Constitution of association panel of tropical field corn (Zea mays L.) for kernel and cob-related traits

Cereal Research Communications Constitution of association panel of tropical field corn (Zeamays L) for kernel and cob-related traits --Manuscript Draft-- Manuscript Number: Full Title: Constitution of association panel of tropical field corn (Zeamays L) for kernel and cob-related traits Article Type: Original Article Section/Category: Breeding Funding Information: ICAR-BMGF Dr Ganapati Mukri Abstract: Maize is one of the model crops for genetic study and has wider variability for yield component traits. Capturing the available variability and utilizing it in the breeding program is the prerequisite for the maize improvement program. On the other hand, the energy and resources required to characterize the germplasm are huge and maintenance of the trait becomes challenging when the population size of the germplasm is large. However, based on the concept of a mini-core collection, the breeder can capture maximum variability by sampling the entire germplasm available and utilizing them in an active crop improvement program. Similarly, the present panel of inbred lines, ‘Field Corn Panel of IARI’, was constituted to use them in trait mapping followed by active utilization in the field corn breeding program of IARI. This panel may serve as the base study material for GWAS analysis, genomic prediction, and identification of tolerant inbred lines for biotic and abiotic stresses and yield component traits, etc. In the present study, a detailed discussion has been made on the construction of a subset of maize germplasm from the available whole set of field corn germplasm of IARI by capturing the maximum variability present. Corresponding Author: Ganapati Mukri Indian Agricultural Research Institute Pusa Campus , New Delhi, Delhi INDIA Corresponding Author Secondary Information: Corresponding Author's Institution: Indian Agricultural Research Institute Corresponding Author's Secondary Institution: First Author: Ganapati Mukri First Author Secondary Information: Order of Authors: Ganapati Mukri Jayant S Bhat, PhD R N Gadag, PhD Kumari Shilpa chandu Singh, PhD R N Dandapani, PhD Navin C Gupta, PhD Digvender Pal, PhD Order of Authors Secondary Information: Author Comments: Development of association panel of tropical field corn (Zea mays L) for kernel and Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation cob-related traits comprising Indian based maize germplasm is first of its kind. The information generated may be utilized for decision making in maize improvement Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation Constitution of association panel of tropical field corn (Zea mays L) for kernel and cob-related traits Ganapati Mukri1*, Jayant S Bhat2, RN Gadag1, Kumari Shilpa1, Chandusingh1, R Dandapani1, Navin C Gupta3 and Digvender Pal1 1ICAR-Indian Agricultural Research Institute, New Delhi, India 2 ICAR-IARI Regional Research Centre, Dharwad, Karnataka, India 3 ICAR-National Institute for Plant Biotechnology, New Delhi, India *ga[email protected], [email protected]ov.in Abstract Maize is one of the model crops for genetic study and has wider variability for yield component traits. Capturing the available variability and utilizing it in the breeding program is the prerequisite for the maize improvement program. On the other hand, the energy and resources required to characterize the germplasm are huge and maintenance of the trait becomes challenging when the population size of the germplasm is large. However, based on the concept of a mini-core collection, the breeder can capture maximum variability by sampling the entire germplasm available and utilizing them in an active crop improvement program. Similarly, the present panel of inbred lines, ‘Field Corn Panel of IARI’, was constituted to use them in trait mapping followed by active utilization in the field corn breeding program of IARI. This panel may serve as the base study material for GWAS analysis, genomic prediction, and identification of tolerant inbred lines for biotic and abiotic stresses and yield component traits, etc. In the present study, a detailed discussion has been made on the construction of a subset of maize germplasm from the available whole set of field corn germplasm of IARI by capturing the maximum variability present. Keywords: Germplasm, core-hunter, field corn, panel, molecular diversity, PCA Introduction Maize has wider genetic variability and phenotypic diversity that can be effectively exploited in maize improvement programs to improve productivity (Flint‐Garcia et al., 2005). Identification of novel genetic variations and mapping of the genomic regions