Crop wild relatives (CWR) intra- and interspecific diversity is essential for crop breeding and food security. However, intraspecific genetic diversity, which is central given the idiosyncratic threats to species in landscapes, is usually not considered in planning frameworks. Here, we introduce an approach to develop proxies of genetic differentiation to identify conservation areas, applying systematic conservation planning tools that produce hierarchical prioritizations of the landscape. It accounts for: (i) evolutionary processes, including historical and environmental drivers of genetic diversity, and (ii) threat processes, considering taxa-specific tolerance to human-modified habitats, and their extinction risk status. Our analyses can be used as inputs for developing national action plans for the conservation and use of CWR. Our results also inform public policy to mitigate threat processes to CWR (like crops living modified organisms or agriculture subsidies), and could advise future research . Although we focus on Mesoamerican CWR within Mexico, our methodology offers opportunities to effectively guide conservation and monitoring strategies to safeguard the evolutionary resilience of any taxa, including in regions of complex evolutionary histories and mosaic landscapes. Crop wild relatives’ genetic diversity is usually not considered in conservation planning. Here, the authors introduce an approach to identify conservation areas based on evolutionary and threat processes, by developing proxies of genetic differentiation, and including taxa’s habitat preferences. CWR are generally more genetically diverse than crops, and carry useful adaptations for a large set of biotic and abiotic stresses, like drought, pests, and diseases, as they have evolved to survive in diverse and often extreme environments. Planning for the conservation of genetic diversity of Mesoamerican wild relatives of some of the world’s most important crops  is particularly urgent, as up to 35% of taxa recently assessed are threatened with extinction according to the International Union for Conservation of Nature (IUCN) Red List2. Many CWR are threatened by anthropogenic activities and habitat modification4 as also are many other wild species in the mosaic landscapes that dominate in the Anthropocene. Thus, innovative conservation solutions are needed to retain the range-wide genetic diversity that can be at risk even in widespread species, which is crucial for maintaining evolutionary resilience6. Systematic conservation planning can aid the process of effectively locating and managing key areas to protect biodiversity by addressing the representation and persistence of diversity across all levels of biodiversity, as well as ecological and evolutionary processes. Nevertheless, most systematic conservation planning assessments focus on taxa representation rather than persistence, while at the same time, the representation of genetic diversity below the taxonomic species level (i.e. range-wide genetic diversity) remains understudied9.Attaining food and nutrition security under global change poses major challenges for humanity. The importance of crop wild relatives (hereafter CWR) consists in their role as a substantial reservoir of genes that can help crops adapt to changing environmental conditions11, which changes according to the climatic and geologic history of a given area12. Most regions where crops were originally domesticated (centers of domestication)—often holding high diversity of CWR—are tropical or topographically heterogeneous13, and therefore areas where long-term population persistence and historical isolation promote genetic differentiation in complex patterns15. To address the processes behind CWR genetic diversity, systematic conservation planning should consider both current and historical drivers of evolution. Another challenge for planning approaches is to account for CWR interaction with agriculture. Most CWR are threatened by land use change linked to the expansion of intensive agriculture, although some thrive in managed and agricultural landscapes and are tolerated or fostered by farmers19. In addition, in some regions gene flow between crops and their CWR can occur, and has been part of the crop domestication process for thousands of years21. However, in modern agriculture, which is linked to the cultivation of highly genetically uniform commercial varieties and living modified organisms, gene flow can become a risk in the form of genetic assimilation  and demographic swamping (wild populations shrinking due to hybridization), as well as causing other unintended consequences, like the evolution of new or more problematic weeds or invasive species23. Gene flow can also occur in the opposite direction, which some farmers perceive as a problem causing a lack of interest in CWR conservation24.As in any wild species, CWR genetic diversity depends on demographic history, population structure, and natural selectione.g.30, including the identification of priority sites