Daphnia galeata is a common and dominant species in warmer waters, and has a strong top-down effect on both phytoplankton and bacteria. The knowledge of its temporal and spatial patterns of genetic diversity is fundamental in understanding its population dynamics and potential ecological function in ecosystems. Its population genetics have been investigated at regional scales but few within regions or at smaller spatial scales. Here, we examined the fine-scale spatial genetic variation of D. galeata within four large, deep reservoirs in wet and dry seasons and the six-year variation of genetic diversity in one of the reservoirs by using cytochrome c oxidase subunit I and microsatellites (simple sequence repeat). Our study shows that fine-scale spatial genetic variation commonly occurred within the reservoirs, indicating strong environmental selection at least in the two of reservoirs with strong longitudinal gradients. Since the environmental gradients established in the dry season was largely reduced in the wet season, the fine-scale spatial genetic variation was much higher in the dry season. The dynamics of local genetic diversity did not follow the theoretical pattern of rapid erosion but peaked in mid or mid-late growth season. The local genetic diversity of D. galeata appears to be shaped and maintained not only by recruitment from resting egg banks but also by gene flow within reservoirs. The temporal and fine-scale genetic variation within a water body suggests that it is necessary to pay attention to sampling periods and locations of a given water body in regional studies. Spatial patterns of biological composition and diversity are a major topic in modern ecology, providing core knowledge for conserving biodiversity and ecosystem management . SpeciesSpatial patterns of genetic variation or diversity are structured in ways similar to those for patterns of species diversity at the community level . IntraspDaphnia is one of the largest genera in Cladocera with more than 100 species found around the world (Daphnia reproduce by cyclic parthenogenesis and produce resting eggs that accumulate in the sediments as a seed bank (Daphnia species at the population level when high-resolution markers  are used  has a clear spatial pattern of genetic diversity within a heterogeneous water body, and 2) has a genetic diversity that peaks in early spring when the species hatch from the sediments and then is quickly eroded. To test the assumptions, we collected D. galeata along a longitudinal gradient from four large reservoirs in tropical and subtropical China for two seasons, and the populations in their growing season in a tropical reservoir for six successive years. Daphnia galeata individuals were sequenced for COI and SSR to measure the genetic diversity of populations.er water . It shower water . D. geleal China , where iDaphnia galeata were collected from four large and deep reservoirs in southern China  and identified under an optical microscope (Olympus U-LH100-3) according to criteria given by rn China : Qiandaorn China , which cD. galeata were picked out from 95% ethanol, rinsed repeatedly with double-distilled water, and transferred individually to a 500 μl tube. We added 200 μl Lysis buffer and 4 μl Protease K (20 mg·ml−1). The mixture was vortexed and subsequently incubated for 2 h at 55°C. Next, 100 μl of chloroform-isoamyl alcohol (24:1) was added and the mixture was centrifuged vigorously at 10,000 rpm at 10°C for 10 min. The supernatant was moved to a new 500 μl tube and added 3 μl beads. After adsorption for 2 min, we added 300 μl Binding buffer and centrifuged at 10,000 rpm for 4 min at 10°C. The supernatant was discarded and the precipitate was washed with 300 μl washing buffer and centrifuged at 10,000 rpm and 10°C for 4 min. Then the supernatant was removed and the precipitate was left to dry at room temperature for 2–3 h. After that, 25 μl Elution buffer was added to the supernatant, which was placed 4°C for 12 h, and centrifuged at 10,000 rpm and 10°C for 4 min. Finally, the supernatant was obtained and stored at −20°C.The genomic DNA was extracted using an Ultra-Sep Gel Extraction Kit . Individuals of Daphnia in Europe (2+plus), 0.25 mM dNTP Mixture 2.5 mmol/l each, 20 μM each primer, 0.5 U Taq (TaKaRa), and 3 μl template DNA. The PCR reaction procedure is used as follows: pre-denaturation at 95°C for 5 min, 35 cycles of 95°C for 30 s, annealing at 55°C for 45 s, extension at 72°C for 30 s, the final extension at 72°C for 3 min, and the reaction ended at 4°C. Polymorphism was assessed on an ABI PRISM 3730 capillary DNA sequencer, using an internal Liz Gene-scan size standard (Applied Biosystems). Amplified fragments for all primers contained between 99 and 234 nucleotides. Genotyping was checked by GeneMarker v2.2. Before merging data, the same criteria were used to check the consistency of alleles, especially the alleles with small differences in fragment lengths.The individual DNA materials were first used for the microsatellite data and the remaining materials were used for sequencing COI. For each population, 32 individuals were randomly picked. DNA amplification was performed by polymerase chain reaction using nine microsatellite markers developed for species of n Europe . The nin2+ plus), dNTP Mixture 2.5 mM each, 0.5 μM of each primer, 0.5 U Taq, and 3 μl DNA template. The PCR conditions consisted of a 1 min initial cycle at 94 ⁰C, followed by 35 cycles of 40 s at 94ºC, 40 s at 51⁰C, 60 s at 72⁰C, then a final extension of 3 min at 72⁰C, end of reaction at 4⁰C. The amplifications were verified and chosen for sequencing using a 1% agarose gel for electrophoresis. The PCR products were then sent to Huayu gene  for sequencing on ABI3730 sequencer. All obtained sequences were checked for the absence of stop codons and ambiguous positions, and the validity of obtained sequences was verified by BLAST comparison in NCBI. The homologous alignment of sequences was performed using Aliview  and HCO2198 . Fis ranges from −1 to 1, where negative values significantly different from zero indicate an excess of heterozygotes and positive values indicate a deficiency of heterozygotes. The deviation from Hardy–Weinberg equilibrium was examined in Arlequin v3.5  within reservoirs was detected by a linear regression model with package ggplot2 (The genetic differentiation (genetic distance) for COI was estimated in MEGA v6  with theuin v3.5  was used ggplot2  in R v3. ggplot2 .Recent immigration rate over the last 3–5 generations and directional gene flow was estimated based on the principle of linkage disequilibrium with BayesAss v3.0 , which eOne hundred and six (106) alleles were detected from 1,018 individuals of 32 populations. The average number of alleles (Na) of all populations was 3.96 (Variance = 0.79). All populations had low genetic diversity . There was no significant seasonal variation in genetic diversity within individual reservoirs. AMOVA showed that genetic variation was very low (< 7%) among populations within seasons and among seasons for each reservoir, and most of the genetic variance for the four reservoirs was contributed by within-population variation  occurred in the dry season  and bay (Bay)), between midstream (Mid) and downstream (Down), and low genetic differentiation (Fst < 0.05) did in the wet season (Fst < 0.15) among sampling sites occurred in the dry season  and haplotype diversity  confirmed this. For Chaishitan, Liuxihe, and Xujiahe reservoirs, their genetic diversities all showed temporal and spatial differences .The haplotype networks revealed the temporal variation of clone composition . In Qianp < 0.05) between seasons occurred in Chaishitan reservoirs. The genetic variation between seasons was 10.64% (p > 0.05) and 16.36% (p < 0.05) in Qiandaohu and Xujiahe reservoirs, respectively. In the two reservoirs, the genetic distance showed weak population genetic differentiation (sequence differences >0.01) between the two sampling seasons.AMOVA analysis showed that total population genetic variation in each reservoir was mainly from within populations  alleles were detected from 959 individuals of 32 temporal populations, and the average number of alleles (Na) for all populations was 3.52 (Variance = 0.10). The genetic diversity varied, and with a random seasonal pattern . In 2014Fst between 6 years also showed low genetic difference (Fst < 0.05). AMOVA indicated that genetic variation was mainly from within populations , and less (3.9%) but significantly from interannual variation  between temporal populations had a mean of 0.088 with a variance of 0.031 in 2013. High genetic differentiation occurred between July and other months in 2017 (Fst < 0.05) occurred in 2014 and also in 2015 except for two pairs of temporal populations in the year . And there was symmetrical and high gene flow between the transitional zone to the riverine zone (Mid1 and Up). In the dry season, gene flow was from the riverine zone to the lacustrine zone (from Up to Down), as well as from the transitional zone or the lacustrine zone to riverine zone . In the wet season in Qiandaohu reservoir, high gene flow mainly occurred from the lacustrine zone or transitional zone to the riverine zone . CompareFor annual populations across 6 years in the Liuxihe reservoir, both forward (from 2013 to 2014) and backward gene flows  were detected . For temD. galeata populations. Spatial fine-scale variation occurred and changed between two sampling seasons, especially in the two reservoirs (Chaishitan and Qiandaohu reservoirs) with longitudinal gradients. Genetic differentiation increased with spatial distance in the dry season, indicating increased environmental selection. Seasonal variation of genetic diversity at a pelagic site of Liuxihe reservoir appears to peak in the mid or mid-late growing season and did not follow an erosion pattern.The present study investigated temporal and spatial fine-scale variation in genetic diversity and structure of the Fst > 0.1, Fst and geographic distance  occurred in the two sampling seasons, their relative frequencies often differed  is suitable for the hatching of dormant eggs, and individuals quickly re-established from the egg bank  between most temporal populations. This seasonal differentiation may suggest recruitment and selection acting together, and which usually occurred in early or end growing season. The contribution of resting eggs to the population may profoundly altered the genetic composition of the population compared to the previous season  can be found at: LeX and B-PH conceived and designed the experiments. QH, LeX, and LiX performed the experiments and analyzed and discussed the results. QH and B-PH wrote the manuscript. PL, ER, and B-PH revised the manuscript. All authors contributed to the article and approved the submitted version.The study was supported by a grant from National Natural Science Foundation of China (32171538 and 31400344) and the open grant (2022-2) from Engineering Research Center of Tropical and Subtropical Aquatic Ecological Engineering, Chinese Ministry of Education.