Accordingly, the present study aimed to establish a systematic categorization of the genetic and phenotypic diversity encountered across carob germplasm identified in situ throughout Cyprus, a historical center of production and genetic diversity for the species. Linking pod morphology, primary and secondary metabolite profiles with genotyped source material originating in different agro-environments and crop seasons would provide a framework for interpreting (a) the interaction of these factors in configuring carob pod physicochemical constitution, and (b) the relative stability of phenotypic traits against environmental and seasonal variation. Microsatellite analysis discriminated 36 genotypes out of the 124 trees located in nine traditional agro-environmental zones and revealed low genetic diversity within the grafted germplasm. Two landraces were identified: “Tillyria,” which is widespread and predominant, and “Kountourka,” which is mainly localized to the northeastern peninsula of Karpasia. Morphological traits, such as seeds-to-pod weight ratio, pod width and thickness were principally under genetic control. Contrarily, compositional traits, particularly total phenolic content—including condensed tannins, in vitro antioxidant capacity and to a lesser extent gallic acid, organic acids and minerals were under agro-environmental control. Agro-environmental zone also modulated principally fructose and glucose; sucrose was modulated equally by genotype and agro-environment, while total sugars were under genetic control. Statistically significant differences between seasons were detected for all traits except for the seeds-to-pod weight ratio, pod length and width. Hierarchical cluster analysis corroborates that Cyprus may be divided into two major agro-environmental zones modulating the compositional properties of the carob pulp. The present study provides a comprehensive insight into the extant carob genetic resources of Cyprus and advances our understanding of how genetic, agro-environmental and seasonal factors interact in shaping carob pod morphology and composition.Carob is a predominantly rainfed tree crop of high nutritive value and a long history of adaptation to the edaphoclimatic stress conditions of the Mediterranean. However, declining attention to the carob tree in recent decades has aggravated genetic erosion. The extant  Ceratonia siliqua L.) is an evergreen, diploid species (2n = 48) that belongs to the Fabaceae family. The carob tree is an important component of the Mediterranean vegetation and it is predominately cultivated in dry and marginal areas, due to its low requirements in agronomic inputs compared to other fruit species. Its ability to thrive in the prevailing calcareous soils has amplified the socio-economic value of the carob in several Mediterranean mild climate dry lands  after the 1960s in Cyprus led to the depreciation of the carob crop and resulted in substantial reduction of the carob cultivated area . Moreovein situ/on farm to dissect the genotypic and the agro-environmental effects on morphological and compositional traits. Extensive sampling was performed from all the traditional environmental zones of carob cultivation, with emphasis on grafted material. Sampling was performed for two consecutive years to evaluate the seasonal effect on phenotypic traits. The genetic and phenotypic variation of Cypriot carob genetic resources is hereby presented. To the best of our knowledge, this is the first study aiming to investigate the genotypic, agro-environmental and seasonal effects on carob morphological and compositional traits.The current study combined genetic and phenotypic data collected from trees Extensive survey of carob genetic resources was carried out during two seasons, 2018 and 2019. Trees were sampled from nine agro-environmental zones based on geographical and geological parameters . “AnogirVernacular knowledge on phenotypic diversity, cultivation practices, trade and processing were collected through interviews with farmers and stakeholders of the local industry. Farmers participated in joint field trips to locate trees of distinct phenotypic traits. Non-grafted trees were also sampled to assess the relatedness between grafted and non-grafted gene pools. Additionally, trees from the nursery plantations of the Department of Agriculture, Ministry of Agriculture, Rural Development and Environment (DOA) which provide budding wood for propagation purposes were sampled. Three accessions, representing the cultivated material in Sicily, were also included in the study. In total, 124 trees were sampled out of which 107 were grafted and 17 non-grafted. The trees were georeferenced and passport data were compiled . Leaves TM 500LIZ (Applied Biosystems) was added to each sample to delineate allele sizes. Data were analyzed using GeneMapper Software version 4.1 .DNA was extracted using the DNeasy Plant Mini Kit . DNA integrity was verified in agarose electrophoresis and DNA quality and quantity was determined by NanoDrop 1000 . Eighteen microsatellites were selected based on the available information from previous studies . AmplifiCarob pods were washed under tap water to remove debris, rinsed with deionized water and patted dry. The thickness of the pod  and the length and width of the pod were recorded using a caliber. Then the pods were coarsely ground in a Vita Prep 3 blender  operated at low speed and deseeded. Pod and seed weight were measured using a Precisa XT120A analytical balance . The carob kibbles were lyophilized in a Christ, Alpha 1–4 lyophilizer  to stable weight, ground to powder of 1–2 mm granulometry using a CT293 Cyclotech mill  and stored at −60°C.∗) of the carob pod and seed were assessed using a Minolta CR-400 Chroma Meter  and that of the powder using a Minolta CR-410 both with a diffusion illumination 0° viewing angle geometry and the color space XYZ, Yxy, L∗a∗b∗, Hunter, L∗C∗h, Munsel as the default.The color lightness , anions  and organic acids  on lyophilized carob powder was performed as previously described in detail by –1 dw. The total protein content was assessed by the Kjeldahl method, with nitrogen-to-protein conversion factor of 6.25 ; and (b) methanol:H2O:acetate (80:19.5:0.5). In addition to the extraction of readily extractable phenolics, the former solution facilitated the extraction of acid-hydrolyzed condensed tannins. Absorbance was measured on a Jasco V-550 UV–vis spectrophotometer  and quantification was performed using linear calibration with external gallic acid standards over the range of 50-500 mg L–1, yielding a regression coefficient R2 > 0.99. TPC was expressed as gallic acid equivalents in g kg–1 pulp dw.The total phenols content (TPC) of the carob powder was determined according to the method of –1 pulp dw.Condensed tannins (proanthocyanidins) were determined using a modification of the vanillin method described by –1. The mobile phase consisted of water (A) and methanol (B), both containing 0.1% formic acid. Gradient elution program was applied as follows: 0 min—5% B, 1.3 min—30% B, 9.3 min—100% B, 11.3 min—100% B, 13.3 min—5% B, 20 min—5% B. The UHPLC system was coupled to a Q Exactive Orbitrap liquid chromatography tandem-mass spectrometry (LC–MS/MS). A heated electrospray ionization source  operating in negative ion mode (ESI–) was used. Ion source parameters were: sheath gas-flow rate 45 units, auxiliary gas-flow rate 10 units, spray voltage −3.5 kV, capillary temperature 275°C, S-lens (RF) level 50, auxiliary gas heater temperature 350°C.Polyphenols were determined on methanolic extracts obtained using the extraction solvent (b) described above. An Ultimate 3000 UHPLC system  was employed equipped with a Kinetex 1.7 μm biphenyl (100 × 2.1 mm) column  maintained at 25°C. The injection volume was 2 μL and flow rate was 0.2 mL minAnalysis was arranged setting two scan events  for all compounds of interest. Full MS data were acquired setting the following parameters: microscans, 1; AGC target, 1e6; maximum injection time, 200 ms; mass resolution, 35,000 FWHM at m/z 200 and m/z range, 80–1,200. AIF scan conditions were: microscans, 1; AGC target, 1e5; maximum injection time, 200 ms; mass resolution, 17,500 FWHM at m/z 200; HCD energy, 10, 20, and 45 and m/z range, 80–1,200. Calibration of the Q Exactive Orbitrap LC–MS/MS was checked daily, in both negative and positive modes, using the commercial calibration solutions provided by the manufacturer. Mass tolerance was kept at 5 ppm in both fullscan MS and AIF modes. Xcalibur software v. 3.1.66.10  was used to perform data analysis and processing. Single phenolic compounds were quantified using calibration curves built with appropriate standards. As some standards were not available, quantitation for some compounds was carried out employing calibration curves of available standard belonging to the same chemical group and with similar response to the mass spectrometer. Analysis of polyphenols by UHPLC-Q-Orbitrap HRMS was performed only on samples harvested in 2018.in vitro Ascorbate Equivalent Antioxidant Activity (AEAC) of carob powder was assayed on pulp methanolic extracts according to the 1,1-diphenyl-2-picrylhydrazyl free-radical (DPPH) scavenging capacity assay of –1 pulp dw. The in vitro antioxidant activity of carob powder methanolic extracts was assayed also according to the ferric reducing antioxidant power (FRAP) assay of –1 pulp dw.The –1. Quantification was performed against calibration curves of fructose, glucose and sucrose external standards (0.2–2.0 g 100–1 mL) with a coefficient of determination R2 > 0.999 and expressed as g 100 g–1 pulp dw.The analysis of water-soluble carbohydrates was performed on aqueous extracts clarified using the Carrez Clarification Kit . Separation and quantification of glucose, fructose and sucrose was performed by liquid chromatography on an Agilent HPLC system  equipped with a 1,200 Series quaternary pump and a 1,260 Series Refractive Index detector operated by Chem-Station software as previously described by k = 1–10) and the validation of the most likely number of clusters K was performed with the Structure Harvester following the ΔK method proposed by Genetic analysis was conducted on allele fragments. Three out of the eighteen primers  amplified more than two alleles per tree sample, therefore, in the analysis, it was assumed that these primers were multilocus. Number of alleles (Na), number of alleles with frequency less than five (N ≤ 0.05), number of private alleles, effective number of alleles (Ne), observed (Ho) and expected heterozygosity (He), fixation index (Fis) and deviation for Hardy Weinberg equilibrium (HW sign) were calculated on the whole set of trees and separately for grafted and non-grafted trees using GenAlEx 6.4 . Polymort-tests were conducted to explore trait differences between seasons and Pearson correlations were estimated to investigate their relationships. Separate Analyses of Variance (ANOVA) were conducted to investigate the genetic and agro-environmental effects on the examined traits. The first ANOVA was executed using as factor treatments the clustering of the trees according to the structure analysis, while the second ANOVA was executed using as factor treatments the agro-environmental zones where the trees are grown. The percentage of variance explained by the main effect, as percentage of the SS to the total SS, is presented. Mean values and standard error of the means were calculated for each structure cluster and agro-environmental zone. Box plots are presented in cases where the percentage of variance explained by the genotypic or agro-environmental effect was particularly high. Hierarchical cluster analysis was conducted to assess the genetic and agro-environmental effects on the overall phenotype. Squared Euclidean distances were calculated on standardized Z-values, with a mean of 0 and a standard deviation of 1. Clustering was performed using the “WARD” method. All analyses were carried out using SPSS .Mean values, standard error of means, coefficients of variation, maximum and