functional validation in maize.

QTL Detection for Kernel Size and Weight in Bread Wheat (Triticum aestivum L.) Using a High-Density SNP and SSR-Based Linkage Map.

High-density genetic linkage maps are essential for precise mapping quantitative trait loci (QTL) in wheat (Triticum aestivum L.). In this study, a high-density genetic linkage map consisted of 6312 SNP and SSR markers was developed to identify QTL controlling kernel size and weight, based on a recombinant inbred line (RIL) population derived from the cross of Shixin828 and Kenong2007. Seventy-eight putative QTL for kernel length (KL), kernel width (KW), kernel diameter ratio (KDR), and thousand kernel weight (TKW) were detected over eight environments by inclusive composite interval mapping (ICIM). Of these, six stable QTL were identified in more than four environments, including two for KL (qKL-2D and qKL-6B.2), one for KW (qKW-2D.1), one for KDR (qKDR-2D.1) and two for TKW (qTKW-5A and qTKW-5B.2). Unconditional and multivariable conditional QTL mapping for TKW with respect to TKW component (TKWC) revealed that kernel dimensions played an important role in regulating the kernel weight. Seven QTL-rich genetic regions including seventeen QTL were found on chromosomes 1A (2), 2D, 3A, 4B and 5B (2) exhibiting pleiotropic effects. In particular, clusters on chromosomes 2D and 5B possessing significant QTL for kernel-related traits were highlighted. Markers tightly linked to these QTL or clusters will eventually facilitate further studies for fine mapping, candidate gene discovery and marker-assisted selection (MAS) in wheat breeding.

QTL mapping and genetic analysis for maize kernel size and weight in multi-environments

Kernel size and weight are important agronomic traits, as well as crucial traits that influence grain yield in maize. In the present study, 150 F7 recombinant inbred lines derived from a cross 178×K12 were evaluated for kernel length (KL), kernel width (KW), kernel thickness (KT), and 100-kernel weight (HKW) across seven environments. Natural variations in KL, KW, KT, and HKW were observed in the population. A set of quantitative trait loci (QTLs) for the kernel-related traits were identified by inclusive composite interval mapping method. For the four kernel traits from seven environments and the best linear unbiased prediction data, a total of 52 QTLs were detected, which distributed on all chromosomes except chromosome 6. The LOD values ranged from 2.52 to 8.91, the additive effect from − 2.22 to 1.37, and the range of individually explaining phenotypic variation was from 5.8 to 23.49%. Amongst these QTLs, most were detected only in one or two environments. Three stable QTLs, qKL4-1 at bin 4.07/4.08, qKW4-2 at bin 4.06 and qKT2-1 at bin 2.02/2.03, were identified across at least three environments. Besides, several overlapping QTLs associated with multiple traits were identified. For example, qKW3-1 for KW and qHKW3-1 for HKW were located in the same marker interval at Bin 3.01/3.02. These stable QTLs and overlapping QTLs found in this study will contribute to the understanding of genetic components of grain yield and provide the foundation for molecular marker-assisted breeding in maize.

Molecular mapping of quantitative trait loci for three kernel-related traits in maize using a double haploid population

Kernel size and kernel weight are important factors possibly involved in the determination of grain yield in maize, so identifying the genetic basis of kernel-related traits provides insights into the breeding of high-yield maize varieties. Kernel length (KL), kernel width (KW) and hundred kernel weight (HKW) were evaluated in three various planting conditions for the 240 field-grown double haploid (DH) lines derived from the single-cross hybrid Xianyu335. Variations in KL, KW and HKW were observed among DH lines, and all three traits showed a broad sense heritability of 76%. A total of 964 single nucleotide polymorphisms (SNPs) from the MaizeSNP3072 chip was utilised to create a high-density genetic map of 1546.4 cM and to identify quantitative trait loci (QTLs). Using composite interval mapping, a total of five, seven and five QTLs have been mapped for KL, KW and HKW, respectively. qkl1-2 and qkl4-1 explained 17.8% and 14.2% of the phenotypic variation in KL, respectively, and the other three QTLs contributed 3.2–4.0%. The phenotypic variation explained (PVE) of seven QTLs responsible for KW ranged from 3.3 to 9.5%. Three QTLs for HKW, qhkw1, qhkw5 and qhkw10 each explained more than 10% of the phenotypic variation, and qhkw4 and qhkw9 accounted for 3.0% and 6.0%, respectively. Due to their detection in multiple planting environments, the loci mapped here appear to be potential targets for the improvement of maize grain yield.