for in situ protection and ex situ collections, thus greatly contributing to CWR conservation globally31. Notwithstanding, most CWR planning apply the “minimum set cover problem” that detects the least number of sites to complement existing reserves, which may not fully represent the genetic diversity spectrum of any given species6. This is particularly important in centers of origin, diversity and domestication of crops, such as Mexico, where genetic diversity is driven by complex climatic, geologic and human histories32, and where crops and their CWR co-exist under different forms of human management and have interacted over long periods of time35.Conservation planning for CWR has increased during the last few years26. It included: (i) a CWR checklist, i.e. list of CWR taxa distributed in Mesoamerica, which were subset to reduce the number of taxa (Supplementary Data 2. This study relies on introducing an approach in step (iv) to account for genetic differentiation in a spatially explicit way, through the use of proxies of genetic differentiation  evolutionary processes, including historical and environmental drivers of genetic diversity, and (ii) threat processes, considering taxa-specific tolerance to human-modified habitats, and their extinction risk status. To accomplish this, in the context of the project ‘Safeguarding Mesoamerican crop wild relatives’, a collaborative partnership between government agencies, local communities, universities, and non-governmental organizations from El Salvador, Guatemala, Honduras, Mexico, the UK, and IUCN, we applied a modified version of a planning framework for CWR conservationion Fig. . SpecifiCapsicum), squash (species of Cucurbita), cotton (species of Gossypium), avocado (species of Persea), bean (species of Phaseolus), husk tomato , potato (species of Solanum sect. Petota), maize (species of Zea and Tripsacum), and vanilla (species of Vanilla) . They are related to nine crops: chili pepper (species of a) Table . Of thes36. Based on SDM, areas of high taxa richness were identified along the Trans-Mexican Volcanic Belt, and in the montane areas of Oaxaca and Chiapas in Southern Mexico , which showed potentially more taxa per area than the global study  of Mesoamerican CWR with more than 20 occurrence records were used to overcome biases in sampling efforts  including not only environmental drivers of genetic differentiation, but also historical drivers that could lead to population differentiation and structure, and (2) defining proxies in an efficient, repeatable, and spatially explicit way that could be incorporated into systematic conservation planning analysis.Given that high-resolution genetic data for all taxa will not be available soon, we propose to use proxies of genetic differentiation as a spatially explicit surrogate of the potential genetic differentiation within the distribution of a given taxon, which can represent different populations across landscapes. Studies that considered genetic variation for spatial analysis of systematic conservation planning are extremely rare47 or among the main Mexican mountain ranges32. Thus, we obtained 102 proxies of genetic differentiation for Mexico; each proxy has a particular distribution area, unique in shape, size, and location that does not overlap with other proxies , which was excluded from the literature review to avoid having its phylogeographic patterns influence the estimation of proxies of genetic diversity for this taxon. This approach allowed us to mimic the scenario presented by the many species without any existing genomic resources. According to a population analysis with >30,000 single nucleotide polymorphisms (SNPs) data, Z. mays subsp. parviglumis is structured in 13 genetic clusters which includes a longitudinal gradient as well as some considerably differentiated genetic clusters , but more empirical data is needed to test this.Proxies of genetic differentiation can be assessed for any taxa without genetic data, which is particularly important if we aim to secure genetic variation within all taxaZea mays subsp. parviglumis would be better reflected. Thus, regardless of the quality of the proxies, our analyses are also a proof of concept of how genetic differentiation can be incorporated in a spatially explicit way into systematic conservation planning.Of course, as any given model or surrogate, the proxies of genetic variation are a simplified version of the reality; thus, the main caveat of our methodology is that the spatial expression of the proxies of genetic differentiation would never be as accurate as actual genetic studies. However, as genomic resources become available for more species, the proxies of genetic differentiation could be further corroborated and fine-tuned. For example, with minor changes to the proxies 5, 10, and 36 . We quantitatively tested different alternatives to incorporate proxies of genetic differentiation into the conservation analysis  CWR potential distribution models subdivided by proxies of genetic differentiation , the conservation of ecological and evolutionary processes shaping biodiversity at all levels  cannot be secured in a small fraction of the territory and with few individuals Fig. . To reprmap Fig.  and selemap Fig.  offer a 8 e.g. Persea are trees distributed in well-preserved vegetation, such as cloud forests, while Gossypium are bushes requiring a certain degree of disturbance43, and Capsicum and Physalis are managed in the wild or tolerated within differentiated human management and traditional agricultural systems, mainly within indigenous territories58. To identify conservation priorities for different land use preferences, we ran three scenarios, including: (a) all taxa, (b) taxa exclusively distributed in well-preserved vegetation, and (c) taxa that can be associated with different habitats and land uses . Results showed different spatial solutions , and to link each biodiversity feature to a certain condition group, e.g. habitat preference. One of the software outputs is a hierarchical representation of the landscape that can inform proactive and reactive conservation measures  and Aichi Target 13 (https://www.cbd.int/sp/targets/) of the Convention on Biological Diversity. To move this field forward, we propose an approach to assess and incorporate indicators of genetic variation to identify areas of high conservation value for Mesoamerican CWR in Mexico, which is needed for strategic planning and decision-making at local, national, and regional scales. We focused on CWR due to their importance for food security, human well-being, the adaptation of crops to changing environments1, as well as their relevance for contributing towards achieving international commitments52. However, as recent suggestions to the Convention on Biological Diversity post-2020 strategy highlight, we also need to conserve and monitor genetic diversity beyond domesticated species and their wild relatives54. A first key step to do so is to spatially delimit genetically differentiated populations for which genetic data is not available54. Our proxies of genetic differentiation methodology could be applied to any taxa to achieve this goal, and thus represents a contribution to incorporate genetic diversity monitoring and conservation into national and regional biodiversity management strategies, which is needed to enhance species evolutionary resilience in the face of climate change and other threats5. As better phylogeographic meta-analysis and genetic data become more available, proxies of genetic differentiation could be fine-tuned or delimited at higher resolutions.Conservation of genetic diversity of crops and their wild relatives is key to tackling the global environmental and social crisis, as communicated in the UN Sustainable Development Goal 2.5 62.Our results support the development of National Strategic Action Plans for the conservation and use of CWR in Mexico, inform public policy regarding crops living modified organisms26 which has been used by numerous countries of Europee.g.64, Americae.g.65, Africa30 and Asia67. We addressed the following main steps of the toolkit (see Spanish version49): (i) CWR checklist, i.e., creating a list of CWR taxa distributed in an area - more than 100 experts from academic, governmental, and non-governmental organizations from El Salvador, Guatemala, Honduras, Mexico, the UK, and IUCN participated in six workshops, shared data, and provided fundamental knowledge and feedback at each project stage to ensure accurate, reliable and robust information for next steps. The checklist, inventory and risk assessment were collaboratively developed between partners of El Salvador, Guatemala, and Mexico . The spatial analysis to identify areas for in situ and ex situ conservation of CWR was done independently by each country.During the process -framed under the project “Safeguarding Mesoamerican crop wild relatives”  and proxies of genetic differentiation.To assess conservation areas of CWR in Mexico, we developed proxies of genetic differentiation that account for evolutionary processes by including historical and environmental drivers of genetic diversity (see the Methods section ‘Proxies of genetic differentiation’). In addition, we used criteria such as information on taxon-specific tolerance to human-modified habitats and IUCN extinction risk category. We applied a systematic conservation planning approach and performed spatial analysis using the software Zonationsee2. In this study, we also included the dry areas of northern Mexico that are part of Aridamerica68 and the Nearctic biogeographic realm69 to account for the full extent of the geographic range of many taxa included in the extinction risk assessment2.Mesoamerica is a cultural region encompassing the territories of Belize, Guatemala, El Salvador, the southern part of Mexico and parts of Honduras, Nicaragua and Costa Rica70. The Mexican territory covers 80% of the landscapes of the region called Mesoamerica. Its high biological diversity is attributed to its