Genetic characterization and population structure of maize populations using SSR markers

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Genetic characterization and population structure of maize populations using SSR markers ⁎

G.B. Adu , F.J. Awuku, I.K. Amegbor, A. Haruna, K.A. Manigben, P.A. Aboyadana CSIR-Savanna Agricultural Research Institute, P.O. Box TL 52, Tamale, Ghana

A R T I C LE I N FO

A B S T R A C T

Keywords: Drought Polymorphic Diversity Microsatellite Heterozygosity

Maize is a major staple food in sub-Saharan Africa. However, due to the threats posed by biotic and abiotic constraints to its production, it is a prerequisite to conserve germplasm with diverse genetic background and to seek for favourable genotypes with such background for improvement of superior genotypes. This study assessed the diversity of 70 white maize populations developed at CSIR-Savanna Agricultural Research Institute with resistance/tolerance to drought and low soil nitrogen (Low-N) using 31 SSR markers. A total of 288 alleles were detected among the germplasm used with a range of 4 to 17 alleles per locus and an average allele number of 9.60 alleles per locus. Polymorphic Information Content values for the SSR markers ranged from 0.32 to 0.85 with a mean value of 0.68. The average heterozygosity obtained from the markers was 0.54 and gene diversity ranged from 0.35 to 0.87 with a mean gene diversity value of 0.71 indicating a high level of polymorphism among the populations. Clustering based on Jaccard's similarity coefficient and an Unweighted Pair Group Method with Arithmetic Mean (UPGMA) revealed 5 clusters for the 70 populations. Evanno method for population structure clustering further revealed two sub-populations. Sub-population 1 was more genetically diverse with an Fst value of 0.18 than sub-population 2 which recorded an Fst value of 0.013. These results indicated that the SSR used were very polymorphic for diversity studies. Also, the studied maize populations are diverse and heterozygous, making them ideal source populations for extraction of drought and low-N tolerant inbred lines.

1. Introduction Maize (Zea mays L.) is known worldwide for its importance as major staple food crop and a model oganism with immense genetic diversity (Prasanna, 2012; Patel et al., 2017). Population increase, changes in climatic conditions and production constraints have resulted in high demand of maize and hence the need for improvement of various agricultural and economic important traits (Xiao et al., 2017). Genetic diversity is the basis of any crop improvement program. Diffusion of modern varieties from crop improvement and/or replacement of old landraces with new resistant/tolerant cultivars coupled with urbanization, population growth and climate change are the major factors contributing to genetic erosion (Prasanna, 2012; Warburton et al., 2008). The success of a plant breeding program depends upon the continued sourcing, creation, and deployment of new useful diversity in order to achieve sustained improvement in crop productivity and genetic gains (Smith et al., 2015). Landraces and wild progenitors of maize are a treasured source of favourable alleles useful for enriching

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the genetic base of existing breeding programs (Lia et al., 2009). In order to access the favourable alleles within a set of germplasm, there is the need for detailed information about the genetic diversity and population structure (van Inghelandt et al., 2010). Analysis of genetic diversity for a given germplasm helps breeders to select parental combinations for developing inbred lines with maximum genetic variability (Semagn et al., 2012; Ertiro et al., 2017). This also provide valuable information for describing heterotic groups and determining the level of genetic variability when defining core subsets selected for specific traits (Flint-Garcia et al., 2009; Semagn et al., 2012). Analysis of population structure provides valuable insight into understanding genetic diversity and serves as guideline for formulation of selection strategies and the choice of breeding options to adopt (Hayward and Breese, 1993). Several types of marker techniques such as phenological, morphological, biochemical and deoxyribonucleic acid (DNA) based markers have been used to study genetic diversity and population structure in crops (Govindaraj et al., 2015). Morphological characterization is often used as the first step towards the assessment of genetic diversity in a

Corresponding author. E-mail address: [email protected] (G.B. Adu).

https://doi.org/10.1016/j.aoas.2019.05.006 Received 19 October 2018; Received in revised form 12 February 2019; Accepted 23 May 2019 0570-1783/ 2019 Production and hosting by Elsevier B.V. on behalf of Faculty of Agriculture, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Please cite this article as: G.B. Adu, et al., Annals of Agricultural Sciences, https://doi.org/10.1016/j.aoas.2019.05.006