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Landraces are considered a valuable source of potential genetic diversity that could be used in the selection process in any plant breeding program. Here, we assembled a population of 600 bread wheat landraces collected from eight different countries, conserved at the ICARDA's genebank, and evaluated the genetic diversity and the population structure of the landraces using single nucleotide polymorphism (SNP) markers. A total of 11,830 high-quality SNPs distributed across the genomes A (40.5%), B (45.9%), and D (13.6%) were used for the final analysis. The population structure analysis was evaluated using the model-based method (STRUCTURE) and distance-based methods [discriminant analysis of principal components (DAPC) and principal component analysis (PCA)]. The STRUCTURE method grouped the landraces into two major clusters, with the landraces from Syria and Turkey forming two clusters with high proportions of admixture, whereas the DAPC and PCA analysis grouped the population into three subpopulations mostly according to the geographical information of the landraces, i.e., Syria, Iran, and Turkey with admixture. The analysis of molecular variance revealed that the majority of the variation was due to genetic differences within the populations as compared with between subpopulations, and it was the same for both the cluster-based and distance-based methods. Genetic distance analysis was also studied to estimate the differences between the landraces from different countries, and it was observed that the maximum genetic distance (0.389) was between the landraces from Spain and Palestine, whereas the minimum genetic distance (0.013) was observed between the landraces from Syria and Turkey. It was concluded from the study that the model-based methods (DAPC and PCA) could dissect the population structure more precisely when compared with the STRUCTURE method. The population structure and genetic diversity analysis of the bread wheat landraces presented here highlight the complex genetic architecture of the landraces native to the Fertile Crescent region. The results of this study provide useful information for the genetic improvement of hexaploid wheat and facilitate the use of landraces in wheat breeding programs. Wheat crop is grown and cultivated on more land area than other commercial crops and provides basic human nutrition. Wheat as food is a source of energy and protein for about two billion people and plays an indispensable role in food security as it provides 20% of world caloric consumption . With thTriticum urartu, Aegilops speltoides, and Aegilops tauschii are the parental sources of genomes A, B, and D, respectively  as a result of natural hybridization and thus does not share any direct wild ancestor with the same genomic constitution . Triticuectively . Hence, The success of breeding programs relies on the presence of significant genetic variability in a source population. High genetic variability boosts the chances to select superior genotypes from a population . On the Morphological traits are not the best indicators to evaluate genetic diversity as largely influenced by environmental conditions . Hence, A wheat diversity panel containing 600 landraces from the International Centre for Agricultural Research in the Dry Areas (ICARDA) was used in this study. The landraces in the panel were obtained from 8 countries, which were Syria (376), Turkey (157), Iran (47), Greece (7), Iraq (7), Spain (3), Jordan (2), and Palestine (1).Fresh leaves were collected from 10-day-old seedlings in labeled Eppendorf Tubes and sunk immediately into liquid nitrogen; then, they were transferred to the lab and stored at −80°C. Leaf samples were grounded using a tissue lyser (TissueLyser II from QIAGEN). Genomic DNA was extracted from 0.1 g powdered leaf samples by using the cetyltrimethylammonium bromide (CTAB) method . ExtractA high-throughput genotyping by sequencing (GBS) method using Diversity Arrays Technology (DArT)  was usedTo reveal the population structure of the wheat diversity panel, a model-based Bayesian cluster analysis was performed with STRUCTURE software (v. 2.3.4) . The proThe discriminant analysis of the principal components (DAPC) was used as a second approach to analyze the population structure. DAPC uses the K-means clustering of principal components to identify the group of each individual. The numbers and nature of the clusters are assessed using the Bayesian Information Criterion (BIC). The DAPC analysis was conducted by using the R package “adegenet”  in R stuNa), number of effective alleles (Ne), Shannon’s index (I), diversity index (He), unbiased diversity index (uHe), and percentage of polymorphic loci (PPL) were measured using GenAIEx v. 6.503  based on >20% missing data and minor allele frequency (MAF) <5%. After QC and SNP filtering, the set of the 11,830 SNPs on the 21 chromosomes was selected for analysis. The highest number of markers was mapped on the B genome  followed by the A genome  and D genome  . The higTwo different approaches, STRUCTURE and DAPC were used to identify the underlying stratification in the whole population panel. For the STRUCTURE program as the first approach, the optimum number of subpopulations was determined by the change of likelihood (ΔK). The results suggested that the optimum population structure was at K = 2. To find the optimal subpopulation number, the plot of K against ΔK  was usedThe results of the PCA revealed that the landraces were grouped into three groups. The first, second, and third PCs explained 15, 18, and 22% of the total variation, respectively.The DAPC was used as the second approach, and the scree plot of ΔK against the proportion of explained variance showed that the landraces were divided into at least three subpopulations . The thrThe results from both STRUCTURE and DAPC analyses showed that there was an admixture between the different geographic regions which can be seen from the results of the minimum spanning network (MSN) and neighbor-joining based clustering analyses , 5.Three AMOVAs were generated based on the results of STRUCTURE and DAPC, as well as on the geographic origin of the landraces .. The STThe low genetic variability among the subpopulations implies a high amount of gene flow between the landraces evaluated in this study. Besides, the higher values of genetic variability within the populations for STRUCTURE (97.5%), DAPC (95.8%), and origin-based AMOVA (95.5%) suggest that the landraces from eight countries shared common ancestries and were highly admixed .The genetic distances were calculated to decipher the levels of diversity between the subpopulations . The genNa) and number of effective alleles (Ne) of two subpopulations determined by STRUCTURE were 1.993 and 1.476, respectively (I), diversity index (He), and unbiased diversity index (uHe) of the two subpopulations were 0.443, 0.288, and 0.288, respectively. STRUCTURE-based analysis showed that subpopulation 2  had a slightly higher genetic diversity than subpopulation 1 .The mean values for the number of different alleles  than subpopulations 1 and 3.For the DAPC approach, the mean ectively . AccordiNa = 1.887, Ne = 1.447, I = 0.416, He = 0.270, and uHe = 0.273. The landraces originating from Syria showed the highest diversity with the diversity parameters of I = 0.442, He = 0.287, and uHe = 0.287, and the lowest genetic diversity was observed within Jordan landraces .The mean values of the genetic indices obtained by geographic origin-based grouping for seven countries  were The membership coefficients of 600 landraces were presented as bar plots in Global wheat production is facing new challenges in terms of climate change and biotic stress resistance; thus, the studies of genetic diversity could prove to be helpful for the effective conservation and improvement of the existing germplasm . In breeAegilops tauschii , followed by genome A , and D , and these findings are in agreement with previous studies . The restauschii . The numtauschii . Similartauschii . After Qtauschii .The understanding of the population structure is crucial for further downstream analysis, for example, genome-wide association studies (GWAS). The evaluation of genetic diversity also provides vital information which can help in the preservation strategies and broadening of the genetic base of crops . The preThe fixation index (Fst) is used to measure the genetic differentiation among the populations . An Fst Based on the genetic diversity indices when the population stratification was estimated by the STRUCTURE program, subpopulation 2 showed higher genetic diversity than subpopulation 1. Subpopulation 2 consisted of 238 landraces and was mainly composed of landraces from Syria and Turkey, in addition to some landraces from Iran, Greece, and Iraq, whereas subpopulation 1 contained 362 landraces. The presence of higher genetic diversity in subpopulation 2 indicated the potential of this group to be used in breeding programs. In the case of three subpopulations, as estimated by DAPC and PCA, subpopulation 2 was the most diverse as it showed the highest values for genetic diversity indices and was composed of landraces from Syria and Iran. The higher genetic diversity in the Syrian and Iranian landraces has also been previously reported . It is tvia STRUCTURE and DAPC methods, potentially provide broad and important genetic diversity. This diversity could be used in current and future wheat genetic enhancement and breeding research programs around the world. High genetic diversity is an important factor in conducting association mapping studies (GWAS) and marker-assisted selection for the mapping and identification of economically important traits in wheat. In addition, these landraces were collected from eight different countries with diverse agroclimatic conditions, therefore these landraces should also be a useful source of genes to be used in breeding programs addressing the challenges of changing global climate.From these results, we can report that the 600 bread wheat landraces used in the current study, in particular, subpopulation 2, estimated The study provided a detailed population structure and genetic diversity analysis of 600 bread wheat landraces collected from eight countries preserved at the ICARDA genebank. Clustering analysis showed distinct population structures in the landraces. The landraces were mainly divided into Turkish, Syrian, and Iranian groups with significant overlapping. This admixture is a result of historical seed exchange between these countries through farmers and traders due to their close geographical proximity to each other. The genetic diversity indices represented high genetic diversity in these wheat landraces. These landraces were collected from a wide range of agroclimatic zones, as a result possess high diversity and capacity to tolerate and resist various abiotic and biotic constraints, and could hence be used as a potential source of new genes/alleles for the genetic enhancement of hexaploid wheat. Therefore, sustainable conservation and use of these landraces preserved in the genebank is important for future breeding strategies of wheat breeding programs worldwide.