minimum values were calculated and frequency plots were constructed for all morphological and compositional traits. Two tailed PhiPT = 0.321, p = 0.01), and Nm value was 0.528.Microsatellite analysis was used to genotype carob trees from nine agro-environmental zones. The 18 primer pairs used in the present study discriminated 36 genotypes. The primers distinguished all non-grafted trees. Moreover, 20 genotypes were identified among the 107 grafted trees. The most common genotype appeared with very high frequency . In totaA Bayesian approach was employed to examine population structure. The analysis grouped trees into two clusters . Ninety-∗) and total sugars content over seasons were 67.99 and 45.27 g 100 g–1 dw, respectively. Variation was very low for these traits. Sucrose and mallic acid were the predominant soluble carbohydrate and organic acid, respectively. Variation was lower for sucrose and fructose compared to glucose. Phosphorous (PO4) and potassium were the major metals detected in carob pods. Average DPPH, FRAP and total phenolics extracted with MeOH-HCL over seasons were 9.04 g AE kg–1 dw, 45.76 g AE kg–1 dw and 29.85 g GAE kg–1 dw, respectively. Gallic acid was the main phenolic compound detected followed by the flavonoid kaempferol-7-O-glucoside. Variation was higher for total phenolic content, condensed tannins, antioxidant activity and individual polyphenols compared to the other compositional and morphological traits  propagated tree with expectedly lower genetic diversity than an annual crop in similar environments, e.g., Cypriot durum wheat landraces . MoreoveThe genetic diversity detected in the present study and the percentage of discriminated genotypes were lower than previous works on carob , which cNon-grafted trees grouped at a different structure cluster than the core of the grafted trees or they were admixtures. Despite the smaller sample size, genetic diversity among non-grafted trees was higher, Fis was close to zero and most loci did not show significant deviation from Hardy Weinberg equilibrium indicating that contrary to grafted material wild populations were under random mating. Nevertheless, gene flow exists between grafted and non-grafted genetic material. Grafted trees producing pods of variable morphology were grouped as admixtures in structure analysis confirming that admixture is an important component of carob genetic diversity . As caroex situ collections should be treated with caution , higher antioxidant activity, higher concentration of total phenolics, condensed tannins, organic acids, calcium and sucrose. These compounds are highly significant for the characterization of carob as a functional food  or those that were admixtures (A) behaved in many cases as outliers within particular environmental zones ; this waGenotyping analysis of carob genetic resources, using microsatellites revealed low genetic diversity levels within Cypriot grafted germplasm. Two carob landraces were identified: “Tillyria” and “Kountourka” consisted of genetically similar or identical trees. “Tillyria” predominates on the island while “Kountourka” is locally common to the Karpasia peninsula. Genetically divergent genotypes from the abovementioned landraces were identified, which appeared though with very low frequency, indicating a low selection pressure from farmers. Morphological traits, particularly the seeds/pod weight ratio, pod width and thickness, and the total sugar content, were mainly under genetic control. On the other hand, agro-environmental conditions had a strong effect on compositional traits, particularly on total phenolics, antioxidant activity, condensed tannins and color of the carob powder. Concerning sugar profile, the agro-environmental effect was stronger on fructose and glucose, while sucrose was equally affected by genotype and agro-environment. Significant differences between seasons for morphological and compositional traits were observed revealing the existence of considerable seasonal effect. Correlations between seasons were stronger for morphological traits, intermediate for powder color, antioxidant activity, total phenolic content and condensed tannins, and lower for carbohydrates suggesting the existence of a strong tree-by-season interaction on sugar content. Based on all the above findings, Cyprus can be divided to two major agro-environmental zones that modulate to a great extent the compositional properties of the carob pulp.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the ethics committee of the Agricultural Research Institute of the Cyprus Ministry of Agriculture, Rural Development and Environment. The participants provided written informed consent to participate in this study.AK, MK, and CA: conceptualization, methodology, and data curation. AK, MK, CA, LP, GG, and YR: formal analysis, writing—original draft preparation, and writing—review and editing. MK, LP, GG, and YR: resources. AK and MK: supervision and project administration. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Physical removal of individuals from groups causes reductions in group sizes and changes in group composition, which may affect the predictive ability of estimates of indirect genetic effects of animals on phenotypes of group mates. We hypothesized that including indirect genetic effects of culled animals and of animals without phenotypes in the analysis affects estimates of genetic parameters, improves predictive ability, and reduces bias of predicted breeding values. We tested this by applying different editing procedures, i.e. omission of individuals or groups from the data, and genetic models, i.e. a classical and an indirect genetic model (IGM) without or with weighting of indirect genetic effects based on the relative proportion of time spent in the pen or space allowance. Data consisted of average daily gain for 123,567 pigs in 11,111 groups, from which 3% of individuals in 25% of groups were prematurely removed from the group.The estimate of total heritability was higher (0.29 to 0.34) than that of direct heritability (0.23 to 0.25) regardless of the editing procedures and IGM used. Omission of individuals or groups from the data reduced the predictive ability of estimates of indirect genetic effects by 8 to 46%, and the predictive ability of estimates of the combined direct and indirect genetic effects by up to 4%. Omission of full groups introduced bias in predicted breeding values. Weighting of indirect genetic effects reduced the predictive ability of their estimates by at least 19% and of the estimates of the combined direct and indirect genetic effects by 1%.We identified significant indirect genetic effects for growth in pigs. Culled animals should neither be removed from the data nor accounted for by weighting their indirect genetic effects in the model based on the relative proportion of time spent in the pen or space allowance, because it will reduce predictive ability and increase bias of predicted breeding values. Information on culled animals is important for prediction of indirect genetic effects and must be accounted for in IGM analyses by including fixed regressions based on relative time spent within the pen or relative space allowance. An indirect genetic model (IGM) for estimation of breeding values includes both direct and indirect genetic effects of animals. It has been suggested that estimated breeding values from such a model can be used to select for social behavior without the need for expensive and tedious recording of complex behavioral traits, such as tail biting in pigs. Ellen et al.  showed tThere are several reasons why culled animals should not be ignored in IGM. First, the phenotypes of all group members are affected when an individual is culled, because the phenotype of an individual is the result of its direct genetic effect plus the sum of the indirect genetic effects of its group mates . Second,Another challenge associated with culling of animals is that differences in time spent in the group and changes in space allowance per animal may also affect the indirect genetic effect of a given animal. Cantet and Cappa  suggesteThe data used were part of routine recording of pigs in performance tests in nucleus herds of the DanBred pig breeding program. Management and welfare procedures were in accordance with Danish national standards and the records obtained in these tests, e.g. weight, require no ethical approval.2 at the start of the performance test. If a pig left the pen because of death, illness, or sale, the date and reason for leaving the pen were recorded. Pigs were neither allowed to re-enter their pen after having been culled, nor to move to any other pen that was part of the performance test. During the test, feeding was ad libitum with dry feed, which at a minimum fulfills the Danish norm requirements given by SEGES [Data were from performance-tested DanBred Landrace pigs in 18 nucleus breeding herds within the period from January 2014 to May 2017. The age of the pigs at the start of the performance test was on average 82 days [standard deviation (sd) = 8.2] and ranged from 42 to 156 days. The average starting weight was 30 kg (sd = 1.9), ranging from 20 to 40 kg, and the average weight at the end of the performance test was 94 kg (sd = 9.0), ranging from 45 to 140 kg. The number of test days per pig was on average 63 days (sd = 6.9), ranging from 34 to 93 days. Average daily gain (ADG) was calculated as:by SEGES .Descriptive statistics after editing (described below), including numbers of animals, litters, and groups, as well as group sizes, percentage of missing phenotypes, mean ADG, and average additive genetic relationship for six datasets that represent different scenarios of editing procedures regarding culling of pigs or missing phenotypes are in Table Group sizes smaller than 8 or greater than 15 at the start of the performance test, because they were considered too rare (68 groups omitted).Groups with one or more pigs having pedigree errors (105 groups omitted).Groups with a smaller group size at the start than the pen size prescribed  or with any pigs unaccounted for at the end of the performance test, i.e. physically not in the pen, but not recorded as having been culled (285 groups omitted).Group sizes that occurred infrequently, either within a herd , or within a sex .Groups with the following characteristics were omitted from all datasets: full) without any further edits based on culling of pigs from the groups or missing phenotypes and five alternative editing procedures based on culling of pigs from groups or missing phenotypes , as described below. Culling of pigs was due to e.g. disease, leg problems, tail bites, or sale and missing phenotypes (for pigs that were still in the pen) were due to recording errors (i.e. missing phenotypes). The five datasets for the alternative scenarios were subsets of the dataset for the full reference scenario.The six datasets consisted of a reference scenario . Group size was defined as the number of pigs in the group when 28% of the performance test days had passed. Group size ranged from 5 to 15, and 3.9% of the pigs and 29.7% of the groups had missing phenotypes. The mean ADG and the average additive genetic relationship within pens were equivalent to those of the full scenario , a standard indirect genetic model (IGM), and three IGM with weighting of indirect genetic effects: (1) with weighting noPhen and testdays, for which an additional fixed effect of group size was included. Vectors entclass1pt{minimaThe IGM was defined as:g, Matrix Parameters in the models were estimated using average information residual maximum likelihood (AIREML) and genetic effects were best linear unbiased predictors (BLUP), which were in both cases implemented in the DMU software .full to 0.992 in noPhenGrp15), full to 1.025 in noPhenGrp), and full to 1.017 in noPhenGrp15) for the IGM, The phenotypic variance entclass1pt{minimaa et al. :\documena et al.  and the entclass1pt{minimaIt follows that Standard errors (SE) on estimates of direct heritability (by Bijma : \documenoPhen