governing the variability is of utmost importance to utilize them in such breeding programs. There are different strategies for capturing these variations in cross-pollinated species like maize. As an outbred species, maize encompasses a high level of phenotypic and molecular diversity. When considering nucleotide diversity, any two random maize lines differ from one another in 1.4% of total DNA, similar to Manuscript Click here to access/download;Manuscript;Maize Panel_final.docx Click here to view linked References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 the divergence observed between humans and chimpanzees (Buckler and Stevens 2005). This kind of variation can be mapped by using different approaches viz., linkage or QTL mapping, and association or linkage disequilibrium (LD) mapping (Xu et al., 2018) to dissect the trait visà-vis breeding perspectives. Maize by its enormous genetic variability (Cooper et al., 2019), availability of SNP information, and rapid LD decay (Mazaheri et al., 2019), make this an ideal crop for GWAS (Xiao et al., 2017). The requirement of a genetically variable population becomes a prerequisite for the mapping of traits underlying natural variations. An ideal population should harbor as much genetic diversity as possible, which is often used to resolve complex trait variation to a single gene or nucleotide. Screening of available germplasm to make a workable collection followed by their utilization in trait mapping may help in fastening the process of trait discovery through association mapping approaches. However, careful selection of germplasm to include existing variation is a challenge since breeder cannot use existing wide variability in the given crop for their improvement followed by their maintenance. Udapdhya and Ortiz (2001) suggested the concept of deriving a mini-core collection or subset population by capturing the maximum variability available in the core population and their ease of handling in subsequent genetic analysis followed by usage of the same in the breeding program. In India, there is limited information available about the formulation of the subset of germplasm harboring extensive variability present in the tropically adapted germplasm (Udapdhya et al., 2012). In this direction, an effort has been made to construct a subset or panel of tropical maize germplasm available in the Indian maize breeding program. Material and method A germplasm collection consisting of 514 distinct genotypes derived from different source populations was used as a base material for this study. These genotypes were evaluated using augmented design during the Kharif-2020 at ICAR-IARI, PUSA, New Delhi. Data on yield and related traits such as Cob Length (CL) (cm), Cob Diameter (CD) (cm), Kernel Row Number (KRN) (No.s), Kernels per Row (KPR) (No.s), Shelling Percentage (SH%), and Grain yield (kg/ha) were recorded and subjected to statistical tools (R package) to calculate descriptive statistics, principal component analysis (PCA), correlation and phenotypic diversity analysis. Further, the population was also subjected to Core Hunter 3.0 (http://www.corehunter.org/r.html) to narrow down the number of genotypes by keeping its diversity intact. Subsequently, the 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 generated subset of 200 genotypes from the main population and six elite breeding lines were combinedly (206 genotypes) evaluated under augmented design during kharif 2021 at the ICARIARI, PUSA campus, New Delhi. Five randomly selected plants were used to record the data on CL, CD, KRN, and KPR and grain yield. Descriptive statistics and a two-sample Welch's t-test (Welch 1947), were used to compare the unequal sample size, original (514), and derived (206) populations, assuming unequal variance. The formula followed for Welch's t-test was as follows. 𝑡= 𝑥ˉ1−𝑥ˉ2 √𝑠12 𝑛1+𝑠22 𝑛2 Where 𝑥ˉ1 and 𝑥ˉ2 are the sample means, 𝑠12 and 𝑠22 are the sample variances, and n1 and n2 are the sample sizes. In addition, a total of 24 SSR markers linked to yield components and heterosis were used to profile these 206 genotypes. DNA was extracted from fresh young leaves of genotypes by the CTAB (Cetyl trimethyl ammonium bromide) method (Saghai-Maroof et al. 1984). The PCR was performed with 1 unit of Taq DNA polymerase, 10× reaction buffer supplied by the manufacturer, 0.1 mM dNTPs, 10 pmol/µl each primer and 50 ng DNA template in a total reaction volume of 25 µl. The PCR amplification conditions were, initial denaturation at 94oC for 5 min followed by 35 cycles consisting of denaturation at 94oC for 30 s, annealing at 55oC for 30 s, extension at 72oC for 60 s and a final extension of 7 min. at 72oC. The PCR-amplified fragments were resolved on 2.5% agarose gel (HiMedia) and gel pictures were archived in a gel documentation framework. The detection of alleles and determination of their sizes were accomplished through an image processor (UNITEC Cambridge). The scoring of the number of peaks and profiles per marker was done by checking the amplification in different accessions. A data matrix involving 62 accessions was then generated, with entries indicating the presence (1) or absence (0) of the amplified SSR fragments. The polymorphic information content (PIC) for each SSR locus was computed using the formula mentioned below. 