Genetic improvement is one of the major means to enhance the productivity of livestock, and well-designed animal genetic improvement schemes are necessary to achieve genetic gains. The objective of the current study was to design an alternative breeding program to improve the productivity of common Tigray highland sheep. Two village- and two central nucleus-based breeding schemes were simulated and evaluated in terms of genetic gain, bio-economic efficiency and operational feasibility.Four breeding schemes were simulated: scheme 1, a one-tier cooperative village-based breeding scheme, scheme 2, a two-tier cooperative village breeding scheme (dispersed village-based nuclei scheme), scheme 3, a central open nucleus-based scheme with 5% nucleus size; and scheme 4, a three-tier breeding schemes . Simulation analyses were performed using the ZPLAN computer program, which is based on a deterministic approach to estimate genetic and economic gains in the breeding programs.Between the two village-based breeding schemes, scheme 2 showed higher genetic gain and economic efficiency in the breeding traits analysed than scheme 1. The central nuclei schemes were more efficient than the village breeding schemes. Scheme 3 was the most efficient with a genetic gain in the breeding objective of US $ 1.03 and a profit of US $ 2.73/ewe/year, but operationally it is also the most difficult to implement as it requires a big central nucleus. A central nucleus linked with the village-based nuclei would be a feasible option to overcome the operational difficulties of the conventional central nucleus schemes. If a village-based breeding program is considered it should be the first step in most low-input systems. In this case, scheme 2 is the most efficient.With some support from the public sector at the outset and a strong collaboration among the stakeholders including smallholders, scheme 4 could lead to substantial genetic gains in the common Tigray highland sheep breed within its habitat that covers large areas of the Tigray region. Therefore, we recommend a long-term breeding program which should include cross-breeding, genomic selection, updated estimates of genetic and economic values for the common Tigray highland sheep breed. Although common Tigray highland sheep play multiple important roles in the smallholders’ sheep farming systems in the Tigray region of Ethiopia, there have been very few successful and sustained genetic improvement programs. Genetic improvement of livestock is a particularly powerful tool to enhance their productivity. It involves the identification of the breeding objectives, the derivation of economic values for the traits, the design of appropriate schemes, which define the population structure, gene flow, selection strategies, and the development and implementation of the breeding plan . LivestoThe current designs of breeding strategies that are suitable for smallholder farming systems can be categorized into village-based and central nucleus-based breeding schemes. The absence of farmers or their minimal participation in the design and implementation of the breeding schemes and inadequate infrastructure have been found to render breeding programs unsuccessful in a low-input production system . To overVillage-based breeding programs are intended to overcome the problems of genotype–environment interactions by avoiding the genetic lag between the animals in the nucleus and village populations and to be suitable for the in-situ conservation of indigenous animal genetic resources. Village-based programs also help to bridge the gaps between the skills of breeders (nucleus centers or breeding companies) and farmers to ensure property rights of farmers for improved genetic material. However, the effectiveness of such a design depends on the commitment of the villagers to participate in the program. Failure or lack of compliance by some villagers will affect the level of controlled breeding and the genetic gain that could be achieved . The effModelling of the breeding program targeted seven districts of the Tigray Regional State, in northern Ethiopia, which represent different agro-ecological conditions in which the common Tigray highland sheep are bred. The study areas lie within 12° 20′ and 14° 16′ N latitude and 39° 15′ and 39° 45′ longitude. The mean annual temperature ranges from 14 to 22 °C, while the mean annual rainfall ranges from about 400 to 970 mm . Most ofThe dominant farming system across all study areas is a mixed crop-livestock production system. Like other parts of Ethiopia, the livestock production system in the study areas is subsistence-oriented, which is characterized by minimal inputs. A low-input production system is found in all livestock production systems prevailing in the country except in the peri-urban and urban systems . TraditiIn the current simulation study, we considered the common Tigray highland sheep farmers’ breeding traits as defined previously in : 6-monthTwo alternative breeding designs that are increasingly advocated for traditional production systems  were conkebeles (four from Atsbie-Wemberta and three from Mekelle area) representing smallholder sheep production systems were selected based on the distribution of this sheep breed, agro-ecological condition, sheep population, accessibility and willingness of the community to participate in the village-based breeding program. Households with at least five breeding ewes were included as members of the breeding cooperative program. Villages are defined as smallholder farmers’ cooperative groups with approximately 2100 breeding ewes (300 ewes per village/kebele) as one breeding unit in the breeding program.Each of these two designs consist of two scenarios (schemes): schemes 1 and 2 as village-based breeding schemes and schemes 3 and 4 as conventional (nucleus) breeding programs. These schemes address genetic improvement activities either at the village level (schemes 1 and 2) or at the regional level that covers all of the common Tigray highland sheep breeding tracts (schemes 3 and 4). Seven villages/The population of common Tigray highland sheep in the seven districts is estimated to be 419,074 individuals. Based on the previous studies , the estkebeles were considered as one breeding unit in the breeding program. All the male lambs in the cooperation flocks are recorded and all 6-month old ram lambs in the villages are evaluated as candidates. Selection of the breeding stock in the village flocks is based on the selected candidates’ own 6-month weight and their dams’ lamb rearing record.Scheme 1 is designed to address genetic improvement within a village , and is a one-tier cooperative village-based breeding scheme that involves cooperation among smallholder farmers. Selection is carried out in the whole village sheep population. Breeding rams are selected by the farmers from all the flocks in the village that are grouped as one breeding flock and use the selected rams communally. Three hundred ewes per village from smallholder farmers’ cooperative groups and seven kebeles nuclei to the whole common Tigray highland sheep population in the region. It is a two-tier cooperative village breeding scheme (dispersed village-based nuclei scheme) that involves more nuclei breeding villages/kebeles where genetic improvement is generated. These serve as sources of improved rams to the whole population of common Tigray highland sheep. The village nuclei are located strategically across the common Tigray highland sheep breeding tract.Since scheme 1 will not show how the genetic improvement within one village can be extended to the different areas where common Tigray highland sheep are raised, scheme 2 is designed to scale up the genetic improvement from two villages/Scheme 3 is a conventional central open nucleus-based breeding scheme (5%) which is developed by the Regional Agricultural Research Institute  and in which genetic improvement is generated in a central nucleus flock that supplies improved rams to the villages. The nucleus size is a major factor for determining the level of genetic progress and profitability of a scheme. Open nucleus breeding systems require that farmers of the base population do some selection on their female progeny. A very important feature of open nucleus breeding systems is that favorable adaptation genes and other breeding preferences of ‘base’ farmers are secured in the males that are obtained from the nucleus since they are born from what the ‘base’ farmers considered as the ‘best’ females. This feature is a tangible participation of the ‘base’ farmer in the definition of the breeding objectives. The population of nucleus breeding ewes comes from 5% of the total population of ewes. Rams that are used as sires in the base population are selected from a subset of rams that did not qualify for inclusion in the nucleus flock based on best linear unbiased prediction (BLUP) estimated breeding values. Selection in the nucleus is based on selection criteria that are applied in the central nucleus with the involvement of data recorders and animal breeding and genetics experts.Scheme 4 is a three-tier breeding schemes (open breeding nucleus linked with multiplier units). Multi-tiered breeding structures are characterized by the presence of multiple livestock population, which generate, transfer and receive continuous and cumulative genetic improvement . A threeMultiplier units serve as a bridge to transfer and multiply the superior rams to the population that is located near the village cooperatives, and this requires finding a compromise for both genetic superiority and overall management practice at the farm level. After their establishment, the multiplier units supply superior rams from the central nucleus and from the base population. The elite rams required for the nucleus and multiplier tiers are sourced from the nucleus. Ram lambs born and selected in the multiplier tier are used as sires of lambs in the smallholder farmers. Young female candidates are selected based on their phenotypic performance from the recorded multiplier tiers which produced their own replacement ewes. Replacement nucleus ewes are sourced from within the nucleus and multiplier tiers. The smallholder farmers produced their own ewe replacements that are based on traditional non-recording methods. The gene flow and its expressions from the multiplier rams to the base population are easily adapted to the smallholders’ production system. . SelectiInput parameters for the simulation studies are in Table The fixed costs include the salaries of the animal breeding experts for genetic evaluation, technical field assistants, and village coordinators, as well as the costs for maintaining nucleus flocks, data processing facilities, supplies and communications. Costs for animal identification, stationery, weighing scale and recording traits are included as variable costs. These costs vary between schemes depending on the nucleus sizes and selection activities across the different tiers of the schemes. The breeding program was planned for 10 years. The genetic parameters and economic values of the traits used to design the breeding schemes are in Table The phenotypic standard deviations, economic weight, estimates of the heritability of traits, genetic and phenotypic correlations used in the simulated selection program for common Tigray highland sheep are in Table idelines . From thidelines . Under fThe marginal economic value for each trait was estimated as a change in profit resulting from an increase by one additive genetic standard deviation in the trait value due to selection by keeping the changes in other traits constant. Positive economic values indicated that genetic improvement in the traits would result in a positive effect on profitability. Additive genetic standard deviations The predicted annual genetic gain for the breeding objectives was calculated by summing the genetic gains in the component traits  weighted by their respective economic weights under four alternative breeding schemes Table . The resThe highest annual genetic gain was obtained for the 6-month weight trait, which improved by 0.821 kg and 0.785 kg annually under schemes 3 and 4, respectively. However, under the village-based breeding schemes 1 and 2, it improved only by 0.288 kg and 0.371 kg per year in the one-tier cooperative village-based breeding scheme and the two-tier cooperative village-based breeding scheme, respectively. The predicted higher annual genetic gain of 6-month weight trait in all breeding schemes is due to its higher phenotypic variation When ewes produce multiple lambs with better growth performance and with short lambing intervals, they are preferred by smallholder farmers and they achieve a faster genetic gain in the flock while their chances of survival decrease due to the competition between twins for consuming colostrum and milk from their dams. Higher pre-weaning lamb survival rate (the percentage of lambs born surviving up to weaning) is scored under the central open nucleus-based breeding scheme compared to the village-based breeding scheme Table . Pre-weaTable All breeding objective scenarios simulated as