Hemp seed flour represents a potential ingredient for protein enrichment of gluten-free bakery products, the nutritional value of which could be further increased by fermentation with sourdough or with beneficial lactic acid bacteria strains. In this study, a metabolomic approach was used to evaluate the effect of hemp seed flour addition and sourdough fermentation on the production of flavoring and health-related volatile organic compounds (VOCs) in a gluten-free bread. Multivariate analysis of VOCs provided an in-depth description of the effects of hemp seed flour addition and sourdough fermentation on flavoring and bioactive compounds. In particular, an increased concentration of antimicrobial compounds, a larger spectrum of bioactive VOCs and a typical flavoring profile was evidenced in comparison to standard products. Furthermore, an increase of fermentation metabolites was observed in comparison to a standard dough, relating to abundances of 2-butanone-3-hydroxy, acetic acid, ethanol, and 1,4-butanediol. This study provides new insights on the evolution of flavoring and bioactive hemp seed flour constituents during sourdough fermentation, evidencing their retention in baked goods, and describes a new approach that could guide the formulation of innovative, fermented food with enhanced nutritional value. Celiac disease (CD) is a chronic systemic autoimmune disorder caused by a permanent intolerance to gluten proteins in genetically susceptible individuals. It affects 1% of the population in Europe .w/w), HSF is characterized by a low content of saturated fats (4.5% w/w) and a good percentage of polyunsaturated fatty acids (PUFA) (38% w/w), including ω3 (8% w/w) and ω6 (27% w/w). Hemp seed flour is naturally free of cholesterol, it has a low glycemic index, 4% (w/w) of dietary fibers, and it is rich in magnesium (0.7% w/w) [in vitro and ex vivo antioxidant activity [w/w) HSF was used to enrich GF bread [Hemp seed flour (HSF) represents a potential ingredient for GF bakery products. Beside its high protein content 33% 3% w/w, Hted fats .5% w/w aFermentation with sourdough or with beneficial lactic acid bacteria (LAB) strains could further increase the nutritional value of HSF-enriched GF products since it provides direct  and indirect  health benefits to consumers . During In this study, a metabolomic approach was used to evaluate VOCs production in a sourdough-fermented, GF bread enriched with HSF and to relate it to flavoring and health properties of the final product. Lactobacillus plantarum 98a, Lb. sanfrancisciensis Bb12 and Saccharomyces cerevisiae LBS were obtained from 30% (v/v) glycerol stocks stored at -80 °C. Bacteria were propagated in de Man–Rogosa–Sharpe (MRS)  broth at 37 °C for at least 48 h and yeasts in Sabouraud broth  at 30 °C for 24 h. Microbial strains belonged to the microbial collection of the Department of Agricultural and Food Sciences, University of Bologna  ,14,15. Lw/w) HSF to obtain a GF formulation suitable to be considered as a protein source. The list of samples and their codes are described in w/w) replaced standard flours. The percentage of HSF to be used for enrichment was set based on the sensory characteristics of the final products. Both types were used for direct fermentations and for sourdough fermentations. Direct fermentations were as follows: (i) not inoculated (X); (ii) inoculated with Log10 6 CFU/mL of an equal LAB mix of L. sanfrancisciensis Bb12 and L. plantarum 98a (L); (iii) inoculated with Log10 7 CFU/mL of S. cerevisiae LBS (Y), and it was conducted for 18 h at 31 °C. Additionally, a direct fermentation with Y was conducted for 6 h at 31 °C. The sourdough fermentations were made by replacing 20% of H and S dough formulations with 140 g of direct L fermented doughs and inoculated with Log10 7 CFU/mL of Y. The sourdoughs were then fermented for 6 h at 31 °C. All fermented doughs were baked at 180 °C for 20 min to produce breads (B). All samples were produced in triplicates in two independent experiments. Flours were commercial organic certified products . Experim10 CFU/mL (Colony Forming Units/mL). Culture independent protocol was performed by qPCR with the SYBR Green I chemistry, applying genus specific primers as Lac1 for Lactobacillus spp., then named LAB, (forward:5’-GCAGCAGTAGGGAATCTTCCA-3’ and reverse: 5’-GCATTYCACCGCTACACATG-3’) [S. cerevisiae LBS, then named yeasts, (forward: 5’-GTTTCCGTAGGTGAACCTGC-3’and reverse: 5’-ATATGCTTAAGTTCAGCGGGT-3’) [10 cells/mL [Microbial quantification was obtained by both culture-dependent and culture-independent protocols. The culture-dependent quantification was done by plating serial dilutions of the samples in physiological solution (0.9% NaCl). LAB were plated on MRS (de Man–Rogosa–Sharpe)  agar and cycloheximide (0.1 g/L)  and incubated aerobically for 24 h at 37 °C. Yeasts were plated on Sabouraud dextrose agar  and chloramphenicol (0.4 g/L)  and incubated aerobically for 48 h at 30 °C. Quantification was calculated as LogCATG-3’)  and ITS GGGT-3’) . ExtractGGGT-3’) . For botGGGT-3’) . Sample cells/mL ,19. The pH was determined at 20 °C with a pH meter  appropriately calibrated with three standard buffer solutions at pH 9.21, pH 4.00, and pH 2.00. The pH values were measured in duplicate at three different times to monitor the fermentation .Evaluation of volatile organic compounds (VOCs) was carried out on an Agilent 7890A Gas Chromatograph  coupled to an Agilent Technologies 5975 mass spectrometer operating in the electron impact mode (ionization voltage of 70 eV), equipped with a Chrompack CP-Wax 52 CB capillary column  . The SPME-GC-MS (solid phase micro-extraction gas chromatography–mass spectrometry) protocol and the identification of volatile compounds were done according to previous reports, with minor modifications ,15. Briep < 0.05. Multivariate analysis was conducted with principal component analysis (PCA), K-mean clustering, and MANOVA (p < 0.01). Pearson correlations were used to generate the heatmap of VOCs prior fermentation. For post hoc test, an HSD Tukey’s test was employed (p < 0.05). Except for the quantification in mg/kg of major metabolites, independently normalized data set was proposed for each chemical class of molecules. The data were normalized using the mean centering method [All statistical analyses were performed using TIBCO Statistica 8.0 . Normality was checked with the Shapiro–Wilk test, while homoscedasticity was evaluated with the Levene’s test . Differeg method ,19. All 10 cell/g and represent the mean value of the two methods since they give similar results when are applied to quantify the evolution of a known and standardize inoculum in an essential food prototype made in a research laboratory [p < 0.05). In all conditions, direct inoculation of LAB was the starter that mostly reduced pH during fermentation (S. cerevisiae LBS (pH 5.81 ± 0.11).Quantification of LAB and yeasts obtained by plate count and qPCR are shown in boratory ,19. In Hentation . In partp < 0.05)  one including not fermented cases and 126 significant molecules ( < 0.05)  and (ii) < 0.05) . Afterwa < 0.05) ; (iv) al < 0.05) . One-hundred-twenty-six VOCs, including 34 alcohols, 23 aldehydes, 29 ketones, 22 organic acids, and 18 alkenes, were detected in flours and in not fermented doughs. The data set was clustered by Pearson analysis in two major groups, the first including HSF samples and HSF not fermented doughs and the second including standard flour samples and related not fermented doughs. A higher quantity and a wider speciation of compounds was identified in HSF samples . In most cases, hexanal was one of the most abundant compounds, reaching the top level in HSF and HSF doughs. In these samples, octanone and ethylcyclopentanone were the second most abundant compounds. Of note, these ketones were not identified in standard samples. As well, 1-octen-3-ol and caryophyllene oxide were detected in significant amount only in HSF samples. The heatmap of relative quantification of VOCs in the different samples is reported in p < 0.05). After baking, in HSF bread, the concentration of aldehydes, ketones, and organic acids was double compared to the standard breads, and that of alkenes was triple . The HSF-enriched sourdoughs (YH+6) produced 1.44-fold more acetic acid than the control, while in the baked samples, those directly fermented by LAB (HLB) had 3.1-fold more acetic acid than the control breads (SLB) (p < 0.05). In comparison to the control, HSF fermented doughs produced a similar content (p>0.05) of 1,4-butanediol, but in the HSF sourdough breads (YH+B) this compound was retained 1.6-folds more (p < 0.05) than in standard bread. S. cerevisiae LBS direct fermentation almost doubled 2-butanone-3-hydroxy concentration in HSF samples (HY18) and breads (HYB) compared to their controls.Quantification of main fermentation metabolites (mg/kg of fermented matrix) is reported in To better evidence differences in the alcohol class, normalization of the dataset and statistical analysis were performed after exclusion of the most abundant compounds, ethanol and 1,4-butandiol. Moreover, flours and not fermented doughs were not included (n = 28) in the dataset due to the scarce abundance of alcohols. PCA grouped samples in three clusters . HSF samS. cerevisiae LBS was marked by higher cyclohexanol abundance, while LAB fermentation generated more 1,2,4-butanetriol. Thymol, borneol, and terpineol were robust descriptors of HSF enriched goods. Lastly, furanmethanol resulted 83% lower in the HSF fermented doughs and in the HSF breads in comparison to the control samples.Direct fermentation performed by p < 0.05). PCA loadings (p < 0.01)  and tetradecanal, while those fermented by LAB were characterized by 2,4-nonadienal . Interestingly, this analysis evidenced that HSF breads accounted for the top proportion in production of 2-heptenal (Z), octadienal dimethyl (Z), 2,4-decadienal , and dodecanal. Since these compounds were not found in the raw material it is conceivable that they were a result of bread making. On the contrary, 2-decenal (E) diminished drastically with bread making. From analysis of variance of all samples (n = 58), 10 aldehydes resulted significantly different (loadings A were clloadings B. HSF sa < 0.01)  indicatep < 0.05). PCA loadings (p < 0.01) -bornane dione being evidenced only in HSF fermented doughs and breads.Analysis of variance including all samples (n = 58) evidenced 18 significantly different ketones (loadings A were clloadings B. HSF sa < 0.01)  emerged p < 0.05). PCA loadings  and eicosene (E), were augmented by this type of fermentation. Independently from fermentation, β-phellandrene, ∆-3-carene, and caryophyllene were robust descriptors of HSF-enriched doughs or breads.From analysis of variance including all samples (n = 58), 16 alkenes, notably, 9 terpenes and 7 α-olefins resulted significantly different . PCA loadings  (S. cerevisiae LBS pushed that of pentadecanoic acid (49%). Interestingly, 32.84% of n-hexadecanoic acid was not derived from fermentation. In fact, its abundance was related to the 33.9% and 63.9% in HSF doughs and HSF flour, respectively. Besides, while medium-chain organic acids, such as heptanoic and nonanoic acids, were mainly present in HSF goods , the long-chain organic acids, such as heptadecanoic and nonadecanoic acids, were majorly derived from FH, accounting for 62.7% and 60.2% of total values. From analysis of variance including all samples (n = 58), 18 organic acids  resulted significantly different (loadings A were clloadings B. HSF sa < 0.01) . For exaS. cereviase LBS provided the maximum production of ethanol, the most important descriptor for an efficient leavening process. The lower acetic acid loads observed in our study compared to the previous one [In all HSF fermented samples, microbial growth was massive and higher than in the standard samples. Microbial growth after sourdough fermentation was higher than in the recent study by Nionelli et al., , probablious one  were proious one  decreaseFlavoring characteristics and putative bioactivity of the main VOCs in HSF-enriched GF doughs and breads are summarized in Among minor alcohols, 1-heptanol, 1-octen-3-ol, 1-nonen-3-ol, tetradecanol, borneol, thymol, and terpineol better discriminated HSF containing samples. 1-heptanol, 1-nonen-3-ol, and 1-octen-3-ol are associated to hemp drinks fermented with probiotics . 