Genetic analysis of QTLs for lysine content in four maize DH populations

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Improving the lysine content is favorable to improve maize quality by optimizing feeding requirement. Understanding the genetic basis of lysine content benefits greatly improving maize yield and optimizing end-use quality. Results Four double haploid (DH) populations were generated and used to identify quantitative trait loci (QTL) associated with lysine content. The broad-sense heritability indicated the majority of lysine content variations were largely controlled by genetic factors. A total of 12 QTLs were identified in a range of 4.42–12.66% in term of phenotypic variation explained (PVE) which suggested that a large number of minor-effect QTLs mainly contributed to the genetic component of lysine content. Five well-known genes encoding key enzymes in maize lysine biosynthesis pathways locate within QTLs identified in this study. Conclusions The information presented will pave a path to explore candidate genes regulating lysine biosynthesis pathways and be useful for marker-assisted selection and gene pyramiding in high-lysine maize breeding programs. Maize DH kernel lysine content QTL Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Maize ( Zea mays L.) is one of the main crops worldwide and serves as an important source of nutrition [ 1 – 3 ]. However, the poor lysine content of maize greatly limited essential amino acids for human and livestock [ 3 – 6 ]. Therefore, the lysine contents in maize endosperm are considered to be one of the most important traits for determining the nutritional quality of food and feed [ 5 ]. In order to improve breeding for balanced amino acid composition of maize kernels, many research efforts has been expended on identify the genes controlling amino acid content in the maize kernel and a large number of mutants related to maize endosperm have been found [ 3 , 7 ]. The opaque-2 ( o2 ) mutation has about a 50% reduction in zeins and nearly doubles the lysine content of maize endosperm compared to normal genotypes [ 3 , 8 – 10 ]. However, the soft and starchy endosperm associated with o2 lead to brittleness and insect susceptibility, both in storage and in the field, thereby decreasing the value of the grain [ 11 – 13 ]. The quality protein maize (QPM) has been used in breeding programs to develop semi-vitreous and vitreous phenotypes with high Lys content and solve the problems [ 14 ]. The development and widespread use of QPM is limited because of the genetically complex germplasm and the technical complexity of the multiple loci [ 5 ]. To elucidate genetic variation in lysine content, several QTL studies have been carried out using different mapping methods and populations, and many QTLs associated with lysine content or OPM related traits in maize kernel have been revealed. For instance, five significant QTLs for opaque2 modifiers were identified in F 2:3 individuals from a cross between two isogenic QPM inbreds and showed influencing the tryptophan content using functional and genomic SSR markers. Holding et al. found that seven major QTLs associated with o2 endosperm modification from developed QPM lines and showed that chromosomes 5, 7 and 9 might be a major hub of opaque2 modifiers[ 11 , 14 ]. Wang and Larkins identified four significant loci that account for about 46% of the phenotypic variance and coincident with the genes involved in free amino acids biosynthetic pathways[ 15 ]. Deng et al. identified four QTLs for lysine content in three RIL populations and one QTL were further validated using molecular[ 7 ]. Different mapping populations with advantages and limitations will cause great impacts on QTL outputs [ 16 ]. Double haploid (DH) segregating populations have been widely used in QTL analysis because of the specific advantages that enable to remove any residual heterozygosity, ensuring replicates genetically identical [ 16 ] and increase selection response by stabilizing heritability of various traits during perse and test cross evaluation with the relatively high genetic variance in DH lines [ 17 – 22 ]. In this study, four DH populations derived from the practical breeding program were used for further dissection of the genetic basis and QTLs controlling the phenotypic variation of lysine content in maize kernels. Our results will provide insights into the genetic basis of lysine biosynthesis in maize kernels and may facilitate marker-based breeding for quality protein maize. Materials and methods Plant materials Four DH populations (AF109, AF116, AF129 and AF170) with 248, 190, 316 and 265 lines, respectively, were developed from eight maize inbred lines (Table 1 ). The eight parents belonged to elite inbred lines used for breeding program at Maize Yufeng Biotechnology LLC and had a relative higher lysine content than other inbred lines (Table 1 ). Plants were grown at Liaoning province, China (LN, 40 ◦ `82′N, 123 ◦ 56′E) with three replication blocks in 2021. Each line was grown in a single-row plot with a row length of 150 cm and 60 cm between rows under natural field conditions. All plants in each row were self-pollinated and harvested after maturity. Kernels from middle part of three well-grown ears were bulked for lysine content measurement. We declare that all the collections of plant and seed specimens related to this study were performed in accordance with the relevant guidelines and regulations by Ministry of Agriculture (MOA) of the People's Republic of China. Lysine content measurement and phenotypic data analysis Near infrared reflectance (NIR) spectrometer (DA 7250, Perten Instruments Inc., Sweden) was used to determine the lysine content in maize kernels. Reflectance spectra (R) from bulk whole grains of at least 50 kernels from each sample were collected at 10-nm intervals in the NIR region from 400 to 2500 nm. The averaged results were obtained from three times scanning of each sample. R Version 4.0.1 ( www.R-project.org ) was used to perform all statistical analyses. The variances of the lysine content were estimated by using the R function ‘AOV’. The model for the variance analysis was y = µ + α g + β e + ε, where α g is the effect of the g th line, β e is the effect of the e th environment, and ε is the error. All of the effects were considered to be random. These variance components were used to calculate the broad-sense heritability as h 2 = σ g 2 / (σ g 2 + σ ε 2 / e ) [ 23 ], where σ g 2 is the genetic variance, σ ε 2 is the residual error, and e is the number of environments. The best linear unbiased predictor (BLUP) value for each line was calculated to eliminate the influence of environmental effects by using a linear mixed model that considered both genotype and environment as random effects in the R function ‘LME4’. The model was y ij = µ + e i + f j + ε ij , where y ij is the phenotypic value of individual j in environment i, µ is the grand mean, e i is the effect of different environments, f j is the genetic effect, and ε ij is the random error. The BLUP values were used for phenotypic description statistics and QTL analysis. Genotyping and constructing genetic linkage map The GenoBaits Maize 2K marker panel containing 10,378 SNP markers, which was developed by Mol Breeding Biotechnology Co., Ltd., Shijiazhuang, China ( http://www.molbreeding.com/ ), based on genotyping by target sequencing platform in maize [ 24 ] was used to genotype all lines. CrossMap Version 2.0.5 was used to convert the SNP positions in the B73 reference genome Version 3 to those in Version 4 [ 25 ]. SNPs with minor allele frequency (MAF) 0.6 were filtered out in each population. Subsequently, the genetic linkage maps were constructed with the high quality SNPs in each population via the R/qtl package functions est.rf and est.map [ 26 ] with the kosambi mapping method. QTL mapping Composite interval mapping (CIM) method implemented in Windows QTL Cartographer 2.5 was used to analyze the QTLs [ 27 ]. Genome was scanned at every 1.0 cM interval between markers using a 10 cM window size. In order to control background from flanking markers, a forward and backward stepwise regression with five controlling markers was conducted. The significant QTLs were identified when the empirical logarithm of the odds (LOD) threshold was determined by the 1,000 permutations at a significance ( p < 0.05) [ 28 ]. These threshold LOD values were from 2.86 to 3.09 in four DH populations, respectively. The LOD threshold of 3.00 was used for four DH populations for simplicity. The confidence interval for each QTL position was estimated with the 1.5-LOD support interval method [ 29 ]. Results Phenotypic variation and heritability in kernel lysine content Four DH populations were developed bu using eight inbred lines, which had the lysine content with a range of 0.18–0.44%. Totally, the four DH populations included 190–316 lines, respectively (Table 1 ). The lysine content exhibited continuously and approximately