alternatives for small holder sheep producers had advantages in terms of genetic gain and profit. The results show that the genetic progress obtained in a village-based scheme with the existing lambing distribution over 1 year (scheme 1) was slow compared to the village-based scheme with an improved lambing distribution (scheme 2). The reason is that few selected candidates were available in each round of selection because of the dispersed lambing and of selection errors. Gizaw et al.  have alrThe three-tier breeding scheme (open breeding nucleus linked with multiplier units) produced a greater annual genetic gain and profit compared to schemes 1 and 2, but a slightly lower gain than the central open nucleus breeding scheme (scheme 3). The three-tier breeding scheme has an advantage over the central nucleus breeding which is due to a reduction in the time necessary for dissemination and adaptation to a particular production system and environment, which have considerable effects on the genetic improvement of the whole common Tigray highland sheep population in the region. Furthermore, linkage of a central nucleus breeding with smallholder farmers through multiplier units increases the accuracy of prediction of breeding values in the elite flock. According to Horton et al. , a benefThe choice of a breeding scheme should not be restricted to central nucleus- versus village-based schemes; a combination of both schemes needs to be considered, as well, to maximize genetic progress/profitability and facilitate the operation of breeding programs. Linkage of central nucleus breeding scheme with a smallholder farmer system through multiplier units  serves as both multiplier and generators of genetic improvement for smallholder sheep producers. This scheme avoids the need to establish a large nucleus, which is costly and requires to limit geographically the central breeding nucleus. Introduction of biotechnology tools, particularly artificial insemination in the breeding program could facilitate the implementation of such breeding programs by overcoming the need for large nucleus flocks and the logistical limitations to transfer genetic gains across the breeding structure. However, this means solving the technical and institutional difficulties for developing efficient artificial insemination services, which have so far been a challenge in most developing countries .The predicted genetic gains for 6-month weight, average daily body weight gain, litter size and pre-weaning lamb survival obtained in the current study are comparable with the results of Yohannes  and HagoRunning central nucleus breeding schemes needs good infrastructures and technologies that have high establishment and operational costs, but in the case of whole common Tigray highland sheep populations in the Tigray region of northern, Ethiopia, it could result in increased genetic gain. The dissemination of the improved rams to smallholder farmers is difficult and less profitable because the overall production activities differ between the center and village levels. However, if smallholder farmers are strongly committed and supported by technologies such as artificial insemination and estrus synchronization, an improved genetic gain could be obtained within a short period of time. It is also recommended to distribute improved males to village cooperatives in a communal grazing system for communal uses because it will increase the mating ratio and the genetic and economic gains. Indeed, Gizaw et al.  noted thBoth central nucleus-based and village-based breeding schemes were designed to improve the productivity of common Tigray highland sheep, and thus to increase its contribution to the livelihood of producers and the national economy. The two alternative breeding schemes evaluated in the current study were modeled on the basis of biological, economic and operational feasibilities. Our study revealed that open central nucleus-based breeding and linkage of a central nucleus with smallholder farmers through multiplier units resulted in higher genetic gain and economic efficiencies for all the breeding traits analysed compared to village-based breeding schemes. Based on the predicted genetic gain, we suggest that scheme 3 and scheme 4 are the most efficient schemes and that they need to be optimized for the genetic improvement and conservation of common Tigray highland sheep. Improved breeding management and technological interventions in village-based breeding schemes could result in more concentrated lambing periods and thus increase the selection intensity and genetic progress. Inter-linking of a central open nucleus breeding system with smallholder farmers through multiplier units in each district could rapidly address the flow of genetically superior animals to the whole common Tigray highland sheep population in their home tract. Further work on the application of a long-term breeding program such as cross breeding, genomic selection and updated estimates of genetic and economic values for the common Tigray highland sheep breed are also needed.


Taro is a valuable staple food crop among resource-poor rural people in countries such as Nigeria and Ghana, among others. Characterization of genetic diversity is a prerequisite for proper management of breeding programs and conservation of genetic resources. Two hundred seventy one taro accessions obtained from Nigeria and Vanuatu were genotyped using DArTseq-based SNP markers with the objectives of investigating the genetic diversity and population structure. In the analysis, 10,391 SNP markers were filtered from the sequence and used. The analysis revealed higher transition than transversion types of SNPs in the ratio of 1.43:1. The polymorphism ranged from 0.26 to 0.29 for the markers, indicating moderate genetic diversity. A model-based Bayesian clustering analysis of taro accessions yielded five subgroups and revealed the admixture situation in 19.19% of all accessions in the study. Vanuatu taro accessions exhibited more genetic diversity than Nigerian taro accessions. The population diversity estimate (PhiPt) was relatively higher (0.52) for accessions originating from Vanuatu than for Nigerian accessions. Analysis of molecular variance (AMOVA) revealed that most variation existed among individuals within a population at 52%. Nei’s genetic distance showed that relatedness is based on geographical proximity. Collection of taro genetic resources should give more emphasis to within regions to utilize diversity in taro breeding program. This study also demonstrated the efficiency of DArTseq-based SNP genotyping for large-scale genome analysis in taro. The genotypic markers provided in this study are useful for association mapping studies. High di-1 . The samTaro is staple food  and reguth among the world’s top 20 edible food crops [The biosphere has more than 30,000 plant species that are thought to be edible . Taro isod crops . Betweenod crops . Despiteod crops . Even thod crops , there iod crops . For manod crops  and its Many researchers had reported on genetic variation among taro accessions. High genetic diversity were reported in Asian taro accessions using AFLP markers , simple In terms of recent advances in molecular markers such as SSR and single nucleotide polymorphism (SNP), taro is an orphan crop. SSR and SNP are the two most reliable markers for assessing genetic diversity and population structure in any organism. SNP markers have a higher population resolution than SSR markers . In thisex-situ collection in Vanuatu contains 125 taro varieties from most of the islands [Taro is an important crop in the Asia-Pacific region’s agriculture . It is e islands . The NigTwo hundred eighty two taro accessions were used in this study, of which 94 accessions were collected from different regions of Nigeria including Enugu, Ebonyi, Imo, Anambara and Abia. The 188 taro accessions were kindly provided by National Root Crop Research Institute of Nigeria (NRCRI) of which 94 accessions were imported from Vanuatu and the remaining 94 taro accessions were obtained from NRCRI in situ conservation. All materials obtained from Vanuatu were hybrids whereas materials from Nigeria were landraces. The th May 2019. Taro leaves were sent to Integrated Genotyping Service and Support (IGSS) platform, currently SEQART AFRICA located at Biosciences Eastern and Central Africa (BecA-ILRI) Hub in Nairobi for Genotyping. DNA extraction was done using Nucleomag Plant DNA extraction kit. The genomic DNA extracted was in the range of 50-100ng/ul. DNA quality and quantity were checked on 0.8% agarose. Libraries were constructed according to Kilian et al. [PstI and MseI) and ligation of barcoded adapters followed by PCR amplification of adapter-ligated fragments. Libraries were sequenced using single read sequencing runs for 77 bases by Hiseq2500. DArTseq markers scoring was achieved using DArTsoft14 which is an in-house marker scoring pipeline based on algorithms. Two types of DArTseq markers were scored, SilicoDArT markers and SNP markers which were both scored as binary for presence /absence  of the restriction fragment with the marker sequence in genomic representation of the sample. Both SilicoDArT markers and SNP markers were aligned to the reference genomes of Taro (Taro_V1), to identify chromosome positions.Two hundred eighty two taro accessions were grown at the University of Ebony state (Nigeria) teaching and experimental nursery on 28n et al. . DArTSeqo), expected heterozygozity (He), and polymorphic information (PIC) content, the R package "adegenet" [ST). 999 permutations were used to determine the statistical significance of the AMOVA and Mantel tests for all populations and loci [For quality control, DArTseq SNP markers were filtered to remove unwanted SNP markers using the software PLINK 1.9 and VCFtools . Markersdegenet"  was useddegenet"  base wasdegenet"  was useddegenet"  and geogand loci .The binary file generated from the VCF file was subjected to admixture analysis with the “adegenet” R package . Using kAfter preprocessing and filtering, 271 taro accessions and 10,391 SNP markers were retained. These 10,391 SNP markers were mapped onto 14 taro chromosomes, with an average of 742 SNP markers per chromosome . In totao, He, MAF, and PIC of the 10,391 SNP markers from 271 taro accessions are presented in  showed relatively higher genetic diversity than landraces (Nigerian accessions)  .In this study , we founPrincipal component and cluster analysis were performed on seven geographical origins, and clustering analysis grouped all geographical origins into two . ClusterAll taro accessions were divided into two groups based on their population type (landrace and hybrid) using a 0.05 cut-off Euclidean distance . ClusterWith a set of 10,391 SNP markers, population structure analysis among 271 taro accessions revealed an optimal K value of five , dividinThe analysis revealed five distinct clusters, three of which are all Nigerian landraces  . ClusterThe best way to achieve efficient management of crop genetic resources to improve breeding programs and understand the ancestry relationships of accessions is to reveal the population structure and diversity of a collection . The curIn the current study, a total of 32,227 SNPs were initially called from the accessions using a variant calling pipeline. The observed number of SNPs in the current study was high when compared to the report by Soulard et al.  but low In the current study, the SNP with the most allelic sites and proportion was GA , while the nucleotide CG  had the smallest read and proportion . Mace anThe observed and expected heterozygozity in the current study ranged from 0.40 to 0.53 and 0.26 to 0.39, respectively, indicating moderate genetic variability among the taro accessions studied. Similarly, Hu et al.  found hiIn this study, we found a high percentage of genetic variation across geographical regions 47%), indicating that regional isolation can be a source of genetic diversity. In marginal populations, genetic differentiation is significantly higher than in the center of the range due to spatial segregation and restricted gene flow . In this%, indicaThis study demonstrated the utility of SNPs in characterizing the genetic diversity and population structure of taro collections. Based on the gene diversity values calculated from the 10,931 SNPs, the Nigerian and Vanuatu accessions appeared to be genetically diverse. Since within-population variation was significant than between populations, we suggest that, during collecting missions, germplasm collectors should increase sampling of more accessions within a location than increasing the number of locations. The degree of genetic relationship and differentiation among genetic resources can be used to increase genetic diversity and combine alleles for valuable agricultural traits. Thus, the collections contain valuable genetic information for future conservation and breeding studies.S1 Table(CSV)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(CSV)Click here for additional data file.S4 Table(CSV)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S1 Data(CSV)Click here for additional data file.