1-heptaS. cerevisiae fermentation. Octadienal dimethyl (Z) is characterized by a nice aroma of lemon [Staphylococcus aureus [HSF breads accounted for about 20% less furfural than standard breads. Furfural has a leading role in the aroma of bakery products , but it of lemon . It has of lemon ,37,38, as aureus , and ants aureus . HSF bres aureus ,42,43. Os aureus  and sours aureus ,β-phellandrene, ∆-3-carene, α-farnesene, camphene, and β-caryophyllene increased in HSF fermented dough and breads compared to HSF doughs prior fermentation. This effect, already observed in hemp drinks ,19, was Among the α-olefins, eicosene (E) was found in higher concentration in HSF than standard samples. Eicosene (E) is a part constituting ceramide, known as C20-sphingosine , and theBacillus species [Bacillus and in minor part by direct fermentation with LAB. The odor of 2P3H is described as caramel-sweet, buttery, and hay-like [HSF samples were described by a larger speciation of ketones than standard samples at all stages of the process. Among ketones, 2-pentanone-3-hydroxy (2P3H) deserves attention. 2P3H is biosynthesized by plants and bacteria, mainly by  species  and lact species . Accordihay-like . Moreovehay-like , resultiIn our study, the profile of organic acids of HSF samples was quantitatively and qualitatively superior to the standard one. Principally, a great amount of organic acids was discovered in the fermented HSF sourdoughs. In agreement with previous study ,18, the S. cerevisiae fermentation, respectively. In addition, increased traits of herbal, fruity, and floral notes  could be provided by HSF addition both in yeast- and LAB-fermented bread, and sour and buttery nuances due to propanoic acid could be predicted in sourdough bread. The organoleptic impact of the increased concentration of medium and short-chain FA is difficult to predict since it greatly depends on the relative FA content. Indeed, except for fatty and creamy background (nonanoic), an excess of rancid-like notes may be ascribed to their unbalance. The addition of HSF and the use of different fermentation types gave rise to specific VOCs profile predicting the organoleptic characteristics of bread. In sourdough bread, pleasant almond-like and lemon-like notes could be related to HSF addition or The health potential of bioactive compounds delivered in the experimental breads is hardly predictable for different reasons. First, although SPME-GC-MS is a quali/quantitative method, bioactive absolute concentration must be handled very carefully in estimating bioactivity. At present, the effective dose of most of the detected bioactive has been evaluated in specific experimental conditions and cannot be generalized. Moreover, the food matrix delivering the bioactives still represents a key parameter both for analysis and bioaccessibility.The enrichment with HSF was based on the sensory characteristics and allowed to increase the protein concentration in the final products, although not to the extent needed for the claim “source of protein”, being about 11% instead of the 12% of the total energy required by the EU regulation (Regulation (EC) No 1924/2006, lastly amended by Regulation (EU) No 1047/2012). Of note, we did not add any flavoring or masking agents to avoid interference in the analyses. In the future, their addition could allow to further increase the percentage of HSF maintaining good sensory characteristics.In this work, HSF was used to formulate a GF bread enriched in proteins. The bread was quantitatively and qualitatively analyzed throughout the process to evaluate fermentation performances and volatilome composition. The metabolomic profiles of HSF-enriched GF breads were considered to investigate the potential of hemp flour as a vehicle for the addition of flavoring and bioactive compounds in bakery products. Multivariate analysis on VOCs provided a deeper description of the effects of HSF addition and sourdough fermentation process on flavoring and bioactive compounds, mainly evidencing an increased concentration of antimicrobial compounds, a larger spectrum of bioactive VOCs, and a typical flavoring profile. The herein reported study was not aimed to the demonstration of any biological activity of the HSF-enriched bread, but it provided new insights on the modulation of flavoring and bioactive constituents of HSF during sourdough fermentation and evidenced the retention or even increase of the most of them in baked goods. Our approach allowed linking specific VOCs to different fermentation types and could be useful to tune the formation of desirable compounds in the final products. Although further studies coupling volatilome analysis to sensorial assessment are needed to meet consumers’ acceptance, the evaluation of the shift of VOCs could represent a comprehensive, sensitive, and reliable method guiding the formulation of innovative food with enhanced nutritional value.


A major challenge of evolutionary ecology over the next decades is to understand and predict the consequences of the current rapid and important environmental changes on wild populations. Extinction risk of species is linked to populations’ evolutionary potential and to their ability to express adaptive phenotypic plasticity. There is thus a vital need to quantify how selective pressures, quantitative genetics parameters, and phenotypic plasticity, for multiple traits in wild animal populations, may vary with changes in the environment. Here I review our previous research that integrated ecological and evolutionary theories with molecular ecology, quantitative genetics, and long‐term monitoring of individually marked wild animals. Our results showed that assessing evolutionary and plastic changes over time and space, using multi‐trait approaches, under a realistic range of environmental conditions are crucial steps toward improving our understanding of the evolution and adaptation of natural populations. Our current and future work focusses on assessing the limits of adaptive potential by determining the factors constraining the evolvability of plasticity, those generating covariation among genetic variance and selection, as well as indirect genetic effects, which can affect population's capacity to adjust to environmental changes. In doing so, we aim to provide an improved assessment of the spatial and temporal scale of evolutionary processes in wild animal populations. On the other hand, Hayward et al.  to quantify strength, direction, and variation in selective pressures, quantitative genetics parameters, and phenotypic plasticity, (b) to do so for multiple traits at the same time in wild populations, and (c) to achieve this across several years, under different environmental conditions and also using different environmental variables. These were the central goals of my research program over the last 15 years see also . To do sBox 1Salmo salar) (not a sturgeon!), testing the theoretical framework of the member/vagrant hypothesis proposed by Sinclair (Centre Interuniversitaire de Recherche sur le Saumon Atlantique (CIRSA), we provided one of the first genetic evaluation of the species’ mating system in the wild  meeting. We have also been involved in several research projects together over the last years, co‐supervising graduate students and producing several contributions related to the impact of stocking in wild populations of Brook trout  ! I had the privilege to complete both my MSc and PhD degrees under the supervision of Louis, co‐supervised by Julian Dodson, at Université Laval. Right from the start, I knew I was in the best place possible to expand my way of thinking about science and improve and expand my research skills. Based on my supervisors original ideas, my MSc research focussed on within‐river population structure in Atlantic salmon  and Eastern chipmunk (Tamias striatus).Relatively few studies of wild vertebrates have obtained the long‐term detailed data required to explain the evolutionary dynamics of quantitative traits in the wild , the dominant canopy tree on our sites and the main source of storable food items for chipmunks, are quantified using seed collectors distributed on the trapping grids . The grids are located in a deciduous forest where we trap chipmunks every week from mid‐May to mid‐September. At each capture, every animal is aged, weighed, sexed, and uniquely marked with ear tags. Behaviors are measured for each individual using standard open field  for tree swallows  in France, I will complement this summary of our contributions using examples from this study system as well.Over the next sections, I summarize achievements regarding three main interconnected research axes of my research program that provided advances in the understanding of evolution in its ecological context. First, we assessed the genetic architecture of important ecological traits and their variability depending on effect of context and environmental conditions. Second, we determined the environmental drivers underlying the variability of selection over temporal and spatial scales for both adults and juveniles. Third, we quantified the effect of variability of phenotypic plasticity of key traits on adaptation to changing environments. To illustrate this, I provide recent examples, mainly from our work on tree swallows and eastern chipmunks, achieved over the last few years. However, since I have also been privileged to collaborate with students and researchers using long‐term datasets of Blue tit , coefficient of additive genetic variation (CVA), and genetic correlation (rA) estimates—resulting from using a “social” instead of a “genetic” pedigree in system with a high rate of extra‐pair paternity (EPP). Then, we also used simulations to show that power of datasets may be an issue for adult traits in populations with low recruitment (around 1% of nestlings), resulting in low connectivity in the pedigree, despite having several thousand individuals in the dataset. This, however, was not an issue for nestling traits in our tree swallow population, given that the high rate of EPP results in several half‐siblings and increased power.Several achievements were fulfilled within this research axis. First, we advanced our understanding of the applicability and reliability of quantitative genetics methods in the wild  in eastern chipmunks and assessed the effects of fluctuating food resources and temporal variability on the patterns observed. Contrary to our predictions—we expected variability in selection through time linked to changes in food abundance associated with masting trees—we found no evidence of fluctuating selection on exploration  levels in females are modulated by reproductive state, being lower during gestation and lactation (Montiglio, Garant, Pelletier, & Réale, 4quantify strength of covariance between genetic variance and selection and its link to environmental variation;describe plasticity evolution over time and space as the strength of selection acting on it; andassess the importance of indirect genetic effects on fitness of individuals and on evolutionary potential of populations.Over the next years, we will build upon our previous findings and recent theoretical and empirical advances to pursue three main interconnected research themes regarding the process affecting evolutionary changes in wild populations. In details, we will:4.1An underappreciated phenomenon concerning evolutionary potential of population is the possible effect of an environmentally driven correlation—positive or negative—between selection and heritable genetic variation acting on traits that could greatly affect their evolutionary response and adaptation rate. As suggested by Wood and Brodie , in case4.2Despite of its importance in the adaptive potential of a population, we still know little about heritability of plasticity and about how selection acts on plasticity (Merilä & Hendry, 4.3Social interactions are often an important part of an individual environment and can affect evolution and adaptation of populations through indirect genetic effects IGEs (where the phenotype of an individual is affected by the genotypes of others, which include maternal effects; McAdam, Garant, & Wilson, 5It is critical to tackle questions about the possible role of evolution in the current contexts of global warming and biodiversity crisis. Previous authors, such as Smith et al. , have suNone declared.


