development of new cowpea ( vigna unguiculata) mutant ... · 22/05/2020 · 81 tolerance to maruca...

Development of new cowpea (Vigna unguiculata) mutant 1

genotypes, analysis of their agromorphological variation, genetic 2

diversity and Population structure 3

Made DIOUF1, Sara DIALLO1, François Abaye BADIANE1,2, Oumar DIACK1 and Diaga 4

DIOUF1* 5

1Laboratoire Campus de Biotechnologies Végétales, Département de Biologie Végétale, 6

Faculté des Sciences et Techniques, Université Cheikh Anta Diop, Dakar-Fann, Code Postal 7

10700, Dakar, Sénégal 8

2Faculté des Sciences et Technologies de l’Education et de la Formation, Université Cheikh 9

Anta Diop, Dakar-Fann, Code Postal 10700, Dakar, Sénégal 10

* Correspondence: 11

[email protected] 12

13

was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (whichthis version posted May 25, 2020. . https://doi.org/10.1101/2020.05.22.109041doi: bioRxiv preprint

https://doi.org/10.1101/2020.05.22.109041

Keywords: Cowpea; Vigna unguiculata; Gamma rays; Induced mutagenesis; Plant breeding; 14

Agromorphological characterization; ISSR 15

Abstract 16

Cowpea is one of the most important legume grain in the SubSaharian region of Africa used 17

for human consumption and animal feeding but its production is hampered by biotic and 18

abiotic constraints raising the need to broaden its genetic basis. For this purpose, the seeds of 19

two cowpea varieties Melakh and Yacine were respectively irradiated with 300 and 340 Gy. 20

The developed mutant populations were agromorphologically characterized from M5 to M7 21

while the genetic diversity of the last were evaluated using 13 ISSR markers. Based on 22

agromorphological characterization, variation of flower color, pod length, seed coat color and 23

seed weight with respectively 78.01, 68.29, 94.48 and 57.58% heritability were recorded in 24

the mutant lines. PCA analyses allowed to identify the elite mutants based on their 25

agromorphological traits while Pearson’s correlation results revealed a positive correlation 26

between yield component traits. Three subpopulations were identified through STRUCTURE 27

analyses but assignment of the individuals in each group was improved using DAPC. 28

Analysis of Molecular Variance revealed that the majority (85%) of the variance rather 29

existed within group than among (15%) group. Finally, our study allowed to select new 30

promising mutant genotypes which could be tested for multi local trials to evaluate their 31

agronomic performance. 32

Introduction 33

Cowpea [Vigna unguiculata (L.) Walp. 2n = 2x =22] is an important crop legume for tropical 34

and subtropical regions grown in Africa, Southern Europe, Latin America, Southeast Asia, 35

and southwestern regions of North America on 12 496 305 hectares 36


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(http://www.fao.org/faostat/en/#data/QC/visualize). Its production is estimated to 7 233 408 37

tons, Nigeria, Niger, Burkina Faso, Ghana, United Republic of Tanzania, Myanmar, Mali, 38

Cameroon, Sudan (including Sudan and South Soudan) and Kenya, are the top producers in 39

the world. In the Sahelian part of Africa, the crop plays a major role in human nutrition. For 40

instance, the fresh seeds, grilled on wood fire, are consumed in Senegal while the dry seeds 41

are used in a wide range of meal compositions. The young leaves cooked as spinach are eaten 42

in Eastern and Southern Africa while the hay as well as the seed are used for livestock feeding 43

in several African countries (for review see [1]). Compared to others legumes, its seed 44

contains higher amount of proteins which is estimated to 25% and a substantial amount of 45

minerals and vitamins based on the recent work published by [2], raising cowpea as a 46

valuable crop to fight malnutrition for the low-income farmers. Based on the estimation 47

realized by [3], cowpea which establishes a symbiosis with Bradyrhizobium, is a good 48

nitrogen fixing crop (70 to 350 kg nitrogen per hectare) leading to soil fertility improvement. 49

Despite its importance, the production of the crop is hampered by a wide range of biotic 50

(virus, bacteria, fungi, parasitic weeds and nematodes) and abiotic (drought, heat, etc.) 51

constraints [1, 4]. This susceptibility to a wide range of biotic and abiotic stresses is attributed 52

to the narrow genetic basis of the crop due to single domestication event and its self-53

pollinating pattern of reproduction [5]. To overcome these constraints, the genetic diversity 54

hidden into the existing germplasms which contains relevant agronomic traits have been 55

exploited during the past decades to increase the production of crops through the development 56

of outstanding cultivars, based on methods such as pure line selection, mass selection, 57

pedigree breeding, single seed descent and back crossing [6]. Despite these efforts, the genetic 58

basis of the realized lines is still narrow which arise the need to develop novel outstanding 59

varieties particularly in the era of climate change. For this purpose, technique such as 60

mutagenesis is a valuable tool to induce genetic variation for cultivar improvements. 61


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Mutagenesis has widely been used for the past seventy years in order to improve many 62

economically important crops leading to the selection of 3,320 varieties which are superior to 63

the natural cultivar in term of productivity, resistance to biotic and/or abiotic constraints 64

(https://mvd.iaea.org/#!Search). According to this database, only 16 cowpea varieties were 65

bred using gamma ray mutation techniques and 5 of them were selected from Africa precisely 66

in Kenya, Zambia and Zimbabwe. 67

Gamma rays are ionizing radiation which deeply penetrate into the cells of the target tissues 68

where they interact with molecules to generate reactive oxygen species (ROS) causing base 69

substitutions, genome rearrangements such as insertions, deletions, inversions and 70

translocations [7]. The base substitution caused by ROS is due to the conversion of guanines 71

into 8-oxo-Gs which induces mispairings with adenine while genome rearrangements are 72

caused by error-prone non homologous end joining (NHEJ) rather than error-free homologous 73

recombination which result from double-strand breaks (DSBs). When DSBs and NHEJ occur 74

in several genomic regions, they create favorable conditions for copy number variations 75

(CNVs), presence/absence variations (PAVs) and translocations [8,7]. Presently, it is well 76

documented that these genetic modifications induce agro morphological variations affecting 77

plant height, growth habit, number of leaves per plant, leaves color, number of branches per 78

plant, days to first flower, flower color, flowering ability, maturity, number of pods per plant, 79

number of seed per plant, pod and seed coat color, seed eye color, weight of 100 seeds and 80

tolerance to Maruca vitrata pod borer which were observed among cowpea mutants [9,10,11] 81

[12,13,14]. In view of these variations, several research teams attempted to characterize 82

cowpea mutant population using morphological traits, yield components [15,16,10,12,13,17] 83

and recently seed storage proteins [10,12]. To overcome the limits of using these traits, 84

Random amplified polymorphic DNA (RAPD; [10]) and inter-simple sequence repeat (ISSR; 85

[10,12]) were recently used to understand the genetic organization of some cowpea mutant 86


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populations. of these markers. The analysis of ISSR has generated more informative results, 87

since these sequences are abundant, widely distributed across the eukaryotic genome, highly 88

reproducible and use SSR as primers allowing the amplification of inter SSR region [18]. 89

ISSR are useful to study genetic diversity, genome mapping or evolutionary biology in many 90

crop species and they overcome the limitations of other markers, such as low reproducibility 91

of RAPD and high cost of AFLP (Amplified fragment length polymorphic) and the use a 92

single primer during the amplification processes [18,19]. ISSR combine the advantages of 93

SSR, AFLP and RAPD markers which do not require prior genome sequence information and 94

are efficient to detect genetic variation among cowpea varieties [20,21] or mutant lines 95

[10,12]. ISSR primers can be unanchored with 1 to 4 degenerate nucleotides at 3’ or 5’ end to 96

avoid their slippery within the repeat units and smears apparition during amplification but 97

previous studies showed that primers anchored at 3’ gave more clear bands [22,12]. 98