normal distribution in each DH population with a range of 0.12–0.50% (Fig. 1 , Table 1 ). ANOVA variance analysis exhibited that genotype variance was greater than environmental variance in nearly all populations. It was demonstrated that lysine content variations were mainly controlled by genetic factors and the alleles responsible for increasing the phenotype reside in both parents. High broad-sense heritability estimates were calculated for lysine content in the four DH populations, with a range of 93–96% (Table 1 ), which indicated that the majority of lysine content variations are controlled by genetic factors and suitable for further QTL mapping. Table 1 Phenotypic performance, variance, and broad-sense heritability of lysine content in the four DH populations Trait a Populations AF109 AF116 AF129 AF170 Parents means ± SD (%) KB319003 0.21 ± 0.03 KB519007 0.34 ± 0.04 KB320005 0.25 ± 0.04 JinQingWL2 0.18 ± 0.01 AJ317001 0.38 ± 0.02 KB717001 0.43 ± 0.03 KB120001 0.37 ± 0.03 KB319004 0.44 ± 0.02 p value b <0.0001**** <0.0002*** <0.0001**** <0.0001**** DHs Size 248 190 316 265 means ± SD (%) 0.32 ± 0.07 0.32 ± 0.07 0.33 ± 0.06 0.27 ± 0.08 Range (%) 0.17–0.49 0.18–0.49 0.18–0.49 0.12–0.50 σ g 2 c 0.018 0.015 0.013 0.016 σ e 2 d 0.012 0.014 0.025 0.043 σ ε 2 e 0.082 0.073 0.134 0.068 h 2 (%) f 95.00% 93.00% 93.00% 96.00% a lysine content; b P value based on a t-test evaluating two parental lines; c genetic variance; d environmental variance; e residual variance ; f broad-sense heritability (h 2 ); *** p < 0.001, **** p < 0.0001. Genotyping and genetic linkage map All DH lines in four populations were genotyped using GenoBaits Maize 2K marker panel containing 10,378 SNP markers, and further refined by eliminating SNPs with MAF 0.6. This resulted in a total of 8,377 SNPs, with their precise physical positions based on the B73 reference sequence Version 4, were polymorphic between their respective parents for AF109, AF116, AF129 and AF170, respectively. In each DH population, the missing rate in most lines were less than 2%. The average genetic distance between every two adjacent markers was 0.98, 0.72, 0.66, and 0.78 cM in each DH population, respectively. Identification of QTLs for lysine content in four DH populations The QTL mapping for lysine content in the four DH populations and the related genetic features were summarized in Table 2 . In total, 12 QTLs were identified with a LOD threshold of above 3.00 in the four populations (Fig. 2 ). These QTLs were located on chromosomes 1, 2, 3, 4, 5, 6, 9 and 10. The average of QTL physical intervals was 15.91 Mb in a range of 0.85–32.17 Mb. The average of the total PVE explained by all identified QTLs in a population was 17.68 and ranged from 8.70 (AF116) to 26.06% (AF129). This was less than broad-sense heritability (Table 1 and Table 2 ), suggesting that only part of QTLs have been detected in bi-parent populations. In AF109, three QTLs ( qLYS-1-1 , qLYS-1-2 and qSC-1-3 ) were detected on chromosome 3, 4 and 9, respectively. Phenotypic variation explained by these QTLs was 4.42–4.98% and explained by the additive effect of all QTLs was 11.94%. The parental AJ317001 allele at qLYS-1-2 on chromosome 4 and qLYS-1-3 on chromosome 9 increased lysine content. The QTL qLYS-1-1 was located on chromosome 3 and explained 4.42% of phenotypic variance. The parental KB319003 allele at this locus decreased lysine content. In AF116, a total of two QTLs ( qLYS-2-1 and qLYS-2-2 ) were identified and accounted for 8.70% of the total phenotypic variance. The QTL qLYS-2-1 was located on chromosome 3 and contributed to 4.46% of the explained phenotypic variance. The QTL qLYS-2-2 on chromosome 9 explained 5.60% of phenotypic variance. Parental KB717001 allele at qLYS-2-2 increased lysine content, while parental KB519007 allele at qLYS-2-1 decreased lysine content. In AF129, a total of three QTLs ( qLYS-3-1 , qLYS-3-2 and qLYS-3-3 ) were detected on chromosome 1, 2 and 6, respectively and explained by the additive effect of all QTLs was 26.06%. Phenotypic variation explained by each individual QTL was 5.29–12.66%. Highly effective QTL qLYS-3-3 were detected on chromosome 6 which explained a phenotypic variance of more than 10%, suggesting that qLYS-3-3 was the major QTL controlling lysine content in AF129. The parental KB320005 allele at qLYS-3-1 on chromosome 1 with a phenotypic variance of 5.57% and QTL qLYS-3-3 on chromosome 6 increased lysine content. The QTL qLYS-3-2 was located on chromosome 2 and explained 5.29% of phenotypic variance. The parental KB120001 allele at this locus decreased lysine content. In AF170, four QTLs ( qLYS-4-1 , qLYS-4-2 , qLYS-4-3 and qLYS-4-4 ) distributed on chromosome 1, 5, 9 and 10, respectively. Phenotypic variation explained by these QTLs was 5.23–8.65% and explained by the additive effect of all QTLs was 24.03%. The parental JinQingWL2 allele at qLYS-4-1, qLYS-4-2 and qLYS-4-3 increased the lysine content with the phenotypic variance of 6.92–8.65%, whereas parental KB319004 at qLYS-4-4 decreased lysine content with a phenotypic variance of 5.23%. Table 2 Individual QTL for lysine content in the four DH populations Populations QTL Chr. a P-Peak (Mb)_V4 b P-Range (Mb)_V4 c LOD PVE% d Add. e Parent f + PVE(%) -ALL g AF109 qLYS-1-1 3 69.67 59.07–97.50 3.994 4.42 -0.041 KB319003 11.94 qLYS-1-2 4 44.01 37.64–65.21 3.867 4.67 0.017 AJ317001 qLYS-1-3 9 130.80 129.70-137.80 4.034 4.98 0.021 AJ317001 AF116 qLYS-2-1 3 161.43 161.43–169.00 3.892 4.46 -0.022 KB519007 8.70 qLYS-2-2 9 132.53 130.80-149.39 4.661 5.60 0.031 KB717001 AF129 qLYS-3-1 1 267.92 267.37-271.96 4.226 5.57 0.083 KB320005 26.06 qLYS-3-2 2 214.01 210.90-219.60 4.552 5.29 -0.014 KB120001 qLYS-3-3 6 131.71 117.49-141.72 6.731 12.66 0.016 KB320005 AF170 qLYS-4-1 1 150.61 143.21-159.07 3.529 7.57 0.031 JinQingWL2 24.03 qLYS-4-2 5 12.70 12.70-13.55 4.111 8.65 0.020 JinQingWL2 qLYS-4-3 9 111.07 93.07-125.24 4.990 6.92 0.028 JinQingWL2 qLYS-4-4 10 14.80 13.61–17.92 3.333 5.23 -0.020 KB319004 a Chromosome; b Physical position of QTL based on the B73 reference sequence (V4); c Physical position range of QTL based on the B73 reference sequence (V4); d Percentage of the phenotypic variation explained by the additive effect of QTL; e Additive effect of QTL; f which parental allele increased lysine content; g Percentage of the phenotypic variation explained by the additive effect of all QTL. Genetic overlap of QTLs in the four DH populations To evaluate genetic overlaps among different mapping populations, the 1.5-LOD support interval of QTLs in the four DH populations and other populations for lysine content previously reported were compared (Fig. 3 ). QTLs with overlapping support intervals were considered as common QTLs. Among the four DH population, a 7.00 Mb overlap was observed between qLYS-1-3 and qLYS-2-2 on chromosome 9. Furthermore, there were only a few of overlaps detected after comparing the results with all types of other populations reported. To the end, there were 8.70 Mb and 7.57 Mb overlap with the QTLs in F 2:3 progeny derived from high free amino acids (FAA) parents Oh545 o2 and Oh51A o2 [ 15 ]. Discussion QTL mapping precision The lysine content in maize kernel is a complex quantitative trait [ 30 ]. Location and relationship of opaque2 modifier genes in most of the QPM germplasm are not clear [ 5 ]. Many of these genes had a complex system of genetic control and spread throughout all the ten chromosomes with dosage effects and cytoplasmic effect [ 5 , 31 ]. Several studies have reported the QTLs for lysine content or QPM related traits by using different types of populations, including F 2:3 families [ 5 , 14 , 15 , 30 ] and RILs [ 7 , 14 ]. These QTLs aid in understanding the genetic basis of lysine quantity and quality, and facilitates genetic improvement of QPM in breeding program. The genetic architecture of a quantitative trait consists of a set of parameters that explain the genetic component of trait variation within or among populations [ 32 ]. These parameters include the number of QTL affecting the trait, their locations in the genome, the frequencies of alternative genotypes segregating at the QTL, the pattern of linkage disequilibria among QTL, and the magnitudes of additive, dominance, and epistatic effects [ 32 ]. The advantages of DH populations are the capability of removing any residual heterozygosity to ensure genetically identical replicates and increasing selection response by stabilizing heritability of various traits during perse and test cross evaluation [ 16 – 19 ]. Besides, by conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved with the development of sequencing technology [ 16 , 33 – 38 ]. In our study, we developed four DH populations by using eight inbred lines with lysine content of 0.18–0.44%. The DH populations showed high heritability for lysine content which explained the important effect of genetic factors. A total of 8,377 SNPs were identified after 2K marker microarray assay. This resulted that