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Doyle (1991), with slight modification as follows: 200 mg of ground dried samples were put into properly labeled separate 2 mL Eppendorf tube containing 1 mL of pre-warmed CTAB buffer (2% CTAB, 20 mM EDTA, 100 mM Tris-HCl (Ph 8.0) and 1.4 M NaCl) with 0.2% mercaptoethanol (2 μL/L mL of CTAB buffer)) was added to each Eppendorf tube and place in a water bath at 60 °C for 1 h. During this time the content of the tubes were mixed gently by inverting for 6–7 times. After which 200 μL of potassium Acetate was added and put on ice for 20 min, 700 μL of chloroform: Isoamyl alcohol (24:1) was added to each tube and mixed gently by inverting and left undisturbed for 5 min. The samples were then centrifuged at 10000 rpm for 15 min and the middle aqueous layer transferred into 2 mL tube. The same volume of chloroform: Isoamyl alcohol was added, mixed and was left to stand for 5 min. Thereafter, the samples were again centrifuged at 10000 rpm for 10 min. The supernatant was transferred into 1.5 mL tubes, 500 μL of ice cold isopropanol was added to get a white precipitate. For maximum precipitation of DNA, the tubes containing the supernatant were left over night at a temperature of −20 °C. The samples were then centrifuged for 5 min at 11000 rpm and the supernatant removed carefully leaving pellets of DNA. The pellets were washed twice with 200 mL of 75% ice cold ethanol, each followed by centrifugation at 15000 rpm for 5 min and allowed to air dry. About 300 mL of X1 TE buffer was added and kept at 4 °C for dissolution of the pellet. 2 μL of RNAse A was added to each tube to get rid of RNA in the DNA and incubated at 37 °C for an hour. The extracted DNA was checked for quality on 2% agarose gel. Samples with faint, smeared or no bands were re-extracted to ensure all samples had clear bands for use in further analysis. Polymerase Chain Reaction (PCR) amplification was carried out in a 10 μL reaction volume, consisting 5 μL of 2× one Taq PCR master mix (0.2 U/μL VWR Taq polymerase, 0.4 mM of each dNTP, 25 mM MgCl2, 0.2% Tween 20, 15 mM (NH4)2SO4, Tris HCl Ph 8.5, Inert red dye), VWR, UK., 3 μL of nuclease-free water, 1 μL of 10 μM each of forward and reverse primer and 1 μL of 50 ng/μL DNA template. The PCR was carried out using the ABI thermal cycler with the following conditions; initial denaturation at 94 °C for 30 s, 35 cycles of denaturation at 94 °C for 30 s, annealing at X (depending on marker, (Table 2)) °C for 30 s and extension at 72 °C for 30 s and final extension at 72 °C for 7 min and held at 4 °C. Amplified products were electrophoresed on a 6% horizontal polyacrylamide gel (hPAGE) system (Cleaver Scientific, UK). The conditions for band separation were carried out at a voltage of 120 V for 3 h. A peristaltic pump was used with the PAGE system to enhance even distribution of running buffer and to maintain equilibrium in conductivity of the buffer. After electrophoresis, the gel was stained with ethidium bromide for 30 min and visualized using a UV transilluminator. A photograph of the gel was taken for further analysis.

given germplasm. However, DNA based markers are preferred over morphological and biochemical markers because DNA markers are not influenced by environmental factors or the developmental stage of the plant (Govindaraj et al., 2015), and thus useful in identification of cultivars, accelerates breeding programs through early selection, allows for selection of genes and genotypes with traits of interest, facilitate the detection of hybridity among crosses, eliminates subjectivism associated with morphological markers (Crouch and Ortiz, 2004; Fall et al., 2003; Powell et al., 1996; Shehata et al., 2009; Vignal et al., 2002). Deoxyribonucleic acid markers such as restriction fragment length polymorphisms (RFLPs) (Botstein et al., 1980), randomly amplified polymorphic DNAs (RAPDs) (Williams et al., 1990), amplified fragment length polymorphisms (AFLPs) (Zabeau and Vos, 1993), simple sequence repeats (SSRs/microsatellites) (Tautz, 1989) and single nucleotide polymorphism (SNP) markers have been used extensively in various genetic analyses (N'da et al., 2016). These markers enhance the efficiency of breeding process and has been used extensively in maize research to investigate germplasm characteristics, identify cultivars, identify linkages between markers and gene of interest and using these linkages to improve lines or populations and finally to understand the genetic relationships and evolution (Larnkey et al., 1998; Prasanna and Hoisington, 2003; Xiao et al., 2017). With the exception of SNPs and SSRs markers, RAPDs and RFLPs have limitations in their usage. RFLPs employ the use of radioactive materials (Williams et al., 1990) while RAPDs are not reproducible due to mismatch annealing (Karp et al., 1997). Single nucleotide polymorphisms and SSRs have therefore become the markers of choice because they are polymerase chain reaction (PCR)-based, easy to use, co-dominant, locus-specific, highly reproducible and informative (Powell et al., 1996). However, SSR markers are more informative than biallelic SNP markers because it can detect multiple alleles per locus (Xu et al., 2013). A study conducted by van Inghelandt et al. (2010) reported that, microsatellites are 7 to 11 times more precise than SNPs and hence more efficient in determining the genetic diversity and structure of populations of maize landraces and other improved lines (Vigouroux et al., 2008; Warburton et al., 2008). Moreover, SSR markers have been successfully and efficiently used to assess the extent of genetic diversity in maize (Shehata et al., 2009; Nepolean et al., 2013; Sserumaga et al., 2014). The aim of this study was to determine the genetic diversity and population structure of 70 maize populations developed for tolerance to drought and low soil nitrogen using simple sequence repeat markers. 2. Materials and methods 2.1. Source of germplasm, field experiments and markers