and predPhen to 3.5% for full). Likewise, the proportion of groups that included pigs without phenotypes ranged from 0 (noPhen and predPhen) to 28.6% (full). One hundred and seventeen sires were represented with offspring in the validation data, but only 49 of them had offspring in the training dataset as well.The predictive ability and bias of predicted breeding values were calculated for the six scenarios by dividing the datasets described in Table To further validate robustness of the results, we also defined validation datasets with two alternative cut-off points, i.e. on July 1, 2016, and January 1, 2016. These yielded similar results as the original validation datasets and are, therefore, not reported.Predictive ability was defined as the Pearson correlation between predicted breeding values and corrected phenotypes (see below) in the validation dataset. Three alternative predicted breeding values were calculated: full scenario. The AIC was used to compare models within a scenario as the fixed parts of the models were equivalent. The IGM, The corrected phenotypes of each pig  to 2675 (g/day)2 (full) for the CGM. Likewise, the editing procedure did not have a significant effect on the estimated indirect genetic variance, which ranged from 9.9 (predPhen) to 13.3 (g/day)2 (noPhen) for the IGM. The estimate of direct heritability (noPhenGrp) to 0.25 (predPhen), regardless of the model. When the IGM was used, the total genetic variance was larger than the direct genetic variance for all scenarios, thus, the estimates of testdays and predPhen) and 42% (noPhen and noPhenGrp) higher than the estimates of predPhen (noPhenGrp (− 2.5) and largest (− 26.1 and − 29.7) for predPhen . The estimate of the genetic correlation between direct and indirect effects (noPhenGrp) and − 0.19 (noPhen), only differed significantly from 0 (− 0.16 and − 0.19 ± 0.04) for predPhen  and noPhenGrp (42%) and noPhen (41%). Thus, the effect of including indirect genetic effects on the estimated total genetic variance was largest when the editing procedure involved omission of either all culled individuals or all groups with culled animals. Within each scenario, the estimate of the total genetic variance tended to be smaller when indirect genetic effects were weighted , There were significant group and litter effects and, although the estimate of the group variance was 5 to 10% smaller for the IGM than for the CGM, this difference was not statistically significantly different from zero, regardless of weighting of indirect genetic effects or editing procedure. Estimates of litter and residual variances also did not differ significantly between models.noPhenGrp and noPhenGrp15 than for the other editing procedure scenarios (between 1 and 3% higher). The predictive ability of noPhen, the differences in predictive abilities were significantly different from zero. The predictive ability of noPhen and highest for noPhenGrp (4% higher than for noPhen). The predictive ability of full, compared to the other scenarios.The predictive ability of the predicted direct breeding values of individual pigs, ta Table . The prepredPhen) for the IGM than for the other models. Likewise, the predictive ability of full), i.e. weighting indirect genetic effects based on relative time spent in the pen or space allowance in the model did not improve predictive ability.Within a scenario, the predictive ability of full, noPhen, and testdays, predPhen, noPhenGrp15 and in particular for noPhenGrp, testdays. Lastly, all IGM models resulted in biased noPhenGrp and noPhenGrp15, with the largest bias of noPhenGrp (1.100). For a given scenario, there was generally no significant difference in bias for predPhen, for which the bias of Bias of predicted breeding values was reflected by the extent to which the regression coefficient of predicted breeding values post-phenotyping on predicted breeding values prior to phenotyping deviated from 1 Table . For theWe showed that, in general, neither editing procedures related to culled animals and missing phenotypes, nor accounting for culled animals in the IGM, affect estimates of genetic parameters significantly. We also showed that editing procedures that remove culled animals or pigs with missing phenotypes but not their group mates, tend to reduce the predictive ability of estimates of indirect genetic effects .noPhen scenario. Bunter et al.  particularly so in comparison to the estimates of direct genetic variances. Such (relatively) low values have in the past been mistakenly interpreted as unimportant [We identified significant indirect genetic effects for ADG, with the mportant , but theentclass1pt{minimafull scenario in our study, which would also have contributed to the higher Several previous studies have also identified significant indirect genetic effects for ADG in pigs, which fall mostly within the same range as in our study. Chen et al.  reportedn et al.  had a gra et al.  found a n et al.  found a Our results showed that indirect genetic effects are not significantly affected by weighting of indirect genetic effects based on the relative proportion of time spent in the pen. This indicates that indirect interactions between pigs within a pen are not affected by the amount of time that the pigs have spent together when only a relatively small proportion of the pigs are removed (3.2% of the pigs across 24.8% of the groups). This implies that an aggressive pig has the same effect on the ADG of its group mates over the test period, regardless of whether it has been in the pen throughout the entire test period or it was culled before the end of the test. This may seem to disagree with results of ethological studies, which show that persisting aggression can affect ADG at the phenotypic level . HoweverOur results also show that estimates of indirect genetic effects are not significantly affected by weighting based on the relative space allowance between pens  should not be an issue when selecting on estimates of indirect genetic effects. In fact, animals that have been culled are important for the estimation of indirect genetic effects and must be included in analyses. Our results also show that weighting of indirect genetic effects of culled animals reduces the predictive ability of estimated indirect genetic effects. Instead, fixed regressions on the average relative proportion of time spent within the pen and the relative space allowance can account for the association of culled animals with ADG within groups. This stresses that it is important to take fixed effects that may otherwise be captured by the indirect genetic variance when modelling indirect genetic effects into account.Our results confirm that there are indirect genetic effects for growth in pigs, but indirect genetic effects of culled animals should not be removed through data editing, as this will reduce predictive ability and increase bias of estimated breeding values. In fact, animals that have been culled are important when estimating indirect genetic effects and must be included in analyses with IGM. Moreover, indirect genetic effects of culled animals should not be weighted based on proportion of time spent in the pen or relative space allowance as this will also reduce predictive ability. Instead, the effects of culled animals on indirect genetic effects should be accounted for by including fixed regressions of the mean proportion of time spent in the pen or relative space allowance on ADG in the model.


Human genome-wide association studies (GWASs) have recurrently estimated lower heritability estimates than familial studies. Many explanations have been suggested to explain these lower estimates, including that a substantial proportion of genetic variation and gene-by-environment interactions are unmeasured in typical GWASs. The human microbiome is potentially related to both of these explanations, but it has been more commonly considered as a source of unmeasured genetic variation. In particular, it has recently been argued that the genetic variation within the human microbiome should be included when estimating trait heritability. We outline issues with this argument, which in its strictest form depends on the holobiont model of human-microbiome interactions. Instead, we argue that the microbiome could be leveraged to help control for environmental variation across a population, although that remains to be determined. We discuss potential approaches that could be explored to determine whether integrating microbiome sequencing data into GWASs is useful.Video abstract Genome-wide association studies (GWASs) aim to identify the genetic variants underlying trait heritability. The genetic variance explained by these genetic variants, typically single-nucleotide polymorphisms (SNPs), in a GWAS can be calculated as the combined effect size of all significant genetic variants . For virThe most widely accepted explanation for missing heritability is that GWASs are not testing the majority of relevant human genetic variation , 7. ThisHowever, there are other types of segregating genetic variation that could improve GWAS-derived heritability estimates. In particular, copy-number variation, variation in the presence of large genomic regions containing genes, is typically not assessed in GWASs. Ignoring these variants is a major limitation of current GWASs because they represent a substantial proportion of genetic variation: at least 5% of human genomes contain instances of copy-number variation greater than 500 kb . In addiA related explanation for missing heritability is that many genetic variants have differential effects depending on environmental variation, termed GxE interactions. These interactions, in addition to independent genetic and environmental effects, are known to contribute to the liability of many human diseases . For exaDespite the importance of GxE interactions, genetic and environmental factors are typically analyzed independently. The independent treatment of these factors is mainly due to the prohibitively large sample sizes required for systematically identifying GxE interactions . In addiAnother explanation for missing heritability is that heritability estimates from classical heritability studies and GWASs may not be directly comparable. GWASs typically estimate narrow-sense heritability, which corresponds to the additive contribution of genetic variants to heritability. In contrast, heritability estimates from classical familial and twin studies are more similar to broad-sense heritability, because they can incorporate non-additive genetic effects . In addiDespite these issues, a meta-analysis of 2748 twin studies suggested that non-additive genetic effects are unlikely to be substantial contributors to the heritability of human traits . This clIt remains controversial whether genetic variants identified through GWASs are the sole contributors to additive genetic variation assessed in classical studies. For instance, it has previously been argued that cultural transmission could be inflating classical estimates of additive genetic effects . SimilarThe human microbiome refers to the microbes living on and within the body and the functions they encode. These microbial functions include roughly 100 times more genes than the human genome . WhetherAn explicit proposal for this perspective was outlined by Sandoval-Motta and colleagues  based onariation –38 . Importantly, this low estimate may change as additional data, such as inter-strain level variation, is integrated into community-wide h2 for other cohorts. However, based on the current data it appears that inter-individual variation in microbial composition is predominantly due to environmental effects.Despite these examples of heritable microbial lineages, a recent analysis of two independent cohorts showed that variation in the overall composition of the gut microbiome is largely determined by environmental factors . AlthougThis observation implies that most microbial genetic variation in the microbiome is a proxy for environmental, and not missing human genetic, variation. One