Quantitative trait loci mapping of yield and related traits using a high-density genetic map of maize

Improving grain yield is the ultimate goal of the maize-breeding programs. In this study, analyses of conditional and unconditional quantitative trait locus (QTL) and epistatic interactions were used to elucidate the genetic architecture of yield and its related traits. A total of 15 traits of a recombinant inbred line population, including yield per plant (YPP), seven ear-related traits, and seven kernel-related traits, were measured in six different environments. Based on the genetic linkage map constructed using 2091 bins as markers, 56 main-effect QTLs for these traits were identified. These QTLs were distributed across eight genomic regions (bin 1.06, bin 4.02/4.05/4.08, bin 5.04, bin 7.04, bin 8.08, and bin 9.04), within the marker intervals of 85.45–6260.66 kb, and the phenotypic variance explained ranging from 5.69 to 11.56 %. One gene (GRMZM2G168229) encoding SBP-box domain protein was located in the small interval of qKRN4-3 and may be involved in patterning of kernel row number. Seventeen conditional QTLs identified for YPP were conditioned on its related traits and explained 6.18–23.15 % of the phenotypic variance. Conditional mapping analysis revealed that qYPP4-1, qYPP6-1, and qYPP8-1 are partially influenced by YPP-related traits at the individual QTL level. Digenic epistatic analysis identified 12 digenic interactions involving 22 loci across the whole genome. In addition, conditional digenic epistatic analysis identified 14 digenic interactions involving 21 loci. This study provides valuable information for understanding the genetic relationship between YPP and related traits and constitutes the first step toward the cloning of the relevant genes.

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 for pod- and kernel-related traits in the cultivated peanut (Arachis hypogaea L.).

BackgroundThe cultivated peanut (Arachis hypogaea L.) is an important oil and food crop in the world. Pod- and kernel-related traits are direct factors involved in determining the yield of the peanut. However, the genetic basis underlying pod- and kernel-related traits in the peanut remained largely unknown, which hampered the improvement of peanut through marker-assisted selection. To understand the genetic basis underlying pod- and kernel-related traits in the peanut and provide more useful information for marker-assisted breeding, we conducted quantitative trait locus (QTL) analysis for pod length and width and seed length and width by use of two F2:3 populations derived from cultivar Fuchuan Dahuasheng × ICG 6375 (FI population) and cultivar Xuhua 13 × cultivar Zhonghua 6 (XZ population) in this study.ResultsTwo genetic maps containing 347 and 228 polymorphic markers were constructed for FI and XZ populations respectively. In total, 39 QTLs explaining 1.25–26.11 % of the phenotypic variations were detected in two populations. For the FI population, 26 QTLs were detected between the two environments, among which twelve were not mapped before. For the XZ population, thirteen QTLs were detected, among which eight were not reported before. One QTL for pod width was repeatedly mapped between the two populations.ConclusionThe QTL analyses for pod length and width and seed length and width were conducted in this study using two mapping populations. Novel QTLs were identified, which included two for pod length, four for pod width, five for seed length and one for seed width in the FI population, and three for pod length, three for pod width and two for seed width in the XZ population. Our results will be helpful for improving pod- and seed-related traits in peanuts through marker-assisted selection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0337-x) contains supplementary material, which is available to authorized users.

Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population.