The aim of this study was to broaden the genetic basis of cowpea using gamma irradiation 99

technique specifically to develop mutant populations for which their genetic diversity and 100

allelic richness were assessed and to use the generated information to select new elite 101

genotypes. 102

Materials and Methods 103

Plant materials and gamma irradiation 104

Two inbred cowpea varieties Melakh and Yacine (Table 1) widely cultivated in Senegal were 105

selected from the national germplasm and used in this study [23]. They belong to the early 106

maturity group which reach physiological maturity 64 days after sowing (DAS) under well-107

watered conditions [24,25]. In total 216 dry and healthy seeds for each variety Melakh and 108

Yacine were exposed respectively to 300 and 340 Gy of gamma ray. The irradiation was 109


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performed at the International Atomic Energy Agency (IAEA), Agriculture and 110

Biotechnology Laboratory, A-2444 Seibersdorf, Austria using a cobalt 60 source Gammacell 111

Model No. 220. The control seeds were not exposed to gamma irradiation. 112

113


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Table 1: Agromorphological characters and pedigree of 40 cowpea mutants and their parent used in this study. 114 115

1 : White ; 2 : White with purple border ; 3 : Light purple ; 4 : Dark purple 116 Genotypes used for DNA analysis are indicated in bold. For the mutant line codes, the first or second letter refers to the parent’s name, the number which 117 appeared before « M » indicates the selected individual during the M generation. 118

Genotype codes Pedigree Growth habit

Leaflet length (cm)

Flower color

Pod length (cm)

Pod weigth (g)

Seed length (mm)

Seed coat color

Eye color

Y1-M4-9M5-2M6-M7 Yacine Erect 11.5 3 11.70 2.49 8.67 Brown Brown Y1-M4-9M5-3M6-M7 Yacine Erect 10 2 8.30 1.03 9.18 White-brown Brown Y1-M4-8M5-1M6-M7 Yacine Erect 9 1 14.40 2.59 10.68 White Brown Y1-M4-3M5-2M6-M7 Yacine Erect 8.1 2 16.60 2.13 9.82 White Brown Y1-M4-11M5-1M6-M7 Yacine Erect 12.3 2 12.40 1.09 7.67 White-brown Brown Y7-M4-1M5-1M6-M7 Yacine Prostrate 12.3 2 8.50 0.74 9.50 White Brown Y7-M4-6M5-1M6-M7 Yacine Erect 11.2 2 10.70 1.27 9.41 White-brown Brown Y7-M4-4M5-3M6-M7 Yacine Erect 10.2 2 12.10 1.71 9.63 Brown Brown Y13-M4-4M5-1M6-M7 Yacine Erect 9.6 1 10.00 0.99 8.11 White Brown Y7-M4-12M5-3M6-M7 Yacine Prostrate 9 4 12.10 1.54 10.80 Brown Brown Y7-M4-10M5-1M6-M7 Yacine Erect 9 4 14.10 1.88 9.77 White Brown Y1-M4-1M5-2M6-M7 Yacine Erect 12 2 13,15 1,66 9,62 White Brown Y1-M4-5M5-1M6-M7 Yacine Erect 10 1 8,70 1,25 10,55 Brown Brown Y1-M4-10M5-2M6-M7 Yacine Prostrate 13,5 2 15,80 2,78 8,43 white Brown Y1-M4-11M5-3M6-M7 Yacine Prostrate 10,5 1 19,00 3,26 9,05 White Brown Y1-M4-12M5-1M6-M7 Yacine Erect 8,5 2 10,65 0,93 9,36 White-brown Brown Y1-M4-14M5-1M6-M7 Yacine Erect 10,5 1 13,55 2,31 11,13 Brown Brown Y1-M4-15M5-1M6-M7 Yacine Erect 10,5 1 14,30 2,52 10,59 Brown Brown Y7-M4-4M5-2M6-M7 Yacine Erect 9,2 4 13,60 2,43 8,60 White Brown Y7-M4-3M5-1M6-M7 Yacine Erect 12 2 18,2 2,88 10,90 White-brown Brown Y7-M4-1M5-3M6-M7 Yacine Semi erect 12,5 2 11,90 1,51 9,84 White Brown Y1-M4-16M5-2M6-M7 Yacine Prostrate 10,5 3 14,40 1,51 8,38 White Brown Y7-M4-7M5-3M6-M7 Yacine Erect 6,5 4 12,45 1,46 9,51 White Brown Y7-M4-7M5-1M6-M7 Yacine Erect 12,6 2 19,30 2,91 9,99 White Brown YD7-M4-4M5-3M6-M7 Yacine Erect 10,2 2 12,10 1,71 9,63 Brown Brown Y7-M4-9M5-2M6-M7 Yacine Erect 8,6 4 10,40 1,27 9,99 Brown Brown Yacine Ndiaga Aw x Melakh Erect 11 1 14.20 2.71 11.02 Brown Brown

was not certified by peer review

) is the author/funder. All rights reserved. N

o reuse allowed w

ithout permission.

The copyright holder for this preprint (w

hichthis version posted M

ay 25, 2020. .

https://doi.org/10.1101/2020.05.22.109041doi:

bioRxiv preprint

https://doi.org/10.1101/2020.05.22.109041

Table 1: Agro-morphological characters and pedigree of 40 cowpea mutants and their parent used in this study (continued). 119 120

1 : White ; 2 : White with purple border ; 3 : Light purple ; 4 : Dark purple 121 Genotypes used for DNA analysis are indicated in bold. For the mutant line codes, the first or second letter refers to the parent’s name, the number which 122 appeared before « M » indicates the selected individual during the M generation. 123

Genotype codes Pedigree Growth habit

Leaflet length (cm)

Flower color

Pod length (cm)

Pod weigth (g)

Seed length (mm)

Seed color Eye color

Me51M4-10M5-1M6-M7 Melakh Prostrate 11.5 2 13.4 1.806 9.065 White Brown Me51M4-11M5-2M6-M7 Melakh Prostrate 12.2 1 14.4 1.49 10.2 White Brown Me51M4-14M5-2M6-M7 Melakh Erect 12.9 1 16.4 2.586 12.155 White White Me51M4-25M5-3M6-M7 Melakh Semi erect 10 1 13.1 2.38 7.46 White White Me51M4-29M5-1M6-M7 Melakh Prostrate 11 1 16.2 1.912 9.315 White White Me51M4-39M5-1M6-M7 Melakh Prostrate 10 1 15.58 2.47 10 White White Me51M4-77M5-M7 Melakh Erect 13 1 6.16 1.25 16.55 White White Me51M4-8M5-3M6-M7 Melakh Erect 10,5 1 16,6 1,73 8,685 White Brown Me51M4-9M5-1M6-M7 Melakh Prostrate 10 2 15,8 1,76 9,035 White Brown Me51M4-9M5-3M6-M7 Melakh Semi erect 12 1 14 134 7,99 White Brown Me51M4-20M5-1M6-M7 Melakh Prostrate 11,3 2 16,74 2,63 9,235 White White Me51M4-24M5-1M6-M7 Melakh Prostrate 10 2 19,67 2,804 11,475 White Wite Me51M4-36M5-1M6-M7 Melakh Prostrate 13,2 1 12,4 1,25 8,455 White White Me51M4-102M5-M7 Melakh Erect 9,33 1 11 0,85 12,33 White White Melakh IS86-292 x IT83s-742-13 Semi erect 12.1 1 13.4 2.423 10.25 White White



o reuse allowed w

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ay 25, 2020. .