possible counterargument might be that environmental factors affecting microbiome composition are irrelevant since this simply reflects the variable aspects of a holobiont’s identity. In this view, the holobiont is no less an individual than a human, which undergoes its own developmental and environmentally mediated changes through time. However, under the strict hologenome model, where the host and the microbiome act as a single evolutionary unit, there must be collective reproduction of the holobiont , 48. ColThere are also operational difficulties with re-expressing heritability in terms of a hologenome. For instance, there is no single microbiome within humans: there are drastically different communities spatially and temporally , 50. ThiA stronger counterargument might be that a non-negligible proportion of microbial genetic variation should nonetheless be integrated into human trait heritability estimates based on a less strict form of the hologenome concept. It has been argued that although the hologenome model itself is flawed, the human holobiont could be considered in terms of functional interactions that can be performed by horizontally acquired microbes . Under tHowever, even under this more limited hologenome concept re-expressing narrow-sense heritability in terms of a hologenome remains problematic. While it may be useful to re-consider the microbiome in terms of functional niches that can be filled by different microbes , collectA separate counterargument is based on evidence that certain microbes are transmitted between close relatives. In particular, mother-offspring microbial transmission is known to occur in humans during childbirth and early life . A recenDespite these observations, such strains are unlikely to make a substantial contribution to the high heritability estimates reported by classical studies. The majority of classical trait heritability estimates are derived from twin studies, which are based on comparing differences in phenotypic similarity between monozygotic twins and dizygotic twins , 54. It Nonetheless, if a sufficient physical mechanism was identified, then vertically transmitted strains could contribute to the issue of missing heritability to some degree. The holobiont model would not be a requirement for this scenario, because the contribution of the microbiome to missing heritability would be a statistical phenomenon, rather than a missing source of human genetic variation. We anticipate three different requirements for this model: 1) emergence of heritable variance in the interactions between microbial and host genes, (2) emergence of interactions between the host and vertically transmitted microbes that increases the chance that children will resemble their parents, and (3) some degree of robustness to disruption by competition with horizontally acquired microbes. These requirements remain to be explored in the future to determine the viability of this model. However, if this model was shown to be reasonable, it would represent a distinct case from the main perspectives we have argued against in this work  as a proxy for how environmental factors differ across individuals in a cohort to better control for GxE interactions. Several “omics” datatypes have previously been suggested as potential means of measuring environmental exposures, including the microbiome , 20. HowBacteroides to Prevotella relative abundances showed particularly pronounced changes and was positively associated with duration of stay. Interestingly, dietary differences could only partially account for these differences, suggesting that other unmeasured environmental factors are being captured by such microbial profiles. Nonetheless, diet is typically the most easily attributable factor affecting microbiome variation. This fact is reflected by the observation that dietary metrics, such as the number of different plants consumed, were identified as major drivers of microbiome variation in the American Gut cohort [Nonetheless, recent studies of the human microbiome highlight that this datatype would be informative for capturing environmental variation across cohorts. For instance, it was recently shown that Thai immigrants to the USA quickly acquire a microbial signature that becomes more pronounced with increased duration of stay . This sit cohort .These recent examples imply that the major axes of variation in the microbiome partially represent cultural variation, which itself partially represents differential exposures to environmental factors. Stratifying a cohort by these axes into discrete groups could be one approach to leverage this information. For instance, if individuals could be clearly clustered into groups based on their microbiome profiles, it might be appropriate to test for significant genetic variants for each group separately. This approach would be analogous to stratifying populations by sex, which has resulted in uncovering genetic variants of interest that were obscured when both sexes were analyzed together . A majorAnother potential approach for integrating microbiome data into GWAS frameworks would be to correct for environmental variation between individuals. This approach would be similar to controlling for variation in shared ancestry across a cohort. It is common to control for population stratification among GWAS participants by separately correcting for the major axes of variation derived from a principal components analysis computed based on the genetic distance between participants . The majFortunately, these are not novel issues for GWASs since there are often clinical phenotypes measured that are related to disease liability or other focal clinical phenotypes. For example, body mass index is an important covariate of waist to hip ratio and waist circumference. GWASs focused on these two waist phenotypes that include body mass index as a covariate can result in biased genetic variant effect estimates . For casThere are multiple fundamental issues with considering the microbiome as an extension of the human genome. Accordingly, non-microbial explanations for missing heritability, such as the presence of many low-effect human genetic variants, are more plausible than that microbial genetic variation is a missing source of human genetic variation. Nonetheless, microbiome sequencing data could still potentially be useful for addressing missing heritability by instead controlling for confounding environmental variation in GWASs. Although several approaches appear promising, the feasibility and benefits of leveraging microbiome data as a proxy for environmental variation in GWASs remain to be determined.


Neotoma magister) is endemic to the eastern United States. Population numbers have decreased rapidly over the last four decades due to habitat fragmentation, disease-related mortality, genetic isolation and inbreeding depression; however, effective management is hampered by limited genetic resources. To begin addressing this need, we sequenced and assembled the entire Allegheny woodrat mitochondrial genome. The genome assembly is 16,310 base pairs in length, with an overall base composition of 34% adenine, 27% thymine, 26% cytosine and 13% guanine. This resource will facilitate our understanding of woodrat population genetics and behavioral ecology.The Allegheny woodrat ( Neotoma magister) is endemic to eastern North America, where population numbers have decreased rapidly since the 1980s  and the Mexican woodrat (N. mexicana), respectively. Assembling the complete N. magister mitogenome will provide important resources for species identification  and contribute to our understanding of population structure across the species range.The Allegheny woodrat ). The sample is stored at −80 °C at Towson University in Baltimore County, Maryland (accession number J5). DNA was extracted with commercially available extraction  and clean-up  kits, according to the instructions from the manufacturers. We conducted one lane of paired-end sequencing using an Illumina HiSeq2000 and used Trimmomatic  . This alignment was used to produce a maximum-likelihood tree using the GTR + G + I model of evolution and 1000 bootstraps .We used a 12S rRNA sequence (accession number DQ179706.1) to seed mitogenome assembly implemented by MITObim and MIRA  is 16,310 bp in length with 22 tRNAs, 2 rRNA genes and 13 protein-coding genes. Overall base composition includes 34% adenine, 27% thymine, 26% cytosine and 13% guanine. Gene order and organization are identical to that of N. fuscipes .The N. magister range , expanding upon our understanding of population structure and allowing us to identify evolutionarily significant units.This new genetic resource has important management implications. For example, our annotation of conserved mitochondrial genes will aid species identification from noninvasively collected samples. In addition, this mitogenome assembly may facilitate haplotyping across the


Hepacivirus infection. We chose a generalist study species, the rodent Proechimys semispinosus, because it is highly abundant in three Panamanian landscapes that differ in their degree of anthropogenic modification. We detected only two TLR7 haplotypes that differed by one synonymous single-nucleotide polymorphism (SNP) position. The TLR4 variability was higher, and we detected four TLR4 haplotypes that differed at one synonymous SNP and at three amino acid positions within the leucine-rich repeat region. Only TLR4 haplotypes had different frequencies in each landscape. Using generalized linear models, we found evidence that nematode loads and virus prevalence were influenced by both specific TLR4 haplotypes and landscape. Here, the variable “landscape” served as a surrogate for the important influential ecological factors distinguishing landscapes in our study, i.e. species diversity and host population density. Individuals carrying the common TLR4_Ht1 haplotype were less intensely infected by the most abundant strongyle nematode. Individuals carrying the rare TLR4_Ht3 haplotype were all Hepacivirus-positive, where those carrying the rare haplotype TLR4_Ht4 were less often infected by Hepacivirus than individuals with other haplotypes. Our study highlights the role of TLR diversity in pathogen resistance and the importance of considering immune genetic as well as ecological factors in order to understand the effects of anthropogenic changes on wildlife health.Toll-like receptors (TLRs) form part of the innate immune system and can recognize structurally conserved pathogen-associated molecular pattern (PAMP) molecules. Their functional importance in the resistance to pathogens has been documented in laboratory experimental settings and in humans. TLR diversity, however, has been rarely investigated in wildlife species. How the genetic diversity of TLRs is associated with various pathogens and how it is shaped by habitat disturbance are understudied. Therefore, we investigated the role of genetic diversity in the functionally important parts of TLR4 and TLR7 genes in resistance towards gastrointestinal nematodes and  Deforestation, agricultural encroachment, and urbanization often result in the alteration of pathogen communities and contact probability between wildlife, livestock, and humans, thereby affecting transmission and host infection patterns in multiple ways that are often intimately intertwined , a multigene family crucial for the adaptive immune response in vertebrates Sommer . Indeed,However, host–pathogen interactions and associated immune responses are complex, and about half of the genetic plasticity towards diseases can be attributed to non-MHC genes such as cytokines and Toll-like receptors (TLRs) , the associated ecological factors of species diversity and host density, and the specific MHC supertypes . Hepaciviruses are directly transmitted from host to host. The genus Hepacivirus (family Flaviviridae) comprises the human hepatitis C virus (HCV), has recently been detected in a wide range of vertebrate hosts, including humans, primates, horses, cattle, bats, and rodents  occur in the direct