a total of 12 QTLs were found and distributed on chromosomes 1, 2, 3, 4, 5, 6, 9. Among them, one QTL ( qLYS-4-2 ) spanned the smallest physical interval only 0.85 Mb. Five QTLs ( qLYS-1-3 , qLYS-2-1 , qLYS-3-1 , qLYS-3-2 and qLYS-4-4 ) spanned the physical interval less than 10 Mb. Six QTLs ( qLYS-1-1 , qLYS-1-2 , qLYS-2-2 , qLYS-3-3 , qLYS-4-1 and qLYS-4-3 ) spanned relatively larger physical intervals (15.86–38.43 Mb), which were still less than 40 Mb. Thus, the resolution in this study is considerably improved because of the large number of markers and the appropriate population type. The resolution is probably on the order of 2–3 cM, since pairs of markers any farther apart rarely have substantial levels of linkage disequilibrium [ 32 ]. Genetic basis of lysine content in DH populations In the four DH populations examined in this study, lysine content exhibited a broad range of phenotypic variation with normal distribution. The genetic analysis showed lysine content is highly heritable in all populations, indicating of superior genetic effect on lysine content in DH populations. Among these QTLs, only two QTLs ( qLYS-1-3 and qLYS-2-2 ) spanned a 7.00 Mb physical interval on chromosome 9. The others shared few intervals by all DH populations, reflecting the complexity of lysine content regulation in diverse maize populations. Most of these QTLs had the PVE less than 10%, except the PVE of qLYS-3-3 was 12.66%. These results suggested that a large number of minor-effect QTLs, mainly contributed to the genetic component of lysine content. The QTLs derived from different populations may exhibit consistency to a certain degree across different germplasms/genetic backgrounds and environments. However, by comparing with other studies, only two QTLs showed less than 10 Mb physical intervals overlap with the results from a F 2:3 progeny derived from high FAA parents Oh545 o2 and Oh51A o2 [ 15 ]. It was showed that, qLYS-2-1 in AF116 shared 8.70 Mb with the QTL between flanking markers bmc1904-bmc1452, where qLYS-3-2 in AF129 shared 7.57 Mb Mb with the QTL between flanking markers bmc1537-bmc2248. Analysis showed that for QTLs between bmc1904-bmc1452 and bmc1537-bmc2248, significant linkage with FAA and the alleles contributed by Oh545 o2 are responsible for the high FAA level. Meanwhile, the other QTLs in this study displayed few overlaps with regions associated for lysine content or related traits in multiple former studies [ 7 , 11 , 14 , 30 ]. These results suggested that although some genetic loci may have a common effect on lysine content among different populations, unique QTLs are constantly specialized in each individual population. Except for qLYS-2-1 and qLYS-3-2 , the other QTLs in this study are newly discovered and definitely worth conducting further research via near-isogenic lines (NILs), fine mapping, molecular marker-assisted selection (MAS) and ultimate cloning. Candidate genes relevant to lysine content in maize genetic and breeding Candidate genes have been effectively used in several molecular breeding programs such as QTL mapping, association mapping, marker-assisted selection and development of transgenics for the improvement of agronomically important traits in crop plants [ 5 , 39 ]. Moreover, the co-location analysis could provide information about functional relationships between gene expression and some QTLs of biosynthesis pathway [ 40 ]. In nature, there are two different lysine biosynthesis pathways [ 6 ]. One is via α-aminoadipate which exists in fungi and Euglena [ 41 ]. The other is the diaminopimelate pathway which exists in bacteria, plants, and archaea [ 42 ]. Dihydrodipicolinate synthase (DHDPS) is the core enzyme in the diaminopimelate pathway and has primary roles in regulating the level of lysine accumulation in plant cells [ 43 , 44 ]. In our study, the gene DHPS1 ( Zm00001d046898 ) encoding dihydrodipicolinate synthase1 was found on chromosome 9 and might be solely participated in lysine biosynthesis pathway network during maize seed development [ 6 ] (Fig. 4 ). Diaminopimelate epimerase (DapF) is one of the crucial enzymes involved in lysine biosynthesis, where it converts l,l-diaminopimelate (l,l-DAP) into d,l-DAP in the diaminopimelate pathway [ 6 , 45 – 47 ]. The DapF1 ( Zm00001d030677 ) identified in our study in QTL qLYS-4-1 might be an important gene involved in lysine biosynthesis. Dihydrodipicolinate reductase (DapB) catalyses the second reaction in the diaminopimelate pathway of lysine biosynthesis in the diaminopimelate pathway [ 6 , 48 – 51 ]. In this study, we identified two DapB genes, DapB1 ( Zm00001d047935 ) and DapB2 ( Zm00001d049956 ) on chromosome 9 and 4, respectively, which might be responsible for the key enzymes in the diaminopimelate- and lysine-synthesis pathways that reduces dihydrodipicolinate to tetrahydrodipicolinate. Aiaminopimelic acid (DAP) is a central intermediate that regulate the lysine biosynthesis in DAP-pathway [ 52 ]. In Arabidopsis, the LL-diaminopimelate aminotransferase was found directly regulate DAP synthesis bypassing the DapD-, DapC- and DapE catalyzed steps [ 6 , 52 ]. It was indicating that the gene diaminopimelate aminotransferase2 ( DAPAT2 ) ( Zm00001d047695 ) identified in AF116 DH population on chromosome 9 might have a unique role in maize lysine biosynthesis. Conclusion In this study, four DH populations exhibited continuously and approximately normal distribution were constructed for genetic analysis of kernel lysine content. One major and eleven minor effect QTLs were identified based on the genetic linkage map with LOD threshold of 3.00 and accounted for 4.42–12.66% of lysine content variation. It suggested that a large number of minor-effect QTLs mainly contributed to the genetic component of lysine content. Ten novel QTLs have never been reported in any previous studies. Besides, five well-known genes encoding key enzymes in maize lysine biosynthesis pathways located within QTLs intervals. Our results provide insight to further understanding of genetic variation in lysine content in maize kernels and will be highly useful for further exploration of candidate genes associated with lysine content and OPM germplasm. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Acknowledgments We thank all members of our laboratories for helpful discussion and assistance during this research. Authors’ contributions BL and XZ conceived the study. 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The B73 maize genome: complexity, diversity, and dynamics. Science. 2009;326:1112–5. Chia JM, Song C, Bradbury PJ, Costich D, de Leon N, Doebley J, et al. Maize HapMap2 identifies extant variation from a genome in flux. Nat Genet. 2012;44:803–7. Bukowski R, Guo X, Lu Y, Zou C, He B, Rong Z, et al. Construction of the third generation Zea mays haplotype map. Gigascience. 2018;7:1–12. Flutre T, Le Cunff L, Fodor A, Launay A, Romieu C, Berger G et al. A genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs. G3 GenesGenomesGenetics. 2022;12:jkac103. Kaur G, Pathak M, Singla D, Chhabra G, Chhuneja P, Kaur Sarao N. Quantitative trait loci mapping for earliness, fruit, and seed related traits using high density genotyping-by-sequencing-based genetic map in bitter gourd (Momordica charantia L). Front Plant Sci. 2022;12:799932. Barret P, Brinkman M, Dufour P, Murigneux A, Beckert M. Identification of candidate genes for in vitro androgenesis induction in maize. Theor Appl Genet. 2004;109:1660–8. Thevenot C. QTLs for enzyme activities and soluble carbohydrates involved in starch accumulation during grain filling in maize. J Exp Bot. 2005;56:945–58. Xu H, Andi B, Qian J, West AH, Cook PF. The α-aminoadipate pathway for lysine biosynthesis in fungi. Cell Biochem Biophys. 2006;46:43–64. Velasco AM, Leguina JI, Lazcano A. Molecular evolution of the lysine biosynthetic pathways. J Mol Evol. 2002;55:445–9. Bittel DC, Shaver JM, Somers DA, Gengenbach BG. Lysine accumulation in maize cell cultures transformed with a lysine-insensitive form of maize dihydrodipicolinate synthase. Theor Appl Genet. 1996;92:70–7. Vauterin M, Frankard V, Jacobs M. The Arabidopsis thaliana dhdps gene encoding dihydrodipicolinate synthase, key enzyme of lysine biosynthesis, is expressed in a cell-specific manner. Plant Mol Biol. 1999;39:695–708. Chatterjee SP, Singh BK, Gilvarg C. Biosynthesis of lysine in plants: the putative role of meso-diaminopimelate dehydrogenase. Plant Mol Biol. 1994;26:285–90. Sagong HY, Kim KJ. Structural basis for redox sensitivity in Corynebacterium glutamicum diaminopimelate epimerase: an enzyme involved in l-lysine biosynthesis. Sci Rep. 2017;7:42318. Singh S, Praveen A, Khanna SM. Computational modelling, functional characterization and molecular docking to lead compounds of bordetella pertussis diaminopimelate epimerase. Appl Biochem Biotechnol. 