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https://doi.org/10.1101/2020.05.22.109041

Experimental design and development of mutagenized 124

populations 125

The development of the mutagenized population was performed in different experimental 126

fields located in different sites in the western part of Senegal. In each site, the seeds of each 127

generation were sown using intra row spacing of 50 cm and 75 cm of inter raw spacing. The 128

irradiated seeds (M1) for each variety (Melakh and Yacine) were sown in a separate field in 129

August 2013 around Bambey during the raining season for the development of M1 130

population. Based on their yield, 12 and 7 mutant plants of Melakh and Yacine respectively 131

were selected and harvested for the development of the M2 population. For these purposes, 132

103 and 87 seeds respectively from M1 plant of Melakh and Yacine were sown during the dry 133

season in April 2014 in the “Centre National de Recherches Agronomiques (CNRA)” at 134

Bambey (Senegal) to develop M2 population. At maturity, the most productive mutant plants 135

12 for Melakh and 7 for Yacine were selected, harvested and their seeds sown in the same site 136

(CNRA) in September 2015 to develop the M3 population. From the M3 mutant plants, the 137

most productive were harvested and 88 and 81 seeds for the Mutant Melakh and Yacine were 138

respectively sown in September 2016 in the experimental field located in Ngolgane in the 139

vicinity of Niakhar (Senegal) in accordance with the same experimental design previously 140

described for M2, for the development of M4 population. At maturity stage, the plants were 141

harvested and a single descent method was used to develop M5 population. Thirty-nine (39) 142

and thirty-six (36) seeds of Yacine and Melakh mutants were respectively sown in December 143

2017 in a pot filled with sand from Sanghalkam (Senegal) and watered with tap water 3 times 144

a week. The mutant plants were grown in the Shadehouse of the “Département de Biologie 145

Végétale” at University Cheikh Anta Diop. The M6 and the M7 population were sown 146


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respectively in May 2017 and August 2017 in the field at the Teaching and Research farm of 147

the “Département de Biologie Végétale” at University Cheikh Anta Diop. 148

Agro-morphological characterization 149

Based on the previous studies it is well documented that irradiation promotes the expression 150

of recessive characters in the advanced generations [26,27]. Therefore, qualitative and 151

quantitative parameters were analyzed from M5 to M7 populations. For instance, in our 152

studies, seed color and pod length variation were noticed in the 5th generation (M5). The 153

scored qualitative parameters encompassed: rate of germination, leaflet abnormalities, leaflet 154

shape, growth habit, flower color, day to first flowering and seed coat color. The quantitative 155

parameters included, percentage of germination, plant height, mean of pod length, number of 156

pod per plant, number of seeds per pod, width and length mean of the seed and weight of 100 157

seeds. The plant height was measured using a tape measure (Cow head brand) from the 158

cotyledonary node to the top of the plant at the appearance of the first flower [28]. The mean 159

length of 5 pods was measured with a tape measure. Width and length mean of the seed was 160

measured using a vernier caliper (Mutshito) and weighted using a balance (sartoruis®). The 161

data of the quantitative traits recorded throughout M5 to M7 were used for statistical analyses. 162

Statistical Analysis of Data 163

Analysis of variance (ANOVA) and correlation of the quantitative traits were carried 164

out using R software, (R Development Core Team, 2011, version 3.6.1). In order to determine 165

the association between quantitative traits such as the seed mean length, seed mean width, 166

seed mean weight, pod mean length, pod mean width, mean number of seed per pod, plant 167

height and qualitative parameters (like leaflet shape, flower color, growth habit and 168

abnormalities, seed color and flowering date), a standardized Principal Component Analysis 169


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(sPCA) was performed with R software using the adjusted means of the measured traits to 170

assess the contribution of each of them on the genetic variability. The phenotypic coefficient 171

of variance, the genotypic coefficient of variation (GCV), the genetic advance (GA) and the 172

broad sense heritability (h2) was calculated using R software (R Development Core Team, 173

2011, version 3.6.1). 174

The genotypic variance (σ2g) was calculated using the following formula: 175

σ2g= (MSG- MSE)/ r, 176

Where MSG is the mean square of genotypes, MSE is the mean square of error, and r is the 177

number of advanced generation. 178

The Phenotypic Variance (σ2p) was assessed as follow: 179

σ2p= σ2g+ σ2e, 180

Where σ2g is the genotypic variance and σ2e is the mean squares of error. Error variance 181

σ2e=MSE. 182

According to [29], the estimation of phenotypic and genotypic coe�cient of variation were 183

calculated as follows: 184

��

�X100 185

�� √��

�X100, X is the mean. 186

GCV and PCV values were considered as low (0-10%), moderate (10-20%), and high (�20%) 187

following [30]. 188

The Heritability Estimate (broad sense) (h2%), h2%�σ2g

σ2p100 189


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The heritability percentage was considered as low (0-30%), moderate (30-60%), and high 190

(�60%) [31]. 191

The Expected and Estimated Genetic Advance (GA) 192

GA= k.σp. h2, and The Genetic Advance as Percentage of Mean (GA %) is carried using 193

��% ��

� 100 194

GA was calculated using the method of [32] and selection intensity (k) was assumed to be 195

5%; where k = 2.06, a constant and σp is the phenotypic standard deviation. 196

Genetic advance as percentage of mean was categorized as low (0-10%), moderate (10-20%), 197

and high (>20%) [33]. 198

The Pearson’s correlation coefficient (r) for trait linkage evaluation were performed 199

using R software to determine the association between quantitative characters. The genetic 200

distance between mutants and their parents based on quantitative traits was tested using 201

multivariate analysis. To generate the dendrogram, similar matrices were used based on the 202

Ward methods cluster analysis [34]. 203

DNA genotyping 204

DNA extraction 205

Five hundred (500) mg of young leaflet collected from each individual plant of 1 month old 206

randomly selected were grounded in mortar in accordance with the protocol developed by 207

Fulton et al. (1995). RNA was removed by adding 50 µg/ml of RNAse A (CalBiochem) in 208

each tube which was incubated at room temperature for 1 h and then DNA was purified 209

according the protocol described by [23]. After precipitation, DNA was dried for 20 min with 210

a Speed Vac ® Plus Sc110 (Savant) and dissolved in 100 µl of TE x 0.1. The quantity and the 211

quality of the DNA extract were performed using a NanoDrop™ One/OneC Microvolume 212


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UV-Vis Spectrophotometer (Thermo Scientific) at A260, A280, A260/A280 and A260/A230, 213

then the samples were stored at -20°C or used for amplification. 214

215


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Amplification of DNA and electrophoresis 216

The amplification reaction was performed in 0.2 ml tube puReTaq Ready-To-GoTM PCR 217

beads (GE Healthcare) containing 2.5 U of lyophilized PuReTaq, 200 µM dNPT and 1.5 mM 218

MgCl2, 0.5 µM of each ISSR primer (TSINGKE, China) and 25 ng of DNA in a final volume 219

of 25 µl. The tubes were loaded in a Prime thermocycler (TECHNE, UK) programmed for 220

pre-denaturating step of 5 min at 95°C followed by 40 cycles of 30 s 95°C, 1 min at 38 to 221

52°C (depending on primer, Table 2), 1 min at 72°C and a final extension of 8 min at 72°C. 222

After amplification, the PCR products were separated on 2% agarose gel (Sigma) for 2 h at 223

70V. The gel was finally stained during 30 min with GelRed X10,000 (Biotium) according to 224

the manufacturer's instructions and photographed under UV light using Gel Doc system (High 225

performance UV Transilluminator UVP). 226

Genetic variation analysis 227

Amplifications were repeated three times for each single ISSR primer in order to retain the 228

clear and reproducible bands. On this basis, the total number of amplified bands was 229

calculated: their size estimated and the percentage of polymorphic bands evaluated. The 230

polymorphic bands were scored using a binary code as presence (1) or absence (0) to 231

construct a data matrix. 232

The Shannon diversity index, heterozygosity (Nei index) and the private alleles were 233

calculated using GenAlex 6.5 software [35]. To evaluate the discriminatory power of each 234

marker, the Polymorphic Information Content (PIC) was calculated using the PowerMarker 235

software 3.25 [36], while assessing the genetic variation among and within groups an analysis 236

of molecular variance (AMOVA) was performed using GenAlex 6.5 software [35]. 237

238


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Population structure analysis 239

The population structure was analyzed using the Bayesian clustering approach implemented 240

in the software STRUCTURE 2.3.3 [37] while the number of subpopulations was tested from 241

1 to 10 independent runs. Using the admixture model [38] each simulation set to 100 000 burn 242

in periods and 10 runs of 200 000 iterations of Markov chain Monte Carlo (MCMC) were 243

performed. These results were uploaded to the online software, Structure Harvester [39] to 244

determine the most likely number of subpopulations using the Evanno Δk method [40]. To 245

assign the individuals into clusters, a membership coefficient q ≥ 0.7 was used. The genotypes 246

within a cluster with membership coefficients q < 0.7 were considered as genetically 247

admixed. 248

Discriminant Analysis of Principal Components (DAPC) was performed on the basis of the 249

binary matrix data, in order to confirm or invalidate the pattern of the genetic structure 250

obtained with STRUCTURE and to identify the loci responsible for possible differentiation 251

between genetic groups. This analysis was performed using the package « adegenet » [41] of 252