Pigeons played a major role in communication before the invention of the telephone and the telegraph, as well as in wars, where they were used to carry information and orders over long distances. Currently, numerous sports competitions and races are held with their participation, and their breeding is demanding not only for breeders, but also for the birds themselves. Therefore, an analysis of the genetic structure of racing pigeons kept in Poland was undertaken on the basis of 16 microsatellite markers, as well as the evaluation of the microsatellite panel recommended by ISAG. For this purpose, Bayesian clustering, a dendrogram, and Principal Coordinate Analysis were conducted. In addition, statistical analysis was performed. Based on this research, it was observed that racing pigeons are genetically mixed, regardless of their place of origin. Moreover, genetic diversity was estimated at a relatively satisfactory level , and no alarmingly high inbreeding coefficient was observed (F = 0.088). Moreover, it was found that the panel recommended by ISAG can be successfully used in Poland for individual identification and parentage testing . Columba livia) are popular all over the world [Pigeons  for the individual identification and parentage testing of these birds.Biological material from feathers was collected from 519 racing pigeons. Some pigeons hatched in Poland, while some were purchased at various European auctions. It is also common for pigeon breeders from different countries to exchange birds with each other. This usually applies to the offspring, i.e., young pigeons whose parents were good players. For these reasons, the population of pigeons kept in Poland is variable and diverse. In this way, the samples were divided according to the identification data on the birds’ rings: Poland , Belgium , Germany , Slovakia , and the Netherlands .DNA was extracted with the Sherlock AX Kit  following the manufacturer’s suggested protocol. DNA concentration and quality were assessed using a NanoDrop .Genomic DNA was amplified using the twelve microsatellite markers recommended by the ISAG as a core panel, four microsatellite markers as additional markers, and one marker, CHD, to determine the sex of each pigeon. The sex marker is not required by the ISAG .The reaction mixture contained 11.2 μL of Type-it Microsatellite PCR Kit , 1.2 μL of primer mix, and 1 μL of DNA (10 ng/μL). The PCR conditions for all reactions consisted of an initial denaturation of 95 °C for 5 min, followed by 35 cycles of 95 °C for 1 min, 55 °C for 1 min, and 72 °C for 1 min, with a final extension step of 72 °C for 30 min. Capillary electrophoresis was performed using a 3130xl Genetic Analyser  and contained 11 μL of formamide, 0.4 μL of GeneScan™ 500 LIZ™ dye Size Standard (Applied Biosystems), and 1 μL of PCR product. Samples were denatured for 5 min at 95 °C. The electrophoresis results were analysed using GeneMapper v. 4.0 (Applied Biosystems).ST (parameter of population differentiation) and gene flow (Nm) values, number of different alleles (Na), number of effective alleles (Ne), Shannon’s Information Index (I), a deficit of heterozygotes (P), the Hardy–Weinberg equilibrium using the chi-square test (HWE), observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding coefficient (F), as well as F-statistics parameters: the inbreeding coefficient within individuals relative to the subpopulation (Fis), the inbreeding coefficient within individuals relative to the total (Fit), and the inbreeding coefficient within subpopulations relative to the total (Fst), were calculated using GenAlEx 6.5 [Pairwise FAlEx 6.5 ,21. The AlEx 6.5 .K ranged from 1 to 10, with 10 iterations for each K. The analysis was performed in two ways. In the first one, all pigeons were used as representatives of one specific breed (racing pigeons). In contrast, in the second, pigeons were divided into five groups according to the country of origin . A STRUCTURE HARVESTER [K using Evanno’s model [Principal coordinate analysis (PCoA) was performed using GenAlEx ,21. A NeARVESTER  was used’s model , and CLU’s model .The pigeons kept in Poland showed a relatively high level of genetic diversity; a total of 146 different alleles were observed. The average number of alleles per locus was 9.125, ranging from 4 alleles in CliμD19 to 19 alleles in PIGN12 . Higher Analogous results were obtained for the parameters strictly related to the analysis of parentage  . In addiF-statistics indicators  for the PCoA was performed on the complete dataset of 519 homing pigeon genotypes  in orderA high level of overlapping was shown in the PCoA scatter plot of the analysis of the entire dataset. The first principal coordinate accounted for 3.34% of the total variation, while the second coordinate accounted for 2.89% of the total variation. There was no clear separation of individuals according to their origin.The obtained dendrogram did not reveal any separate genetic clusters related to the origin of the pigeons , confirmK = 4)  . In thisK = 4) . The samST values  and, therefore, comparable internationally with the results from other laboratories that also have standardization. The first proficiency PCT took place in 2013/2014 and to date, four such PCTs have already been carried out. The last one was organized in collaboration with ISAG and the National Research Institute of Animal Production .The CHD marker given in this study was not included in the later analyses due to the fact that in pigeons it determines only two variants, the W allele and the Z allele, and is used to determine sex. This marker is also not recommended in the basic and additional ISAG panel. The analysis with its participation is important since in young pigeons it is often impossible to determine the sex from the external appearance. In pigeon breeding, at its early stages, it is important to know whether the bird is a female or a male; therefore, this analysis is very popular.The population of racing pigeons kept in Poland is diverse, as breeders, not only from Poland but also from Europe, buy and sell birds at various auctions, and, sometimes, they even exchange them. The owners are still looking for the perfect birds with which they could achieve the highest results in sports competitions. However, this sport, as all others, is very demanding. The breeding of racing pigeons requires intensive selection and breeding work, training, feeding, and keeping the birds in proper condition. In addition, various sophisticated methods are often used to increase the incentives for the pigeons to return to the nest .In our population, the Bayesian approach revealed four major genetic clusters unrelated to the pigeon’s country of origin. This can be explained by the fact that Belgium is the creator of the modern racing pigeon, and it was in this country that the breeding of these birds began; then, the knowledge and passion spread to other European countries and beyond. The four genetic clusters likely form the descendants of the first genetic lines of pigeons that were selected and then crossed with relatives, as a result of which a new breed of pigeons was created. However, this hypothesis should be confirmed by examining more samples with racing pigeons from other countries. Nevertheless, the topic is worth further research. Interestingly, there were also four main genetic clusters obtained in the wild pigeon group tested in Italy . Not onlThe PCoA of all individuals, revealed the genetically mixed nature of pigeons tested by us. The analysis did not confirm that the pigeons were grouped according to their country of origin of the birds. There was a slight variation in genotypes, indicating a high level of admixture between individuals. Most likely, this can be explained by the fact that all racing pigeons were created in one country, and were bred for one purpose—to quickly find a way home over often very long distances, while the next step was their distribution spread worldwide. Genetic relatedness between racing pigeons was also demonstrated through genome sequencing , where pUPGMA analysis indicated no obvious segregation of genetic profiles among all pigeons, which confirms the genetically mixed nature of the population, as also demonstrated by the PCoA analysis. Inconsistencies between the phylogenetic clades of different species of pigeons were also noticed in the analysis of mitochondrial DNA, analysed to determine the genetic structure of these birds and to study the universality of genetic primers . The autThis paper provided a detailed analysis of the population structure and genetic diversity of racing pigeons kept in Poland. Research revealed that their genetic structure is mixed; the pigeons did not differentiate into groups depending on their original place of origin. The genetic diversity of pigeons kept in Poland was found to be at a relatively satisfactory level. Despite the specific breeding procedures used, no alarming values were recorded for the inbred index. In addition, it was also found that the panel recommended by ISAG was useful for individual identification and parentage testing; however, in the future, ISAG could be used to replace low polymorphic markers and those with a tendency to null alleles with others. It also seems justified to monitor the genetic diversity of Polish pigeon populations in the future, due to the specific breeding procedures used for this group of animals.


