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

doi:10.21203/rs.3.rs-4290194/v1

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

https://doi.org/10.21203/rs.3.rs-4290194/v1

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published 11 Sep, 2024

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Background

Low 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.

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

kernel

lysine content

QTL

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.

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 h² = σ_g²/ (σ_g² + σ_ε²/e) [23], where σ_g² is the genetic variance, σ_ε² 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) < 0.1 or missing rate > 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].

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
Trait ^a	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
means ± SD (%)	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
h² (%) ^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 (h²); *** 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 < 0.1 or missing rate > 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
AF116	qLYS-2-2	9	132.53	130.80-149.39	4.661	5.60	0.031	KB717001	8.70
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 Oh545o2 and Oh51Ao2 [15].

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 Oh545o2 and Oh51Ao2 [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 Oh545o2 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.

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.

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. HG designed the experiments. LZ and JW performed the experiments. ZC and JL analyzed the results. XZ wrote the manuscript. HW and LC provided scientific suggestions and revised the manuscript.All authors have read and agreed to the published version of the manuscript. Both authors read and approved the final manuscript.

Funding

This 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).

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No competing interests reported.

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published 11 Sep, 2024

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

Status:

Journal Publication

Version 1

Abstract

Background

Results

Conclusions

Figures

Introduction

Materials and methods

Plant materials

Lysine content measurement and phenotypic data analysis

Genotyping and constructing genetic linkage map

QTL mapping

Results

Phenotypic variation and heritability in kernel lysine content

Genotyping and genetic linkage map

Identification of QTLs for lysine content in four DH populations

Genetic overlap of QTLs in the four DH populations

Discussion

QTL mapping precision

Genetic basis of lysine content in DH populations

Candidate genes relevant to lysine content in maize genetic and breeding

Conclusion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1