Ethiopia has been considered as a center of diversity and the second possible center of domestication of durum wheat. Genetic diversity and population structure analysis in the existing Ethiopian durum wheat germplasm have enormous importance in enhancing breeding effort and for sustainable conservation. Hence, 192 Ethiopian durum wheat accessions comprising 167 landraces collected from major wheat-growing areas of the country and 25 improved varieties released from Debre Zeit and Sinana Agricultural Research Centers, Ethiopia in different years (1994–2010) were assembled for the current study.The panel was genotyped with a High-density 90 K wheat SNP array by Illumina and generated 15,338 polymorphic SNPs that were used to analyze the genetic diversity and to estimate the population structure. Varied values of genetic diversity indices were scored across chromosomes and genomes. Genome-wide mean values of Nei’s gene diversity (0.246) and polymorphism information content (0.203) were recorded signifying the presence of high genetic diversity within this collection. Minor allele frequency of the genome varied with a range of 0.005 to 0.5 scoring a mean value of 0.175. Improved varieties clustered separately to landraces in population structure analysis resulted from STRUCTURE, PCA and neighbor joining tree. Landraces clustering was irrespective of their geographical origin signifying the presence of higher admixture that could arise due to the existence of historical exchanges of seeds through informal seed system involving regional and countrywide farming communities in Ethiopia.Sustainable utilization and conservation of this rich Ethiopian durum wheat genetic resource is an irreplaceable means to cope up from the recurrent climate changes and biotic stresses happening widely and thereby able to keep meeting the demand of durum productivity for the ever-growing human population. Triticum aestivum , and tetraploid/hard or durum wheat, T. durum  used for pasta, macaroni, couscous and low-rising bread, and the former accounts for about 95% of world wheat production and durum covers the other 5% [Modern wheat cultivars generally refer to two species: hexaploid bread wheat, other 5% .In Ethiopia, wheat is the second most widely produced cereal crop after corn and the third most important staple food behind corn and sorghum . Hard reTriticum turgidum ssp. dicoccoides) to domesticated emmer (T. turgidum ssp. dicoccum) with the loss of fragility of spikes (disarticulation into spikelets), and second, from cultivated emmer to durum with the appearance of naked kernels or free threshing kernel [Durum wheat is the result of two successful domestication events by ancient farmers: first, from wild emmer  are the most abundant class of DNA markers. Lower rates of recurrent mutation make them evolutionarily stable. They are excellent markers for studying complex genetic traits and for understanding the genomic evolution. They have been widely used in genome-wide association studies, genetic resource characterization, marker-assisted breeding and genomic selection . HybridiMolecular characterization of Ethiopian durum wheat accessions has been investigated in DNA markers with a very limited number, such as microsatellites , 20, 21.From 81,587 SNP probes available on the chip, 30,510 SNP calls  were reproducible in the current Ethiopian durum wheat panel. From these markers, 18,788 SNPs had a known position but only 15,338 (81.63%) were polymorphic and used for the current study  and gene diversity (0.282) (Table The SNP markers exhibited a wide range of polymorphic information content (PIC) and Nei’s gene diversity across chromosomes and genomes of Ethiopian durum wheat. Frequency distribution of SNPs for gene diversity, polymorphic information content, and frequency of the minor allele values of the genome is presented in Fig. The optimal sub-population of accessions was inferred through two approaches: The first method was the STRUCTURE-based clustering approach that was inferred based on the second order rate of change of the likelihood (∆K) Table . The res. However, clustering gets distorted when additional principal components were considered  was analyzed with all Polymorphic SNPs generated from the panel. The first, second and third principal components explained 24.29, 6.61 and 3.74% of the total variance, respectively. The smaller numbers of variance explained by the second and consecutive PCs indicated that only few PCs couldn’t encapsulate the existing genetic variance in Ethiopian durum wheat. The first PC (PC1) distantly clustered varieties from landraces and the second PC grouped the two landrace subgroups Fig. -a. The fred Fig. -b.Fig. The Bayesian model based stratification and PCA clustering methods grouped landraces and varieties in distinct places except in a single variety, Selam, which was clustered alongside landraces. The numbers of polymorphic SNPs were slightly higher in varieties than landraces. Varieties scored higher gene diversity 0.297), PIC (0.240) and the mean frequency of minor allele (0.218) than landraces   revealed the presence of higher genetic variance between STRUCTURE-inferred sub-populations 52.41%) than among individuals within clusters (47.59%)  than individuals within the group (38.98%)  were grouped in sub-population one while 25 landraces from the same origin clustered in sub-population three  were reproduced in the present study from the 90 K wheat SNP array than previously reported by Mengistu et al.  on EthioThe 90 K wheat SNP array was a platform made to capture the most reliable gene-associated SNP markers available in the wheat genome worldwide and could not enable to mine new loci. The less number of SNPs reproduced from the array in the current panel indicates the possibility of existence of novel alleles and further studies would be benefited from the employment of both hybridization and sequencing techniques to provide a thorough description of Ethiopian durum wheat genome.Comparing with previous reports, higher genetic diversity indices were scored in Ethiopian durum wheat panel Table  that strTriticum durum subs. Abyssinicum or T. aethiopicum [Genetic stratification analysis based on the Bayesian clustering model of the second order rate of change of the likelihood  revealedhiopicum . This phhiopicum .In this study, 192 Ethiopian durum wheat accessions comprising 167 landraces and 25 improved varieties were assembled and genotyped with a high density 90 K wheat SNP array to analyze the existing genetic diversity and population structure within accessions. Clustering analysis showed a higher genetic admixture between landraces despite their geographic origin resulted from the existence of higher rate of historical seed exchange throughout the country. Diversity indices revealed the presence of higher genetic diversity in Ethiopian durum wheat accessions. Landraces adapted to wider agroecology and with the genetic capacity to tolerate various stresses could be used as a source of unique alleles in the enhancement of durum breeding through marker assisted selection or marker assisted backcrossing. Hence, sustainable conservation and utilization of Ethiopian durum wheat genetic resource is key for future breeding strategies in Ethiopia and worldwide.One hundred sixty seven Ethiopian durum landrace accessions collected from major wheat growing areas of the country and twenty five improved varieties released in different years and have been cultivated in Ethiopia were assembled for the present study. Improved varieties were released by Debre Zeit Agricultural Research Center (DZARC) and Sinana Agricultural Research Center (SARC), Ethiopia in different years (1994–2010). All landrace accessions and varieties are maintained by these two agricultural research centers as a single seed descent (SSD) progenies.Landraces were originally collected from major wheat-producing areas of Ethiopia .SNP markers were generated using the Illumina iSelect® 90 K wheat SNP assay comprising 81,587 gene-associated SNPs . Marker Numbers and percent of polymorphic loci, polymorphism information content (PIC), Nei’s gene diversity and minor allelic frequency (MAF) were calculated using Power Marker v 3.25 . PIC wasTwo approaches were implemented to infer the optimal clusters/subpopulations existed in 192 Ethiopian durum wheat accessions. First, a Bayesian model-based clustering approach was used to estimate the optimal subpopulations and the membership probability of each genotype to the subpopulations using STRUCTURE v.2.3 . To infeThe second approach was based on the discriminant analysis of principal components (DAPC) implemented using a package “adegenet”  in R stuAdditional file 1 : Table S1. Accession names and types, cultivated areas, seed sources and population structure of 192 Ethiopian durum wheat accessions used for the current study.Additional file 2 : Table S2. List of polymorphic SNPs, their alleles and chromosomes used for genetic diversity analysis in Ethiopian durum wheat accessions.Additional file 3 : Figure S1. Map of Ethiopia showing the major wheat producing areas where 167 landraces were originally collected ; http://www.ifpri.org/node/3763).Additional file 4 : Figure S2. Frequency distributions for Nie’s gene diversity score of polymorphic SNPs across chromosomes in Ethiopian durum wheat accessions.Additional file 5 : Figure S3. Frequency distributions for polymorphic information content (PIC) values of polymorphic SNPs across chromosomes.Additional file 6 : Figure S4. Frequency distributions for minor allele frequency (MAF) values of polymorphic SNPs across chromosomes.