surroundings, and forest fragments embedded in an agricultural matrix exist about 25 km apart. Thus, a system of three different landscapes has been created with similar forest types but with different degrees of human disturbance. Recent studies have indicated that small mammal species diversity and the population density of the most abundant terrestrial mammal in the study system, namely Tome’s spiny rat Hepacivirus prevalence in the widely distributed and ecologically generalistic P. semispinosus. We chose these two TLRs because of their above-mentioned importance in multicellular and viral pathogen resistance. Our specific aims were (1) to characterize the genetic diversity of TLR4 and TLR7 in P. semispinosus and to infer their phylogenetic relationship with other mammals, since TLR4 and TLR7 sequence data from wildlife species are still rare. We have also studied (2), by using generalized linear models (GLMs) (genetic models), whether TLR diversity explains gastrointestinal nematode infection intensity and Hepacivirus infection. We expected that nucleotide polymorphisms influence the function of TLR4 and TLR7 haplotypes in pathogen resistance. Since high MHC polymorphism is maintained by pathogen-driven selection either because of the effects of specific MHC alleles  are taken into account. Thus, we have investigated (3) the relative importance of TLR constitution, ecological factors, and landscape attributes in gastrointestinal nematode infection intensity and Hepacivirus infection by comparing the best genetic model with the best ecology & landscape model   and two Sherman traps , i.e., 300 traps were set up in total. Traps were opened at dusk and baited with a mixture of peanut butter, banana, bird seeds, dog food, and oat flakes for five nights, and were examined and closed the following morning at dawn. Ecological data and samples for genetic analyses were collected as described previously ; nevertheless, we also present models with “season” included.We further examined the prevalence and infection intensity of gastrointestinal nematodes. Individual fecal egg counts (FEC) were calculated by using a modification of the widely used McMaster technique  in the closely related degu (Octodon degus). A similar location was assumed in the study species. For primer design, TLR4 and TLR7 sequences of several rodent species  domain of the TLR4 and TLR7 genes, identified by using the online tool LRRfinder (TM PCR Master Mix (Thermo ScientificTM), 0.2 µL of each primer (10 µM), 3.6 µL of demineralized water, and 1 µL of template DNA. PCRs for TLR7 were set at a volume of 15 µL, with 7.5 µL of DreamTaqTM PCR Master Mix (Thermo ScientificTM), 0.3 µL of each primer (10 µM), 5.4 µL of demineralized water, and 1.5 µL of template DNA.The designated region of TLR4 was amplified in a volume of 10 µL, consisting of 5 µL of DreamTaqTM) and Sanger-sequenced on an ABI 3130 Genetic Analyzer (Applied BiosystemsTM) with an injection time of 12 s for TLR4 and 30 s for TLR7. For the separation of the genotype into haplotypes, i.e., to allow consideration of the nucleotide sequence of one chromosome only, the PHASE algorithm , we concluded that a sample size close to 100 individuals (N = 95) was sufficient to address our research questions. We thus additionally focused on TLR4 and genotyped N = 158 individuals, aiming at a minimum sample size of 10 individuals per study site  by using Arlequin ver. 3.5.2.2 (Excoffier and Lischer The genetic diversity of TLR4 and TLR7 in Chelonia mydas) were used as the outgroup to root the tree. The phylogenetic tree for TLR4 (597 bp long) was constructed by using maximum likelihood and the general time-reversible model with gamma distribution with invariant sites. The TLR7 tree (700 bp long) was constructed by using the maximum likelihood and the Kimura 2-parameter model with gamma distribution with invariant sites. The phylogenetic trees were built with 10,000 bootstrap replicates that were subsequently condensed by using a 50% cut-off value.The phylogenetic relationships among the available TLR gene sequences of diverse mammalian, monotremata, marsupial, and bird species  as the explanatory factor. Models for TLR4 and TLR7 were constructed separately to minimize the number of confounding factors.The association between the individual TLR haplotypes and nematode infection intensity was tested by using a GLM with a negative binomial error structure constructed with the MASS package  of the genetic models with the AICc of the ecology & landscape model  for the best models were obtained using emmeans ; dN/dS ratio = 3.0).In the highly abundant studied rodent, four TLR4 haplotypes differing in their amino acid sequence , TLR4_Ht3 (5.1%) and TLR_Ht4 (9.6%) were less often encountered. All possible genotype combinations between the four haplotypes were retrieved. The rarest genotype occurred in one individual (TLR4_Ht3.TLR4_Ht4 and TLR4_Ht4.TLR4_Ht4) and the most common genotype in 78 individuals (TLR4_Ht1.TLR4_Ht2). In total, 61 animals (38.6%) were homozygous for one genotype or the other, and 97 individuals (61.4%) were heterozygous.O. degus (XM_004641503.2), dN/dS ratio = 0).Two different TLR7 haplotypes were detected in 95 individuals , homozygous for TLR7_Ht1 with two copies , heterozygous , homozygous for TLR7_Ht2 with one copy , and homozygous with two copies .P. semispinosus and of a further 50 taxa revealed two main branches, one including all the mammal species and one including the birds, with the reptiles as an outgroup  and one for the Aves, with the reptiles as an outgroup. In both phylogenetic trees, the spiny rat was placed next to its closest relative, the O. degus.The results for TLR7  and C and I , but not for A and I . TLR4_Ht1 was less abundant in landscape A than in landscapes C and I , whereas TLR4_Ht2 showed the opposite pattern and was more abundant in A than in the two other landscapes  , but their frequencies differed  Fig. . Differep value = 0.75 + −0.01): TLR7_Ht1 was slightly less frequent in A (0.36) than in C (0.42) and I (0.47), whereas TLR7_Ht2 showed the corresponding opposite pattern . Further, more heterozygote than homozygote animals were observed than expected in landscape I, but this deviation was not significant . Homozygote and heterozygote individuals were equally abundant in landscape A   Fig. .Hobs = 0.320, Hexp = 0.470, p = 0.186; landscape A: Hobs = 0.400, Hexp = 0.533, p = 1.000; landscape I: Hobs = 0.600, Hexp = 0.497, p = 0.414)  Fig. .All genetic models including TLR heterozygosity or one of the four haplotypes explained the variance in nematode infection intensity equally well    than in TLR7 . In both receptor-encoding genes, the encountered polymorphisms were located within the LRR region and, thus, might have direct effects on binding probabilities with pathogens. Although silent mutations are traditionally assumed to have no impact on the binding probabilities, they can still have an impact on the functionality, such as pathogen resistance , 10 different TLR4 haplotypes were reported  for TLR2 (three haplotypes) and TLR4 (one haplotype), together with a very low level of heterozygosity (<5%). Whether the low TLR diversity of P. semispinosus also holds true in other parts of its biogeographic range remains to be investigated, as does whether low levels of diversity are also present in other TLRs that have not been analyzed in our study but that might be of importance, such as TLR8, which is known to recognize ssRNA in humans . Up to seven different nematode morphotypes per species have been identified in a study on five small mammal species in the coastal rainforest Mata Atlântica of Brazil  sampled along a precipitation gradient from the Cape of South Africa to northern Namibia. In the long-tailed giant rat (Leopoldamys sabanus) in Borneo, 11 different helminth morphotypes have been identified , with 0–4 morphotypes per individual  living in temperate forests, eight different gastrointestinal nematode morphotypes, with 1–3 different nematode morphotypes per individual  population from northwest Scotland. The mechanism behind the difference in TLR4 distribution was attributed to parasite-mediated selection, a mechanism also possibly explaining the differences encountered in our study. Further studies have shown an association between TLR2 haplotypes and Borrelia burgdorferi in bank vole (Myodes glareolus) populations from Sweden , which causes acute and chronic liver infections in humans , but without further elaborating on the effect of specific haplotypes on infection status. Loots et al.  less severe impact on the health of the animals and, thus, selection pressure on this TLR. We have also observed no sex-specific differences, even though TLR7 is located on the X-chromosome in mice and, therefore, probably also in the spiny rat.Regarding TLR7, in spiny rats all genetic models had a lower AICc than the ecology & landscape or genetics & landscape model, and the delta AICc of the effects of the two identified haplotypes  and the heterozygous genotype was similar. The best model, however, did not assess a genetic variable as a significant contributor. Thus, contrary to our expectations, we detected no effect of the genetic constitution of TLR7 and the infection status with Hepacivirus prevalence. All individuals carrying TLR4_Ht3 were detected as Hepacivirus-positive. Yet, the AICc of the model including TLR4_Ht3 was only slightly lower than the model including the other rare haplotype TLR4_Ht4 (delta AICc < 0.1). Individuals carrying TLR4_Ht4 were less infected in landscapes C and A than individuals carrying the haplotypes TLR4_Ht1, Ht2, or Ht3. Whereas the models including these specific TLR haplotypes explained differences in the prevalence of Hepacivirus infections, the ecology & landscape model resulted in a much lower AICc that was only topped by a combination of ecological and genetic factors at a landscape level (genetics & landscape model). TLR4 is known to detect a wide range of PAMPs, with the envelope proteins of human endogenous retroviruses activating the innate immune system through CD14/TLR4 (Rolland et al. Hepacivirus infections.On the contray, an individual’s TLR4 constitution had a strong effect on Hepacivirus prevalence was lowest in landscape A, probably because of shifts in species assemblages affecting population density and sex ratio (so-called “dilution effect”, Schmid et al. Hepacivirus infections, beneficial effects of this receptor with respect to other pathogens not investigated in the present study might explain this discrepancy. Possibly, the differences in the haplotype frequencies could be attributable to pathogen-driven selection promoted by pathogens that were only present in landscape A (vicinity to humans, domestic animals, and possible non-native species, e.g., Rattus norvegicus, Flacke et al. Hepacivirus-positive, where those carrying the rare haplotype TLR4_Ht4 were less often infected with Hepacivirus than individuals with other haplotypes. We further detected strong differences in the frequency distribution of haplotypes across human-modified landscapes together with differing pathogen pressures. Further studies using genome-wide SNP markers will help to judge whether differences in TLR haplotype frequencies across landscapes are due to genetic drift or differences in selection acting in different landscapes. Our study highlights the importance of considering immune genetic as well as ecological factors in order to understand the effects of anthropogenic changes on wildlife health. Since changes in pathogen pressures attributable to anthropogenic habitat modifications and a higher contact probability of wildlife, domestic animals, and humans are increasingly being reported, disentanglement of the mechanisms influencing susceptibility/resistance to infections is crucial for wildlife health and the prevention of potential spillovers from pathogen reservoirs causing zoonotic diseases.In conclusion, we detected evidence for the advantage/disadvantage of carrying specific haplotypes in pathogen resistance, which is in agreement with the assumptions of a rare allele advantage/frequency-dependent selection. Individuals carrying the common TLR4_Ht1 haplotype were less intensely infected by the most abundant strongyle nematode. Individuals carrying the rare TLR4_Ht3 haplotype were all TLR sequences are archived with GenBank under accession numbers MT136926–MT136931.Supplementary Tables 1_11Supplementary Files 1_3Supplementary Figures 1_3