2023;195:6675–93. Christensen JB, Soares Da Costa TP, Faou P, Pearce FG, Panjikar S, Perugini MA. Structure and function of cyanobacterial DHDPS and DHDPR. Sci Rep. 2016;6:37111. Lee CW, Park SH, Lee SG, Park HH, Kim HJ, Park H, et al. Crystal structure of dihydrodipicolinate reductase (PaDHDPR) from Paenisporosarcina sp. TG-14: structural basis for NADPH preference as a cofactor. Sci Rep. 2018;8:7936. Watkin SAJ, Keown JR, Richards E, Goldstone DC, Devenish SRA, Grant Pearce F. Plant DHDPR forms a dimer with unique secondary structure features that preclude higher-order assembly. Biochem J. 2018;475:137–50. Mackie ERR, Barrow AS, Giel MC, Hulett MD, Gendall AR, Panjikar S, et al. Repurposed inhibitor of bacterial dihydrodipicolinate reductase exhibits effective herbicidal activity. Commun Biol. 2023;6:550. Hudson AO, Singh BK, Leustek T, Gilvarg C. An LL-diaminopimelate aminotransferase defines a novel variant of the lysine biosynthesis pathway in plants. Plant Physiol. 2006;140:292–301. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Sep, 2024 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 26 Apr, 2024 Submission checks completed at journal 23 Apr, 2024 Editor assigned by journal 23 Apr, 2024 First submitted to journal 18 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4290194","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":295788555,"identity":"4823328b-5d21-48ce-9ed4-d42bc9dde2e8","order_by":0,"name":"Xiaolei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYFAC5gYgYcPDz95AtBZGkNI0GcmeA6RpOWxjcMOBSA3yMxLbHvP8Oc/DcIOB8cPHHGLs6DnYbszDc5uHcXYDs+TMbURoYWZvbJPmkbjNwyxzgI2ZlxgtbMyMQC0G53jYJBKI1MIDtiXhAA8P0VokeA62Sc45kMwDZDQT5xf5GcnHJN78sbO3P9588MNHYrSAABMPmAJHEJGA8QfxakfBKBgFo2AkAgB/fS85/An/5wAAAABJRU5ErkJggg==","orcid":"","institution":"Heilongjiang Provincial Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Xiaolei","middleName":"","lastName":"Zhang","suffix":""},{"id":295788556,"identity":"10c69e98-022b-4e60-ac44-aa46b8dc4fbd","order_by":1,"name":"Hongtao Wen","email":"","orcid":"","institution":"Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hongtao","middleName":"","lastName":"Wen","suffix":""},{"id":295788557,"identity":"e34db1b5-6c13-4294-b7c6-a8f0f5f2bfac","order_by":2,"name":"Jing Wang","email":"","orcid":"","institution":"Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":295788558,"identity":"669855f6-6dbd-4ec5-b730-12353f3a9396","order_by":3,"name":"Lin Zhao","email":"","orcid":"","institution":"Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhao","suffix":""},{"id":295788560,"identity":"1ace0789-aa4d-41a3-8f5a-c6bd6508c183","order_by":4,"name":"Lei Chen","email":"","orcid":"","institution":"Heilongjiang Provincial Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Chen","suffix":""},{"id":295788562,"identity":"070c4fb8-df9a-4032-aca2-a7c74904112e","order_by":5,"name":"Jialei Li","email":"","orcid":"","institution":"Food Processing Institute, Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jialei","middleName":"","lastName":"Li","suffix":""},{"id":295788564,"identity":"d68ebf7f-af43-46fe-b240-0d94c58c26a0","order_by":6,"name":"Haitao Guan","email":"","orcid":"","institution":"Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Haitao","middleName":"","lastName":"Guan","suffix":""},{"id":295788566,"identity":"45967837-38ae-4280-a45b-b81fb934247b","order_by":7,"name":"Zhenhai Cui","email":"","orcid":"","institution":"Key Laboratory of Soybean Molecular Design Breeding/State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhenhai","middleName":"","lastName":"Cui","suffix":""},{"id":295788567,"identity":"ae057542-cd57-4701-800d-c0545362ca32","order_by":8,"name":"Baohai Liu","email":"","orcid":"","institution":"Quality and Safety Institute of Agricultural Products, Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Baohai","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-04-19 01:29:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4290194/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4290194/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-024-10754-9","type":"published","date":"2024-09-11T15:58:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55550366,"identity":"7a061fca-7078-4fa7-915c-a0d38b3768f8","added_by":"auto","created_at":"2024-04-29 21:11:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":322167,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic variation of lysine content in the four DH populations. The \u003cem\u003ex\u003c/em\u003e-axis showed the lysine contentand the \u003cem\u003ey\u003c/em\u003e-axis indicated the frequency\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4290194/v1/22f06c1f233051e4c6595cd9.png"},{"id":55550364,"identity":"03ffd99e-93ef-4628-883b-96eda9f06ecc","added_by":"auto","created_at":"2024-04-29 21:11:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":378744,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of QTLs across the entire genome in the four DH populations. \u003cstrong\u003eA-D\u003c/strong\u003e designated AF109, AF116, AF129 and AF170 population, respectively\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4290194/v1/56e1f45b86fe7ff7d593ae3e.png"},{"id":55550365,"identity":"0c96b227-7fa2-4b30-a935-5f537dbc08a4","added_by":"auto","created_at":"2024-04-29 21:11:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183102,"visible":true,"origin":"","legend":"\u003cp\u003eOverlap of lysine content QTLs in maize kernels identified in the present and previous studies. The QTLs identified in this study were represented on top. QTLs detected in previous studies were displayed in the form of references. The lower layer showed the number of detected QTLs\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4290194/v1/0c781f9e09c54b07a4224205.png"},{"id":55550367,"identity":"421f8595-e999-44af-abbd-47926c0833e0","added_by":"auto","created_at":"2024-04-29 21:11:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":309110,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of candidate genes with kernel lysine content QTLs. The QTLs identified in four DH populations are represented as vertical rectangles of different colors next to each chromosome. The left labels denote known genes that co-localized with the QTLs\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4290194/v1/208850e342c1177c3b023f5d.png"},{"id":64619593,"identity":"ef072eff-837b-49ef-8a22-8ca6d557d0d8","added_by":"auto","created_at":"2024-09-16 16:16:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1903287,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4290194/v1/7259a347-7a3a-4f6e-b327-0f77aee96dae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic analysis of QTLs for lysine content in four maize DH populations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) is one of the main crops worldwide and serves as an important source of nutrition [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the poor lysine content of maize greatly limited essential amino acids for human and livestock [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, the lysine contents in maize endosperm are considered to be one of the most important traits for determining the nutritional quality of food and feed [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn order to improve breeding for balanced amino acid composition of maize kernels, many research efforts has been expended on identify the genes controlling amino acid content in the maize kernel and a large number of mutants related to maize endosperm have been found [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The \u003cem\u003eopaque-2\u003c/em\u003e (\u003cem\u003eo2\u003c/em\u003e) mutation has about a 50% reduction in zeins and nearly doubles the lysine content of maize endosperm compared to normal genotypes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the soft and starchy endosperm associated with \u003cem\u003eo2\u003c/em\u003e lead to brittleness and insect susceptibility, both in storage and in the field, thereby decreasing the value of the grain [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The quality protein maize (QPM) has been used in breeding programs to develop semi-vitreous and vitreous phenotypes with high Lys content and solve the problems [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The development and widespread use of QPM is limited because of the genetically complex germplasm and the technical complexity of the multiple loci [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo elucidate genetic variation in lysine content, several QTL studies have been carried out using different mapping methods and populations, and many QTLs associated with lysine content or OPM related traits in maize kernel have been revealed. For instance, five significant QTLs for \u003cem\u003eopaque2\u003c/em\u003e modifiers were identified in F\u003csub\u003e2:3\u003c/sub\u003e individuals from a cross between two isogenic QPM inbreds and showed influencing the tryptophan content using functional and genomic SSR markers. Holding et al. found that seven major QTLs associated with \u003cem\u003eo2\u003c/em\u003e