Vietnam exhibits great cultural and linguistic diversity, yet the genetic history of Vietnamese populations remains poorly understood. Previous studies focused mostly on the majority Kinh group, and thus the genetic diversity of the many other groups has not yet been investigated. Here we analyze complete mtDNA genome sequences and ~2.3 Mb sequences of the male-specific portion of the Y chromosome from the Kinh and 16 minority populations, encompassing all five language families present in Vietnam. We find highly variable levels of diversity within and between groups that do not correlate with either geography or language family. In particular, the Mang and Sila have undergone recent, independent bottlenecks, while the majority group, Kinh, exhibits low levels of differentiation with other groups. The two Austronesian-speaking groups, Giarai and Ede, show a potential impact of matrilocality on their patterns of variation. Overall, we find that isolation, coupled with limited contact involving some groups, has been the major factor influencing the genetic structure of Vietnamese populations, and that there is substantial genetic diversity that is not represented by the Kinh. Southeast Asia (SEA) is a melting pot of ethnolinguistic diversity shaped by many demographic events, beginning with the initial arrival of anatomically modern humans at least 65 kya , 2, and Vietnam is a multiethnic country that occupies a key position within MSEA and exhibits both geographic and ethnolinguistic diversity. The northern part of the country consists of highlands and the Red River delta; the central part also comprises highlands, while the southern part encompasses mostly coastal lowlands and the Mekong River delta. There are 54 official ethnicities in Vietnam, and a total of 109 different languages are spoken in the country. These belong to all of the five major language families present in SEA .AN speakers are found all over ISEA and Oceania, and trace at least a part of their ancestry to aboriginal Taiwanese AN-speaking populations, supporting a start of the AN expansion out of Taiwan about 4 kya , 7. The While several genetic studies have focused on SEA, research on the ethnic groups in Vietnam remains rather limited –23. Mostwww.gso.gov.vn and the 2009 Vietnam Population and Housing census, accessed September 2018) and the Ethnologue [We analyzed DNA from 600 male Vietnamese individuals  and the available genetic markers in our target region. Labels denoted with an asterisk in the text and figures are paragroups that do not include subgroups.We enriched for ~2.3 million bases of the MSY from the same libraries used for mtDNA capture enrichment . The MSYH) values using Arlequin version 3.5.2.2 [H we calculated the percentage difference from the mean for each population. Arlequin version 3.5.2.2 [ST distances) among the populations and the analyses of molecular variance (AMOVA) for both markers. The p values of the genetic distances were corrected for multiple testing by applying the Benjamini–Hochberg procedure. The ΦST distances were used to compute nonmetric multidimensional scaling (MDS) plots. We created two-dimensional projections (R function: isoMDS package: MASS) and calculated heatplots with five dimensions, showing per-dimension standardized values between 0 and 1. We calculated Mantel matrix correlation tests between genetic distances and great circle distances of the average geographical location per population using Pearson’s correlation with 10,000 times random resampling. The correspondence analyses were computed in R using the libraries “vegan", “fields,” and “ca”. The haplotype sharing analysis was based on sequence haplotypes via string matching. We excluded Ns and indels for the mtDNA sequences; this step was not necessary for the MSY sequences because indels were not called and there were no Ns after imputation.For both markers, we calculated the mean number of pairwise differences by averaging over the sum of nucleotide differences for each pair of sequences within a population (R function: dist.dna package: ape) divided by the total number of pairs. The nucleotide diversity (π) and its variance were computed using the R function nuc.div (package:pegas). Because the MSY information is based on a set of linked SNPs without recombination, we use the term haplotype throughout this paper to refer to the MSY sequences, and not to STR profiles as has been done in the past. We calculated the number of unique haplotypes for each population and obtained the haplotype diversity  to MSY /bp/year  was usedU test.We constructed MP trees for both markers and counted the mutations from the outgroup per sample. The mutation counts were used to compare the average distance of macrohaplogroups to the base of the trees as a measurement of branch length heterogeneity. We tested for significant differences in branch length distributions of major haplogroups with the Mann–Whitney http://www.ncbi.nlm.nih.gov/SNP/). Fifty-seven different haplogroups were present in the 17 populations . After filtering, there were 3932 SNP positions, including 1908 novel sites that have not been described previously  for mtDNA and MSY sequences varied substantially among populations  compared with the mean (4.72 × 10−5), reflecting the unusual MSY haplogroup composition of this group, dominated by haplogroup O1b-B426, which had a frequency of 97%. The HM groups Pathen and Hmong had higher than average π values for the MSY, which reflects the higher frequency of C and D haplogroup sequences in these two groups  and haplotype diversity  and TK (except Lachi) groups. The Kinh group showed overall low genetic distances with other groups  via AMOVA Table . With reTo further assess the impact of geography on the genetic structure of Vietnamese populations, we tested for correlations between the geographic and genetic distances for both mtDNA and MSY sequences Table . This anWe additionally carried out maximum parsimony and Bayesian analyses of the MSY sequences and tested for branch length heterogeneity; the results of these analyses  are discussed in Supplementary Material Previous genetic studies of Vietnam have focused largely on the Kinh majority as representative of the country , 43, 44.FST = 23% vs. mtDNA FST = 10%)  Table . PreviouOverall, it appears that the genetic structure of Vietnam has been primarily influenced by two main factors. The first is isolation and genetic drift, leading to high levels of genetic differentiation between groups and variable levels of genetic diversity within groups. Second, there has been limited recent contact between some groups, leading to some haplotype sharing. The levels of genetic differentiation among groups of 10% based on mtDNA and 23% based on the MSY Table . The lowIn addition to the general aspects of Vietnamese genetic diversity discussed above, our results provide some insights into the genetic profile and history of specific groups. These are discussed in detail in Supplementary Text Material The sequencing of 2.34 Mb of the MSY chromosome and the complete mtDNA genome of 17 different ethnic groups  enabled us to carry out the first comprehensive analysis of the genetic diversity within Vietnam. Overall, isolation leading to genetic drift has had an important impact on Vietnamese groups, with recent contact limited to some TK and HM groups. There are several differences between the maternal and paternal genetic history of some populations; in particular, a matrilocal vs. patrilocal residence pattern appears to be one of the major drivers of differences between the MSY and mtDNA signals in AN vs. other groups. There is also a profound impact of genetic drift for the Mang and Sila, especially in the MSY lineages, suggesting male-specific bottlenecks or founder events. And although we find genetic evidences for a central position of the Kinh as the majority group within the country, there is substantial genetic diversity in the other ethnic groups that is not represented in the Kinh. Genetic studies of the remaining ethnic groups in Vietnam, and expansion of the genetic data to include genome-wide variation will provide further insights into the genetic history of this key region of MSEA.Supplementary TextSupplementary Figures S1-S17Supplementary Tables S1-S7Data 1Data 2


Aequiyoldia eightsii from the shallows of southern South America and the WAP to test the role of the ACC and the APCC in its dispersal. We found strong genetic structure across the ACC (between southern South America and Antarctica) and moderate structure between populations of the WAP. Migration rates along the WAP were consistent with the APCC being important for species dispersal. Along with supporting current knowledge about ocean circulation models at the WAP, migration from the tip of the Antarctic Peninsula to the Bellingshausen Sea highlights the complexities of Southern Ocean circulation. This study provides novel biological evidence of a role of the APCC as a driver of species dispersal and highlights the power of genomic data for aiding in the understanding of the influence of complex oceanographic processes in shaping the population structure of marine species.The Antarctic Circumpolar Current (ACC) dominates the open-ocean circulation of the Southern Ocean, and both isolates and connects the Southern Ocean biodiversity. However, the impact on biological processes of other Southern Ocean currents is less clear. Adjacent to the West Antarctic Peninsula (WAP), the ACC flows offshore in a northeastward direction, whereas the Antarctic Peninsula Coastal Current (APCC) follows a complex circulation pattern along the coast, with topographically influenced deflections depending on the area. Using genomic data, we estimated genetic structure and migration rates between populations of the benthic bivalve  The forconcinna , the octpapillae  and the uelensis  are onlyIn contrast to the ACC, the Antarctic Peninsula Coastal Current (APCC), located in the Western Antarctic Peninsula (WAP), follows a complex circulation pattern along the coast and around the many islands of the WAP, forced by freshwater discharge and downwelling-favourable winds near the coast . AlthougAequiyoldia eightsii is one of the most abundant benthic species from the shallows of the Antarctic Peninsula [A. eightsii in shallow waters.The bivalve eninsula . It repreninsula , a type eninsula . The speeninsula . While tGenome-wide molecular data such as that obtained from restriction site associated DNA sequencing (RADSeq) have the power to estimate structure and patterns of gene flow at a range of different spatial scales