R software [42]. 253

A Neighbor Joining [43] dendrogram was constructed using the inter-individual distance 254

matrix, calculated on the basis of the [44, 45] index. This analysis was performed using 255

Darwin 6.0 software [46]. 256

Results 257

Agromorphological characterization of the munants 258

Variation of qualitative traits among the mutants 259


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The germination rate of the irradiated seeds of Melakh and Yacine was respectively 98.15% 260

and 99.08% in the M1 generation. This germination rate was respectively for Melakh and 261

Yacine 92.23% and 96.5 in the M2, 93.3% and 92.4% in the M3, 63.64 and 81.5% in M4, 262

90% and 92% in M5, 88% and 93% in M6 and 87.5% and 93.82% in M7 while it was 100% 263

for the control. These results suggested that gamma irradiation at 300 or 340 Gy affected 264

negatively the germinative power of the seeds. Growth habit variability appeared in the M5 265

for Melakh mutants where 94% were prostrate, 3% erected and 3% semi erected like the 266

control. During the M6, 3% were prostrate, 97% erected and semi erected phenotype was not 267

observed among the mutants. The M7 included 62% prostrate, 7% semi erected and 31% 268

erected. In the Yacine mutants the prostrate phenotype appeared in the M5 with 38% and 61% 269

were erected like the control but in the M6 the percentage of the individuals with these traits 270

was respectively 4% and 96%. The semi erected phenotype appeared for the first time in the 271

M7 with 4% while 18% and 78% were respectively prostrate and erected (Table 1). The 272

leaflet shape within the M7 mutants of Melakh revealed the existence of 4 leaflet forms which 273

were globular (6%), subglobular (76%), hastate (6%) and subhastate (12%) while the leaflet 274

form of the control was subhastate. The leaflet of Yacine was subglobular but 4% of its 275

mutants had subhastate leaflet indicating that gamma irradiation induced leaflet shape 276

variability (Fig 1A-D). Foliar number abnormalities were 11.11%, 11.5% and 0% respectively 277

in M5, M6 and M7 (S1 Fig). These foliar number abnormalities, such as three primary leaves 278

around the node in some mutants instead of two opposite leaves and tetraleaflet, revealed that 279

the gamma irradiation affected the genes controlling leaflet shape in these mutants. 280

Phenotypic variability in flower color was first observed in the M5 of the mutants of Yacine 281

and Melakh. Among the mutants of Melakh 22%, 50% and 36% had white flowers with 282

purple border respectively in M5, M6 and M7 unlike to the control which had white flowers 283

(Fig 1E-H). In contrast, three patterns of flower coloration were observed among the mutants 284


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of Yacine. In the M5, 42.5% of the mutant showed white flowers like the control but 35 and 285

22.5% had white flowers with purple border and purple flowers respectively. The proportion 286

of the mutants with white flowers with purple border was 48% in M6 and 43% in M7 whereas 287

the mutants with purple flowers represented 13 and 26% in the M6 and M7 respectively. 288

These data suggested that gamma irradiation affected the genes controlling flower color of the 289

cowpea samples. Sterility characterized by flower abortion was observed only among the 290

mutants of Melakh in M5, M6 and M7 with 11%, 5% and 2% respectively. Among our 291

population, 50% of flowering was reached 45 days after sowing (DAS) in M6 and M7 for 292

Melakh mutants while this value was respectively 46 and 50 DAS for the mutants of Yacine 293

(S2 Fig). The color of the seed coat was unchanged from M1 to M7 for the mutants of Melakh 294

but some of them showed brown or beige eye. In the M4 of Yacine mutants, the seed coat was 295

brown like the control except one mutant where brown seeds, white seeds and pigmented 296

brown and white were harvest (Fig 1J). In the M5, 43% had white seeds, 38% of the mutants 297

had brown seeds as the control and 19% had light brown seeds (Fig 1K-M). 298

Fig 1. Variation of qualitative traits observed in the populations. 299

A-D: Leaflet shape, Bar = 6.25 cm; E-H: Flower color, Bar = 0.84 cm; I-M: Seed coat color, 300

Bar = 1 cm 301

A: Globular (Yacine); B: Hastate; C: Subglobular; D: Suhastate (Melakh); E: White flowers 302

(Yacine and Melakh), F: White flower with purple border; G: Dark purple; H: Light purple; I: 303

Brown seed coat (Yacine); J: Brown, white, white pickleed brown seed coat (from on single 304

plant in M4 of the mutant of Yacine); K: light brown speckled in dark brown seed coat; L: 305

White seed coat; M: Light brown seed coat 306

Variation of quantitative traits and yield components among the mutants 307


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To advance the mutant populations from M1 to M4, pedigree method was used and the 308

selection criteria was based on the plant yield and no shattering pods for the mutants of 309

Yacine only. In contrast, from M5 to M7, single seed descent method was used. In M5, the 310

mutants of Melakh were selected based on 100 seed weights but in M6 and M7 one more 311

yield component (pod length) was including in the selection criteria. The average pod length 312

of Melakh control measured across generations was 19 cm while the value obtained in the 313

mutants ranged from 12.5 to 25 cm cross generation M5, M6 and M7 (Fig 2A). At the same 314

time, the average pod length of Yacine control was estimated to 14.65 cm but variability of 315

the pod length was observed among the mutants of Yacine ranging from 8.70 to 19 cm cross 316

generations M5, M6 and M7. 317

The number of pods per plant ranged from 1 to 6, 1 to 20 and 2 to 15 in M5, M6 and M7 318

respectively for the mutants of Yacine, for the control it ranged from 3 to 6. These values 319

ranged from 1 to 7, 2 to 35 and 1 to 43 for the mutants of Melakh and from 3 to 10 for the 320

control. The mean seed length varied from 8.75 to 10.72, 8.1 to 11.66, 7.46 to 12.16 mm 321

respectively for M5, M6 and M7 for the mutants of Melakh and 9.2 mm for the control (Fig 322

2B). For the mutants of Yacine the mean seed length varied from 6.12 to 10.04, 9.12 to 11.20 323

and 8.11 to 11.13 mm respectively for M5, M6 and M7 and 10 mm for the control. The 324

number of seeds per pod varied from 9 to 15, 4 to 12 and 3 to 12 for the mutants of Melakh 325

respectively for M5, M6 and M7 and 10 for the control. For the mutants of Yacine, the 326

number of seeds per pod ranged from 7 to 13, 5 to 12 and 4 to 16 respectively in M5, M6 and 327

M7 and 9 for the control. The 100 seed-weight ranged from 16.67 to 28.52, 20.07 to 32.28 328

and 13.55 to 38 g respectively for M5, M6 and M7 for the mutants of Melakh but the values 329

recorded for the control ranged from 18.35 to 32.12 g. At the same time, 100 seed-weight 330

ranged from 10.78 to 24.5, 17.27 to 33.16, 13.83 to 30.03 g respectively in M5, M6 and M7 331

population of the mutants of Yacine and from 16.4 to 30.40 g for the control. Two mutant 332


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lines of Melakh produced more seeds (44 to 184) per plant regardless of the generation. 333

Similar results were observed among the mutants of Yacine (17 to 150 seeds). 334

Fig 2. Variation of the pod length (A) and seed size (B) in M5 among the mutants of Melakh. 335

On the top: Pods and seeds harvested from the mutant Me51M4-39M5. Bar = 1.54 cm 336

On the bottom: Pods and seeds harvested from the parent control Melakh. Bar = 1 cm 337

Genotypes clustering based on Principal Components Analysis and 338

correlation between traits 339

The projection of agromorphological parameters collected from M7 in the PCA Biplot 340

showed that the axis 1 explained 28% of the variation. This axis encompassed the elite 341

mutants in term of seed length, pod weight and seed weight (Y7-M4-3M5-1M6-M7 and 342