geographic, topographic, climatic, geological and cultural characteristics, which, among other factors, shaped the distribution of an extraordinary variety of ecosystems and species with high levels of endemism and species turnover among different regions73. In particular, the high genetic variation within populations of landraces and CWR is the result of past and ongoing sociocultural processes occurring in a wide range of distinct environmental conditions75.For the assessment of conservation areas, we focused on Mexico, which is one of the most biodiverse countries in the worldThe compiled CWR checklist included ~3000 species and subspecies of 92 genera and 45 families of plants that belong to the same genus of a crop cultivated in Mesoamerica, or wild plant collected for food or other uses in the region (Supplementary Data The first set of criteria were established in preparation for the first stakeholder workshop. The following criteria were applied at the genus level to compile the CWR inventory: (1) occurrence of wild relatives of cultivated plants or crops that were domesticated in Mesoamerica; (2) existence of research groups working on taxa that could support the extinction risk assessment; and (3) relation to a crop of economic and nutritional importance at local, national and regional levels, or cultivars known to require genetic improvement.76. The primary gene pool consists of wild plants of the same species as the crop and thus their mating produces strong fertile progeny. The secondary gene pool is composed of wild relatives distinct from cultivated species but closely related as to produce some fertile offspring  native distribution in Mesoamerica, incl. Aridamerica; 2) related to a crop of economic or social importance based on production and nutritional value; (3) related to a taxon for which Mesoamerica is the center of origin or domestication; (4) constitutes part of the primary or secondary gene pool, and in some cases the tertiary gene pool related The list had to be further reduced due to time and funding restrictions to include those genera which when added together would include no more than 250 taxa, and that the taxonomic groups could be comprehensively assessed and their taxa evaluated throughout their entire range. Thus, not all species in the group necessarily met the criteria previously mentioned. See the final Mesoamerican CWR inventory in Supplementary Data 2. Summarizing, during the process 224 taxa were evaluated according to the International Union for Conservation of Nature, IUCN, Red List Categories and Criteria78. The IUCN Red List is a critical indicator to identify species most vulnerable to extinction considering a set of criteria, i.e., species’ population trends, size, structure, and geographic ranges. A Red List workshop with the participation of 25 experts from different project partner institutions and IUCN specialists was organized to assess the extinction risk of taxa. The threat analysis included not only species, but subspecies and subpopulations (i.e. races) for some groups , the Global Biodiversity Information Facility , and Mexico’s Biodiversity Information System .To compile occurrence records, hundreds of data sources were consulted, including published and personal databases of the project participantshttps://www.iucn.org/news/species/202109/threats-crop-wild-relatives-compromising-food-security-and-livelihoods). Spatial resolution of the SDM was 1 km2. SDM were obtained for taxa with more than 20 unique occurrence data in a 1 km2 grid covering the study extent to reduce uncertainty when using smaller sample sizes83. We used 19 bioclimatic variables and other climatic variables, such as annual potential evapotranspiration, aridity index, annual radiation, slope, and altitude86. Climate data represents annual and seasonal patterns of climate between 1950 and 2000. Also, we used a variable that described the percentage of bare soil and cultivated areas87. Collinearity between variables was assessed with the ‘corselect’ function of the package fuzzySim version 1.088, using a value of 0.8 and the variance inflation factors as criteria to exclude highly correlated variables.To generate potential species distribution models (SDM), we used more than 13,000 occurrence records . This method penalizes overly complex models and helps to choose those with an optimal number of parameters. However, it has been shown that the number of model parameters may not correctly estimate degrees of freedom98, and that model selection should not be selected solely with one measure99. Thus, we used 30% of the withheld data to test the area under the curve (AUC) of the receiver operating characteristic, and the omission error under a 10 percentile training threshold.To reduce model complexity without compromising model performance, we built several models by varying the feature classes (FC) and regularization multipliers (RM)  within the main mountain ranges, while lowland populations were ephemeral . So, gene flow among mountain ranges was more