providing the means to detect fine scale genetic subdivision and assess the magnitude and direction of ongoing gene flow . UnderstIn this study, we use genomic data to (i) evaluate genetic structure across the ACC, i.e. between South America and Antarctica, and within both these regions, and (ii) estimate patterns of directional gene flow to test the role of ocean currents (ACC versus APCC) on species dispersal along the WAP.2.2.1.Aequiyoldia eightsii is widely distributed in the Southern Ocean, but owing to sampling limitations and the need for recently collected specimens, this study focused on the southern South America and the WAP regions. Forty-one individuals of A. eightsii were collected from five sites, two from southern South America and three from the WAP (c oxidase subunit I (COI) gene was amplified using universal primers from [2. Amplification parameters were as follows: 95°C for 2 min followed by 35 cycles of 95°C for 30 s, 48°C for 30 s, 72°C for 90 s, and a final extension of 72°C for 7 min. Purification and sequencing were conducted at MACROGEN Inc. (South Korea). Chromatograms were edited in CodonCode Aligner 8.0.2 . Sequences were imported to BioEdit 7.0.5.2 [lustal W algorithm, available within BioEdit, and checked by eye. All sequences were deposited in GenBank (accession numbers MT176643–MT176683). the WAP . Genomicers from  with fin 7.0.5.2 , alignedGenome-wide data were obtained through the Nextera-tagmented reductively amplified DNA protocol  of  to impro2.2.p-distance) between populations were calculated using the R-package Adegenet [opart [Fst values using Adegenet.To estimate levels of population differentiation for the COI data, genetic distance  tree using the program RA. 8.1.16  with a c. 8.1.16 . Given te manual . Supporttructure 2.3.4 [K-values) ranged from 1 to 5. Ten independent runs per K were conducted, each with 100 000 burn-in and 300 000 Markov chain Monte Carlo (MCMC) iterations, using the ‘Admixture Model' and ‘Correlated Allele Frequency Model' with default settings, except for the Lambda parameter, which was empirically estimated in a preliminary run. Results were not different using more burn-in or MCMC iterations. Structure Harvester [istruct [K was chosen based on ΔK [tructure analyses, a discriminant analysis of principal components (DAPC) was conducted using the R-package Adegenet [K-means clustering algorithm and Bayesian Information Criterion to select the best-fit model (number of clusters) [K-value, and the actual locations (five) as groupings for comparison, retaining in each case two principal components. A dataset using 811 SNPs, which contained loci that were present in at least 20 individuals, was used for the DAPC analysis . We complemented these cluster analyses by also calculating pairwise Fst values between sites using the same dataset used for the Structure analysis and the R-package StAMPP [Subsequently, the software Sre 2.3.4 , that pearvester  and Dist[istruct  were useAdegenet . This anAdegenet . First, lusters) . Subseque StAMPP .A. eightsii has a pelagic life stage, and its dispersal is likely to be influenced by ocean currents, rates of migration were tested using divMigrate-online [ayesAss3-SNPS [igrate-online method is based on defining a hypothetical pool of migrants for a given pair of populations and estimating an appropriate measure of genetic differentiation between each of the two populations and the hypothetical pool. The directional genetic differentiation can then be used to estimate the relative levels of migration between the two populations where the larger of the two relative values indicates the population that is probably a source population, while the smaller of the two estimates indicates the sink population. The D distance [tructure analysis, but converted to a genepop input file using Adegenet [ayesAss3-SNPS [ayesAss [ayesAss3-SNPs to extensively calibrate parameters. Subsequently, the final analysis was conducted by running five replicates for 5 000 000 MCMC generations, sampling every 500 generations and discarding the first 500 000 generations as burn-in. Convergence of parameter estimations were checked with Tracer v1.7.1 [Given that e-online  and Bayess3-SNPS . The divdistance  was usedAdegenet  , while the average number of nucleotide differences within regions was 1.78 (0.28%) and 2.28 (0.36%) for South America and Antarctica, respectively. The haplotype network confirmed the high genetic divergence between regions  showed high genetic differentiation between South America and Antarctica. Within regions, statistically significant differentiation between some Antarctic populations was also found. There was significant differentiation between O'Higgins Base and the other two Antarctic sites, Yelcho  and Rothera , but no significant differentiation was found between Yelcho and Rothera  nor between the two South American populations .Genetic differentiation in  regions , and sho3.2.The total number of loci found by ipyRAD was 352 263. After the filtering process and quality controls, the total amount of loci was 297 417. As with many RADSeq datasets, which are characterized by moderate to high levels of missing data ,40, mosta). Support for the different clades varied from very high at the Rothera site (bootstrap support = 100) to weak in the Puerto Williams clade (bootstrap support = 53). Antarctica and South America were recovered as the two major clades, with all populations within these regions being recovered as reciprocally monophyletic with high support (bootstrap support = 100).ML phylogenetic relationships showed strong phylogeographic structure, with clades strongly reflecting geography a. Supportructure analyses confirmed the strong genetic differentiation between the South American and Antarctic regions found in the ML tree (a). For the entire dataset, including all samples in a single analysis, the highest supported K (as suggested by the Evanno test) was K = 2, matching exactly the Antarctic and South American regions. After rerunning Structure independently for each region, the results did not support genetic substructure in South America. On the other hand, the analysis for the Antarctic region supported the existence of two clusters (K = 2) with varying degrees of admixture. One cluster was mainly associated with Rothera, while the second cluster was mainly present in Yelcho. The northernmost site, O'Higgins Base, presented individuals with different levels of admixture from both clusters.The S ML tree a. For thtructure results in that it clearly separated South America and Antarctica. However, there were some discrepancies regarding structure between sites at the WAP . The two first PC axes explained, cumulatively, 25.36% of the total variance, with the first, second and third PC axes explaining 21.91% and 3.45% of the total variance, respectively . At the WAP, Rothera was the most differentiated site, whereas the O'Higgins Base and Yelcho sites showed less differentiation, and a greater overlap in genetic space (a). The Magellan Strait and Puerto Williams sites in the South American region showed moderate differentiation. The Bayesian information criterion supported K = 2 clusters as the optimal solution , consistent with a separation between the South American and Antarctic regions (c). Two individuals from the South American region showed admixed memberships (PW_04 and EM_06) with the Antarctic locality Yelcho, a result that may be influenced by a high level of missing data for these individuals which, coincidentally, presented the lowest number of loci in the dataset . Fst values for the genomic dataset were concordant with the Structure analysis, showing high differentiation between South America and Antarctica, and also showing Yelcho as the most differentiated site at the WAP  revealed an important asymmetry in the migration rates between populations in the WAP (b). A moderate, but significantly higher migration rate was detected from Yelcho to Rothera (D = 0.30) as well as from Yelcho to O'Higgins Base (D = 0.17) in agreement with a role for the APCC on larval dispersal. Strikingly, the highest rate of migration occurred from O'Higgins Base to Rothera (D = 0.69). High migration rates were inferred between the two South American sites, although no significant asymmetrical migration was found .The analysis of directional gene flow  was slightly higher than from Rothera to Yelcho (0.022) and the mean migration rate from Yelcho to O'Higgins Base (0.026) was slightly higher than from O'Higgins Base to Yelcho (0.022). However, confidence intervals were very wide indicating no statistical support for asymmetrical migration between any population pair . Furthermore, difficulties to obtain adequate acceptance rates for parameter estimations and to achieving convergence for the Markov chains make these results hard to interpret.The B4.A. eightsii to assess the role of the ACC and the APCC on shaping connectivity patterns and genetic differentiation. Genome-wide markers have been recently used to support ongoing gene flow across the Drake Passage in the brittle star Astrotoma agassizii [A. eightsii confirming the results found by previous studies based on fewer markers (e.g. [xML phylogeny and Structure) showed a strong differentiation between these regions. Thus, it is likely that the conflicting evidence correspond to an artefact owing to high levels of missing data for some samples. Although the impact of missing data has not been thoroughly studied in the context of genetics, it is known that missing data can severely impact PCA performance, especially when missing data are not distributed randomly [In this study, we aimed to investigate patterns of genetic structure for the Antarctic bivalve gassizii , in contgassizii . There igassizii . Thus, drs e.g. ,44). Alt. AltA. erandomly . In factFst patterns from mtDNA and SNP, and Structure and DAPC results regarding which site was the most differentiated were evident. Fst patterns between COI and SNPs datasets showed different levels of differentiation for Yelcho, being the mtDNA data unable to detect significant differentiation. This may be owing, at least in part, to the lower amount of information available in the single-gene COI data. Regarding differences between Structure and DAPC, Rothera was identified as the most distinct by DAPC, whereas Yelcho was the most distinct based on Structure. We are not certain about the reasons for the difference between analyses. Perhaps, it could be related with the underlying assumptions of each programme, as Structure assumes populations are at Hardy-Weinberg equilibrium [Within regions, genetic structure was more evident in the WAP. Both COI and genomic data evidenced some degree of genetic structure between populations, although some discrepancies between ilibrium , whereasilibrium . Alternailibrium .b). A predominant migration from Yelcho to O'Higgins Base and from Yelcho to Rothera matches the circulation of the APCC in this area, which is conceived to flow meridionally away around Yelcho (Anvers Island). From Anvers Island, one branch flows southwestward towards Margarite Bay, where Rothera Research Station is located, whereas the other branch flows northeastward towards the opposing flow of the Antarctic Coastal Current which flows around the tip of the Peninsula from the Weddell Sea through the Bransfield Strait [et al. [A. eightsii because it would mean that larval transportation from the tip of the Antarctic Peninsula to Rothera can occur following this southward current at some distance from the coast, avoiding the near-coast, northeast-flow branch of the APCC. Based on the genomic results we suggest that, although current systems in this area are complex and still poorly constrained by direct observations, movements from the tip of the Peninsula toward the Bellingshausen Sea area are frequent enough to facilitate larval dispersal.We found evidence of asymmetrical migration between Antarctic populations that was broadly consistent with the circulation patterns of the APCC figures  and 2b. 2d Strait . More cod Strait ,46. Renn [et al. ,46, usinA. eightsii, species in the subclass Protobranchia are known to exhibit either pelagic lecithotrophy or brooding larval development [A. eightsii, a pelagic lecithotrophic larval development has been suggested for the species [A. eightsii. The APCC is believed to exhibit strong seasonality, being pronounced during November–June and less so during July–October, in concordance with freshwater input cycles [A. eightsii observed between April and May [Although there is no direct evidence of the larval type of elopment . Based o species , a hypott cycles . The pea and May  suggests and May . Efforts and May , where iThe role of the ACC on species dispersal has been well documented for several taxa, but much less is known in this regard of the importance of other, more localized (and more nearshore) currents. As far as we are aware, this is the first study providing evidence of the role of the APCC on the dispersal of a marine invertebrate species. Our data also suggest that complex and less studied current dynamics may also be important for transporting larva from the tip of the Antarctic Peninsula towards the Bellingshausen Sea area , althoug5.A. eightsii supported the role of the ACC as a biogeographic barrier between these continental shelves and confirmed previous findings that suggested the existence of two different species (across the Drake Passage). At the Western Antarctic Peninsula, patterns of directional migration were generally consistent with the directional fluxes of the APCC, suggesting a role of this current in the dispersion of the species. Future studies could increase the number of populations and species to test the generality of our findings and incorporate direct measurements of ocean circulation pathways to further investigate the impact and the complexities of the Southern Ocean currents.Strong genomic differentiation between the Antarctic and South American lineages of


Vigna unguiculata (L.) Walp. is an important grain legume grown in the dry agro-ecologies of the tropics with considerably low yield due to lack of improved varieties, aggravated by prevalent narrow genetic base. Thus, induced mutagenesis was employed using sodium azide and gamma rays to increase genetic variability in cowpea genotypes that resulted in isolation of eleven high yielding mutant lines at the M4 generation from the genetic background of cowpea varieties Gomati VU-89 and Pusa-578. In order to analyze the induced genetic divergence among the mutant lines and parent genotypes, biochemical and molecular characterization was carried out with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), simple sequence repeat (SSR) and CAAT box derived polymorphism (CBDP) markers. Activity of nitrate reductase (NR) and content of chlorophyll, carotenoid, protein and mineral were found to be significantly high in the selected mutant lines compared to their respective parent genotypes. SDS-PAGE profile of seed proteins generated 54 and 28 polymorphic bands and a total polymorphism of 62.06 and 41.17% in Gomati VU-89 and Pusa-578, respectively. SSR primers amplified a total of 16 and 24 polymorphic bands with an average polymorphism of 20.69 and 50.74% in Gomati VU-89 and Pusa-578, respectively. CBDP markers, used for the first time in mutagenized population, generated 175 bands with 77 bands being polymorphic in Gomati VU-89 and 121 bands with 59 bands being polymorphic in Pusa-578. Physiological, biochemical and molecular profiling of the selected promising mutants lines showed that Gomati VU-89-G and Pusa-578-C are genetically most diverged high yielding genotypes with significant increase in protein and micronutrient content, therefore, could be recommended for further research considerations. Thus, the favorable combination of genes induced in the novel cowpea mutants selected through the present study are valuable to correspond farmers requirements for new improved cultivars (direct or hybrids).Cowpea,  Cowpea [Vigna unguiculata (L.) Walp.] is a vital leguminous crop growing in the semi-arid tropics of Asia, Africa, Southern Europe, Southern United States, and Central and South America2. Cowpea leaves, green pods, and grains are excellent dietary source of protein, thus used as food and feed for healthy growth of both humans and livestock3. Cowpea is reportedly considered to be first originated and domesticated in Southern Africa, and later spread to east and West Africa and Asia4. Worldwide about 6.5 million metric tons of cowpea are produced annually on about 14.5 million hectares5. Cowpea yields are considerably low in India compared to the world average due to the unavailability of high yielding varieties and prevalence of biotic and abiotic stresses5. Cowpea being a self-pollinated crop, possess a very narrow genetic base which greatly restricted improvement endeavour, because the availability of genetic variations is an important prerequisite to initiate any breeding programme. Hence, a systematic breeding approach is required to circumvent the issue of low genetic variability and to generate improved cultivars associated with higher yielding capacity and stress resilience, which can significantly reduce the prevailing yield gap in cowpea.Pulses are the chief components of the agricultural system, effectively boosting food and nutrition, revenue and environment across the globe and hence assumed ideal for acquiring food security in the developing world including India. Even though India is the leading producer of pulses with 26% of the total world production harvested at about 35% of the total world area, yet a prominent yield gap of 18% exists and one of the leading importer nation6. With the advent of modern genomic techniques and advancement in molecular markers, the possibilities in mutation breeding are now amplified tremendously. In India, mutation breeding has led to the development and official release of only 9 mutant varieties of cowpea. This reflects the unexplored status of cowpea genetic resources and necessitates the use of mutation breeding for cowpea improvement6. In mutation breeding program, the assessment of mutagen induced genetic variability is imperative for the effective and efficient utilisation of genetic resources. For the accurate and quick assessment of genetic variability several morphological, physiological, biochemical and molecular markers are used. Although, the morphological and physiological markers based assessment of genetic variability is valuable for early field based characterization and selection but these markers are influenced by environmental flux to a great extent7. In order to validate the morphological and physiological marker based data, the study needs to be supplemented with more robust genetically linked molecular and biochemical markers for selection precision. Among the molecular markers SSRs and very recently developed CBDP markers are widely used to validate the morphological data8. The biochemical markers have been employed in several crops to assess the extent of genetic diversity9. Among the biochemical markers, SDS-PAGE shows better validity and ease for ascertaining genetic diversity of crop species and consequently it is employed in a wide range of crops for diversity analysis10. The SDS-PAGE profile of seed storage proteins has been used by several researchers for the assessment of genetic variability in cowpea11. However, the use of SDS-PAGE for the evaluation of genetic variability in the mutagenized populations of cowpea is very meagre. The introduction of molecular techniques equipped researchers to analyse genetic variability. Simple Sequence Repeats (SSRs) are unique among molecular markers having repeats of short (2–6 bp) DNA sequences and widely used in marker-assisted breeding and evaluation of diversity/polymorphism13. SSRs are highly polymorphic, co-dominant, reproducible, simple and inexpensive which make them better than other markers for the evaluation of genetic variation14. In the present study, we report the first time use of CBDP markers for the molecular characterisation of mutant varieties and evaluation of genetic diversity in the mutagenized population. The CBDP marker system is an outstanding approach for evaluation of genetic variability in the mutagenized population of cowpea. Earlier, CBDP markers have also been employed for the genetic diversity analysis, DNA fingerprinting, linkage mapping/QTLs and MAS in several crops such as cotton and jute15. CBDP markers are eighteen nucleotides long and harbour central CCAAT nucleotides with a consensus sequence GGCCAATCT located ~80 bp upstream of the start codon and filler sequence at the 5′ end and di- or trinucleotides at the 3′ end16.Mutations are a necessary occurrence for evolution and hence for speciation and domestication of both plants and animals. Therefore, induced mutagenesis has been considered as a powerful tool to create contrast in traits of interest. Amongst the conventional and modern approaches of plant breeding, mutation breeding is a rapid, cost-effective and coherent method to accelerate the course of developing and screening crop genotypes with novel