Thunnus albacares, is an important global fishery and of particular importance in the Eastern Pacific Ocean (EPO). According to the 2019 Inter-American Tropical Tuna Commission (IATTC) assessment, yellowfin tuna within the EPO is a single stock, and is being managed as one stock. However, previous studies indicate site fidelity, or limited home ranges, of yellowfin tuna which suggests the potential for multiple yellowfin tuna stocks within the EPO, which was supported by a population genetic study using microsatellites. If numerous stocks are present, management at the wrong spatial scales could cause the loss of minor yellowfin tuna populations in the EPO. In this study we used double digestion RADseq to assess the genetic structure of yellowfin tuna in the EPO. A total of 164 yellowfin tuna from Cabo San Lucas, México, and the Galápagos Islands and Santa Elena, Ecuador, were analysed using 18,011 single nucleotide polymorphisms. Limited genetic differentiation (FST = 0.00058–0.00328) observed among the sampling locations  is consistent with presence of a single yellowfin tuna population within the EPO. Our findings are consistent with the IATTC assessment and provide further evidence of the need for transboundary cooperation for the successful management of this important fishery throughout the EPO.Yellowfin tuna,  Thunnus albacares [Overfishing is one of the greatest threats to the yellowfin tuna, lbacares . The Intlbacares . Five inlbacares . Some oflbacares . Four tRlbacares . Effortslbacares , which hlbacares .Various studies suggest that, at a global level, yellowfin tuna is split into several populations. Genetic assessments by tRFMOs have reported the presence of four different stocks, which are located in the Atlantic Ocean, Indian Ocean, Eastern Pacific Ocean (EPO), and West Central Pacific Ocean , 7–9. HoIn addition to genomic analyses, a number of other studies suggest that yellowfin tuna have a home range of 1,800 km, and exhibit high regional fidelity –21. StudHauser and Carvalho  emphasizCSL-MEX: Cabo San Lucas , La Paz, México, covering the northern tropical EPO; [GAL-ECU: Santa Cruz , Galápagos Islands, Ecuador, covering the central tropical EPO; and [STR-ECU: Santa Rosa fishing port , Santa Elena, Ecuador, covering the continental coast of Ecuador and northern Perú  for transportation and further processing. Sexing individuals requires the extraction of their gonads, which in turn reduces the commercial value of the fish. Consequently, we could not determine our samples’ sexes as fish were sampled on the proviso that they retained their market value.Fork length (FL) of each individual fish was measured to the nearest centimetre (cm) using a fish board; fish ranged in size from 34–200 cm FL. A sample of red muscle, approximately 1cmPstI and SphI methylation-sensitive restriction enzymes. These enzymes were chosen as they avoid the repetitive fractions of the genome, which is uninformative for SNP discovery [PstI and SphI compatible adapters, which included an Illumina flow cell attachment sequence and a PCR primer sequence, were ligated to the restriction fragments. Additionally, the PstI adapter incorporated unique barcode sequences to identify samples within pooled libraries. Only fragments capped with both PstI and SphI adaptors were amplified. PCR conditions consisted of an initial denaturation step at 94°C for 1 min, followed by 30 cycles at 94°C for 20 sec, 58°C for 30 sec, and 72°C for 45 sec, with a final extension step at 72°C for 7 min. Bridge PCR was then applied to further amplify and sequence the libraries on the Illumina HiSeq 2500  using MJ Tetrad PCR thermocyclers  [DNA extraction, double digestion RAD library preparation and sequencing, and SNP genotyping were carried out by Diversity Arrays Technology . The patented protocol DArTseq™ was used as it is a cost-effective methodology, and has been optimized for the analysis of yellowfin tuna high-throughput genotyping data , 37, 38.iscovery , 38, andThe resulting sequences were processed using DarTseq analytical pipelines DarT-Soft14 version . The priThe SNP dataset was further filtered using the package radiator  in R v3.ST values were calculated using DartR [st), the absolute allele frequency difference (AFD) [a priori grouping of samples through alpha optimization (via the dapc and a.score.optim commands).Basic genetic diversity measures of the quality controlled data set were conducted in R v3.5.3 . Populatng DartR , and theng DartR . The meang DartR . Populatce (AFD) , Discrimce (AFD) . PCA andce (AFD)  and CLUMce (AFD)  softwarece (AFD) . STRUCTUce (AFD)  and Clumce (AFD)  to evaluce (AFD) . Structuce (AFD)  uses fouce (AFD) . The DAPwithin individuals, with a mean value of 96.3%. Variation among individuals within sampling areas was 3.6%, while only 0.1% of the variation was explained by differences among sampling areas  was 0.14 and the overall observed heterozygosity (Ho) was 0.13 . No statng areas .ST = 0.00328) was observed across the 164 individuals. Pairwise FST analyses identified similarly low genetic differentiation between the three sampling areas, with the greatest differentiation observed between CSL-MEX and STR-ECU (FST = 0.0005 followed by CSL-MEX and GAL-ECU (FST = 0.0004) and the lowest between GAL-ECU and STR-ECU (FST = 0.0003) (Limited genetic differentiation (F 0.0003) . No isol 0.0003) . The inb 0.0003) . Similar 0.0003) . This la 0.0003) .st-values.Sample sizes per sampling area are given in parentheses after the sampling area name; values above the diagonal are p-values; values below are FStock clustering analyses or structure are largely consistent with those of DAPC and PCA, showing a lack of structure among individuals in each group, and any differences between groups are minimal . All StrST scores and AFD values suggest high levels of genetic homogeneity among yellowfin tuna from the three sampling sites. This is further supported by the Mantel test analysis and the low inbreeding coefficient. The absence of deficit or excess of heterozygotes allowed us to discard the Wahlund effect in the population [A lack of genetic structure was observed for yellowfin tuna among the three sampling areas and no genetic difference by isolation was observed across spatial distances of 1,000–4,000 km, suggesting that yellowfin tuna within the EPO should currently be managed as a single stock. Pairwise analyses identified lowest genetic differences among individuals sampled at the two closest sampling sites, the Galápagos Islands and Santa Elena, Ecuador, separated by approximately 1,050 km. However, the greatest genetic differentiation was not observed between the two most distal locations  as might be expected if migratory distances played a role in the species differentiation, but rather among individuals from southern México and the Galápagos Islands . Structure analyses support the limited genetic differentiation observed among individuals from the different locations; low Fpulation . The lacpulation . Our finOur results showed a similar allelic richness among the three locations. This result and the similar patterns found in the PCA of each region, support the idea of limited differentiation among the sampling areas. Nevertheless, allelic richness is only one measure of genetic diversity at a given time , so contIn addition to overfishing and anthropogenic impacts , tuna populations in the EPO face other important threats like oxygen-minimum zones, which occur at elevated frequencies in this region , and havSeveral countries in the region, especially Ecuador and México, profit from the intense fishing of this stock and, therefore, regional management and conservation strategies are required for this shared resource. Hillborn et al.  provide To inform adaptive management, it is essential to perform NGS population studies of the yellowfin tuna fishery, in combination with ecological and reproductive biology studies. Moreover, it is important to design management policies that do not only focus on controlling fishing activity, but also manage other threats, such as climate change, illegal and unrecorded catches, and pollution, as part of an ecosystem-based management approach. Despite the existence of international tuna regulation agencies, it is critical that governments and local entities that promote the research collaborate in the development of common regulations that guarantee the survival of this species. Understanding key aspects of the status of the species  is critical for stakeholders to develop and establish appropriate management measures that promote the biological sustainability of the stocks, which ultimately guarantees the survival of the species and food security in the region .S1 Table(TIF)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S1 File(XLSX)Click here for additional data file.S2 File(RAR)Click here for additional data file.