Me51M4-14M5-1M6-M7) and the early maturing mutant lines (Me51M4-36M5-1M6-M7, 343

Y1-M4-16M5-2M6-M7 and Y7-M4-1M5-1M6-M7) compared to their control parents Yacine 344

and Melakh. The axis 2 explaining 19.7% of the variation, was constituted by the mutant lines 345

(Y1-M4-11M5-3M6-M7, Y7-M4-1M5-3M6-M7, Y1-M411M5-3M6-M7, Me51M4-39M5-346

1M6-M7, Me51M4-29M5-1M6-M7, Me51M4-20M5-1M6-M7, Me51M4-10M5-1M6-M7, 347

Me51M4-9M5-1M6-M7, Me51M4-11M5-1M6-M7) which acquired a new growth habit i.e 348

prostrate compared to their parents (Fig 3). The evaluation of the Pearson’s coefficient 349

between agromorphological characters cross generation (from M5 to M7) showed that steam 350

pigmentation (Pg) was significantly and negatively correlated to seed color (SC) (r = -0.5, p = 351

0.01 in M5; r = -0.59, p = 0.001 in M6 and r = -0.64, p = 0.001 in M7), the number of seeds 352

per plant (NSP) was significantly and positively correlated to the pod length (PdL) (r = 0.42 p 353

= 0.05 in M5, r = 0.62 p = 0.001 in M6 and r = 0.82 p = 0.001 in M7), Seed weight (SWg) and 354

Seed Length (SL) (r = 0.86 p = 0.001 in M5, r = 0.60 p = 0.001 in M6 and r = 0.74 p = 0.001 355

in M7) (S1 Table, S2 Table and Table 2). In addition to these, in the M7, which is supposed to 356


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be more stable, the yield component parameters such as the pod number (PdN) and the growth 357

habit (GH) are significantly correlated (r = 0.37 p = 0.05) and pod weight (PWg) and PdL (r = 358

0.78 p = 0.001). The pod width is significantly and positively correlated to the SWg (r = 0.54 359

p<0.001), PWg (r = 0.40.01 to SL (r = 0.46 p = 0.01) as do the pod weight (PdWg) and SWg 360

(r = 0.56 p = 0.001). In contrast, Seed width (SW) and PdW are negatively and significantly 361

correlated (r= -0.47 p = 0.01, Table 2). 362

Fig 3. Principal Component Analysis of 40 mutant M7 lines and their parent based on agro-363

morphological data. 364

365


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Table 2. Estimation of Pearson’s correlation between the agromorphological characters in the M7 of the mutant lines. 366 367 PH Pig GH DF FC PdL PdW SL SW SWg NSP PdN PdWg SC PH 1 Pig 0,31* 1 GH 0,72*** 0,38* 1 DF 0,10 0,05 0,11 1 FC -0,12 0,13 -0,09 -0,07 1 PdL 0,18 0,17 0,29 0,14 -0,18 1 PdW 0,01 -0,44** -0,14 -0,04 0,01 0,23 1 SL -0,09 -0,26 -0,19 -0,27 -0,10 0,13 0,46** 1 SW 0,11 0,15 0,22 -0,19 -0,17 -0,04 -0,47** 0,02 1 SWg -0,02 -0,29 -0,13 -0,31* -0,18 0,33* 0,54*** 0,74*** 0,27 1 NSP 0,31* 0,18 0,36* 0,11 -0,20 0,82*** 0,26 0,10 0,04 0,31* 1 PdN 0,33* 0,20 0,37* -0,19 0,00 0,19 0,15 0,17 0,14 0,19 0,30 1 PdWg 0,10 -0,06 0,13 0,10 -0,13 0,78*** 0,40** 0,20 0,06 0,55*** 0,67*** 0,06 1 SC -0,35* -0,64*** -0,40** -0,04 0,20 -0,40** 0,33* 0,25 0,03 0,29 -0,34* -0,18 -0,08 1

* significant at 5% level of probability; ** significant at 1% level of probability; *** significant at 0.1% level of probability 368



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Relationship among mutants based on agromorphological parameters 369

Based on the agro-morphological characters the M7 population was divided into 3 groups. 370

The first group which encompassed 52% of the individuals are divided into 3 subgroups. The 371

subgroup 1 included only the mutants (Me51M4-102M5-M7, Me51M4-36M5-1M6-M7, and 372

Me51M4-77M5-M7) which are sister of Y13M4-4M5-1M6-M7, Y1M4-11M5-1M6-M7 and 373

Me51M4-25M5-3M6-M7. The subgroup 2 only comprised mutants of Yacine except 374

Me51M4-8M5-3M6-M7 which clustered with genotypes sharing the same characters such as 375

erected port, white brown-eyed seed. The subgroup 3 included only mutants of Yacine. The 376

second group included 14% of the genotypes which had all erected port and brown-eyed seed 377

and contained only mutants of Yacine and their parent. The third group was the second largest 378

one with 33% of the genotypes was divisible into 2 main subgroups. The first subgroup 379

encompassed Melakh its mutants and 2 mutants of Yacine. The second included mutants of 380

Melakh and 2 mutants of Yacine which shared several agromorphological characters with 381

Melakh among these long pod and white seed (Fig 4). 382

Fig 4. Hierarchical classification of 40 mutant lines and their parent based on agro-383

morphological characters using Ward’s method in R software. 384

Heritability of agromorphological characters in the mutants 385

Using R software, it was possible to analyze the phenotypic (PCV) and genotypic (GCV) 386

coefficient of variance and the heritability values for the different measured traits in the 387

mutant populations. The PCV varied from 100.59 for pod number (PdN) to 9.12 for Seed 388

length (SL). Plant height (PH) recorded high value (75.1%) of PCV in contrast, day of 389

flowering (DF), pod width (PdW), eye color (HC) and leaflet length (LF) showed respectively 390

9.37, 10.14, 10.32 and 15.89%. The recorded GCV values ranged from 67.12% for pod 391

number (PdN) to 5.97% for date of flowering (DF). All the studied traits showed a genetic 392


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coefficient of variance (GCV) below 50% except pod number and seed color (SC). The 393

genetic advance (GA) as a percentage of the mean recorded in the mutants ranged from 0.45% 394

for petiole length to 92.27% for pod number. High values of heritability were recorded for 395

seed color (94.48%), steam pigmentation (92.83%), flower color (78.01%), eye color 396

(68.98%), pod length (68.29%), pod width (59.06%), seed weight (57.58%), number of seed 397

per plant (52.11%), seed length (51.19%), the remaining characters showed values below 50% 398

(Table 3). 399

400


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Table 3. Estimation of mean values of phenotypic coefficient of variance (PCV), genotypic coefficient of variation (GCV %), broad sense 401

heritability (h2bs%) and genetic advance as% of the mean (GA %) of eighteen traits (quantitative and qualitative) in the M7 of 40 402

cowpea mutant lines. 403

PH LF LW PeL Pig GH DF FC PdL PdW SL SW SWg NSP PdN PdWg SC EC Mean 51.8 10.4 5.8 7.04 - - 49.6 - 15.4 8.8 9.5 6.6 0.2 10.6 6.4 1.9 - - PCV% 75.1 15.89 24.34 27.45 20.85 46.9 9.37 46.67 21.73 10.14 9.12 14.65 25.69 20.1 100.59 25.76 54.1 10.32 GCV% 23.67 6.7 13.31 9.28 20.09 17.59 5.97 41.22 17.96 7.79 6.53 9.48 19.49 14.51 67.12 17.97 52.59 8.57 GA % @ 5%

20.18 5.82 14.98 0.45 - - 7.84 - 30.56 12.33 9.61 12.62 30.47 21.57 92.27 25.82 - -

h2(%) 17.14 17.78 29.89 11.43 92.83 14.06 40.62 78.01 68.29 59.06 51.19 41.85 57.58 52.11 44.53 48.66 94.48 68.98

404

PH: Plant Height, LF: Leaflet Length; PeL: Petiole; Pig: Pigmentation; GH: Growth Habit; DF: Date of Flowering; FC: Flower Color; PdL: Pod Length; 405 PdW: Pod Weight; SL: Seed Length; SW: Seed Weight; NSP: Seed Number per Plant; PdN: Pod Number; PdWg: Pod Weight; SC: Seed Color; EC: Eye 406 Color 407