and improved agronomic traits2 to M4 generations. The biochemical and molecular marker based analysis of divergence was done to assess the induced genetic deviations among the genotypes for comparative selection of mutants.Therefore, the objective of this study was to characterize the high yielding mutant genotypes generated through induced mutagenesis in two widely grown cowpea varieties of India, after continuous selections from M17. The cowpea varieties Gomati VU-89 and Pusa-578 were procured from NBPGR, IARI, New Delhi, India. The dry and healthy seeds (moisture 10.0%) of both the varieties were directly irradiated with 100, 200, 300 and 400 Gy of gamma rays with Cobalt-60 radioisotope (60Co) at National Botanical Research Institute, Lucknow. For chemical treatments, seeds were pre-soaked in water for 6 hours and then treated with different doses (v/v) of sodium azide (SA) viz, 0.01%, 0.02%, 0.03% and 0.04% at room temperature of 25 ± 2 °C for 9 hrs. Also, a combination treatment sets of seeds viz. 100 Gy γ rays + 0.01% SA, 200 Gy γ rays + 0.02% SA, 300 Gy γ rays + 0.03% SA and 400 Gy γ rays + 0.04% SA, were prepared by directly treating the gamma rays treated seeds with sodium azide concentrations. Initially, the doses of chemical and physical treatments were optimised by calculating LD50 values based on germination rate and survival percentage. The 300 seeds from each treatment and control population were grown in 10 replications of 30 seeds each in the agricultural field of Aligarh Muslim University, Aligarh, India during mid-april 2014-October 2014 along with respective controls in a randomized complete block design (RCBD) to raise the M1 generation. Each block contained one replication from each of the 12 treatments and 1 control i.e., 13 plot (1.8 × 3 m) per block per variety. The seed to seed and row to row spacing was kept at 0.30 m and 0.60 m inside each block in the overall field size of 23.5 × 40 meter , Gomati VU-89-B (isolated at 100 Gy γ rays + 0.01% SA), Gomati VU-89-C (isolated at 200 Gy γ rays), Gomati VU-89-D (isolated at 200 Gy γ rays + 0.02% SA), Gomati VU-89-E (isolated at 100 Gy γ rays), Gomati VU-89-F (isolated at 200 Gy + 0.02% SA) and Gomati VU-89-G (isolated at 100 Gy γ rays) were screened in var. Gomati VU-89 and four high yielding mutant lines viz., Pusa-578-A (isolated at 0.02% SA), Pusa-578-B (isolated at 200 Gy γ rays + 0.02% SA), Pusa-578-C (isolated at 200 Gy γ rays), Pusa-578-D (isolated at 100 Gy γ rays + 0.01% SA) screened in var. Pusa-578 (Supplementary Table 2) and genetic advance (GA) of selected mutant lines have been evaluated to assess the stability and expressivity of the yield and yield attributing traits18. The genetic parameters were calculated for the traits to predict the extent of induced genetic variability. These parameters include the following:2 g (an estimate of genotypic variance) = (MSG − MSE)/rThe experimental procedure followed in the present study is illustrated in Fig. MSG is an estimate of mean square of tested accession;MSE is an estimate of mean square of error2 g is the genotypic component of variancer refers to the number of replications.2 p is the phenotypic component of variance.2bs = Broad-sense heritabilityσσp refers to Phenotypic standard deviation of the mean performance of treated populationK = 2.64, constant for 1% selection intensity19. Leaves (200 mg) were chopped in 2.5 ml phosphate buffer  (Appendix 1-A), 0.5 ml potassium nitrate (0.2 M) solution (Appendix 1-B), 2.5 ml isopropanol (5%) (Appendix 1-C) and were incubated for 2 hours at 30 °C in dark. 0.3 ml sulphanilamide solution (0.1%) (Appendix 1-D) and 0.3 ml NED-HCL (0.02%) (Appendix 1-E) were added to 0.4 ml incubated mixture. The mixture was diluted to 5 ml with double distilled water and the absorbance was read at 540 nm. The absorbance of each sample was compared with that of the calibration curve and NRA (nmol NO2.g−1.h−1FW) was computed on a fresh mass basis.The high yielding mutants were evaluated for Nitrate reductase activity (NRA) as per the protocol of Jaworski20. About 1 g of finely cut fresh leaves was grounded to a fine pulp in 20 ml of acetone (Appendix 1-F). The mixture was centrifuged at 5,000 rpm for 5 minutes and the supernatant was collected in 100 ml volumetric flask. The residue was washed three times, using 80% acetone. The absorbance was read at 645 and 663 nm for chlorophyll and 480 and 510 nm for carotenoid against the acetone (80%) as blank. The chlorophyll and carotenoid contents present in the extracts of leaves was calculated according to the equation given by Arnon21.The chlorophyll and carotenoid contents of leaves were estimated by the method of MacKinney645, OD663, OD480, OD510 = Optical densities at respective wavelengthODV = Volume of an extractW = Mass of leaf tissuesd = Length of light path (d = 1.4 cm)22. The data were subjected to Duncan’s multiple range test to assess the significance degree.Seeds were pounded to the flour and 400 μl sample buffer was added to seed sample (0.01 g) as extraction buffer containing 0.5 M Tris-HCl (pH 6.8), 2.5% SDS, 10% glycerol and 5% 2-mercaptoethanol. Few drops of Bromophenol Blue was employed as a tracking dye to watch the movement of protein in the gel. Assessment of seed mineral elements such as iron (Fe), copper (Cu) and zinc (Zn) was carried out in an atomic absorption spectrophotometer23. Coomassie Brillant Blue G-250 die was added to 10% polyacrylamide gel and kept overnight. There are three main steps of SDS PAGE analysis:SDS-PAGE analysis was employed to characterize the protein pellets as per the protocol formulated by LaemmliTen per cent lower gel (separating gel) by adding the solutions (Appendix II-A) was prepared. A small layer of isopropanol to the top of the gel was added prior to polymerization for straightening the level of the gel. After the polymerisation of gel is complete, stacking gel (5%) was prepared by adding the solutions (Appendix II-B) and TEMED was added.2O. Based on the Lowry Assay, the same amount of protein samples was prepared and mixed with the same volume of 2X protein sample buffer (Appendix II-C). The samples were boiled at 95 °C for 10 min. The samples were spanned at the maximal speed for 1 min in a tabletop centrifuge and left the samples at room temperature until ready to load onto the gel.The stored samples were thawed and vortex and diluted with ddHElectrophoresis running buffer (10×) (Appendix II-D) was poured into the opening of the casting frame between the gel cassettes. The region outside of the frame was filled with 10X running buffer. An equal amount (10 μl) of prepared protein samples was slowly loaded into each well and 10 μl of protein MW marker was also loaded. The standard protein molecular weight marker (Fermentas®), containing  five kinds of low molecular weight proteins was used. The established mold was then connected with the power supply and ran the gel at 50 mA until the dye reaches the bottom of the glass plate.et al.24 for SSR and CBDP analysis. 82 genic-SSR primers were screened and finally, nine primers lead to the generation of polymorphic bands (Sup. Table 2). Briefly, DNA amplification was carried out in a 96 well thermocycler in a volume of 20 μl each containing PCR grade water, 1.5–2 µl of 50 ng template DNA, 0.5ul of 10 µM of each primer, 0.5 µl of 10 mM of the dNTPs mix, 2 µl of 10X Taq buffer and 0.2 µl of Taq DNA Polymerase. The touchdown PCR was performed in a thermocycler following the program in which the first 5 cycles of 30 s at 94 °C, 45 s at 61 °C with a 1 °C decrease in annealing temperature per cycle and 1 min at 72 °C followed by 30 cycles at 30 s at 94 °C, 45 s at 57 °C and 1 min at 72 °C and a final extension at 72 °C for 10 min. The DNA bands were resolved on a 3% agarose gel stained with ethidium bromide, prepared by combination of MetaPhor and normal agarose in the ratio of 1:1 using 1X TBE buffer.The leaf DNA was extracted from 10-days-old seedlings using CTAB (Cetyl Trimethyl Ammonium Bromide) method as per Agbagwa 15. However, these markers have never been used to assess the induced genetic diversity in any mutagenized population. 25 CBDP markers were screened and finally, nine primers lead to the generation of polymorphic bands (Sup. Table 3). We report first time applicability of CBDP markers in the mutant lines. The master mix consisted of 50 ng template DNA, 1X PCR buffer, 200 mM dNTPs, 0.5 μM CBDP primer and 1 U of Taq DNA polymerase in a total volume of 25.0 μl. The touchdown PCR was performed in a thermocycler following the program in which the first 5 cycles were run at 94 °C; 1 min for denaturing, 35 °C; 1 min for annealing, and 72 °C; 1 min for the extension. The annealing temperature was subsequently raised to 50 °C for another 35 cycles with a final extension for 10 min at 72 °C.The CBDP marker system is an excellent approach for assessment of genetic variability in the mutagenized population of cowpea. Previously, CBDP markers have been employed for the genetic diversity analysis, DNA fingerprinting, linkage mapping/QTLs and MAS in several crops25. Per cent of polymorphism was determined as:The SDS-PAGE protein, SSR and CBDP DNA bands were recorded as presence (1) or absence (0) and used to calculate Jaccard’s similarity coefficients and generate a dendrogram using PAST 326.Polymorphism information content (PIC) value was determined as4 high yielding mutant lines revealed an ample range of variability for polygenic traits such as pods bearing branches, number of pods and seed yield per plant. The quantitative data and genetic data for different traits of mutant lines and their controls are provided in Tables 2 and GA in mutant lines Gomati VU-89-A, Gomati VU-89-B, Gomati VU-89-C, Gomati VU-89-D, Gomati VU-89-E, Gomati VU-89-F and Gomati VU-89-G and Pusa-578-A, Pusa-578-B, Pusa-578-C and Pusa-578-D compared to their respective control, which in turn suggests the selection for quantitative traits in the M3 generation was very successful. Heritability (h2) and genetic advance (GA) has been the most dependable statistical measure to ascertain the genetic stability of quantitative trait(s) in plant breeding. The significantly high heritability and genetic advance estimated in this study confirmed that the high yielding mutant lines were least influenced by the environment and were genotypically stable for further advancement.The M−1 g−1 FW) was recorded for the mutants Gomati VU-89-G and Pusa-578-C, respectively. The correlation studies reflected a positive relationship of NRA with the yield and similar correlations have been reported in lentil27. Likewise, a