𝑃𝐼𝐶=1 − ∑_(𝑖=1)^𝑛 𝑝_𝑖^2 − ∑_(𝑖=1)^(𝑛 − 1) ∑_(𝑗=𝑖+ 1)^𝑛 2𝑝_𝑖^2 𝑝_𝑗^2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 where: n – number of alleles; pi – frequency of the ith allele; pj – frequency of the jth allele (Botstein et al. 1980). The molecular diversity was assessed using Darwin Software (https://Darwin.cirad.fr). Results and Discussion The descriptive statistics of 514 genotypes indicated that a wide range of variability exists for each trait. Cob length ranged from 5.5 to 21.45 cm with a mean value of 13.52 cm. Cob diameter recorded a mean of 3.295 cm and it ranged from 1.43 cm to 4.56 cm. The mean values for Kernel Row Number (KRN) and Kernels per Row (KPR) were found to be 14 and 27.55, respectively. The KPR was found to have a range of 11.4 to 48.45, while for KRN it was 10 to 22. Similarly, the mean Shelling percentage recorded was 70.91%, with a range of 62.24 to 89.10%. The grain yield ranged from 480 kg/ha to 5810 kg/ha with a mean of 2090 kg/ha (Table 1). The existing variability in the core population itself can directly be utilized in the maize improvement program (Antony et al., 2024). However, due to the problem in handling such variability, one should classify the total variability into its components followed by the grouping of genotypes based on the major components (Upadhyaya et al., 2012; Syafii et al., 2015). In the present study, the PCA analysis (Fig.1) subdivided total variability into six different components, which can also be utilized for prioritizing the traits for selection (Bradu and Gabriel, 1978). The highest eigenvalue was recorded by cob diameter (2.83) followed by cob length (1.05). On the other hand, the lowest eigenvalue was recorded grain yield (0.16) followed by shelling percentage (0.32). Kernel-related traits viz., KRN and KPR exhibited eigenvalues, 1.01 and 0.63 respectively (Table 2). Cob diameter contributes through PC1(0.49) and PC2 (0.14) and cob length contributes majorly through PC6 (0.67) and PC1 (0.49). The KPR contributes mainly through PC1 (0.53) and PC5 (0.29) and KRN was through PC2 (0.54) and PC5 (0.46) Similarly, PC1(0.4) and PC3 (0.23) were coordinated by Shelling percentage, whereas grain yield was contributed by PC2 (0.72) and PC3 (0.63) (Table 2). This information can be further utilized for selecting the genotypes for trait-based crop improvement (Daudo and Olakojo, 2007). Further phenotypic data on CL, CD, KRN, KPR, SH%, and grain yield were subjected to the correlation of traits among the diverse germplasm comprising 514 genotypes as per the method given by Pearson (Pearson, 1901) to understand the relative association of traits among each other (Mukri et al., 2021). Notably, KPR exhibited a significantly positive correlation with CL (0.72) and yield (0.48). Cob diameter showed a positive and significant association with KRN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 (0.49) and yield (0.43). Shelling Percentage indicated a significant positive correlation with grain yield (0.53). In addition, intercorrelation among these four traits showed that they also have a significant association with each other (Fig. 2) and also influence the expression of grain yield positively (Kumar et. al., 2017, Nagarajan and Nalla thambi, 2017; Yi Q et. al., 2019). Having known variability and association between traits, the genotypes were subjected to diversity analysis. The hierarchical cluster method indicated that the genotypes under study were equally distributed to all coordinates and spread across ten groups (Mukri et al., 2018) suggesting high phenotypic diversity among them (Fig. 3). Further, this set of 514 genotypes was subjected to Core-hunter 3.0 with 38% selection intensity (Thachuk et al., 2008). Using both the information together, a subset of 200 genotypes was then carefully selected from the original pool of 514 inbred lines which is a representative subset of the maize germplasm maintained at IARI, New Delhi. Six breeding lines that are already in use at the IARI maize breeding program were added to the 200 selected genotypes that combinedly