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Genetic characterization of the mutants 408

Genetic diversity induced by mutagenesis revealed by ISSR markers 409

In total, the 13 ISSR markers used in this study gave polymorphism amplifying 129 bands 410

(loci) in the 18 mutant lines and their 2 parents. The size of the amplified bands ranged from 411

150 to 2000 bp. Of these, 111 (86%) bands were polymorphics with numbers ranging from 5 412

(UBC827) to 12 (UBC825 and UBC844) for each primer (Table 4). The percentage of the 413

polymorphic bands per primer ranged from 60% (UBC809) to 100% (UBC825, UBC844, 414

17899A, 17899B and, HB10). The band frequencies ranged from 0.318 (UBC841) to 0.808 415

(HB12) with a mean of 0.473±0.145. The genetic diversity ranged from 0.142 (UBC841 and 416

UBC823) to 0.293 (UBC844 and 17899B) with a mean of 0.220±0.049. The Polymorphic 417

Information Content (PIC) values for each primer varied between 0.167 (UBC841) and 0.307 418

(HB09) with a mean of 0.238±0.044 (Table 5). Based on these data, our analyses showed that 419

the average diversity level for all the mutants and their parents was equal to 0.222 (Nei 420

index). The level of the genetic diversity observed in group 1 (h = 0.308) was higher than the 421

one in group 2 (h = 0.146; p = 0.0001) and in group 3 (h = 0.212; p = 0.0001). The level of 422

genetic diversity in group 3 was higher than in group 2 (p = 0.016). The genotypes which 423

belong to group 1 also recorded a greater number of private alleles (17 bands vs. 4 for group 2 424

and 7 for group 3, Table 6). The Shannon’s information index was higher in group 1 (0.463) 425

than in group 2 and 3 with respectively 0.211 and 0.319 but for the entire population this 426

value was 0.331. To investigate the genetic variance within and among genetic pools, 427

AMOVA was carried out in this study. Our results revealed that the majority of the variance 428

rather existed within group (85%) than among group (15%) (Table 7). 429

430


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Table 4. List of the Inter-simple sequence repeat (ISSR) primers with their annealing temperatures (Tm) used to genotype the mutants 431

M7 and their parent, total number of bands, number of polymorphic bands, percentage of polymorphic bands and the band size range. 432

ISSR primer codes

Sequences (5’-3’)

Annealing temperature (°C)

Total number of bands

Number. of polymorphic bands

Percentage of polymorphic bands

(%)

Band size range (pb)

UBC809 (AG)8G 52.2 10 6 60 1400-200 UBC825 (AC)8T 49.8 12 12 100 1500-200 UBC841 (GA)8YC 52.6 14 11 78.57 1800-150 UBC844 (CT)8AC 52.6 12 12 100 1300-300 17899A (CA)7AG 49.2 11 11 100 2000-250 17899B (CA)7GG 51.7 11 11 100 1500-300 HB8 (GA)6GG 44 8 6 75 1300-250 HB9 (GT)6GG 44 10 8 80 1200-250 HB10 (GA)6CC 44 8 8 100 1000-350 HB12 (CAC)3GC 38 7 6 85.71 1500-300 UBC827 (AC)8G 52.2 7 5 71.42 1000-400 UBC823 (TC)8C 52.2 10 8 80 1500-400 UBC807 (AG)8T 49.8 9 7 77.77 1500-200



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Table 5: Band frequency, genetic diversity and polymorphism information content (PIC) of 433

each ISSR locus. 434

Marker Band Frequency Gene diversity PIC

ISSR 825 0.338 0.203 0.219

ISSR841 0.318 0.142 0.167

ISSR823 0.381 0.142 0.172

ISSRHB-10 0.369 0.232 0.228

ISSRHB-8 0.575 0.231 0.236

ISSR17899A 0.468 0.212 0.224

ISSRUBC-807 0.55 0.228 0.242

ISSR827 0.46 0.236 0.293

ISSR844 0.417 0.293 0.288

ISSRHB-09 0.413 0.274 0.307

ISSR17899B 0.368 0.293 0.274

ISSR HB-12 0.808 0.164 0.196

ISSR809 0.683 0.211 0.258

Mean 0.473±0.145 0.220±0.049 0.238±0.044

435

Table 6. Statistical analyses of genetic diversity level. 436

Population Number of gentoypes

% of polymorphic

loci Nei diversity

Shannon's Information

Index Group1 7 85.59 0.308 0.463

Group2 4 34.23 0.146 0.211

Group3 9 61.26 0.212 0.319

All genotypes 20 60.36 0.222 0.331

437

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Table 7. Genetic variance within and among groups based on Analysis of Molecular 439

Variance (AMOVA). 440

Source of variance Degree of freedom

Sum of Square

Mean Square

Estmated Variance

Percentage of total variance

Among group 2 65.008 32.504 2.729 15 Within group 17 257.992 15.176 15.176 85 Total 19 323.000 17.905 100

441

Population structure and genetic relationship among genotypes 442

Using STRUCTURE software [37], the evaluation of the delta k according to the Evanno 443

method showed the highest peak at k = 3 (Fig 5A) and the mean value of the logarithm of 444

likelihood (LnP (D) for k = 1 was lower than that of k = 3 which is the higher peak (Fig 5B). 445

The representation of the ancestry at k = 3, revealed three (3) genetic pools (Fig 5C). Group I 446

included 30% of the individuals while group II and group III encompassed respectively 50 447

and 20% of the genotypes. The genetic relationship revealed by the dendrogram was in 448

agreement with STRUCTURE analysis which clearly distinguished three groups. The first 449

group contained 2 mutants of Melakh (Me51M4-14M5-2M6-M7 and Me51M4-39M5-1M6-450

M7) and 4 other mutants of Yacine (Y1-M4-8M5-1M6-M7, Y1-M4-3M5-2M6-M7, Y1-M4-451

11M5-1M6-M7 and Y7-M4-4M5-3M6-M7). This group encompassed the highest number of 452

admixed individuals. The second genetic pool contained exclusively the mutants of Yacine. 453

Of these, Y1-M4-9M5-2M6-M7 and Y1-M4-9M5-3M6-M7clustering with a high bootstrap 454

value (96%). The admixed Y7-M4-1M5-1M6-M7 clustered with Y7-M4-6M5-1M6-M7 with 455

80% bootstrap value. The third genetic pool contained Yacine, 3 mutants of Yacine (Y13-M4-456

4M5-1M6-M7, Y7-M4-12M5-3M6-M7 and Y7-M4-10M5-1M6-M7) and 5 other mutants of 457

Melakh (Me51M4-10M5-1M6-M7, Me51M4-11M5-2M6-M7, Me51M4-25M5-3M6-M7 and 458

Me51M4-29M5-1M6-M7). The variety Melakh was clustering with its mutant, Me51M4-459

77M5-M7 with a high bootstrap value (92%) (Fig 6A). 460


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The clustering of the genotypes resulting from the DAPC analysis identified 3 groups which 461

showed any individual genetically admixed (Fig 6B). This results suggested that DAPC 462

analysis was appropriate to assess mutant populations structure by achieving better separation 463

among group as it was also showed by the number of clusters identified (Fig 7A). Group 1 464

and Group3 differed from each other based on the first axis of the DAPC. This classification 465

was based on the loci 84, 37, 57, 105, 35, 10 and 106 which were the most discriminating, in 466

decreasing order (Fig 7B). Group 2 differed from the others on the second axis. These 467

findings were based on the loci 97, 78, 87, 21, 105, 7, 82, 64, 47 and 12 which were the most 468

discriminating, in decreasing order (Fig 7C). 469

Fig 5. Structure of the mutant population. 470

A: Probability of subdivision into genetic groups by the method of Evanno et al. 2005; B: 471

Probability of subdivision into genetic groups by the log mean likelihood method and C: 472

Ancestry for K = 3. 473

Fig 6. Neighbor Joining Dendrogram based on ISSR data representing the grouping of 18 474

cowpea mutant lines and their parent. The dendrogram was constructed using the distance 475

matrix between individuals, calculated using the Jaccard, (1902, 1912) Index. 476