statistically significant increase in the chlorophyll and carotenoid contents were also observed in the M4 mutants of cowpea. Average chlorophyll contents revealed a significant rise to 2.47 mg g−1 FW (Gomati VU-89-G) and 2.68 mg g−1 FW (Pusa-578-C) against the controls of Gomati VU-89 and Pusa-578 with mean values of chlorophyll contents as 2.05 and 2.55 mg g−1 FW, respectively. The close correlation between pigment contents and seed yield as recorded in M4 high yielding mutants has also been reported in wheat28. Carotenoids are precursors of vital vitamins and are essential antioxidants that reduce the oxidative stress to a greater extent29. Similarly, carotenoid contents also reflected a significant increase up to 0.84 and 0.91 mg g−1 FW in the Gomati VU-89-G and Pusa-578-C, respectively. Enhancement in carotenoid contents in the mutants as recorded in the present study in cowpea play a vital role in promoting the plant health and also possesses protective health benefits for humans and livestock; hence, development of high carotenoid varieties is an emerging area of interest for mutation breeders. The high carotenoid content mutants were isolated from gamma irradiated seeds, as the gamma rays induced oxidative stress in the irradiated material it can be concluded that carotenoid synthesizing genes might be overexpressed in these mutant lines due to the plant’s natural defence mechanisms. The increased carotenoid contents in response to gamma rays induced oxidative stress ultimately enhanced the normal plant growth and subsequent fixation of the micromutations. Hence, considerable augmentation in the carotenoid contents in M4 high yielding mutants may be attributed to the mutagen caused hypoactivity or hyperactivity of carotenoid biosynthesis genes. The mutagen induced up-regulation of carotenoid biosynthesis genes may impart tolerance to a wide range of climate induced stresses such as drought and salinity28. EMS induced Arabidopsis thialiana and Hordeum vulgare mutants tolerant to the paraquat and imidazolinone herbicide respectively were successfully isolated by Gechev et al.30 and Lee et al.31 using induced mutagenesis. Hence, the studies revealed that the high yielding mutants may also be tolerant to a range of environmental flux and can be identified, screened and allowed for after trait fixation in advanced generations.Compared to controls substantial increase in NRA, chlorophyll and carotenoid contents was recorded in the mutant lines Table 4 high yielding mutants evaluated for micronutrient and protein quantity to ascertain the opportunity of an increase in yield coupled fortification of micronutrients. Selections were exclusively based on the quantitative traits up to the M4 generation, any augmentation in the nutrient contents in M4 high yielding mutants is an added value to the mutagenesis research. The average values and coefficient of variation (C.V.%) for the proteins and minerals of M4 mutants have been estimated  was estimated in the mutants Gomati VU-89-B and Pusa-578-D, respectively. The mutants Gomati VU-89-D (3.46 mg 100 g−1) and Pusa-578-D (3.33 mg 100 g−1) while as Gomati VU-89-F (2.37 mg 100 g− 1) and Pusa-578-A (2.24 mg 100 g−1) were found to contain maximum zinc and copper contents, respectively. The results were in agreement with the findings in soybean mutants induced by gamma rays37. Tomlekova et al.38 also reported the increased concentration of minerals such as iron, copper and zinc in pepper mutants with higher β-carotene contents. Hence, it can be concluded that the increase in the mineral elements might be due to the augmentation of β-carotene contents in the M4 high yielding mutants. Even though there are several reports of mutagen induced variations in quantitative traits of cowpea but the literature is very scanty on the mutagen induced variation in Fe, Zn and Cu contents. The results confirmed the scope of isolating mutant lines with increased micronutrients content coupled with a higher yield.The estimation of micronutrients resulted in a slight but significant increase in the mean seed protein contents in Gomati VU-89-A, B and E and Pusa-578-A, B, C and D mutant lines. The enhancement in the seed micronutrient contents of pulses through mutation breeding could purge the micronutrient deficiencies across the globe4 cowpea mutants are given in Tables et al.27 reported substantial genetic divergence between and within mutants of Lens culinaris which were not always reflected at the phenotypic level. Laskar et al.27 further reported that SDS-PAGE analysis of seed protein could be employed as an efficient means for screening mutants that cannot be identified solely by morphological data alone. Nonetheless, in conformity with the results reported by Laskar et al.27 on Lens, we were able to make use of SDS-PAGE as a tool for determining genetic relationships within a mutagenized population of Vigna unguiculata. The variations in the electrophoresis patterns of the seed proteins after mutagen treatments have also been reported in various pulse crops39. Hong et al.40 advocated that the response of mutants towards a wide range of environmental stresses can also be determined using SDS-PAGE analysis and would be useful in screening the stress tolerant mutants with high yield. Belele et al.41 in Phaseolus vulgaris, Auti and Apparao42 in Vigna radiata, Nakagawa et al.43 in Glycine max, Khadke and Kothekar44 in Vigna acconitifolia, Kozgar et al.45 in Cicer arietinum, Khursheed and Khan46 in Vicia faba and Laskar and Khan17 in Lens culinaris also noticed mutagen induced variations in the electrophoresis patterns of the seed storage proteins. The unique pattern of protein bands may also serve as markers for screening mutant lines. In general, the appearance of mutagen induced new bands with varied intensities reveals the new protein expression that might be attributed to the higher yield in mutant lines.The SDS-PAGE profile of seed proteins revealed variations in the number and intensity of protein bands of mutant lines Figs. –4. Theseet al.47 most of the agro-economical traits, particularly yield and yield attributes are governed by small additive effects of multiple genes and environmental factors, hence, to screen and isolate mutants with better traits, there is a need to integrate marker assisted selection (MAS). MAS has huge potential to enhance the efficiency and accuracy of traditional breeding approaches48. MAS has been widely employed for the assessment of genetic diversity due to the availability of a huge number of DNA markers49. Among the DNA markers, simple sequence repeats (SSRs) have vast distribution in both non-coding and coding sequence as tandem repeats of 1–6 DNA nucleotide motifs50.Even though protein markers play a significant role in the assessment of genetic diversity in the mutagen treated plants, however, DNA markers are more effective and have wide acceptance for studying the genetic divergence induced by mutagens. In addition, the interaction of mutagen with the DNA leads to the altered expression of genes and variations in protein profiles, hence, DNA markers divulge more precise state of gene mutations. As per the suggestions of Kumar 4 high yielding mutants of cowpea. Earlier SSR markers have been employed for the assessment of genetic diversity of cultivated and wild cowpea species51, however, the literature is scanty on the assessment of genetic diversity of the mutagenized population using SSR markers. Out of 82 genic-SSR primers used, 9 primers generated polymorphic bands, 53 primers generated monomorphic bands and the remaining 20 primers failed to amplify DNA fragments. The selected 9 primers amplified 76 bands with 16 bands being polymorphic in the var. Gomati VU-89 and 47 bands with 24 bands being polymorphic in the var. Pusa-578  and Pusa-578-A (0.238) were most divergent from the control population Tables  and 11. 4 high yielding mutants.The SSR based relative similarity and dissimilarity matrix of mutant and control population generated dendrograms are presented in Figs. et al.27.The CBDP based genetic dissimilarity coefficient of the mutants and controls ranged from 0.172 (between Gomati VU-89 and Gomati VU-89-F) to 0.524 (between Gomati VU-89-C and Gomati VU-89-D) in the var. Gomati VU-89, while it ranged from 0.111 (between Pusa-578-A and Pusa-578-D) to 0.500 (between Pusa-578 and Pusa-578-C) in the var. Pusa-578 Tables  and 15. 4 mutants developed can be employed as mutant varieties directly or as parents in crossbreeding programmes for advanced improvement and fixation of the traits in cowpea.Apart from the little differences both the markers resulted into a similar pattern of clustering and creation of dendrograms like Gomati VU-89-F and Gomati VU-89-G were always grouped together and similarly, Pusa-578-B and Pusa-578-D were also grouped together in both the marker analysis. The dendrogram showed control and mutant plants in separate clusters, which reconfirmed that considerable degree of genetic variability was generated by gamma rays and SA treatments used alone or in combination. Hence, the genetically diverged M2 and GA. Assessments on NRA, chlorophyll, carotenoids, protein and mineral elements showed improved physiological and biochemical activity in the high yielding mutant lines of cowpea. SDS-PAGE analysis revealed the expression of new polymorphic protein bands among the selected high yielding mutant lines. CBDP and SSR markers have generated a considerable amount of polymorphism which in turn validates the genetic divergence of mutants from the control population. Cluster analysis separated the control plants of both the cowpea varieties into separate clusters evidently confirmed that the employed mutagens successfully created heritable genetic changes with considerable genetic gains in the M4 high yielding mutants compared to their respective controls. The genetic divergent analysis using molecular markers confirmed that the selected M4 high yielding mutants, especially Gomati VU-89-G and Pusa-578-C, induced in the present study contains novel combination of genes and has significant breeding implications in the development of new high yielding cowpea varieties, either directly or as parents in cross breeding programmes.The current research led to the development of high yielding cowpea mutant lines and the seeds of mutant lines are stored in the department seed bank for further farmer’s field based characterization and dissemination to the cowpea breeders. A good degree of genetic variability has been induced in the mutant lines as revealed by the manifold increase in GCV, hSupplementary Info.