Gattaca has emerged as a canonical pop culture reference used to discuss modern controversies in genetics and bioethics. It appeared in theaters a few years prior to the announcement of the “completion” of the human genome (2000), as the science of human genetics was developing a renewed sense of its social implications. The story is set in a near-future world in which parents can, with technological assistance, influence the genetic composition of their offspring on the basis of predicted life outcomes. The current moment—25 years after the film’s release—offers an opportunity to reflect on where society currently stands with respect to the ideas explored in Gattaca. Here, we review and discuss several active areas of genetic research—genetic prediction, embryo selection, forensic genetics, and others—that interface directly with scenes and concepts in the film. On its silver anniversary, we argue that Gattaca remains an important reflection of society’s expectations and fears with respect to the ways that genetic science has manifested in the real world. In accompanying The 1997 film  Gattaca, using the moment as an opportunity to examine contemporary issues—both technical and ethical—in modern human genetics.In this article, Ogbunugafor and Edge commemorate the 25th anniversary of the release of the film  Gattaca, written and directed by Andrew Niccol, envisions a “not-too-distant” future in which predictions made on the basis of genotypes play a central role in structuring society. Despite positive reviews from critics, Gattaca underperformed at the box office in its initial theatrical run. Since then, however, Gattaca has become a cult classic, and its title is a stand-in for the idea of genetic dystopia in popular parlance (e.g. The 1997 film nce e.g. .Gattaca’s protagonist is Vincent Anton Freeman (Ethan Hawke), who was conceived the old-fashioned way, without the aid of modern technology. So-called “faith births” have become taboo in Gattaca’s society—such people are labeled as “in-valids,” “uteros,” or “God children” and barred from opportunities and status. Based on what is in his genome, Vincent is predicted at birth to be at risk for several conditions, including a heart disorder foretold to lead to early death, which precludes his participation in educational and social institutions and in most employment opportunities.Gattaca’s world—doors are closed to Vincent that would allow him to pursue his lifelong dream of becoming an astronaut at the Gattaca Aerospace Corporation. This goal is unreachable until he takes bold and desperate action, adopting the genetic identity of another person: an illegal, underground process called a “borrowed ladder.” Vincent pays to adopt the genetic persona of a cooperating “valid”—that is, a person born under genetic supervision and assistance—Jerome (Jude Law), a former elite swimmer whose paraplegia prevents him from competing.Because of this genetic discrimination—outlawed in principle but common in practice in Gattaca remains as relevant as ever, a lens on modern uses of genetics. A quarter century later, we look back on the film, considering it from our perspective as geneticists. We organize our discussion around major scenes in the movie, taking up its depiction of genetic discrimination, genetic prediction, embryo selection, genetic investigations of crime, gene-by-environment interactions, and related topics. Along the way, we briefly review some technical aspects of contemporary genetics work related to Gattaca. For clarity, we have provided a brief definition of these concepts , with genetics the instrument of discrimination in this fictional world. The science of genetics has already been a close partner of discrimination in its history, including via the eugenics movement  can propagate their privilege by ensuring that their children are born with genetic assistance. Privilege also becomes naturalized, justified by scientific prediction. Economic barriers to genetic assistance undercut Vincent’s narrative claim that class is “no longer determined by social status.” Through his experiences, Vincent realizes that though genetic variation matters, the social force of genetic prediction can be at least as powerful. Uncertainty about whether Vincent will continue to outwit the genetically defined elite, in part by capitalizing on their misplaced faith in an overstated genetic determinism, is an important driver of tension in the film’s plot.The world of Gattaca despite their uneven performance. Some commentators have taken the overly precise predictions in Gattaca at face value. For example, futurist Ramez Naam wrote , as well as about the correlations of genetic liability for the diseases considered.One question raised by the probabilities recited at Vincent’s birth  who have an identical twin, the twin has bipolar I disorder only about 40% of the time  manifest, as they can be modified environmentally .Box 1.“the time .The potential to predict a trait from genetic information is limited by the trait’s heritability, the proportion of trait variance explained by genetic variation in a given population and range of environments. Heritability can be estimated in many ways , in which the associations between genotypes at genome-wide loci and a phenotype are estimated in a large sample of individuals.P-value (thresholding). Once m loci are selected, the polygenic score A widely applied method for estimating a polygenic score is clumping and thresholding, in which the goal is to select loci for inclusion in the polygenic score that are not strongly correlated (clumping) and that each pass a threshold for evidence of association with the phenotype, based on a j and jX is the number of effect alleles carried by the individual at locus j. The thresholds for P-value and for correlation among sites can be chosen empirically to maximize predictive accuracy , they are told, “this child is still you, simply the best of you.” This line and others, as well as the image of 4 candidate embryos on a screen in the geneticist’s office, strongly suggest that “genetic assistance” is some form of embryo or gamete selection rather than CRISPR-like editing of parental genetic material.The geneticist details the features available in the candidate embryos for consideration. The parents opt for a boy and confirm their preference for some cosmetic features—hazel eyes, dark hair, fair skin.  As the geneticist continues through a list of traits he has “eliminated,” Vincent’s parents appear uncomfortable and protest, saying that although they are comfortable preventing diseases, “we were wondering if it’s good to just leave a few things to chance.” The geneticist replies, “You want to give your child the best possible start. Believe me, we have enough imperfection built in already.”Gattaca was released, beginning in 1989 Box 3.“You could conceive naturally a thousand times and never get such a result”Gattaca takes the form of embryo selection from a large number of embryos, what might the results be? How different from a couple’s average child (produced the old-fashioned way) might we expect the “assisted” child to be? Several recent papers have taken up the related question of how embryo selection on the basis of polygenic scores might perform  would tend to lower risk for several other diseases , rendering moot the need for parents to carry the desired alleles . But whiGattaca’s original ending, cut for the theatrical release but included in an advance screening for scientists  straight out of noir, complete with fedora and trenchcoat, and his younger, valid colleague (Loren Dean), who outranks him. Much of the tension in Vincent is not the murderer, but the investigation nonetheless threatens to uncover his genetic impersonation of Jerome. Though Vincent is fastidious in his effort to keep his identity secret, he nonetheless sheds an eyelash near the crime scene, which ultimately alerts police to the presence of an in-valid in the facility.Vincent’s illicit presence makes him a prime suspect, leading to a genetic manhunt. When Vincent’s eyelash is analyzed, detectives learn he is an “unregistered” in-valid—i.e. one who is not included in an implied central database—and they see a picture of Vincent, complete with his preimpersonation hairstyle and eyeglasses. They do not receive a name, but they do learn that he used to work as a janitor at the facility, perhaps by questioning his former supervisor (Ernest Borgnine).Gattaca’s release, following its origin in the United Kingdom in the mid-1980s  has opened large segments of the population to de facto genetic search. In an IGG search, genetic genealogists upload genome-wide genotypes—not the handful of markers included in CODIS—to platforms typically used for recreational genealogy.pulation . At the Gattaca raises a puzzle: If Vincent is unregistered, how do the detectives get his picture? And further, how do they have access to his picture but no name attached? One possibility is that the film wants us to assume that the detectives predict his phenotype, generating the picture from the genetic information in the eyelash. The detectives do make reference to other phenotype predictions, including Vincent’s short predicted lifespan and a “violent temperament,” which, in the eyes of the older detective, replaces the need for a plausible motive.Speaking of universal databases, the genetic investigation in Phenotype predictions in forensic contexts, termed forensic DNA phenotyping, have been explored in the academic literature . ForensiGattaca’s use of phenotype prediction specifically to investigate a crime is more than a plot device used to explore the ways in which forensic genetics would work in this fictional world. Debates about innateness have been central in conversations around criminal behavior and its social consequences. For example, the search for criminal “types” based in and recognizable in terms of biology has a long history with deep connections to the eugenics movement  has several roles: she is Vincent’s love interest and a staff member at the Gattaca Corporation who is assigned to assist the murder investigation. The interactions between Vincent and Irene generate windows onto the interpretation of genetic “imperfections” in Gattaca’s world. Early in the film, Irene takes a nonconsensual look at Vincent’s genetic identity by taking a hair from his desk drawer to a corner genomic sequencing service.  When Irene confesses her clandestine genetic monitoring, she also shares her insecurity about a heart defect that prevents her from going to space.Gattaca-world doctrine of biological determinism—some people are born with the equipment to play certain tunes.One especially critical moment involving Irene takes place on her first date with Vincent. They attend a performance, and we see a pianist playing an impressive serenade. At the end of the concert, Vincent (posing as Jerome) and Irene stand near a poster advertising the event that highlights the pianist’s 12 fingers. Vincent says, “One finger or twelve, it’s how you play,” to which Irene responds “that piece can only be played with twelve”.Gattaca: should we interpret the presumptive celebration of the 6-fingered pianist as a symbol of acceptance or of exoticism? Perhaps the scene highlights that certain conditions are tolerated insofar as they entertain and do not threaten the valid class.The film is not clear about whether the 12-fingered pianist was the product of genetic assistance  or was a “God child” from a natural birth. Whatever the mechanism, the 12-fingered pianist was born with some version of polydactyly, in which vertebrates have additional digits on their extremities (more than 5 digits in humans).Gattaca’s world of physical conformity. But they also let him play a transcendent piece of music that few others can manage. One lesson is that fixed notions of impairment are imprecise. This is not only an ethical message but also a technical one: gene-by-environment interactions are a central concept in modern genetics, defined by how the phenotypic manifestation of genotypic information is dependent on context.In many settings, conversations around abnormalities, impairments, and disability have become more sophisticated, as scientists, scholars, and advocates have challenged scientific fields to rethink normative definitions of fitness, health, and disability , and broader questions regarding how new technologies (e.g. gene editing via CRISPR) should be used . And unfA recurring problem at the interface of the science and ethics of genetics, participated in by both scientists and users of science, is a kind of giddiness—an unjustified optimism that genetic research will imminently solve the most relevant questions surrounding human nature and society. For example, Charles Davenport once wrote with exhilaration about the potential for tying all sorts of human traits to the Procrustean bed of Mendelism, including “nomadism,” “shiftlessness,” and love of the sea (Gattaca’s world are based on science advanced beyond our own, but they mirror ones in our own history. Reflecting on Gattaca implores us to look ahead, toward creating a world different from the one it portrays, where science is no longer misused to reinforce social inequality, to limit possibilities, and to transform hopes into dreams deferred.The abuses of science in iyac142_Supplementary_DataClick here for additional data file.