A and B: Dendrograms based on STRUCTURE and DAPC analyses respectively. 477

Fig 7. Discriminant Analysis of Principal Components (DAPC) for 18 cowpea mutant lines 478

and their parent based on ISSR data. 479

A: graphical representation of the groups; B: contribution of the alleles based on the first axis; 480

C: contribution of the alleles based on the second axis 481

482

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Discussion 484

Wide genetic variability is a prerequisite for a successful breeding program particularly in the 485

era of climate change with its adverse effects leading to the erosion of the plant genetic 486

resources. Thus, to broaden the crop’s genetic basis, a wide range of techniques have been 487

used in the last decade but among these, induced mutagenesis has been proved to be best for 488

creating novel variation in crop genome and was used in this study to expand variability in 489

cowpea which experienced a single domestication event during the course of evolution. 490

Agromorphological variability analysis of the mutants 491

In this study the percentage of germination of the irradiated seeds decreased compared to the 492

control (Melakh and Yacine) regardless of the dose and the generation used. These results 493

were in agreement with the findings of Melki and Marouani [47], Horn et al. [13] and 494

Olasupo et al. [17] who recorded similar observations during their studies. In contrast, Horn et 495

al. [13] recorded zero germination of cowpea irradiated seeds at 300 Gy, in this study 98.15% 496

of field establishment were observed for Melakh M1 white-seeded at this dose, this value 497

reached 99.08% for Yacine M1 brown-seeded at 340 Gy. These results were in agreements 498

with the findings of Olasupo et al. [17] who suggested that radio-sensitivity is genotype 499

dependent as it was associated with seed characteristics (seed coat color, water content, 500

thickness and weight). Presently, it is well documented that ionizing radiation is injurious of 501

enzymes and growth hormone leading to biochemical and physiological modifications, cell 502

death, abnormal cell division, tissue and organ failure and growth disturbance [48, 49, 50]). 503

We can assume that these type of changes occurred in our irradiated seed materials as 11.11% 504

and 11.5% of the Melakh mutant lines in M5 and M6 respectively showed abnormities of 505

leave numbers, corroborating the discoveries of Girija and Dhanavel [51] and Nair and Mehti 506

[11] performed in cowpea mutants that were bifoliated, tetrafoliated, pentafoliated and 507


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hexafoliated (S1 Fig). These disturbances would explain the change observed in leaves shape 508

of our mutants with 3 new forms e.i. globular, subglobular and hastate (Fig 1). In contrast, the 509

investigations performed by Gnanamurthy et al. [16] led to the discovery of only a globular 510

form within their mutant populations. Similar modifications induced early flowering in some 511

Melakh and Yacine mutant lines which is an important agronomic trait for farmers and 512

breeders particularly in the Sahel zone where the rainy season has become shorter and shorter. 513

In addition, mutagenesis treatment affected flower coloration which changed from white to 514

white with purple border in Melakh and in Yacine mutant population it changed from white to 515

white with purple border, purple or dark purple with a high heritable value (h2 = 78.01%) (Fig 516

1, Table 3). These findings were in accordance to the observations of Horn et al. [13] and 517

Girija et al. [52] who also noticed flower color variation in their cowpea mutant populations. 518

In this study, similar seed coat color variation was observed in the mutants of Yacine, first 519

time during the M4 generation where one single plant produced white seeds, brown seeds and 520

white-browned seed (Fig 1J) but from M5 brown seed and white were only recorded as it was 521

previously reported that seed coat color is an important trait for consumer preference 522

depending on the regions [53]. On the other hand, the seed coat color of Melakh mutants 523

remained unchanged e.g white as the control regardless of the generation. These results 524

explained the high heritability value (h2 = 94.48%; Table 3) recorded for seed coat color in 525

our populations. In accordance with this study, mutants seed coat color variation was also 526

recorded by Gaafar et al. [12] and Horn et al. [13] suggesting that mutation affected the 527

candidate genes involved in the control of late stages in flavonoid biosynthesis pathway 528

namely the basic helix–loop–helix gene for the C locus, the WD-repeat gene for the W locus 529

and the E3 ubiquitin ligase gene for the H locus [54]. 530

During this study, yield component (pod length, pod width, number of seeds per plant, seed 531

length and seeds weight) variation with a high heritability values was noticed among the 532


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mutant genotypes compared to the control. Similar results were recorded by Gnanamurthy et 533

al. [16] Goyal and Khan [55] and Horn et al. [13] which suggested that mutagenesis can be 534

used to improve crop yield, one of the most important agronomic characteristics for breeders. 535

High heritability, genetic advance and genetic coefficient of variation values of pod length 536

and number of seeds per plant recorded in this study suggest that these traits can be 537

considered as attributes for the improvement through selection of the mutants. In addition, 538

regardless of the generation (M5 to M7) and the environment, the pod length and the number 539

of seeds per plant were positively and significantly correlated as did seed length and seed 540

weight during this study. In contrast, the variation of the correlation values between traits 541

recorded might be due to the effect of interaction genotype x environment which could induce 542

epigenetic modifications impacting gene expression or gene segregation over generations. 543

The genetic relationship based on the agromorphological characters revealed that the mutant 544

populations were subdivided into 3 groups (Fig 4). The first group included genotypes which 545

notably deviated from their respective parents, thereby suggesting that the irradiation doses 546

used were efficient enough to induce heterogeneous population as any polytony was observed 547

in the dendrogram. In the remaining two others groups, most of the mutants were clustering 548

with their respective control revealing their high phenological diversity as reported by Laskar 549

and Khan [56]. Taking together, these findings meet the recommendation of Rohman et al. 550

[57] who suggested that the cluster contributing to the greatest divergence can help to choose 551

the parent in a breeding program. Based on these, PCA results (Fig 3) revealed in this study 552

that yield related traits such as pod length, number of seeds per plant and seed weight are 553

major contributor to the genetic divergence which was in accordance with the report of 554

Afuape et al. [58] who suggested that PCA is accurate to select the best genotypes for future 555

breeding progam. 556

557


https://doi.org/10.1101/2020.05.22.109041

Genetic diversity of mutants based on molecular markers 558

In the present study, the gene diversity measures through the average heterozygosity and the 559

genetic distance among individuals in a population and the PIC values which is a good 560

indicator of the usefulness of a marker to determine its inheritance between the offspring and 561

the parent were estimated in our mutants. Our results showed that, in general, the gene 562

diversity and the PIC were close (Fig 5), suggesting the evenness of allele’s frequencies in the 563

mutant populations as reported by Shete et al. [59]. According to Botstein et al. [60], a marker 564

is considered highly informative if the PIC is ≥ 0.50, moderately informative with a PIC 565

values ranging from 0.25 to 0.5 and slightly informative at less than 0.25. Based on this, the 566

ISSR827, ISSR844, ISSRHB9, ISSR17899B and ISSR809 were moderately informative, the 567

remaining markers were slightly informative which were higher than the scores recorded in 568

the cowpea mutant population developed by Gaafar et al. [12]. These differences could be 569

attributed to the low irradiation dose (50 Gy) used which might induce less mutations and less 570

variability compared to the 300 Gy and 340 Gy employed in this study. In addition, Gaafar 571

and coworkers [12] used ethidium bromide for DNA staining which is less sensitive than 572

GelRed, the one we used in our study leading to more DNA bands scoring. In contrast, the 573

PIC scores reported in the natural populations of Vigna [61], mungbean, blackgram [62] and 574

in Vigna unguiculata [21] were a bit higher probably due to several mutations undergone by 575

these genomes during evolution. Our results show that ISSR are accurate markers to 576

discriminate cowpea mutants for new genotypes identification and selection. In this study, the 577

ISSR primers UBC825 (AC repeat motif), UBC844 (CT repeat motif), 17899A and 17899B 578