2.3. Statistical analysis

Seventy intermediate maturing (105 days to physiological maturity) white maize populations with diverse tolerance to drought and Low-N, developed at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute (CSIR-SARI), Ghana were used in this study. The populations were generated from bi-parental crosses and backcrosses between drought and Low-N tolerant inbred lines from CIMMYT and locally preferred genotypes (Table 1). For genotyping purposes, all the 70 populations were planted at research fields of CSIRSARI in Nyankpala, Ghana in 2016. Thirty-one SSR markers (Table 2) that showed polymorphism in previous studies (Senior et al., 1998) among maize hybrids were used in the present study.

The bands on the gels were scored for presence ‘1’ and absent ‘0’ of alleles for each sample per marker. Band sizes (allele size) were scored using the 50 bp ladders loaded as a guide. Data generated from the molecular screening was analyzed using Darwin V6 (Perrier and Jacquemoud-Collet, 2006) and PowerMarker V3.2.5 software (Liu and Muse, 2005). Cluster analysis was carried out using the Jaccard similarity test and an UPGMA clustering method (Durvasula and Rao, 2018) using the software Darwin V6. To estimate the genetic differentiation parameters for the populations, bands sizes were score as x/y where ‘x’ is the lower band/allele size and ‘y’ the upper band/allele size. These parameters were generated with the aid of PowerMarker V3.2.5. The data from the 31 polymorphic SSR markers were subjected to population structure analysis based on the admixture model-based clustering method in the software package STRUCTURE 2.3.4 (Falush et al., 2007). This model was run by varying the number of clusters (k) from 1 to 12 with 4 iteration for each K. A burn-in period of 50,000 and Markov Chain Monte Carlo (MCMC) replications of 100,000 after each

2.2. DNA extraction, amplification, and electrophoresis Leaf tissues were sampled from six to eight plants per genotype grown in the field at two weeks after planting (WAP) into zip-lock bags containing silica gel for drying. The leaf samples were dried for a week and pulverized using a mortar and pestle into powder. The powdered tissue was subjected to total genomic DNA extraction using protocol by 2

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Table 1 Pedigree information on backcross populations used in the study. Entry name

Progeny

Donor parent

Recurrent parent

Entry name

Progeny

Donor parent

Recurrent parent

SARI/I1/15 SARI/I2/15 SARI/I3/15 SARI/I4/15 SARI/I5/15 SARI/I6/15 SARI/I7/15 SARI/I8/15 SARI/I9/15 SARI/I10/15 SARI/I11/15 SARI/I12/15 SARI/I13/15 SARI/I14/15 SARI/I15/15 SARI/I16/15 SARI/I17/15 SARI/I18/15 SARI/I19/15 SARI/I20/15 SARI/I21/15 SARI/I22/15 SARI/I23/15 SARI/I24/15 SARI/I25/15 SARI/I26/15 SARI/I27/15 SARI/I28/15 SARI/I29/15 SARI/I30/15 SARI/I31/15 SARI/I32/15 SARI/I33/15 SARI/I34/15 SARI/I35/15

BC2 BC2 BC1 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC1 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC1 BC2 BC2 BC2 BC2 BC2 BC2 BC1 BC2

CML546 CML546 CML546 CML542 CML395 CML541 CML442 CML445 CML489 CML543 CML543 CML543 CML545 CML541 CML488 CML488 CML444 CML488 CML488 CML441 CML536 CML536 CML536 CML537 CML548 CML537 CML548 CML548 CML537 CML440 CML395 CML197 CML197 CML546 CML542

GH12/I2 GH90/I2 GH12/I2 IITA/I1 GH12/I2 GH90/I2 GH90/I3 GH90/I3 IITA/I1 GH90/I2 GH90/I3 GH12/I2 GH12/I1 IITA/I1 GH90/I3 GH12/I1 GH90/I2 GH12/I1 GH12/I2 GH90/I2 GH90/I3 GH12/I1 GH12/I2 GH12/I2 GH12/I2 IITA/I1 GH12/I2 GH90/I3 GH90/I3 GH12/I2 IITA/I1 GH90/I3 GH90/I2 GH12/I1 IITA/I2