endosperm modification from developed QPM lines and showed that chromosomes 5, 7 and 9 might be a major hub of \u003cem\u003eopaque2\u003c/em\u003e modifiers[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Wang and Larkins identified four significant loci that account for about 46% of the phenotypic variance and coincident with the genes involved in free amino acids biosynthetic pathways[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Deng et al. identified four QTLs for lysine content in three RIL populations and one QTL were further validated using molecular[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferent mapping populations with advantages and limitations will cause great impacts on QTL outputs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Double haploid (DH) segregating populations have been widely used in QTL analysis because of the specific advantages that enable to remove any residual heterozygosity, ensuring replicates genetically identical [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and increase selection response by stabilizing heritability of various traits during perse and test cross evaluation with the relatively high genetic variance in DH lines [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, four DH populations derived from the practical breeding program were used for further dissection of the genetic basis and QTLs controlling the phenotypic variation of lysine content in maize kernels. Our results will provide insights into the genetic basis of lysine biosynthesis in maize kernels and may facilitate marker-based breeding for quality protein maize.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eFour DH populations (AF109, AF116, AF129 and AF170) with 248, 190, 316 and 265 lines, respectively, were developed from eight maize inbred lines (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The eight parents belonged to elite inbred lines used for breeding program at Maize Yufeng Biotechnology LLC and had a relative higher lysine content than other inbred lines (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Plants were grown at Liaoning province, China (LN, 40\u003csup\u003e◦\u003c/sup\u003e`82\u0026prime;N, 123\u003csup\u003e◦\u003c/sup\u003e56\u0026prime;E) with three replication blocks in 2021. Each line was grown in a single-row plot with a row length of 150 cm and 60 cm between rows under natural field conditions. All plants in each row were self-pollinated and harvested after maturity. Kernels from middle part of three well-grown ears were bulked for lysine content measurement. We declare that all the collections of plant and seed specimens related to this study were performed in accordance with the relevant guidelines and regulations by Ministry of Agriculture (MOA) of the People's Republic of China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLysine content measurement and phenotypic data analysis\u003c/h2\u003e \u003cp\u003eNear infrared reflectance (NIR) spectrometer (DA 7250, Perten Instruments Inc., Sweden) was used to determine the lysine content in maize kernels. Reflectance spectra (R) from bulk whole grains of at least 50 kernels from each sample were collected at 10-nm intervals in the NIR region from 400 to 2500 nm. The averaged results were obtained from three times scanning of each sample.\u003c/p\u003e \u003cp\u003eR Version 4.0.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.R-project.org\" target=\"_blank\"\u003ewww.R-project.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to perform all statistical analyses. The variances of the lysine content were estimated by using the R function \u0026lsquo;AOV\u0026rsquo;. The model for the variance analysis was y\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;α\u003csub\u003eg\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ee\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε, where α\u003csub\u003eg\u003c/sub\u003e is the effect of the g\u003csup\u003eth\u003c/sup\u003e line, β\u003csub\u003ee\u003c/sub\u003e is the effect of the e\u003csup\u003eth\u003c/sup\u003e environment, and ε is the error. All of the effects were considered to be random. These variance components were used to calculate the broad-sense heritability as \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;σ\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e/\u003c/em\u003e (σ\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u003cem\u003e+\u003c/em\u003e\u0026thinsp;σ\u003csub\u003e\u003cem\u003eε\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003ee\u003c/em\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], where σ\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e is the genetic variance, σ\u003csub\u003e\u003cem\u003eε\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e is the residual error, and \u003cem\u003ee\u003c/em\u003e is the number of environments. The best linear unbiased predictor (BLUP) value for each line was calculated to eliminate the influence of environmental effects by using a linear mixed model that considered both genotype and environment as random effects in the R function \u0026lsquo;LME4\u0026rsquo;. The model was y\u003csub\u003eij\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;e\u003csub\u003ei\u003c/sub\u003e + f\u003csub\u003ej\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eij\u003c/sub\u003e, where y\u003csub\u003eij\u003c/sub\u003e is the phenotypic value of individual j in environment i, \u0026micro; is the grand mean, e\u003csub\u003ei\u003c/sub\u003e is the effect of different environments, f\u003csub\u003ej\u003c/sub\u003e is the genetic effect, and ε\u003csub\u003eij\u003c/sub\u003e is the random error. The BLUP values were used for phenotypic description statistics and QTL analysis.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eGenotyping and constructing genetic linkage map\u003c/h2\u003e \u003cp\u003eThe GenoBaits Maize 2K marker panel containing 10,378 SNP markers, which was developed by Mol Breeding Biotechnology Co., Ltd., Shijiazhuang, China (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.molbreeding.com/\u003c/span\u003e\u003cspan address=\"http://www.molbreeding.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), based on genotyping by target sequencing platform in maize [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] was used to genotype all lines. CrossMap Version 2.0.5 was used to convert the SNP positions in the B73 reference genome Version 3 to those in Version 4 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. SNPs with minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;0.1 or missing rate\u0026thinsp;\u0026gt;\u0026thinsp;0.6 were filtered out in each population. Subsequently, the genetic linkage maps were constructed with the high quality SNPs in each population via the R/qtl package functions est.rf and est.map [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] with the kosambi mapping method.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQTL mapping\u003c/h2\u003e \u003cp\u003eComposite interval mapping (CIM) method implemented in Windows QTL Cartographer 2.5 was used to analyze the QTLs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Genome was scanned at every 1.0 cM interval between markers using a 10 cM window size. In order to control background from flanking markers, a forward and backward stepwise regression with five controlling markers was conducted. The significant QTLs were identified when the empirical logarithm of the odds (LOD) threshold was determined by the 1,000 permutations at a significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These threshold LOD values were from 2.86 to 3.09 in four DH populations, respectively. The LOD threshold of 3.00 was used for four DH populations for simplicity. The confidence interval for each QTL position was estimated with the 1.5-LOD support interval method [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic variation and heritability in kernel lysine content\u003c/h2\u003e \u003cp\u003eFour DH populations were developed bu using eight inbred lines, which had the lysine content with a range of 0.18\u0026ndash;0.44%. Totally, the four DH populations included 190\u0026ndash;316 lines, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The lysine content exhibited continuously and approximately normal distribution in each DH population with a range of 0.12\u0026ndash;0.50% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). ANOVA variance analysis exhibited that genotype variance was greater than environmental variance in nearly all populations. It was demonstrated that lysine content variations were mainly controlled by genetic factors and the alleles responsible for increasing the phenotype reside in both parents. High broad-sense heritability estimates were calculated for lysine content in the four DH populations, with a range of 93\u0026ndash;96% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which indicated that the majority of lysine content variations are controlled by genetic factors and suitable for further QTL mapping.