limited than within them. As a result, genetic differentiation among mountain ranges of different biogeographic provinces has been widely documentedZea mays subsp. parviglumis, which was not included in the literature review in order to test the usefulness of our approach regarding the lack of genetic data. The dataset includes ca. 1800 occurrence records and ca. 30,000 SNPs48. Sampling localities were not used for distribution modeling. Admixture groups per population were estimated for K1 to 60. According to the population analysis, Z. mays subsp. parviglumis is structured in 13 genetic clusters along a longitudinal gradient  according to the following categories: (i) well-conserved vegetation (i.e. primary vegetation), (ii) human-impacted vegetation (i.e. secondary vegetation), (iii) less intensive rainfed and moisture agriculture, (iv) intensive rainfed and moisture agriculture, (v) irrigated agriculture, (vi) induced and cultivated grasslands and forests, and vii) urban areas , and selected the municipalities with the established average maize yield. We combined the municipality layer with the land cover map to differentiate areas of high and low agricultural intensity. To generate taxon-specific habitat layers, we associated the habitat preference classes established by experts to the land cover map aggregated into seven major land cover categories, using R 3.6.096 and the following packages: raster version 3.4-5111 and rgdal version 1.4-8112. We obtained habitat maps for 116 taxa with SDM.We considered habitat preference to refine the presence of CWR in the planning process; thus minimizing commission errors and highlighting areas that more probably contain taxan = 116); (ii) “SDM + LZ” scenario, included 116 SDM and 27 layers representing Holdridge life zones to consider environmental variation (n = 143); (iii) “SDM + PGD” scenario, included 116 SDM and 102 layers representing each proxy of genetic differentiation individually (n = 218); (iv) “SDM*PGD” scenario, included 5004 input layers representing the intersection of SDM and PGD ; (v) “SDM and PGD as ADMU” scenario, included 116 SDM as the main conservation features, while integrating one single layer of proxies of genetic differentiation to consider each of them as planning units by using the ‘Administrative units’ function. Analysis was done in Zonation119.We generated five preliminary scenarios to explore different approaches to include conservation features for maximizing the representation of intraspecific diversity as given by taxa and proxies of genetic differentiation, i.e., representation of proxies within a taxa range  to maximize the representation of all conservation features in a minimal possible area51. Zonation generates two main outputs: (a) a hierarchical landscape priority rank map, that allows decision makers establishing different area thresholds to highlight areas of conservation interest; and (b) a representation curve showing species or conservation features range distribution in a given area. The curve also allows identifying how much area is needed to cover a certain taxon range or the distribution of a feature of conservation interest.We identified areas of high conservation value for CWR in Mexico by using the software Zonation version 4.0For the conservation scenarios, we integrated the following inputs in the Zonation software: (1) 5,004 layers, i.e., SDM intersected with proxies of genetic differentiation . This option can only be used for taxa represented by a raster layer, and is not available for SSI taxa included via occurrence records. By enabling the “landscape condition” option of Zonation, each habitat map is linked to a specific conservation feature layer. Areas with unfavorable habitats will quickly be masked out during the selection of cells in order to obtain a solution that favors conservation areas within areas of preferred habitat.We generated three final scenarios to identify conservation areas for (a) all taxa, (b) taxa exclusively distributing in natural vegetation, and (c) taxa associated with a wider range of habitats such as natural vegetation, agricultural and urban areas. The Zonation configuration remained similar among the three scenarios. When taxa were not included in a given scenario, we assigned a value weight of 0. This excluded the feature to be considered for the hierarchical prioritization of the landscape, but still allowed to evaluate the taxa during post-processing.125 and indigenous regions127 , and thus generally do not adequately plan for wild and native genetic resources129. We also discussed different approaches to consider connectivity for taxa, habitats and proxies of genetic differentiation in the Zonation processing. Still, we finally decided to run the analysis without particularly accounting for connectivity as we had no taxa-specific information on dispersal abilities or possible effects of fragmentation, and we did not want to lose efficiency of the solution to represent taxa by or include lower-quality habitats by forcing the solution to an aggregation of pixels.We discussed the proposed methodological framework, input layer and criteria during a fourth workshop in Mexico. It is worth mentioning that we ran several analyses including additional layers, such as areas where indigenous communities live that promote the presence of CWR in the landscapeFurther information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesPeer Review FileSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12