Kernel development is an important dynamic trait that determines the final grain yield in maize. To dissect the genetic basis of maize kernel development process, a conditional quantitative trait locus (QTL) analysis was conducted using an immortalized F2 (IF2) population comprising 243 single crosses at two locations over 2years. Volume (KV) and density (KD) of dried developing kernels, together with kernel weight (KW) at different developmental stages, were used to describe dynamic changes during kernel development. Phenotypic analysis revealed that final KW and KD were determined at DAP22 and KV at DAP29. Unconditional QTL mapping for KW, KV and KD uncovered 97 QTLs at different kernel development stages, of which qKW6b, qKW7a, qKW7b, qKW10b, qKW10c, qKV10a, qKV10b and qKV7 were identified under multiple kernel developmental stages and environments. Among the 26 QTLs detected by conditional QTL mapping, conqKW7a, conqKV7a, conqKV10a, conqKD2, conqKD7 and conqKD8a were conserved between the two mapping methodologies. Furthermore, most of these QTLs were consistent with QTLs and genes for kernel development/grain filling reported in previous studies. These QTLs probably contain major genes associated with the kernel development process, and can be used to improve grain yield and quality through marker-assisted selection.

Detection of quantitative trait loci for ear row number in F2 populations of maize

Ear row number (ERN) is not only a key trait involved in maize (Zea mays L.) evolution but is also an important component that is directly related to grain yield. In this study, quantitative trait loci (QTLs) for ERN were detected across two F2 populations that were derived from a same cross between B73 with 16 rows (N = 233) and SICAU1212 with four rows (N = 231). As a result, 33 QTLs were associated with 12 agronomic traits: three plant traits, four ear-related traits, and five kernel-related traits. The total phenotypic variation explained by the QTLs for each trait ranged from 8.60 to 72.67%, and four QTLs were identified for ERN in the two populations. Each QTL explained between 6.78 and 36.76% of the ERN variation. Notably, three of the four QTLs (qERN2-1, qERN4-2, and qERN8-1) were associated with ERN, and qERN8-1 simultaneously influenced grain yield, plant diameter, ear diameter, and kernel length. In addition, only one significant epistatic interaction was detected in all 33 QTLs. This study should provide a foundation for further fine-mapping and map-based cloning of these consistent QTLs, and for controlling maize ERN by marker-assisted breeding.

SELF-INCOMPATIBILITY AND EFFECT OF RECIPROCAL CROSS AND OPEN POLLINATION ON FRUIT SET AND FRUIT CHARACTERISTIC IN JORDANIAN ALMOND LANDRACES

Limited studies were carried out to identify self- and cross-compatibility relationships among Jordanian almond landraces. Therefore, this study a imed at studying the level of self-incompatibility and the effect of cross and open pollination on fru it set and shell, nut and kernel related traits in almond. To achieve this objective, field experiment s were carried out during 2012 and 2013 cropping season on five Jordanian almond landraces and one wild bitter almond ( A. communis ) available in the farmer's fields at Ajloun district, Jordan. Fruit s et was recorded in the field after open-pollination , selfpollination by bagging the branches with flower bud s and cross pollination after emasculation of the floral buds. All almond genotypes showed complete s elf-incompatibility. Genotypes showed variable percentages in fruit set with similar trend in the two growing seasons. Following cross pollination treatment, genotypes exhibited fruit set ranging fr om 40.3% in Hajari to 94.0% in Fark and from 34.1% in Mukhmaly to 93.3% in Fark in 2012 and 2013, respectively. Results showed that cross pollination of Hami Hallo (79.7-81.7 and 77.8-89.2% in 2012 and 2013 respectively) and Fark (83.094.0 and 86.1-93.3% in 2012 and 2013 respectively) with other landraces led to high level of fruit set indicating high cross-compatibility. Slight differe nces were recorded in fruit set in reciprocal cross es. Pollen source did not affect shell, nut and kernel traits. As a conclusion, results obtained from this study showed that, all of the genotypes were self i n-compatible and all genotypes showed high level of cross-compatibility with variable degree among geno types.