Gammarus pulex  (clade E) was investigated on a regional scale. The site‐specific AOM levels and their toxic potentials were determined in water and G. pulex tissue sample extracts for 34 sites along six rivers in central Germany impacted by wastewater effluents and agricultural run‐off. Population genetic parameters were determined for G. pulex from the sampling sites by genotyping 16 microsatellite loci. Genetic differentiation among G. pulex from the studied rivers was found to be associated with geographic distance between sites and to differences in site‐specific concentrations of AOM. The genetic diversity parameters of G. pulex were found to be related to the site‐specific AOM levels. Allelic richness was significantly negatively correlated with levels of AOM in G. pulex tissue (p < 0.003) and was reduced by up to 22% at sites with increased levels of AOM, despite a positive relationship of allelic richness and the presence of waste‐water effluent. In addition, the inbreeding coefficient of G. pulex from sites with toxic AOM levels was up to 2.5 times higher than that of G. pulex from more pristine sites. These results indicate that AOM levels commonly found in European rivers significantly contribute to changes in the genetic diversity of an ecologically relevant indicator species.Anthropogenic chemicals in freshwater environments contribute majorly to ecosystem degradation and biodiversity decline. In particular anthropogenic organic micropollutants (AOM), a diverse group of compounds, including pesticides, pharmaceuticals, and industrial chemicals, can significantly impact freshwater organisms. AOM were found to impact genetic diversity of freshwater species; however, to which degree AOM cause changes in population genetic structure and allelic richness of freshwater macroinvertebrates remains poorly understood. Here, the impact of AOM on genetic diversity of the common amphipod  In the previous study on the genetic divergence of G. pulex from polluted and nonpolluted sites within a river, G. pulex individuals from the different sites were found to not be genetically different  across the whole width of a river with at least five locations per sampling site. Sampled amphipods were morphologically identified using a taxonomical key  and 26 AOM‐polluted sites along six rivers (Altenau River (A), Eine River (E), Holtemme River (H), Parthe River (P), Saale River (S), Wipper River (W)) belonging to three catchments in the states of Lower Saxony, Saxony, Saxony‐Anhalt, and Thuringia in central Germany Figure . The rived Table . Gammaru2.2G. pulex tissue samples were analyzed by a Thermo Ultimate 3000 liquid chromatography (LC) system coupled with a quadrupole‐orbitrap high‐resolution mass spectrometer  as described in Švara et al. . TUs were calculated based on the lethal concentrations for 50% of individuals in standard toxicity tests (LC50) for the respective compounds given in the EPA ecotoxicology database . Data from the database were retrieved as a text file (exotox_ascii_15_09_2020) and the mean was calculated from all LC50 values obtained in 24 or 48 h exposure experiments for G. pulex for a respective compound. If no LC50 data for a compound were available for G. pulex, LC50 data for Daphnia magna Straus, 1820 were used. The freely dissolved fraction (Cfd) of each compound i was estimated based on the measured tissue concentrations according to the equilibrium partitioning theory using the following equation:CtG is the total measured concentration [ng/g of wet tissue] of a compound in G. pulex; fLIPID, the lipid fraction value predicted for G. pulex by Ashauer et al.  and expected (He) heterozygosity and the inbreeding coefficient (FIS) using the R package ‘Hierfstat’  values, with the deviation from zero tested by applying 10,000 permutations of the analyzed loci. The effective population size at each site was estimated with the linkage disequilibrium model in ‘NeEstimator 2.0.2’ . The impact of environmental pollution on genetic differentiation between sites was assessed by partial Mantel tests correlating pairwise Fst values against distances based either on total AOM levels or TUs, while accounting for the effect of waterway distances.The association between distance and genetic differentiation among sites was tested using the Mantel test by comparing pairwise 2.4FIS), effective population size (Ne), and G. pulex abundance, were analyzed by linear mixed‐effect models (LME). Genetic diversity indicators were explained by linear fixed effects of distance from the spring, toxic units based on the AOM from G. pulex tissue samples, total concentration of detected AOM in amphipod tissue, presence of WWTPs before the sampled sites, conductivity, pH, and saturation of water with oxygen, allowing a random intercept for each river. The values of the total concentration of detected compounds and effective population sizes were log‐transformed to avoid the effects of very low values close to 0. The sites, for which, due to limited numbers of G. pulex, AOM‐tissue concentrations were not available, were excluded from the analysis . Using the function lmer in the package ‘lme4’  were used to fit allelic richness against total AOM concentration and distance from the source for all sites in each river. The models were fitted by generalized least squares by applying the sem function from the R package ‘lavaan’ Rosseel, . In the 33.1Numbers of detected AOM and their concentrations in the water samples indicated different site‐specific pollution patterns. In total, 236 compounds were detected in water samples from 34 sites. Most compounds were found in water samples from sites P4, S5, P5, and W7 with 152, 131, 130, and 104 compounds, respectively. The highest total AOM concentrations were 107.8 and 96.4 µg/L in water from sites W5 (Wipper River) and P4 (Parthe River), respectively Figure . Total AG. pulex tissue samples, a total of 253 compounds were found at 31 sites. Most compounds were detected in tissue samples from sites E6 (155 compounds), P4 and P5 (109 compounds), E5 (103 compounds), and E3 and E4 (83 compounds). The highest (8.4 µg/g at H3) and the lowest (0.1 µg/g at H1) total AOM concentrations were detected in tissue samples from the Holtemme River . Several compounds were detected in tissue samples from only one site; for detailed information see Table G. pulex tissue extracts , the Parthe River , and the Saale River  and their transformation products, often contributing more than 95% of the total TUs Figure . Another3.3G. pulex samples from the studied rivers belonged to a single genetic lineage. Similarities of all sequences were greatest with those from G. pulex samples from the Brandenburg region , consistent with K = 3 from the structure analysis , followed by 0.075 for G. pulex from sites E1 and E6 (Eine River) and 0.055 for G. pulex from sites W1 and W6 . Surprisingly high Fst values were detected for two sites located close to each other . Within rivers, partial Mantel tests were not significant, except for the Altenau River. Genetic differentiation did not increase with differences in TUs neither regionally nor locally (Mantel p > 0.3).The genetic differentiation of rs Table . Of 81 p 0 Table . A signi3.4G. pulex genetic diversity parameters and AOM concentrations detected in G. pulex tissue was indicated by LMEs. A significant contribution of fixed effects to LME was indicated for 4 of the 5 analyzed model indicators, including allelic richness, Fis, abundance, and Ne (Table The association between Ne Table . The disNe Table .p < 0.05). The presence of WWTP effluent and total concentration of AOM best‐described changes in allelic richness in G. pulex . Finally, the most informative fixed effect associated with the effective population size of G. pulex was the presence of WWTP effluent, with a positive but nonsignificant relationship  and a positive relationship between distance to the river source and allelic richness  and, on the other hand, the significant genetic divergence among G. pulex in geographically proximate rivers , which could be related to high nutrient levels from WWTP effluents and thus the high abundance of food for G. pulex. The higher genetic diversity of G. pulex at sites WWTP effluents may thus be due to the abundance of G. pulex at these sites that is comparatively high because of the increased availability of food. Furthermore, comparatively high genetic diversity at downstream sampling sites may be due to the comparatively long distance to the river source. According to the LME, distance from the source was not an important parameter for increased allelic richness within a river , or fungi (carbendazim) that G. pulex feeds upon. It may be assumed that these indirect effects lead to an increase in intraspecific competition and genetic drift; however, there is no experimental evidence yet for the consequences of these indirect effects. Furthermore, effects caused by freely dissolved AOM in water and by AOM in G. pulex tissue should be compared in future studies, as the detected compound concentrations and exposure duration often largely differ. For instance, AOM may persist over long time periods in the tissue of G. pulex, while the exposure of G. pulex to some freely dissolved compounds would only occur at the time of events such as the release of wastewater from the WWTP.From the detected AOM, insecticides can be expected to exert selective pressure because of their high toxic potential. Particularly, the detected insecticides fipronil, imidacloprid, thiacloprid, clothianidin, and acetamiprid were shown to cause adverse effects in G. pulex, no information on acute toxicity for G. pulex is available , where no G. roeselii were found, indicating a genetic bottleneck for G. pulex. Comparable abundance patterns were shown for amphipod species that occupy similar ecological niches in streams, suggesting that colonizing history majorly influences species composition (Little & Altermatt, Reduced abundance and genetic diversity of a species may cause changes in interspecific competition and species community structure. In the Eine River, G. pulex to respond to environmental changes in the long run. In populations with critically reduced genetic diversity the effects of environmental stress factors may be more pronounced, increasing species mortality and threatening its survival (Pearman & Garner, G. pulex to additional acute stress (Švara et al., G. pulex populations exhibiting low genetic diversity.In addition to interspecific competition, low genetic diversity at sites with comparably high AOM levels can affect the ability of G. pulex. A decline in the genetic diversity of the species may lead to decreased species robustness to environmental stress that, in the long run, can affect the survival of this keystone species and enhance the risk of the loss of its ecosystem function.In conclusion, our results indicate that AOM contamination of rivers and streams can significantly shape the population genetic diversity of The authors declare no conflict of interest.Supplementary MaterialClick here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Table S5Click here for additional data file.