constitute the “Field Corn Panel of IARI”. Subsequently, this entire set of 206 inbred lines was evaluated over the next growing season to revalidate (Upadhyaya et al., 2003) and ascertain the extent of variability. For the 206 genotypes comprising the ‘Field Corn Panel of IARI’, the mean values for CL, CD, KRN, KPR, and grain yield were calculated to be 11.96 cm, 3.28 cm, 13.12, 19.47, and 2355 kg/ha respectively. Similarly, the median recorded for the traits viz., CL, CD, KRN, KPR, and grain yield were 11.50cm,3.26cm,13.00,19.33, and 2.26 (t/ha) respectively. The corresponding ranges for CL, CD, KRN, KPR and grain yield were found to be 7.17 cm to 20.4cm, 1.88-4.85 cm, 8 to 24, 9.33 to 36.33 and 649-5469 kg/ha, respectively (Fig. 4). Considering the deviation between the means and median of the population (514) and the selected Field Corn Panel of IARI (206), it was understood that variation present in the population (514) is available in the panel derived from the same too (Upadhyaya and Ortiz., 2001). However, according to Welch's ttest, due to the unequal variance, CL, KRN, KPR, and grain yield showed significant differences among the original population and panel, but CD recorded non-significant differences (Table 3). On the other hand, there is no significant deviation among the range of each trait available in both the original population and panel (Hu et al., 2000, Kim et al., 2007). Hence, it is an opportunity for breeders to capture and exploit the total variability that existed in the original 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 population (514) by executing the selection strategy in the newly constituted panel (Upadhyaya et al. 2006). To understand the molecular diversity available in the panel, the 206 genotypes were subjected to molecular characterization with 24 SSR (Simple Sequence Repeat) markers spanning 10 chromosomes (Kumar et al., 2021, Patil et al., 2023). The molecular diversity analysis indicated that the extracted subset (panel) harbors diverse inbred lines. The 206 inbred lines are further divided into ten clusters containing 5 to 42 inbred lines (Fig. 5). This distribution supports a high level of molecular diversity existed among the genotypes (Samiha et al., 2024), suggesting substantial genetic variability within the studied ‘Field Corn Panel of IARI’ (Zhang et al., 2012). Conclusion The constitution of the panel of maize is a prerequisite for the genetic analysis targeting trait dissection and mapping. Considering the amount of energy required for maintaining the germplasm vis-a-vis capturing the maximum variability for the crop improvement program. The Constitution of a panel comprising the breeding material available in the IARI maize breeding program was undertaken. The newly constituted “Field Corn Panel of IARI” harbors available variability present in the whole germplasm of the field corn breeding program of IARI. Further, the breeder can select, and utilize the promising lines for the maize improvement program along with the trait dissection. Authors contribution GM, Conceptualized, designed, and planned the experiment. GM, NCG, and Dhandapani performed the statistical analysis and GM, RNG, and Shilpa prepared the manuscript. DP, CS, and JSB facilitated the conduct of experiments and data collection. All authors have read, edited, and approved the final draft of the manuscript. Acknowledgment The authors are thankful to the Director, IARI for facilitating field-based experimentation and the ICAR-BMGF project for financial support. Declaration of competing interest All the authors have no conflict of interest to declare. Data availability Data will be available on request. Informed consent 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Informed consent was acquired from all independent participants involved in the current study. References Antony, B. J., Kachapur, R. M., Naidu, G. K., Talekar, S. C., Zerka, M., & Harlapur, S. I. (2024). Genetic variability and character association among maize (Zea mays L.) Inbred lines. Bangladesh J. Bot. 53(1): 57-65 Botstein D., White R.L., Skolnick M., Davis R.W. (1980): Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics, 32: 314. Bradu, D., & Gabriel, K. R. (1978). The biplot as a diagnostic tool for models of two-way tables. Technometrics, 20(1), 47-68. Buckler, E. S., & Stevens, N. M. (2006). Maize origins, domestication, and selection. 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Indian Journal of Agricultural Sciences, 91(11), 1622-6. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Figure 5: Depiction of Diversity among the Field Corn Panel of IARI by SSR markers 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65