QTL Analysis of Kernel-Related Traits in Maize Using an Immortalized F2 Population

Kernel size and weight are important determinants of grain yield in maize. In this study, multivariate conditional and unconditional quantitative trait loci (QTL), and digenic epistatic analyses were utilized in order to elucidate the genetic basis for these kernel-related traits. Five kernel-related traits, including kernel weight (KW), volume (KV), length (KL), thickness (KT), and width (KWI), were collected from an immortalized F2 (IF2) maize population comprising of 243 crosses performed at two separate locations over a span of two years. A total of 54 unconditional main QTL for these five kernel-related traits were identified, many of which were clustered in chromosomal bins 6.04–6.06, 7.02–7.03, and 10.06–10.07. In addition, qKL3, qKWI6, qKV10a, qKV10b, qKW10a, and qKW7a were detected across multiple environments. Sixteen main QTL were identified for KW conditioned on the other four kernel traits (KL, KWI, KT, and KV). Thirteen main QTL were identified for KV conditioned on three kernel-shape traits. Conditional mapping analysis revealed that KWI and KV had the strongest influence on KW at the individual QTL level, followed by KT, and then KL; KV was mostly strongly influenced by KT, followed by KWI, and was least impacted by KL. Digenic epistatic analysis identified 18 digenic interactions involving 34 loci over the entire genome. However, only a small proportion of them were identical to the main QTL we detected. Additionally, conditional digenic epistatic analysis revealed that the digenic epistasis for KW and KV were entirely determined by their constituent traits. The main QTL identified in this study for determining kernel-related traits with high broad-sense heritability may play important roles during kernel development. Furthermore, digenic interactions were shown to exert relatively large effects on KL (the highest AA and DD effects were 4.6% and 6.7%, respectively) and KT (the highest AA effects were 4.3%).

Quantitative trait loci mapping for yield components and kernel-related traits in multiple connected RIL populations in maize

Grain yield is one of the most important and complex quantitative traits in maize breeding. In the present study, a total of 11 connected RIL populations, derived from crosses between elite inbreed “Huangzaosi” as the common parent and 11 elite inbreeds, were evaluated for five yield components and kernel-related traits under six environments. Quantitative trait loci (QTL) were detected for the traits under each environment and in joint analysis across all environments for each population. A total of 146 major QTL with R2 > 10 % in at least one environment and also detected based on joint analysis across all environments were identified in the 11 populations. Lqkwei4 conferring kernel weight and Lqklen4-1 conferring kernel length both located in the adjacent marker intervals in bin 4.05 were stably expressed in four environments and in joint analysis across six environments, with the largest R2 over 27 and 24 % in a single environment, respectively. Moreover, all major QTL detected in the 11 populations were aligned on the IBM2 2008 neighbors reference map. Totally 16 common QTL (CQTL) were detected. Seven important CQTL (CQTL1-2, CQTL1-3, CQTL4-1, CQTL4-2, CQTL4-3, CQTL4-4, and CQTL6-1) were located in bin 1.07, 1.10, 4.03, 4.05, 4.08, 4.09 and 6.01–6.02, respectively. These chromosomal regions could be targets for fine mapping and marker-assisted selection.

QTL analysis for yield components and kernel-related traits in maize across multi-environments

Huangzaosi, Qi319, and Ye478 are foundation inbred lines widely used in maize breeding in China. To elucidate genetic base of yield components and kernel-related traits in these elite lines, two F(2:3) populations derived from crosses Qi319×Huangzaosi (Q/H, 230 families) and Ye478×Huangzaosi (Y/H, 235 families), as well as their parents were evaluated in six environments including Henan, Beijing, and Xinjiang in 2007 and 2008. Correlation and hypergeometric probability function analyses showed the dependence of yield components on kernel-related traits. Three mapping procedures were used to identify quantitative trait loci (QTL) for each population: (1) analysis for each of the six environments, (2) joint analysis for each of the three locations across 2years, and (3) joint analysis across all environments. For the eight traits measured, 90, 89, and 58 QTL for Q/H, and 72, 76, and 51 QTL for Y/H were detected by the three QTL mapping procedures, respectively. About 70% of the QTL from Q/H and 90% of the QTL from Y/H did not show significant QTL×environment interactions in the joint analysis across all environments. Most of the QTL for kernel traits exhibited high stability across 2years at the same location, even across different locations. Seven major QTL detected under at least four environments were identified on chromosomes 1, 4, 6, 7, 9, and 10 in the populations. Moreover, QTL on chr. 1, chr. 4, and chr. 9 were detected in both populations. These chromosomal regions could be targets for marker-assisted selection, fine mapping, and map-based cloning in maize.