SARI/I36/15 SARI/I37/15 SARI/I38/15 SARI/I39/15 SARI/I40/15 SARI/I41/15 SARI/I42/15 SARI/I43/15 SARI/I44/15 SARI/I45/15 SARI/I46/15 SARI/I47/15 SARI/I48/15 SARI/I49/15 SARI/I50/15 SARI/I51/15 SARI/I52/15 SARI/I53/15 SARI/I54/15 SARI/I55/15 SARI/I56/15 SARI/I57/15 SARI/I58/15 SARI/I59/15 SARI/I60/15 SARI/I61/15 SARI/I62/15 SARI/I63/15 SARI/I64/15 SARI/I65/15 SARI/I66/15 SARI/I67/15 SARI/I68/15 SARI/I69/15 SARI/I70/15

BC2 BC2 BC2 BC2 BC2 BC2 BC1 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC1 BC2 BC2 BC1 BC2 BC2 BC2 BC2 BC1 BC2 BC2 BC2 BC2 BC2 BC1 BC2 BC2 BC2 BC2

CML440 CML540 CML544 CML489 CML545 CML440 CML542 CML538 CML538 CML542 CML202 CML444 CML536 CML546 CML488 CML541 CML545 CML548 CML544 CML540 CML489 CML548 CML538 CML540 CML545 CML544 CML548 CML542 CML547 CML538 CML544 CML544 CML442 CML445 CML546

IITA/I2 IITA/I2 GH12/I1 GH90/I3 IITA/I2 GH90/I3 IITA/I2 IITA/I1 GH12/I2 GH12/I2 GH12/I1 GH90/I1 GH90/I1 IITA/I2 GH90/I1 IITA/I2 GH12/I1 IITA/I2 GH12/I2 IITA/I2 IITA/I2 GH90/I1 IITA/I2 GH12/I1 IITA/I2 GH90/I1 GH90/I2 GH90/I1 IITA/I2 IITA/I1 IITA/I2 IITA/I2 IITA/I1 GH90/I2 GH12/I1

Table 2 List of SSR markers used in the study with their sequences and annealing temperatures. Marker name

Forward sequence

Reverse sequence

NC130 NC133 PHI008 PHI015 PHI034 PHI046 PHI053 PHI059 PHI070 PHI072 PHI084 PHI087 PHI108411 PHI109188 PHI109275 PHI109642 PHI112 PHI233376 PHI328175 PHI331888 PHI374118 PHI420701 PHI423796 PHI448880 PHI452693 PHI453121 UMC1061 UMC1136 UMC1143 UMC1196 UMC1279

GCACATGAAGATCCTGCTGA AATCAAACACACACCTTGCG CGGCTACGGAGGCGGTG GCAACGTACCGTACCTTTCCGA TAGCGACAGGATGGCCTCTTCT ATCTCGCGAACGTGTGCAGATTCT AACCCAACGTACTCCGGCAG AAGCTAATTAAGGCCGGTCATCCC GCTGAGCGATCAGTTCATCCAG ACCGTGCATGATTAATTTCTCCAGCCTT AGAAGGAATCCGATCCATCCAAGC GAGAGGAGGTGTTGTTTGACACAC CGTCCCTTGGATTTCGAC AAGCTCAGAAGCCGGAGC CGGTTCATGCTAGCTCTGC CTCTCTTTCCTTCCGACTTTCC TGCCCTGCAGGTTCACATTGAGT CCGGCAGTCGATTACTCC GGGAAGTGCTCCTTGCAG TTGCGCAAGTTTGTAGCTG TACCCGGACATGGTTGAGC GATGTTTCAAAACCACCCAGA CACTACTCGATCTGAACCACCA CGATCCGGAGGAGTTCCTTA CAAGTGCTCCGAGATCTTCCA ACCTTGCCTGTCCTTCTTTCT AGCAGGAGTACCCATGAAAGTCC CTGCATACAGACATCCAACCAAAG CGTGGTGGGATGCTATCCTTT CGTGCTACTACTGCTACAAAGCGA CAATCCAATCCGTTGCAGGTC