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhenotypic performance, variance, and broad-sense heritability of lysine content in the four DH populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrait \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003ePopulations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAF109\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAF116\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAF129\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eAF170\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParents\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003emeans\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKB319003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKB519007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKB320005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJinQingWL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAJ317001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKB717001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKB120001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKB319004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;0.0001****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;0.0001****\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026lt;0.0001****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDHs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emeans\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.17\u0026ndash;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.18\u0026ndash;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.18\u0026ndash;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.12\u0026ndash;0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eσ\u003c/b\u003e\u003csub\u003e\u003cb\u003eg\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e2 c\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eσ\u003c/b\u003e\u003csub\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e2 d\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eσ\u003c/b\u003e\u003csub\u003e\u003cb\u003eε\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e2 e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eh\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e(%)\u003c/b\u003e \u003csup\u003e\u003cb\u003ef\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e95.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e93.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e93.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e96.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e lysine content; \u003csup\u003eb\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e value based on a t-test evaluating two parental lines; \u003csup\u003ec\u003c/sup\u003e genetic variance; \u003csup\u003ed\u003c/sup\u003e environmental variance; \u003csup\u003ee\u003c/sup\u003e residual variance ; \u003csup\u003ef\u003c/sup\u003e broad-sense heritability (h\u003csup\u003e2\u003c/sup\u003e); *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping and genetic linkage map\u003c/h2\u003e \u003cp\u003eAll DH lines in four populations were genotyped using GenoBaits Maize 2K marker panel containing 10,378 SNP markers, and further refined by eliminating SNPs with MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.1 or missing rate\u0026thinsp;\u0026gt;\u0026thinsp;0.6. This resulted in a total of 8,377 SNPs, with their precise physical positions based on the B73 reference sequence Version 4, were polymorphic between their respective parents for AF109, AF116, AF129 and AF170, respectively. In each DH population, the missing rate in most lines were less than 2%. The average genetic distance between every two adjacent markers was 0.98, 0.72, 0.66, and 0.78 cM in each DH population, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of QTLs for lysine content in four DH populations\u003c/h2\u003e \u003cp\u003eThe QTL mapping for lysine content in the four DH populations and the related genetic features were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In total, 12 QTLs were identified with a LOD threshold of above 3.00 in the four populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These QTLs were located on chromosomes 1, 2, 3, 4, 5, 6, 9 and 10. The average of QTL physical intervals was 15.91 Mb in a range of 0.85\u0026ndash;32.17 Mb. The average of the total PVE explained by all identified QTLs in a population was 17.68 and ranged from 8.70 (AF116) to 26.06% (AF129). This was less than broad-sense heritability (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting that only part of QTLs have been detected in bi-parent populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn AF109, three QTLs (\u003cem\u003eqLYS-1-1\u003c/em\u003e, \u003cem\u003eqLYS-1-2\u003c/em\u003e and \u003cem\u003eqSC-1-3\u003c/em\u003e) were detected on chromosome 3, 4 and 9, respectively. Phenotypic variation explained by these QTLs was 4.42\u0026ndash;4.98% and explained by the additive effect of all QTLs was 11.94%. The parental AJ317001 allele at \u003cem\u003eqLYS-1-2\u003c/em\u003e on chromosome 4 and \u003cem\u003eqLYS-1-3\u003c/em\u003e on chromosome 9 increased lysine content. The QTL \u003cem\u003eqLYS-1-1\u003c/em\u003e was located on chromosome 3 and explained 4.42% of phenotypic variance. The parental KB319003 allele at this locus decreased lysine content.\u003c/p\u003e \u003cp\u003eIn AF116, a total of two QTLs (\u003cem\u003eqLYS-2-1\u003c/em\u003e and \u003cem\u003eqLYS-2-2\u003c/em\u003e) were identified and accounted for 8.70% of the total phenotypic variance. The QTL \u003cem\u003eqLYS-2-1\u003c/em\u003e was located on chromosome 3 and contributed to 4.46% of the explained phenotypic variance. The QTL \u003cem\u003eqLYS-2-2\u003c/em\u003e on chromosome 9 explained 5.60% of phenotypic variance. Parental KB717001 allele at \u003cem\u003eqLYS-2-2\u003c/em\u003e increased lysine content, while parental KB519007 allele at \u003cem\u003eqLYS-2-1\u003c/em\u003e decreased lysine content.\u003c/p\u003e \u003cp\u003eIn AF129, a total of three QTLs (\u003cem\u003eqLYS-3-1\u003c/em\u003e, \u003cem\u003eqLYS-3-2\u003c/em\u003e and \u003cem\u003eqLYS-3-3\u003c/em\u003e) were detected on chromosome 1, 2 and 6, respectively and explained by the additive effect of all QTLs was 26.06%. Phenotypic variation explained by each individual QTL was 5.29\u0026ndash;12.66%. Highly effective QTL \u003cem\u003eqLYS-3-3\u003c/em\u003e were detected on chromosome 6 which explained a phenotypic variance of more than 10%, suggesting that \u003cem\u003eqLYS-3-3\u003c/em\u003e was the major QTL controlling lysine content in AF129. The parental KB320005 allele at \u003cem\u003eqLYS-3-1\u003c/em\u003e on chromosome 1 with a phenotypic variance of 5.57% and QTL \u003cem\u003eqLYS-3-3\u003c/em\u003e on chromosome 6 increased lysine content. The QTL \u003cem\u003eqLYS-3-2\u003c/em\u003e was located on chromosome 2 and explained 5.29% of phenotypic variance. The parental KB120001 allele at this locus decreased lysine content.\u003c/p\u003e \u003cp\u003eIn AF170, four QTLs (\u003cem\u003eqLYS-4-1\u003c/em\u003e, \u003cem\u003eqLYS-4-2\u003c/em\u003e, \u003cem\u003eqLYS-4-3\u003c/em\u003e and \u003cem\u003eqLYS-4-4\u003c/em\u003e) distributed on chromosome 1, 5, 9 and 10, respectively. Phenotypic variation explained by these QTLs was 5.23\u0026ndash;8.65% and explained by the additive effect of all QTLs was 24.03%. The parental JinQingWL2 allele at \u003cem\u003eqLYS-4-1, qLYS-4-2\u003c/em\u003e and \u003cem\u003eqLYS-4-3\u003c/em\u003e increased the lysine content with the phenotypic variance of 6.92\u0026ndash;8.65%, whereas parental KB319004 at \u003cem\u003eqLYS-4-4\u003c/em\u003e decreased lysine content with a phenotypic variance of 5.23%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndividual QTL for lysine content in the four DH populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Peak (Mb)_V4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Range (Mb)_V4\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePVE%\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdd.\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eParent\u003csup\u003ef\u003c/sup\u003e+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePVE(%) -ALL\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAF109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-1-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.07\u0026ndash;97.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKB319003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e11.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-1-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.64\u0026ndash;65.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAJ317001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-1-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129.70-137.