Parkinson's disease (PD) is a neurodegenerative disorder caused by loss of dopaminergic neurons in substantia nigra pars compacta (SNc). Although the exact cause of cell death is not clear, the hypothesis that metabolic deficiency is a key factor has been gaining attention in recent years. In the present study, we investigated this hypothesis using a multi-scale computational model of the subsystem of the basal ganglia comprising the subthalamic nucleus (STN), globus pallidus externa (GPe), and SNc. The proposed model is a multiscale model in that interaction among the three nuclei are simulated using more abstract Izhikevich neuron models, while the molecular pathways involved in cell death of SNc neurons are simulated in terms of detailed chemical kinetics. Simulation results obtained from the proposed model showed that energy deficiencies occurring at cellular and network levels could precipitate the excitotoxic loss of SNc neurons in PD. At the subcellular level, the models show how calcium elevation leads to apoptosis of SNc neurons. The therapeutic effects of several neuroprotective interventions are also simulated in the model. From neuroprotective studies, it was clear that glutamate inhibition and apoptotic signal blocker therapies were able to halt the progression of SNc cell loss when compared to other therapeutic interventions, which only slowed down the progression of SNc cell loss. Parkinson's disease (PD) is predominantly considered as a motor disorder, which affects more than 6 million people around the world , which is precipitated by energy deficiency and on understanding the mechanism behind neurodegeneration during excitotoxicity. Moreover, it aims to suggest therapeutic interventions that can reduce the metabolic burden on the SNc neurons, which in turn can delay the progression of SNc cell loss in PD. In Muddapu and Chakravarthy , we propRRID:SCR_001622) platform, where all models were numerically integrated with a time step of 0.1 ms.The proposed hybrid excitotoxicity model (HEM) in PD consisted of three nuclei from basal ganglia, namely STN, SNc, and globus pallidus externa (GPe). The SNc neuron was modeled as a biophysical neuronal model, and STN and GPe neurons  was adjusted to match the firing rate of nuclei with published data , and the membrane recovery variable (ux):The Izhikevich neuron model of GPe and STN consisted of two variables, namely membrane potential , respectively, x at the location , respectively, Cx is the membrane capacitance of a neuronal type x, {a, b, c, d} are Izhikevich neuronal model parameters, x (where x = STN or GPe).where, vSNc) is given as,The biophysical neuron model of SNc in the proposed HEM was adopted from Muddapu and Chakravarthy . The detF is the Faraday's constant, CSNc is the SNc membrane capacitance, vcyt is the cytosolic volume, Jm,Na is the sodium membrane ion flux, Jm,Ca is the calcium membrane ion flux, Jm,K is the potassium membrane ion flux, and Jinp is the overall input current flux. A more detailed description of the SNc neuron model was provided in Muddapu and Chakravarthy  of the SNc neuron , and endoplasmic reticulum (ER) , and Jout,mt is the calcium efflux from MT through sodium-calcium exchangers, mitochondrial permeability transition pores (PTPs), and non-specific leak flux.where, The synaptic connectivity among different neuronal populations was modeled as a standard single exponential model of postsynaptic currents  current was regulated by voltage-dependent magnesium channels which were modeled as,x to neuronal type y , τRecep is the decay constant for the synaptic receptor, x at time t, Wx → y is the synaptic weight from neuron x to y, y at the location , ERecep is the receptor-associated synaptic potential (Recep = GABA/AMPA/NMDA), and [Mg2+] is the magnesium ion concentration. The time constants of Gamma-Amino Butyric Acid (GABA), Alpha-amino-3-hydroxy-5-Methyl-4-isoxazole Propionic Acid (AMPA), and NMDA in STN and GPe were chosen from Götz et al. , dij,pq is the distance of neuron at the location  from the center neuron , Rm is the standard deviation of Gaussian, and Am is the amplitude of lateral synaptic strength (where m = GPe or STN or SNc).where, The lateral connections within SNc and GPe populations were considered as inhibitory and within STN as excitatory , EGABA is the GABAergic receptor reversal potential, and x and y at the location  (where x → y = {SNc → SNc} only). The parametric values of α, β, θg, where, d Terman  and giveDopamine (DA) modulated lateral connection strength in STN, SNc, and GPe populations. As DA levels increased, the lateral connection strength in GPe and SNc increased, whereas, in the case of STN, it decreased. DA-modulation of lateral connection strength is modeled as,STN, GPe, and SNc, respectively, where dopamine effect on the basal spontaneous activity of the neuronal population was minimal, cdstn, cdgpe, and cdsnc are the parameter values which modulated the influence of dopamine on the lateral connections in STN, GPe, and SNc populations, respectively, DAs(t) is the instantaneous DA level. The instantaneous DA was estimated by the spatial average DA concentration of all the terminals at a given instant. All parameter values are given in where, The post-synaptic effects of DA in SNc, STN, and GPe were modeled similar to Muddapu et al. ,wx → y is the synaptic weight , cd2 is the parameter that affects the post-synaptic current, and DAs(t) is the instantaneous dopamine level.where, SNc neuron at the lattice position  is the summation of the glutamatergic input from the STN neurons, considering both NMDA and AMPA receptor activation, and lateral GABAergic current from other SNc neurons.The total synaptic current received by an STN neurons corresponding to NMDA and AMPA receptors activation, respectively; SNc neurons; FSTN → SNc is the scaling factor for the glutamatergic current from STN neuron.where, GPe neuron at the lattice position  is the summation of the glutamatergic input from the STN neurons considering both NMDA and AMPA receptors activation and the lateral GABAergic current from other GPe neurons.The total synaptic current received by a STN neuron considering both NMDA and AMPA receptors activation, respectively; GPe neurons.where, STN neuron at the lattice position  is the summation of the GABAergic input from the GPe neurons and the lateral glutamatergic input from other STN neurons considering both NMDA and AMPA receptors activation.The total synaptic current received by a GPe neuron; STN neurons considering both NMDA and AMPA receptors activation, respectively.where, Calcium plays a dual role in living organisms—as a survival factor or a ruthless killer . Under such conditions, the particular SNc neuron gets eliminated as follows,The SNc neuron undergoes ER-stress-induced apoptosis when calcium levels in ER cross a certain threshold .where, Caer]) dynamics is given by,The ER calcium concentration . Then that particular SNc neuron will be eliminated as follows,The SNc neuron undergoes mitochondria-induced apoptosis when calcium levels in mitochondria cross a certain threshold , and i, j).where, Camt]) dynamics is given by,The MT calcium concentration  , the excitatory drive from STN neurons to SNc neurons is reduced by altering synaptic weight from STN to SNc  is the change in synaptic weight of STN to SNc connection based on the number of SNc cells surviving (Nsc(t)) at a particular time (t), Nsc(t) is the number of SNc neurons surviving at a particular time (t), δGII is the extent of glutamate inhibition, pcl is the percentage of SNc cell loss (25 %) at which intervention of a particular treatment was employed (pcl = 0.25), Psnc is the population size of SNc neurons, and Tl represents the number of SNc cells surviving at which intervention of a particular treatment was employed. In the present study, the therapeutic intervention was given at 25% SNc cell loss.where, DAA) , the dopamine drive to STN neurons is restored by the increased administration of external dopamine  is the change in dopamine level based on the number of SNc cells surviving at a particular time (t) (Nsc(t)), DAs(t) is the instantaneous DA level, Nsc(t) is the number of SNc neurons surviving at a particular time (t), δDAA is the extent of dopamine content restoration, and Tl represents the number of SNc cells surviving at which intervention of a particular treatment was employed.where, les)  is the number of SNc neurons surviving at a particular time (t), and Tl represents the number of SNc cells surviving at which intervention of a particular treatment was employed.where, δADBS), frequency DBS) were adjusted by using clinical settings as a constraint  was generated as the following,In our previous study  considering the lattice position  as the electrode contact point at a time (t), PBW(t) is the biphasic current waveform at time t, and σDBS−STN is used to control the spread of stimulus current in the STN network.where, In the proposed excitotoxicity model, the DBS therapy was implemented by the following criterion,i, j) based on the number of SNc cells surviving at a particular time (t), Nsc(t) is the number of SNc neurons surviving at a particular time (t), and Tl represents the number of SNc cells surviving at which intervention of a particular treatment was employed.where, DBS therapy in STN based on the disruptive hypothesis proposed earlier , WASF is the weight matrix based on the percentage of axonal and synaptic failures . The DBS parameters are given in where, It was reported that calcium channels in the SNc neuron contribute to neurodegeneration in PD  based on the number of SNc cells surviving at a particular time (t), Nsc(t) is the number of SNc neurons surviving at a particular time (t), δCB is the extent of calcium channel blockers, and Tl represents the number of SNc cells surviving at which intervention of a particular treatment was employed.where, ECP). In the proposed excitotoxicity model, the enhancement of CBPs expression therapy was implemented by the following criterion,It was reported that the expression of CBPs, in general, is very low in the case of SNc neurons  (Nsc(t)), x = {Calb, Cam}, δECP is the extent of CBP enhancement, Tl represents the number of SNc cells surviving at which intervention of a particular treatment was employed, and Nsc(t) is the number of SNc neurons surviving at a particular time (t).where, It is known that neurons which are under stress  undergo neurodegeneration through apoptosis  is the apoptotic signal based on the number of SNc cells surviving at a particular time (t) (Nsc(t)), [APOPij](t) is the instantaneous apoptotic signal, δAB is the extent of apoptotic signal blockage, Tl represents the number of SNc cells surviving at which intervention of a particular treatment was employed, and Nsc(t) is the number of SNc neurons surviving at a particular time (t).where, [We have investigated the neuronal model of SNc for their characteristic firing patterns  and studHz) and high-frequency regular phasic or bursting (~ 20 Hz)  × 10−6mM observed experimentally , the SNc neuron exhibited phasic bursting with more than two spikes per burst  compared to normal conditions (~ 14 × 10−6mM)  .To understand the effect of energy deficiency on excitotoxicity in SNc, glucose and oxygen levels were reduced to very low values. As the percentage of SNc cells in energy deficiency increased, the percentage of surviving SNc cells decreased in a sigmoidal manner . When thto 1 with a step size of 0.1). The percentage of surviving SNc cells, at low values of Wstn → snc (0.1 and 0.2) showed a linear decrease with an increase in the percentage of