TGTGGATGACGGTGATGC GCAAGGGAATAAGGTGACGA GATGGGCCCACACATCAGTC ACGCTGCATTCAATTACCGGGAAG GGGGAGCACGCCTTCGTTCT TCGATCTTTCCCGGAACTCTGAC CTGCCTCTCAGATTCAGAGATTGAC TCCGTGTACTCGGCGGACTC CCATGGCAGGGTCTCTCAAG GACAGCGCGCAAATGGATTGAACT CACCCGTACTTGAGGAAAACCC ACAACCGGACAAGTCAGCAGATTG CGTACGGGACCTGTCAACAA GGTCATCAAGCTCTCTGATCG GTTGTGGCTGTGGTGGTG GAGCGAGCGAGAGAGATCG AGGAGTACGCTTGGATGCTCTTC CGAGACCAAGAGAACCCTCA CGGTAGGTGAACGCGGTA ACTGAACCGCATGCCAAC TGAAGGGTGTCCTTCCGAT ATGGCACGAATAGCAACAGG CGCTCTGTGAATTTGCTAGCTC CCATGAACATGCCAATGC CGCGAACATATTCAGAAGTTTG CAAGCAAGACTTTTGATCAGCC TATCACAGCACGAAGCGATAGATG CTCTCGTCTCATCACCTTTCCCT GACACTAGCAATGTTCAAAACCCC AGTCGTTCGTGTCTTCCGAAACT GATGAGCTTGACGACGCCTG

3

Annealing temp. (°C) 59 58 65 64 65 64 64 64 64 64 64 64 60 61 61 63 64 61 61 58 60 69 59 59 60 60 63 63 62 63 63

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of 200,000 at 1 iteration. Each genotype was assigned to a sub-population based on probability of association greater than or equal to 60%, genotypes with less than 60% level of association were assigned to mixed group (admixture).

Table 3 Genetic differentiation parameters revealed by polymorphic SSR markers; Major allele frequency, allele number, gene diversity, heterozygosity and polymorphic information content (PIC). Marker

NC 130 NC 133 PHI 008 PHI 015 PHI 034 PHI 046 PHI 053 PHI 059 PHI 070 PHI 072 PHI 084 PHI 087 PHI 10841 PHI 109188 PHI 109275 PHI 109642 PHI 112 PHI 233376 PHI 328175 PHI 331888 PHI 374118 PHI 420701 PHI 423796 PHI 448880 PHI 452693 PHI 453121 UMC 1061 UMC 1136 UMC 1196 UMC 1279 UMC1143 Mean

Major allele frequency

Allele number

Gene diversity

Heterozygosity

0.243 0.550 0.507 0.200 0.286 0.279 0.350 0.279 0.364 0.521 0.650 0.593 0.793 0.436 0.257 0.493 0.221 0.393 0.386 0.307 0.429 0.643 0.671 0.321 0.336 0.486 0.679 0.343 0.371 0.443 0.600 0.433

8.0 7.0 13.0 14.0 14.0 10.0 9.0 8.0 7.0 9.0 6.0 5.0 4.0 11.0 10.0 7.0 17.0 7.0 17.0 10.0 12.0 10.0 4.0 7.0 9.0 12.0 8.0 12.0 10.0 6.0 5.0 9.6

0.82 0.63 0.66 0.87 0.84 0.81 0.77 0.81 0.76 0.68 0.54 0.59 0.35 0.76 0.85 0.68 0.87 0.71 0.80 0.79 0.75 0.56 0.49 0.78 0.75 0.72 0.52 0.78 0.81 0.71 0.59 0.71

0.27 0.47 0.63 0.89 0.74 0.83 0.71 0.66 0.24 0.81 0.70 0.01 0.19 0.73 0.93 0.73 0.57 0.59 0.81 0.29 0.81 0.44 0.13 0.34 0.43 0.71 0.34 0.69 0.23 0.81 0.00 0.54

PIC

3. Results and discussion 0.79 0.59 0.62 0.85 0.82 0.79 0.73 0.79 0.72 0.65 0.51 0.54 0.32 0.74 0.83 0.64 0.85 0.66 0.78 0.76 0.73 0.54 0.43 0.75 0.71 0.70 0.49 0.75 0.79 0.67 0.54 0.68