(CA repeat motif) and HB12 (GA repeat motif) gave 100% polymorphic bands which 579

suggested that these motifs are abundant in the genome of cowpea. Similar observations have 580

been reported in Arachis hypogea [63,64] and in Vigna mungo [65]. 581


https://doi.org/10.1101/2020.05.22.109041

Population structure and genetic relationship of the mutants based on ISSR 582

markers 583

Analyzing population structure in mutants is relevant to understand the organization of the 584

genetic variation which is driven by the combined effect of recombination, mutation, 585

demographic history and natural selection. Based on this, and due to the informative nature of 586

the ISSR markers used, the generated data subjected to STRUCTURE analysis showed 3 587

subpopulations (optimal K = 3). These results were consistent with the organization of the 588

dendrogram (Fig 6A). The clustering of the Me51M4-39M5-1M6-M7 and Y1-M4-8M5-1M6-589

M7, two genotypes tolerant to the nematode Meloidogyne incognita, compared to their 590

parents, according to our preliminary studies, might suggest that this character is distributed in 591

this group (group 1). In addition, this group encompassed genotypes with long pod length 592

compared to their respective parents. These findings are relevant to select the best mutant 593

genotypes which can be proposed for variety registration and popularization but also for gene 594

discoveries and breeding programs. These results demonstrate the ability of gamma rays to 595

induce large genetic changes in DNA material as among the 18 mutants genotyped 7 derived 596

from 1 Melakh mutant and 11 from 1 Yacine mutant. The findings were in accordance with 597

previous studies recently performed on several crops such as cowpea [12,13,66] and chickpea 598

[67]. The dendrogram analysis revealed a group encompassing only Yacine mutants. In this 599

group 2, the Y7-M4-4M5-3M6-M7 genotype could be included as it shares several 600

morphological characters such as the erected shape, except Y7-M4-1M5-1M6-M7 which is 601

prostate, white flower with purple border except the mutant Y1-M4-9M5-2M6-M7 showing 602

light purple flower, brown coat seed, except Y7-M4-1M5-1M6-M7 with white coat seed, Y7-603

M4-6M5-1M6-M7 a white-brown seeded genotype as is Y1-M4-9M5-3M6-M7. Thus, ISSR 604

are accurate markers to discriminate these genetic lines. In contrast, the group 3 is more 605

heterogeneous including Yacine, its mutants and its progenitor Melakh and its mutants. Those 606


https://doi.org/10.1101/2020.05.22.109041

results suggested that the assignation of Melakh to the group could be an artefact. The 607

clustering of Yacine and its relative Melakh in the same group 1 demonstrate the 608

discriminating power of the DNA based molecular markers. 609

Genetic differentiation within and between groups 610

In the present study, STRUCTURE analysis and the dendrogram results showed 3 groups 611

which aroused our curiosity to understand the variability within and between clusters by 612

assessing the genetic parameters such as Nei’s genetic diversity and Shannon’s information 613

index which are important to measure the degree of genetic diversity among and within group 614

in a population. Our results showed that group 1 had a high Nei’s genetic diversity and 615

Shannon’s information index and a high number (17) of private alleles unlike group 2 which 616

had the lowest diversity, suggesting that the gamma irradiation doses were efficient to induce 617

new alleles in the mutants (Table 6). Identification of private alleles are important in plant 618

breeding and conservation as their presence in a single population might be linked to specific 619

agronomic traits usable for new genotypes selection in a mutant population. In accordance 620

with this, the AMOVA results revealed that a large majority of the total variation (85%) was 621

noticed within group variation suggesting a high level of differentiation while only 15% of the 622

variation were recorded between group (Table 7). According to Seyoum et al. [68], small 623

variation between groups might be an advantage due to its usefulness to study marker-traits 624

association. In contrast, a large variation between groups could reduce the possibility to detect 625

the effects of single genes in a genome wide association study [69]. Based on these and taking 626

into account the important agronomic traits recorded in our mutant populations a genome 627

wide association can be performed in order to detect the molecular markers associated with 628

these characters and usable in a molecular breeding program. 629


https://doi.org/10.1101/2020.05.22.109041

To analyze population structure, different methods such as STRUCTURE, Principal 630

Coordinate Analysis and DAPC can be used. The latter provides complementary information 631

leading to better assignments of individuals in the accurate group and its advantage is related 632

to the fact that the target population has not to be in Hardy-Weinberg equilibrium. In this 633

study, DAPC results divided the mutant populations in well-defined 3 groups with less 634

admixture compared to STRUCTURE results. Indeed, the ancestry value recorded in 635

STRUCURE analysis allowing the assignment Me51M4-14M5-2M6-M7 and Melakh to 636

group 1 was less than 70% in contrast, this abnormality was resolved using DAPC as it did 637

with Y7-M4-1M5-1M6-M7 and Yacine to group 2 and 3 respectively. These results were in 638

accordance with previous studies carried out in potato [70], in landraces and bred cultivars of 639

Prunus avium [71] and Ginseng germplasm [72] which revealed that DAPC gave a better 640

grouping resolution than STRUCTURE. In addition, DAPC analysis showed that the loci such 641

as 35 and 37 (amplified by ISSR primer HB10), 57 (amplified by ISSR primer 807) greatly 642

contributed for the discrimination of group 1 and group 3. In contrast loci 97, 78, 21 and 105 643

amplified respectively by 17899B, ISSR844, ISSR 841 and ISSRHB12 was also involved in 644

the differentiation of group 2 from the others (Fig 7B). This latter ISSR primer was suspected 645

by Gaafar et al. [12] to be usable in a marker assisted selection for high yield genotypes. 646

These results suggested that DAPC is an appropriate method to analyze the organization of 647

the genetic variation within and between mutant population in order to identify the best 648

genotype as shown in our study, the individuals belonging to the same group shared several 649

agromorphological characters. For instance, group 1 (Fig 6B) included white-seeded 650

genotypes and long pod length unlike group 3 which showed short pod length. 651

Conclusion 652


https://doi.org/10.1101/2020.05.22.109041

In this study, it was obviously noticed that gamma irradiation is a powerful tool to induce 653

genetic variation in cowpea to broaden its genetic basis in order to overcome the bottleneck 654

effect undergone by this crop during its domestication. This assertion has been supported by 655

the various agromorphological variations affecting plant growth habit, flower color, pod 656

length and weight, seed color, size and weight noticed among our mutant populations. These 657

phenotypic variations were explained by the genetic variation revealed by 13 ISSR markers 658

and the use of the datasets generated by these molecular markers enhanced our understanding 659

on the mutant population structure. Overall, efficient exploitation of both agromorphological 660

and molecular data led to the identification of promising high yielding new mutant genotypes 661

which can be proposed for multi trial tests to assess their performance and stability in 662

different agroecological conditions in Senegal and abroad but also their nutritional value. 663

These mutant genotypes are valuable genetic resources usable for breeding programs but also 664

for gene discoveries such as the ones involved in growth habit, pod length or seed size in 665

cowpea. 666

Acknowledgments 667

We thank the IAEA for funding this project. We thank also Thuloane Bernard TSEHLO and 668

Dr Fatma SARSU respectively Programme Management Officer and Technical Officer for 669

this project at the IAEA for their advices and support. I would also like to thank the 670

anonymous reviewers for their valuable comments. 671

672


https://doi.org/10.1101/2020.05.22.109041

Author Contributions 673

MD, FAB and DD designed the study. MD and SD performed DNA extraction and 674

genotyping. MD and SD performed data analyses, OD run STRUCTURE and DAPC 675

analyses. MD, SD, FAB and DD drafted the manuscript. All authors contributed to the final 676

version. 677

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896

Supporting information 897

S1 Table. Estimation of Pearson’s correlation between the agromorphological 898

characters in the M6 of the mutant lines. 899

S2 Table. Estimation of Pearson’s correlation between the agromorphological 900

characters in the M5 of the mutant lines. 901

S1 Fig. First Leave abbnormalities observed among the Melakh mutants. 902

A: Two opposite leaves observed during the juvenile stage; B: Three leaves observed on the 903

mutant 904

C and D: Leaves observed during adult stage on the mutant (tetraleaflet) and the control 905

respectively 906

S2 Fig. Variation of the flowering date after sowing (DAS) in the populations. 907

A: Melakh and its mutants; B: Yacine and its mutants 908

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development of new cowpea ( vigna unguiculata) mutant ... · 22/05/2020 · 81 tolerance to maruca...

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