SNc cells under energy deficiency (Wstn → snc (0.3 to 0.6) with an increase in the percentage of SNc cells under energy deficiency . As the extent of glutamate inhibition increased, the percentage of surviving SNc cells increased in a linear fashion . As the extent of dopamine restoration increased, the percentage of surviving SNc cells increased in a linear fashion with noisy response , the percentage of surviving SNc cells was around 60%. However, at intermediate levels of δDAA (0.9 < δDAA ≤ 1.1), the percentage of surviving SNc cells peaked close to 75%.According to the protocol mentioned in the methods section, the neuroprotective effect of dopamine agonists on the percentage loss of SNc cells was implemented. The dopamine restoration therapy was initiated at 25% of SNc cell loss (indicated by red trace in response . At loweLES). As the extent of subthalamotomy increased, the percentage of surviving SNc cells increased in a hyperbolic fashion . As the extent of axonal and synaptic failures increased, the percentage of surviving SNc cells increased in a linear fashion . As the extent of calcium channel blockers increased, the percentage of surviving SNc cells increased in a sigmoidal fashion . As the extent of enhancement of CBP increased, the percentage of surviving SNc cells increased in a sigmoidal fashion . As the extent of apoptotic signal blockers increased, the percentage of surviving SNc cells increased in a sigmoidal fashion , the percentage of surviving SNc cells was maximum as a result of the total restoration of the dopaminergic drive to the STN neuronal population. However, beyond 1.1, δDAA results were in the lower percentage of surviving SNc cells when compared to intermediate levels. The reason behind the lower percentage of surviving SNc cells at higher values of δDAA might be due to the excessive activation of autoreceptors on the SNc neurons. There are four types of D2 autoreceptors on the SNc neurons, where they regulate neuronal activity and control dopamine synthesis, release, and uptake  in the SNc neuron which was activated due to excess calcium accumulation through ER-stress and mitochondrial-induced apoptotic mechanisms. Thus, calcium channel blocker, enhancement of CBP, and apoptotic signal blocker therapies have been proven to be neuroprotective to SNc neurons under energy deficiency. Moreover, apoptotic signal blocker therapy seems to halt the SNc cell loss.The relationship between the percentage of surviving SNc cells and δr values . The reayre, CCB . SimilarCCB , and antioxidants which did not show neuroprotective effects in the present model. This could be due to the fact that direct ATP supplements and antioxidants might not be enough to show the neuroprotective effect in the present model.Though the proposed model captured the exciting results of excitotoxicity, it is not without limitations. In the proposed model, the ischemic condition was implemented by modulating glucose and oxygen levels, which can be extended by adding a blood vessel module  is available on the ModelDB server (McDougal et al., The code of the proposed multiscale excitotoxicity model (The datasets generated for this study are available on request to the corresponding author.VM and VC conceived, developed the model, and prepared the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mirabilis himalaica (Edgew.) Heimerl in the HHM. We amplified four cpDNA regions , and a low copy nuclear gene (G3pdh) from 241 individuals of 29 populations. SAMOVA, genealogical relationships, and phylogenetic analysis revealed four spatially structured phylogroups for M. himalaica with the onset of diversification in late Pliocene (c. 3.64 Ma). No recent demographic growth was supported by results of neutrality tests, mismatch distribution analysis and Bayesian skyline plot. Paleo-distribution modelling revealed the range dynamics of M. himalaica to be highly sensitive to geo-climatic change with limited long-distance dispersal ability and potential evolutionary adaptation. Furthermore, river drainage systems, valleys and mountain gorges were identified as the corridors for population genetic connectivity among the populations. It is concluded that recent intense mountain uplift and subsequent climatic alterations including monsoonal changes since Pliocene or early Pleistocene formulated fragmented habitats and diverse ecology that governed the habitat connectivity, evolutionary and demographic history of M. himalaica. The integrative genetic and geospatial method would bring new implications for the evolutionary process and conservation priority of HHM endemic species.The genetic architecture within a species in the Himalaya-Hengduan Mountains (HHM) region was considered as the consolidated consequence of historical orogenesis and climatic oscillations. The visualization of dispersal corridors as the function of population genetic connectivity became crucial to elucidate the spatiotemporal dynamics of organisms. However, geodiversity and physical barriers created by paleo geo-climatic events acted vigorously to impact notable alterations in the phylogeographic pattern and dispersal corridors. Therefore, to achieve detailed phylogeography, locate dispersal corridors and estimate genetic connectivity, we integrated phylogeography with species distribution modelling and least cost path of  The Himalaya and the Hengduan Mountains (HHM) are one of the main biodiversity hotspots of the Northern Hemisphere formed as a result of the collision between the Indian and Eurasian plate, started c. 55–50 million years ago (Ma) . Mulch aThe incredible biodiversity and genetic architecture in the HHM region were considered as the aftereffect of historical orogenesis independently or in combination with climatic oscillations . MoreovePhylogeographic approach integrated with ensemble species distribution modelling (SDM) analysis provides comprehension of how paleo-environmental alterations in landscape and climate have governed species distributions and demographical history . In addiPrimula tibetica related to the effects of Quaternary climatic oscillations in QTP  that distributes from the Western Himalaya to the Hengduan Mountains that stretch from N India, WC Nepal, Bhutan, and S Xizang, SW Gansu, N Sichuan, NW Yunnan in the HHM region  to identify genetic diversity and phylogeographic structure; 2) to date the divergence time between lineages and locate population genetic connectivity during the late Quaternary in the HHM region; 3) and to elucidate how paleo geological change and climatic oscillations have affected comprehensive evolutionary and demographic history of M. himalaica. This study represents the integrative approach of maternally inherited (cpDNA) and biparentally inherited (G3pdh) population genetic data in combination with the SDM and LCP approach throughout the distribution range of species to outreach the role played by the historical processes on the present-day spatial population structure of organisms in the HHM.The present study focuses on the HHM endemic M region . The gen–3,800 m . For theMirabilis himalaica , 15 µl 2x Taq Plus Master Mix with dye (Tiangen Biotech.), 1 µl 10 µM of each primer .The sequences of four cpDNA regions were concatenated, revised manually and aligned in Gus 7.0.2 . Chromatus 7.0.2  using PHnaSP 5.0 . The uniG3pdh were determined in DnaSP 5.0 (HT) and average within-population diversity (HS)   were calulations . NST watructure . We comp. We also performed an analysis of molecular variance (AMOVA) to examine the genetic variation using Arlequin v3.5 (F-statistic (FST/FSC/FCT) was calculated, and significance was tested for overall as well as regional populations. “Isolation by distance” (IBD) was evaluated by regressing the net nucleotide divergence between populations (DA) in contrary to their geographical distance, applying GenAlEx 6.5 (http://www.fluxus-engineering.com) was employed to construct median-joining networks of the cpDNA and G3pdh sequences to assess their genealogical relationships. All variable sites were included and weighted equally.Spatial analysis of molecular variance  as outgroups were constructed by Bayesian inference (BI) in MrBayes v3.2  of chlorotypes/haplotypes. We assumed a strict clock , based on a likelihood-ratio test  and RagH) ‘goodness of fit’ was tested, using 1,000 parametric bootstrap replicates.uin v3.5 , with 1,uin v3.5  was perfeast v1.8.4 was used to reconstruct the effective population size fluctuations since the time of the most recent common ancestor for each marker and the combined markers (cpDNA and G3pdh). The MCMC chains (nchains = 4) were run for 20 million generations sampling every 100 generations, with effective sample size (ESS) greater than 200. We used the same settings for three independent runs to ensure the consistency of the results. The demographic history through time was reconstructed using TRACER v1.7.1 /absence (0) applying thresholds that allow a maximum of 50% probability of the suitable habitat   methods using SDMtoolbox v2.0  under thG3pdh) D. FirstpsbE/838 bp, rps16-trnK/602 bp, rps16 intron/883 bp, trnS-trnG/967 bp) with 28 polymorphic sites and 71 indels (amova) harboured two chlorotypes each (6.9%), WHN group contained four chlorotypes (13.8%), and the remaining 21 were occupied by HM group (72.4%); indicating a very high level of molecular diversity in HM region. Nineteen chlorotypes  were relatively rare, each of them restricted to a single population, whereas 15 of them were distributed in the HM region.The total aligned sequences of the four combined cpDNA regions are 3290 bp  are heterozygous for partial Hd)/nucleotide diversity (π) for the cpDNA and G3pdh sequences were 0.901/0.0013 and 0.777/0.00173, respectively. For cpDNA, the highest value of Hd and π falls in population HS ; whereas for G3pdh, population JS exhibited particularly a high value  (HT) in the overall population of M. himalaica were 0.917/0.803 (cpDNA/G3pdh) (G3pdh (HS = 0.303/0.340) was also relatively high. Both, HT and HS are highest in the HM group . The inter-population differentiation (GST) of the two datasets i.e. cpDNA and G3pdh was 0.669 and 0.577, respectively. For both datasets, NST was significantly greater than GST , indicating the existing phylogeographical structure in M. himalaica  and revealed a substantial spatial population genetic structure with five groups, i.e. HM (Hengduan Mountain) group with 18 populations, QTP (Qinghai-Tibetan Plateau) group with three populations, WHN  I group with three populations , WHN II group with four populations  and WHI  group with one population  of cpDNA. Thus, based on four phylogroups, there is greater genetic partition among groups (78.58%/52.92%), than among populations within groups (17.30%/25.05%) or within populations (4.11%/22.03%)   showed that the population of M. himalaica was quite stable after an ever-increasing phase during 0.6–0.075 Ma  varies in some parts of the Western Himalaya and the HM . The result varies from the CCSM4 model, which was showing relatively more expansion towards the west in all regions  were visualized using cpDNA and  markers Figure 5Mirabilis himalaica might have migrated from North America to the Himalayas either by Beringia or through long-distance dispersal, evolving allopatrically into the extant species , and the separation of the lineages spanning late Pliocene to early Pleistocene between 3.64–2.33 Ma  to shape the current phylogeographical pattern and the effective population size of all lineages remained relatively constant throughout the evolutionary period. From SDM analysis, during LGM the species was progressively confined to southwards in the HM, and traces of habitat expansion occurs in WHI, southeastern QTP region .The haplotype sequence data have been deposited in GenBank with accessions: petL-HR and HS planned and designed the research. HR and SR carried out sampling and performed experiments. HR and DL analyzed data. HR and DL conceived the manuscript with the support of SR and HS.