3.1. Polymorphic/informative markers Analysis of all the 31 marker loci revealed that all the SSR markers were polymorphic and highly effective in discriminating the 70 populations used in this study. A total number of 288 alleles were detected, ranging from 4 alleles per locus (PHI 10841 and PHI 423796) to 17 alleles per locus (PHI 112 and PHI 328175), with an average of 9.60 alleles per locus (Table 3). The values recorded in the present study vary from previous studies using SSRs markers on maize inbred lines (Lanes et al., 2014; Sserumaga et al., 2014; van Inghelandt et al., 2010; Vega-Alvarez et al., 2017; Xiao et al., 2017). This study recorded lower total allele number as compared to reports of 471 alleles and 649 alleles by Lanes et al., (2014) and Vega-Alvarez et al., (2017), respectively but high allele number in comparison to report of 145 alleles by Xiao et al., (2017). The differences in the number of alleles between the present study and the other studies could be attributed to the genetic materials used as well as the methodologies adopted for detection of polymorphic markers. The mean allele number of 9.60 per locus across the individuals analyzed in this study was higher than the mean values of 3.85 and 4.03 recorded by Wasala and Prasanna (2013) in Indian maize landrace accessions. The set of SSR loci analyzed in this study had high PIC values, ranging from 0.32 (PHI 10841) to 0.85 (PHI 112), with a mean value of 0.68 indicating the availability of high allelic variation in the marker loci and their distribution within the populations under study. Botstein et al. (1980) reported that the PIC values can be classified into three categories (i) if the PIC value of the marker is more than 0.5, the marker is considered as highly informative, (ii) if the PIC value ranged from 0.25 to 0.5, the marker is moderately informative, and (iii) if the PIC value is less than 0.25, then the marker is slightly informative. Several loci showed PIC values greater than 0.70, indicating that the SSR markers used in the present study were highly informative. The reason for this result can be attributed to the multi-allelic nature of the

burn-in was used. The Evanno method was used to estimate the delta K which gives the best estimate for an optimum number of clusters (Evanno et al., 2005) through the online based STRUCTURE HARVESTER (Earl and vonHoldt, 2012) software. The admixture model was repeated for the best K with a burn-in period of 100,000 and an MCMC

PHI 233376

PHI 328175 Fig. 1. Segregation pattern of some polymorphic markers (PHI233376 and PHI328175) across the first 48 populations (1–48) run on 6% horizontal polyacrylamide gel with 50 bp ladder (L). 4

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SARI/I26/15 SARI/I34/15 SARI/I27/15 SARI/I28/15 SARI/I38/15 SARI/I25/15 SARI/I2/15 SARI/I36/15 SARI/I1/15 SARI/I30/15 SARI/I3/15 SARI/I24/15 SARI/I13/15 SARI/I46/15 SARI/I8/15 SARI/I45/15 SARI/I9/15 SARI/I41/15 SARI/I56/15 SARI/I42/15 SARI/I12/15 SARI/I35/15 SARI/I29/15 SARI/I48/15 SARI/I17/15 SARI/I39/15 SARI/I54/15 SARI/I11/15 SARI/I53/15 SARI/I10/15 SARI/I60/15 SARI/I16/15 SARI/I52/15 SARI/I18/15 SARI/I67/15 SARI/I66/15 SARI/I22/15 SARI/I69/15 SARI/I23/15 SARI/I31/15 SARI/I64/15 SARI/I63/15 SARI/I5/15 SARI/I61/15 SARI/I7/15 SARI/I68/15 SARI/I4/15 SARI/I51/15 SARI/I6/15 SARI/I14/15 SARI/I59/15 SARI/I55/15 SARI/I21/15 SARI/I32/15 SARI/I33/15 SARI/I58/15 SARI/I57/15 SARI/I37/15 SARI/I65/15 SARI/I44/15 SARI/I62/15 SARI/I20/15 SARI/I15/15 SARI/I19/15 SARI/I47/15 SARI/I40/15 SARI/I43/15

SARI/I50/15

0

SARI/I70/15

0.1

SARI/I49/15

Fig. 2. Unrooted UPGMA clustering of maize populations developed for tolerance to drought and Low-N., using Jaccard similarity coefficient.

3.2. Heterozygosity among the germplasm

SSR markers and their inherently high mutation rates. High level of polymorphism observed for SSR markers with moderate to high PIC values support their application in genetic studies such as genetic diversity, genetic linkage map construction and QTL mapping. This further suggested that the SSR markers used, are highly effective and promising in differentiating and discriminating among the 70 maize populations. This result is in close agreement with previous studies by Yang et al. (2011) and Wasala and Prasanna (2013). The present study identified only one major SSR allele in different loci (PHI 10841) with a frequency greater than 0.75 as most frequent SSR allele across the 70 maize populations. The frequency of the most common (‘major’) alleles across the accessions ranged from 0.20 (PHI 015) to 0.75 (PHI 10841). This result indicated that the SSR markers used in this study are very informative, and so can be use in genetic diversity studies and in marker assisted breeding in Maize.

The average heterozygosity obtained was 0.54 among the markers used in the study with a range from 0 to 0.93 (Table 3). In the present study, SSR marker PHI 109275 showed the highest heterozygosity of 0.93, while the marker UMC1143 showed the lowest heterozygosity of 0.00. Also, 58.10% of the total number of markers used showed heterozygosity (0.57 to 0.93) higher than the mean value of 0.54. Heterozygosity measures the amount of genetic variation within a population. The high heterozygosity observed at some SSR loci indicated that there is the need for further selfing to enhance homozygosity, while maintaining desirable genetic and phenotypic characteristics of the population (Yan et al., 2010). The high heterozygosity also means high level of genetic variability, which implied that the populations studied may possess high genotypic variations of any useful adaptive traits identified. On the other hand, gene diversity ranged from 0.35 (PHI 5

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45 40 35

K

30 25 20 15 10 5 0

1

2

3

4

5

6

7

8

9

10

11

12

K Fig. 3. Estimation of best number of population (K) from an assumed range of 1–12 based on Evanno method.