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAJ317001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAF116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-2-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161.43\u0026ndash;169.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKB519007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-2-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130.80-149.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKB717001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAF129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-3-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e267.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e267.37-271.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKB320005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e26.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-3-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e210.90-219.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKB120001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-3-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117.49-141.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKB320005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAF170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-4-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143.21-159.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eJinQingWL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e24.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-4-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.70-13.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eJinQingWL2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-4-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.07-125.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eJinQingWL2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqLYS-4-4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.61\u0026ndash;17.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKB319004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Chromosome; \u003csup\u003eb\u003c/sup\u003e Physical position of QTL based on the B73 reference sequence (V4); \u003csup\u003ec\u003c/sup\u003e Physical position range of QTL based on the B73 reference sequence (V4); \u003csup\u003ed\u003c/sup\u003e Percentage of the phenotypic variation explained by the additive effect of QTL; \u003csup\u003ee\u003c/sup\u003e Additive effect of QTL; \u003csup\u003ef\u003c/sup\u003e which parental allele increased lysine content; \u003csup\u003eg\u003c/sup\u003e Percentage of the phenotypic variation explained by the additive effect of all QTL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic overlap of QTLs in the four DH populations\u003c/h2\u003e \u003cp\u003eTo evaluate genetic overlaps among different mapping populations, the 1.5-LOD support interval of QTLs in the four DH populations and other populations for lysine content previously reported were compared (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). QTLs with overlapping support intervals were considered as common QTLs. Among the four DH population, a 7.00 Mb overlap was observed between \u003cem\u003eqLYS-1-3\u003c/em\u003e and \u003cem\u003eqLYS-2-2\u003c/em\u003e on chromosome 9. Furthermore, there were only a few of overlaps detected after comparing the results with all types of other populations reported. To the end, there were 8.70 Mb and 7.57 Mb overlap with the QTLs in F\u003csub\u003e2:3\u003c/sub\u003e progeny derived from high free amino acids (FAA) parents Oh545\u003cem\u003eo2\u003c/em\u003e and Oh51A\u003cem\u003eo2\u003c/em\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQTL mapping precision\u003c/h2\u003e \u003cp\u003eThe lysine content in maize kernel is a complex quantitative trait [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Location and relationship of \u003cem\u003eopaque2\u003c/em\u003e modifier genes in most of the QPM germplasm are not clear [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Many of these genes had a complex system of genetic control and spread throughout all the ten chromosomes with dosage effects and cytoplasmic effect [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Several studies have reported the QTLs for lysine content or QPM related traits by using different types of populations, including F\u003csub\u003e2:3\u003c/sub\u003e families [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and RILs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These QTLs aid in understanding the genetic basis of lysine quantity and quality, and facilitates genetic improvement of QPM in breeding program. The genetic architecture of a quantitative trait consists of a set of parameters that explain the genetic component of trait variation within or among populations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These parameters include the number of QTL affecting the trait, their locations in the genome, the frequencies of alternative genotypes segregating at the QTL, the pattern of linkage disequilibria among QTL, and the magnitudes of additive, dominance, and epistatic effects [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The advantages of DH populations are the capability of removing any residual heterozygosity to ensure genetically identical replicates and increasing selection response by stabilizing heritability of various traits during perse and test cross evaluation [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Besides, by conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved with the development of sequencing technology [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In our study, we developed four DH populations by using eight inbred lines with lysine content of 0.18\u0026ndash;0.44%. The DH populations showed high heritability for lysine content which explained the important effect of genetic factors. A total of 8,377 SNPs were identified after 2K marker microarray assay. This resulted that a total of 12 QTLs were found and distributed on chromosomes 1, 2, 3, 4, 5, 6, 9. Among them, one QTL (\u003cem\u003eqLYS-4-2\u003c/em\u003e) spanned the smallest physical interval only 0.85 Mb. Five QTLs (\u003cem\u003eqLYS-1-3\u003c/em\u003e, \u003cem\u003eqLYS-2-1\u003c/em\u003e, \u003cem\u003eqLYS-3-1\u003c/em\u003e, \u003cem\u003eqLYS-3-2\u003c/em\u003e and \u003cem\u003eqLYS-4-4\u003c/em\u003e) spanned the physical interval less than 10 Mb. Six QTLs (\u003cem\u003eqLYS-1-1\u003c/em\u003e, \u003cem\u003eqLYS-1-2\u003c/em\u003e, \u003cem\u003eqLYS-2-2\u003c/em\u003e, \u003cem\u003eqLYS-3-3\u003c/em\u003e, \u003cem\u003eqLYS-4-1\u003c/em\u003e and \u003cem\u003eqLYS-4-3\u003c/em\u003e) spanned relatively larger physical intervals (15.86\u0026ndash;38.43 Mb), which were still less than 40 Mb. Thus, the resolution in this study is considerably improved because of the large number of markers and the appropriate population type. The resolution is probably on the order of 2\u0026ndash;3 cM, since pairs of markers any farther apart rarely have substantial levels of linkage disequilibrium [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenetic basis of lysine content in DH populations\u003c/h2\u003e \u003cp\u003eIn the four DH populations examined in this study, lysine content exhibited a broad range of phenotypic variation with normal distribution. The genetic analysis showed lysine content is highly heritable in all populations, indicating of superior genetic effect on lysine content in DH populations. Among these QTLs, only two QTLs (\u003cem\u003eqLYS-1-3\u003c/em\u003e and \u003cem\u003eqLYS-2-2\u003c/em\u003e) spanned a 7.00 Mb physical interval on chromosome 9. The others shared few intervals by all DH populations, reflecting the complexity of lysine content regulation in diverse maize populations. Most of these QTLs had the PVE less than 10%, except the PVE of \u003cem\u003eqLYS-3-3\u003c/em\u003e was 12.66%. These results suggested that a large number of minor-effect QTLs, mainly contributed to the genetic component of lysine content.