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genetic diversity and population structure studies of local olive germplasm are important to safeguard biodiversity, for genetic resources management and to improve the knowledge on the distribution and evolution patterns of this species. In the present study Algerian olive germplasm was characterized using 16 nuclear (nuSSR) and six chloroplast (cpSSR) microsatellites. Algerian varieties, collected from the National Olive Germplasm Repository (ITAFV), 10 of which had never been genotyped before, were analyzed. Our results highlighted the presence of an exclusive genetic core represented by 13 cultivars located in a mountainous area in the North-East of Algeria, named Little Kabylie. Comparison with published datasets, representative of the Mediterranean genetic background, revealed that the most Algerian varieties showed affinity with Central and Eastern Mediterranean cultivars. Interestingly, cpSSR phylogenetic analysis supported results from nuSSRs, highlighting similarities between Algerian germplasm and wild olives from Greece, Italy, Spain and Morocco. This study sheds light on the genetic relationship of Algerian and Mediterranean olive germplasm suggesting possible events of secondary domestication and/or crossing and hybridization across the Mediterranean area. Our findings revealed a distinctive genetic background for cultivars from Little Kabylie and support the increasing awareness that North Africa represents a hotspot of diversity for crop varieties and crop wild relative species. Olea europaea L.) is one of the most important fruit species of the Mediterranean region [Olive  to evaluate the genetic diversity of the main Algerian olive varieties; (ii) to assess, for the first time, the genetic relationships between this germplasm and olive accessions from public datasets; (iii) to provide useful knowledge for future cultivation expansion and breeding programs.A total of 34 Algerian varieties from the ITAFV national olive germplasm collection  were samTotal genomic DNA was extracted from 0.1 g of dry leaves following the Doyle and Doyle  CTAB , expected (He) and observed (Ho) heterozygosity and Polymorphic Information Content (PIC) by using PowerMarker , HaplotyK) in STRUCTURE was calculated following Evanno et al. [K were carried out using the admixture model with correlated marker frequency and default parameters. The runs were averaged using CLUMPP (CLUster Matching and Permutation Program) [To identify the number of genetic groups in the Algerian germplasm, cluster analysis was carried out for nuSSR and cpSSR separately according to the UPGMA  algorithm and two phylogenetic trees were generated using the R package Adegenet . The levo et al. . Twenty Program)  and the Program) . IndividProgram) , among MProgram) . For GstFor the comparison with the WOGB dataset , three SThe six cpSSR markers showed a total of 12 alleles, with an average of two alleles per locus and major allele frequency values ranging from 0.647 to 1.000 . Five ouNuSSRs markers amplified a total of 140 alleles, ranging from five to 11 for EMO90 and DCA07, respectively, with an average of 7.2 alleles per marker , which iThe UPGMA phylogenetic tree based on nuSSRs underlined a main genetic group (A) of 21 varieties, 13 of which belonged to Little Kabylie (LK), split in four subclusters. A second group (B) of 13 cultivars separated in two subclusters a. WithinSTRUCTURE analysis clearly assigned 30 varieties  to one of the six identified genetic groups , while tWe further analyzed the nuclear genetic profiles by PCoA . The resIn order to frame the genetic relationships of the Algerian cultivars within the three main Mediterranean lineages, we compared our profiles with a large dataset of cultivated accessions across the Mediterranean Basin and Near East  using hip < 0.01) the most unrelated, followed by Algeria vs. France and Algeria vs. Turkey. On the contrary, the most related cultivars were those from Turkey vs. East-Mediterranean, Italy vs. Balkan, Balkan vs. Turkey and Iberian vs. Maghreb. The PCoA analysis was able to separate a wide number of the Western varieties from the Central-Eastern group. The Algerian samples showed a bimodal distribution with a group of varieties located in the center of the graph with the Turkish and Near Eastern cultivars, and a second group clustering mainly with the central Mediterranean varieties from Italy and Balkan Peninsula  was in agreement with previous results [STRUCTURE analysis of the whole dataset revealed that the most likely number of clusters of Mediterranean varieties . In total, 18 Algerian cultivars and 126 accessions from the entire Mediterranean region were investigated. The analysis allowed to identify four subclusters, including 84 varieties with strong association , while the remainders 60 cultivars were mosaics . SubclusFor thousands of years, olive cultivation has been central in the culture and economy of many Mediterranean and Middle Eastern regions. The ancient civilizations that thrived in this wide geographical area selected and diffused countless varieties across the different countries facing the Mediterranean Sea. Due to these complex historical events, an endless debate arose among scholars about olive domestication, and in particular whether there has been a single or multiple independent domestication events ,55. n = 13) from different central Mediterranean regions  [n = 18), but highlighted some divergence (i) possible mislabeling errors in the WOGB collection of Marrakech; ii) errors in the published datasets, at least for the same six varieties coming from the above mentioned collection that showed different chlorotypes between the dataset of Besnard et al. [iii) different clones of the same varieties. The different clustering methods adopted in our study highlighted a clear genetic group mainly consisting of 13 LK cultivars, except for a few varieties (four) from other regions (emigrants). Conversely, a few cultivars (four) from LK clustered in other genetic groups (in-migrants). We can formulate some speculative hypotheses to explain these few exceptions to the general geographic and genetic division between LK region and other parts of Algeria. Emigrant varieties might share a wide genetic background with the LK group because they were selected in this region, but later, for some ecological/historical/agronomic reasons, their cultivation disappeared from the LK area and the knowledge of the original native region was lost. This could be the case of Mekki, Neb Djemel and Hamra, today cultivated only in the driest mountain area of the Aurès or Kenchela or in the coastal area, respectively, or in contrast diffuse throughout Algeria (Sigoise). It has been documented that the historical distribution of Mekki was connected to Phoenician, Greek and Roman dominations [in-migrant cultivars had different haplotypes as compared to other LK accessions. In a wider perspective, the combined use of nuSSR and cpSSR, that are differently affected by evolutionary processes , allowed us to investigate the genetic relationship of Algerian varieties with cultivars from other Mediterranean countries and shed light on the geographic origin of Algerian germplasm and relative patterns of crossing/migration across the Mediterranean Region. Our results are compatible with two different hypotheses: (i) local domestication from oleaster and Laperrine’s olive; (ii) local selection by crossing of cultivars imported from other Mediterranean region with local germplasm. The gene flow between Algeria and the rest of the Mediterranean area has been probably limited, suggesting that development of new cultivars possibly proceeded through crossing of few imported varieties with local germplasm, namely oleaster and Laperrine’s olive, as testified by cpSSRs pattern of genetic diversity. We found that the highest proportion of Algerian varieties shared the same haplotype with the majority of Mediterranean cultivars (E1) and the Laperrine’s olive. Interestingly, 30% of Algerian varieties belonged to the other two lineages E2 and E3, unravelling the contribution of the Western Mediterranean olive lineage to the origin of North African cultivars. This result provides new evidence on the role of Algeria as possible and important secondary domestication/selection center, considering that only 4.9% and 4.4% of the Mediterranean cultivars belong to E2 and E3 lineages, respectively [n = 10), France (n = 2) and in one variety from Italy and Spain each. In the last decades, the development of molecular markers such as nuclear and chloroplast SSRs have made it possible to investigate the genetic fingerprint of cultivated and wild olives and disentangle the clues left by migrations and crossing among varieties across the entire olive distribution area. Despite many recent studies provided nuclear and chloroplast SSRs genetic profiles for hundreds of wild and cultivated olive accessions, the germplasm from Central and Southern Mediterranean regions, especially from the Maghreb area, is highly underrepresented. Here, for the first time, we characterized the official Algerian collection of olive varieties from ITAFV by mean of both nuSSRs and cpSSRs. Our results filled the gaps left by previous studies ,16,17 asTunisia) . ChemlalTunisia) . Our resvergence ; in partd et al.  and Haouinations . This vaFinally, regardless of the true origin of the Algerian germplasm, LK varieties can be considered an exclusive genetic core, selected and developed during the different historical periods by the civilizations that thrived in this area from Phoenicians to Arabs until the present. Genetic differentiation parameters and results of the hierarchical STRUCTURE provided evidence that Algerian varieties are more genetically related to Central-Eastern Mediterranean cultivars than to the West. In particular, the second round of STRUCTURE highlighted four main subclusters among the group of Eastern varieties. The Algerian germplasm grouped mostly in two subclusters, both with Central and Eastern affinity. Subcluster B1 was particularly interesting because it was dominant in Algeria and Maghreb, suggesting gene flow between Near East and North Africa. We speculate that the two STRUCTURE clusters could correspond to bottleneck events due to the arrival of different civilization, i.e., Phoenician and Romans. According to the chlorotype lineages identified, 67% of Algerian varieties showed affinity to the Eastern Mediterranean germplasm and 33% to the West. Nuclear DNA confirmed this pattern, with 80% of varieties showing affinity to the Eastern cluster and 20% to the Western cluster. These results might depend on recurrent reticulation events during the diffusion of olive culture  and reflOlea europaea subsp. sylvestris) and Laperrine’s olive (O. europaea subsp. laperrinei), that were already exploited by local human population, laying the foundations for the development of olive cultivation. Subsequently, phoenicians introduced Eastern Mediterranean olive trees to North Africa, triggering gene flow with the local germplasm (oleaster and Laperrine’s olive) and with cultivated olive coming from other Mediterranean areas. Local people and settlers from abroad  eventually selected the cultivars better suited for the cultivation in the different environmental conditions of each Algerian region, thus originating the distribution pattern that we observe today. Our present data do not allow discriminating between the two different hypotheses, namely the occurrence of a secondary domestication event or introgression of imported cultivars with local germplasm, including natural populations (oleaster and Laperrine’s olive). We can hypothesize that before foreign civilizations arrived in Algeria, wild olive tree populations consisted of two taxa, oleaster , there is no international database of olive varieties to use as a reference, and the genetic profiles are not always available. The creation of a public database for olive germplasm would greatly foster the elucidation of the history of domestication for this important crop species.