Fig. 4. Estimated population structure of 70 maize populations as revealed by 31 polymorphic SSR markers for (K = 2), Red indicates sub-population 1, green indicates sub-population 2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3.3. Cluster analysis and groupings

10841) to 0.87 (PHI 112) with an average value of 0.71 (Table 3). Genetic diversity is defined as the probability that two randomly chosen haplotypes (alleles) are different in a sample. Genetic diversity recorded in this study is comparable to the expected heterozygosity for diploid data. The average genetic diversity of 0.71 detected among the 70 maize populations depicted high levels of polymorphisms within the maize populations studied. This result is in close agreement with an earlier study by Wasala and Prasanna (2013), who reported heterozygosity values ranging from 0.13 to 0.75 with an average of 0.63 and a polymorphic information content values of 0.20 to 0.90 with a mean of 0.60. For the identification of Low-N and drought tolerant-linked DNA markers, polymorphism survey was conducted among the 70 populations using the set of SSR markers used in the present study. These SSR markers screened provided 100% polymorphic fragments. The banding pattern of some of the polymorphic marker shown in Fig. 1 which showed great diversity among the populations. As presented in Table 3, seven alleles for PHI233376 marker and 17 for PHI328175 were observed. This suggested that the allele for PHI328175 was more abundant in the populations than that for PHI233376. The high polymorphic nature of the markers among this populations points to the fact that these markers can be used in conjunction with phenotypic data for indirect selection of maize genotypes for Low-N and drought tolerance.

Cluster analysis of the SSR data showed five groups among the 70 populations, indicating significant genetic diversity (Fig. 2). The majority (91.4%) of the populations were in Cluster II (32 populations), Cluster III (14 populations) and Cluster IV (17 populations) and 9.4% in Cluster I (3 populations) and Cluster V (3 populations). The populations were grouped based on their pedigree (Table 1). Variance within clusters, ranged from 0 to 16.7, showing greater diversity among individuals within the different clusters. The genetic distance observed between paired individuals ranged from 0.3 to 0.7. The most related populations were SARI/I42/15 and SARI/I35/15 which share similarity of 74.55%. SARI/I42/15 and SARI/I35/15 are likely to be genetically more similar because they were derived from the same donor and recurrent parents (Table 1). In general, the study revealed high levels of intra-population as well as inter-population diversity in the selected maize populations. The optimum number of sub-populations K, which best explain the population structure of the accession was estimated to be 2 (K = 2) using the Evanno method (Fig. 3). This indicated the presence of two sub-populations. The population structure from the structural analysis is shown in Fig. 4, which depicts the two sub-populations composed of the 70 maize populations studied. Sub-population 1 consisted of 30 populations representing 42.86%, whilst sub-population 2 was made up of 33 populations representing 47.14% of the total number of the genotypes tested. Seven of the maize populations were classified as 6

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Table 4 Summary of population assignment into sub-populations (1 and 2) from Structure analysis and population assignment into clusters (I, II, III, IV and V) from UPGMA clustering. Entry name

Population 1

SARI/I4/15 SARI/I6/15 SARI/I7/15 SARI/I10/15 SARI/I13/15 SARI/I14/15 SARI/I15/15 SARI/I16/15 SARI/I17/15 SARI/I18/15 SARI/I19/15 SARI/I20/15 SARI/I21/15 SARI/I22/15 SARI/I23/15 SARI/I24/15 SARI/I25/15 SARI/I26/15 SARI/I27/15 SARI/I28/15 SARI/I30/15 SARI/I31/15 SARI/I32/15 SARI/I33/15 SARI/I34/15 SARI/I41/15 SARI/I44/15 SARI/I45/15 SARI/I46/15 SARI/I53/15 SARI/I1/15 SARI/I2/15 SARI/I3/15 SARI/I8/15 SARI/I11/15

0.77 0.95 0.71 0.85 0.89 0.93 0.96 0.74 0.92 0.87 0.97 0.93 0.88 0.95 0.96 0.71 0.83 0.94 0.93 0.70 0.73 0.94 0.91 0.94 0.96 0.88 0.73 0.89 0.77 0.71 0.09 0.04 0.04 0.09 0.21