\u003c/p\u003e \u003cp\u003eThe QTLs derived from different populations may exhibit consistency to a certain degree across different germplasms/genetic backgrounds and environments. However, by comparing with other studies, only two QTLs showed less than 10 Mb physical intervals overlap with the results from a F\u003csub\u003e2:3\u003c/sub\u003e progeny derived from high FAA parents Oh545\u003cem\u003eo2\u003c/em\u003e and Oh51A\u003cem\u003eo2\u003c/em\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It was showed that, \u003cem\u003eqLYS-2-1\u003c/em\u003e in AF116 shared 8.70 Mb with the QTL between flanking markers bmc1904-bmc1452, where \u003cem\u003eqLYS-3-2\u003c/em\u003e in AF129 shared 7.57 Mb Mb with the QTL between flanking markers bmc1537-bmc2248. Analysis showed that for QTLs between bmc1904-bmc1452 and bmc1537-bmc2248, significant linkage with FAA and the alleles contributed by Oh545\u003cem\u003eo2\u003c/em\u003e are responsible for the high FAA level. Meanwhile, the other QTLs in this study displayed few overlaps with regions associated for lysine content or related traits in multiple former studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These results suggested that although some genetic loci may have a common effect on lysine content among different populations, unique QTLs are constantly specialized in each individual population. Except for \u003cem\u003eqLYS-2-1\u003c/em\u003e and \u003cem\u003eqLYS-3-2\u003c/em\u003e, the other QTLs in this study are newly discovered and definitely worth conducting further research via near-isogenic lines (NILs), fine mapping, molecular marker-assisted selection (MAS) and ultimate cloning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCandidate genes relevant to lysine content in maize genetic and breeding\u003c/h2\u003e \u003cp\u003eCandidate genes have been effectively used in several molecular breeding programs such as QTL mapping, association mapping, marker-assisted selection and development of transgenics for the improvement of agronomically important traits in crop plants [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, the co-location analysis could provide information about functional relationships between gene expression and some QTLs of biosynthesis pathway [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn nature, there are two different lysine biosynthesis pathways [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. One is via α-aminoadipate which exists in fungi and \u003cem\u003eEuglena\u003c/em\u003e [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The other is the diaminopimelate pathway which exists in bacteria, plants, and archaea [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Dihydrodipicolinate synthase (DHDPS) is the core enzyme in the diaminopimelate pathway and has primary roles in regulating the level of lysine accumulation in plant cells [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In our study, the gene \u003cem\u003eDHPS1\u003c/em\u003e (\u003cem\u003eZm00001d046898\u003c/em\u003e) encoding dihydrodipicolinate synthase1 was found on chromosome 9 and might be solely participated in lysine biosynthesis pathway network during maize seed development [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Diaminopimelate epimerase (DapF) is one of the crucial enzymes involved in lysine biosynthesis, where it converts l,l-diaminopimelate (l,l-DAP) into d,l-DAP in the diaminopimelate pathway [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The \u003cem\u003eDapF1\u003c/em\u003e (\u003cem\u003eZm00001d030677\u003c/em\u003e) identified in our study in QTL \u003cem\u003eqLYS-4-1\u003c/em\u003e might be an important gene involved in lysine biosynthesis. Dihydrodipicolinate reductase (DapB) catalyses the second reaction in the diaminopimelate pathway of lysine biosynthesis in the diaminopimelate pathway [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In this study, we identified two DapB genes, \u003cem\u003eDapB1\u003c/em\u003e (\u003cem\u003eZm00001d047935\u003c/em\u003e) and \u003cem\u003eDapB2\u003c/em\u003e (\u003cem\u003eZm00001d049956\u003c/em\u003e) on chromosome 9 and 4, respectively, which might be responsible for the key enzymes in the diaminopimelate- and lysine-synthesis pathways that reduces dihydrodipicolinate to tetrahydrodipicolinate. Aiaminopimelic acid (DAP) is a central intermediate that regulate the lysine biosynthesis in DAP-pathway [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In Arabidopsis, the LL-diaminopimelate aminotransferase was found directly regulate DAP synthesis bypassing the DapD-, DapC- and DapE catalyzed steps [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. It was indicating that the gene \u003cem\u003ediaminopimelate aminotransferase2\u003c/em\u003e (\u003cem\u003eDAPAT2\u003c/em\u003e) (\u003cem\u003eZm00001d047695\u003c/em\u003e) identified in AF116 DH population on chromosome 9 might have a unique role in maize lysine biosynthesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, four DH populations exhibited continuously and approximately normal distribution were constructed for genetic analysis of kernel lysine content. One major and eleven minor effect QTLs were identified based on the genetic linkage map with LOD threshold of 3.00 and accounted for 4.42\u0026ndash;12.66% of lysine content variation. It suggested that a large number of minor-effect QTLs mainly contributed to the genetic component of lysine content. Ten novel QTLs have never been reported in any previous studies. Besides, five well-known genes encoding key enzymes in maize lysine biosynthesis pathways located within QTLs intervals. Our results provide insight to further understanding of genetic variation in lysine content in maize kernels and will be highly useful for further exploration of candidate genes associated with lysine content and OPM germplasm.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all members of our laboratories for helpful discussion and assistance during this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBL\u0026nbsp;and\u0026nbsp;XZ\u0026nbsp;conceived the\u0026nbsp;study.\u0026nbsp;HG\u0026nbsp;designed the experiments.\u0026nbsp;LZ\u0026nbsp;and\u0026nbsp;JW\u0026nbsp;performed the experiments. ZC\u0026nbsp;and JL\u0026nbsp;analyzed the results.\u0026nbsp;XZ\u0026nbsp;wrote the manuscript.\u0026nbsp;HW\u0026nbsp;and LC\u0026nbsp;provided scientific suggestions and revised the manuscript.All authors have read and agreed to the published version of the manuscript.\u0026nbsp;Both authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (32201798), Heilongjiang Scientific Research Business Expenses Project of China (CZKYF2023-1-C001) and Scientific and Technological In Novation 2030 Agenda of China (2022ZD040190803).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNelson O, Pan D. Starch synthesis in maize endosperms. Annu Rev Plant Biol. 1995;46:475\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalter M. Plant science. 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Plant Physiol. 2006;140:292\u0026ndash;301.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Maize, DH, kernel, lysine content, QTL","lastPublishedDoi":"10.21203/rs.3.rs-4290194/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4290194/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLow level of lysine in maize endosperm is considered to be a major problem for determining the nutritional quality of food and feed. Improving the lysine content is favorable to improve maize quality by optimizing feeding requirement. Understanding the genetic basis of lysine content benefits greatly improving maize yield and optimizing end-use quality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour double haploid (DH) populations were generated and used to identify quantitative trait loci (QTL) associated with lysine content. The broad-sense heritability indicated the majority of lysine content variations were largely controlled by genetic factors. A total of 12 QTLs were identified in a range of 4.42\u0026ndash;12.66% in term of phenotypic variation explained (PVE) which suggested that a large number of minor-effect QTLs mainly contributed to the genetic component of lysine content. Five well-known genes encoding key enzymes in maize lysine biosynthesis pathways locate within QTLs identified in this study.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe information presented will pave a path to explore candidate genes regulating lysine biosynthesis pathways and be useful for marker-assisted selection and gene pyramiding in high-lysine maize breeding programs.\u003c/p\u003e","manuscriptTitle":"Genetic analysis of QTLs for lysine content in four maize DH populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 21:11:23","doi":"10.21203/rs.3.rs-4290194/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-26T10:20:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-24T01:53:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-24T01:53:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-04-19T01:22:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70abb25c-1622-44be-aa03-59f738fc96da","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:10:24+00:00","versionOfRecord":{"articleIdentity":"rs-4290194","link":"https://doi.org/10.1186/s12864-024-10754-9","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2024-09-11 15:58:30","publishedOnDateReadable":"September 11th, 2024"},"versionCreatedAt":"2024-04-29 21:11:23","video":"","vorDoi":"10.1186/s12864-024-10754-9","vorDoiUrl":"https://doi.org/10.1186/s12864-024-10754-9","workflowStages":[]},"version":"v1","identity":"rs-4290194","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4290194","identity":"rs-4290194","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}