Inferred population

UPGMA cluster #

Entry name

2 0.23 0.05 0.29 0.15 0.11 0.08 0.04 0.26 0.08 0.14 0.03 0.07 0.12 0.05 0.04 0.29 0.17 0.07 0.07 0.30 0.28 0.06 0.09 0.06 0.04 0.12 0.27 0.11 0.23 0.29 0.91 0.96 0.96 0.92 0.80

Population 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

II II II II II II II II II II II II II II II III III III III III III II II II III III II III III IV II II II II II

SARI/I12/15 SARI/I29/15 SARI/I35/15 SARI/I38/15 SARI/I40/15 SARI/I42/15 SARI/I43/15 SARI/I47/15 SARI/I48/15 SARI/I49/15 SARI/I50/15 SARI/I52/15 SARI/I54/15 SARI/I55/15 SARI/I56/15 SARI/I57/15 SARI/I58/15 SARI/I60/15 SARI/I61/15 SARI/I62/15 SARI/I63/15 SARI/I64/15 SARI/I65/15 SARI/I66/15 SARI/I67/15 SARI/I68/15 SARI/I69/15 SARI/I70/15 SARI/I5/15 SARI/I9/15 SARI/I36/15 SARI/I37/15 SARI/I39/15 SARI/I51/15 SARI/I59/15

0.16 0.13 0.27 0.06 0.12 0.30 0.12 0.25 0.07 0.08 0.04 0.06 0.06 0.05 0.11 0.08 0.05 0.13 0.03 0.09 0.04 0.06 0.10 0.04 0.05 0.09 0.09 0.04 0.55 0.33 0.32 0.35 0.35 0.54 0.36

Inferred population

UPGMA cluster #

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Admixture Admixture Admixture Admixture Admixture Admixture Admixture

II II III II I III I I III V V III IV IV II IV IV IV IV IV IV IV IV IV IV IV IV V II II III II II IV IV

2 0.84 0.87 0.73 0.94 0.88 0.70 0.88 0.76 0.94 0.92 0.96 0.94 0.94 0.95 0.89 0.92 0.96 0.87 0.97 0.92 0.97 0.94 0.90 0.96 0.95 0.91 0.91 0.96 0.45 0.67 0.68 0.65 0.65 0.46 0.64

research and also can be very useful in marker assisted selection. In addition, high genetic diversity among the maize populations indicates an opportunity to exploit the genetic materials for the development of varieties with tolerance to Low-N and drought. This could also lead to a start of extracting inbred lines from the various populations and consequently pedigree breeding to develop promising inbred lines.

admixtures. The net nucleotide distance which explains the allele-frequency divergence between the two sub-populations was 0.0397. This indicated minimal changes in allele frequencies between the two subpopulations. The expected heterozygosity between the two sub-populations did not differ much. The less heterozygous group was sub-population 1. It recorded an expected heterozygosity value of 0.44, which is 13.70% less than that of sub-population 2 with 0.51 expected heterozygosity. However, sub-population 2 showed less genetic diversity with an Fst value of 0.013, while sub-population 1 showed higher genetic diversity with an Fst value of 0.18. Maize populations within clusters II and III mostly clustered in sub-population 1 (Table 4) of the Structure analysis, which indicated that these group of maize populations are less genetically diverse and less heterozygous. On the other hands, maize populations in sub-population 2 were spread across the UPGMA clusters, with all genotypes in cluster V and most genotypes of cluster IV. The mixtures of genotypes of different clusters within sub-population 2 might be what is making this sub-population more genetically diverse and more heterozygous as compared to sub-population 1.

Acknowledgement This work was financially supported by the Alliance for a Green Revolution in Africa (AGRA) and the United States Agency for International Development (USAID). We are most grateful to Mr. Ofosu Aning Asante and Mr. Yelkuro Mwintuana for the help during the laboratory analysis. We are also grateful to the staff of the maize improvement programme of CSIR-SARI for their technical support during the development and characterization of the genotypes used in this study. References Botstein, D., White, R.L., Skolnick, M., Davis, R.W., 1980. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 32, 314–331. Crouch, J.H., Ortiz, R., 2004. Applied genomics in the improvement of crops grown in Africa. Afr. J. Bot. 3, 489–496. Doyle, J., 1991. DNA protocols for plants. In: Hewitt, G.M., Johnston, A.W.B., Young, J.P.W. (Eds.), Molecular Techniques in Taxonomy. NATO ASI Series (Series H: Cell Biology). vol. 57 Springer, Berlin, Heidelberg. Durvasula, R., Rao, D.V.S., 2018. Extremophiles: Nature's Amazing Adapters. CRC Press, Boca Raton. Earl, D.A., vonHoldt, B.M., 2012. STRUCTURE HARVESTER: a website and program for

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