Sarcocheilichthys parvus (Cypriniformes: Gobionidae) is a stream fish which is endemic to sub-tropical coastal drainages in southern China, thus offering a valuable model for understanding how genetic divergence arises in stream-adapting freshwater fishes in this region. Using the mitochondrial Cyt b gene, integrative analyses of phylogeny, population demography, and ancestral area and paleo-drainage reconstructions are carried out to explicitly explore the role of landscape evolution in genetic diversification of S. parvus. The time-calibrated phylogeny of S. parvus indicates the splitting of two major lineages (A and B) at ∼3.66 Ma. Lineage A inhabits the Poyang Lake sub-drainage of the middle Yangtze River, Han River and Pearl River, and can be split into two sub-lineages (A-I and A-II), where sub-lineage A-II can be further sub-divided into three infra-sub-lineages . Except for the infra-sub-lineage A-IIc, which is restricted to the Han River and Pearl River, the other sub-lineages and infra-sub-lineages live exclusively in the Poyang Lake sub-drainage. Lineage B lives in the lower Yangtze River, Qiantang River, Jiaojiang River and Ou River, displaying close genetic relationships among the drainages. Rapid population expansion has occurred since the Late Pleistocene. Our findings indicate that the splitting of lineages A and B could be attributed to geographic isolation due to the Zhe–Min Uplift, acting as a biogeographic barrier before the late Early Pleistocene. Furthermore, the strong genetic divergence within Lineage A could be explained by the isolation role of the Nanling Mountains and Poyang Lake acting as an ecological barrier; while the lack of phylogenetic structure within Lineage B may have been the result of paleo-drainage connections or episodic freshwater connections during the eustatic low stand of sea level in the late Middle–Late Pleistocene. Phylogeographic research on freshwater fish has continued to play an important role in understanding the underlying causal factors shaping inter- and intra-specific genetic variations of freshwater fish biodiversity across contemporary isolated drainages . LandscaThe Yangtze River Basin is generally thought to lie in the northernmost part of southern China, based on delimitation of the northern boundary of the subtropical zone across China . After tA long-term biogeographic barrier, the Zhe–Min Uplift , formed in the Late Mesozoic, and extends from the southern Korean Peninsula to Zhejiang and Fujian Provinces in southern China . It was 2 during high water levels, and is connected with the Yangtze River by a narrow outlet (Huigobio chenhsienensis (Cypriniformes: Gobionidae) among the major tributaries flowing into the Poyang Lake  is a small stream freshwater gudgeon with body length less than 7 cm  gene, integrative analyses of phylogeny, population demography, and ancestral drainage and paleo-drainage reconstructions were carried out to explicitly explore the role of landscape evolution in the genetic diversification of S. parvus. We also test special hypotheses regarding the Zhe–Min Uplift as a biogeographic barrier before the late Early Pleistocene and the Poyang Lake as an ecological barrier leading to genetic divergence of S. parvus.Despite much progress having been made in understanding how landscape evolution has shaped the phylogeographic patterns of freshwater fishes in southern China over time , a detaiSarcocheilichthys parvus were collected, with the help of local fishermen, across the six coastal drainages in southern China from November 2011 to December 2019  were carried out using 30.0 μL final volumes containing 15.0 μL 2 × Es Taq MasterMix, 0.6 μL of each primer with 20 μM, 1.2 μL genomic DNA and 12.6 μL dd H2O. All PCR reactions were run as follows: Thermal cycling began with 95°C denaturation for 5 min, followed by 35 cycles of 95°C denaturation for 50 s, 52.4°C–56.0°C annealing , and manually adjusted if necessary. Nucleotide sequences for each gene were aligned using the MAFFT v7.427 software . HaplotyS. parvus was inferred using haplotype sequences in the BEAST v2.6.6 software . Another fossil species, †Gnathopogon macrocephala . The birth–death model and relaxed log-normal clock model were employed to specify the priors. Two duplicates of 400,000,000 MCMC generations were run, with a sample frequency of 10,000 and 30% burn-in. All parameters reached convergence (ESSs >200).The time-calibrated phylogeny of software . Due to elatives , on the elatives . The bMoelatives . The firyuanensi , occurriyuanensi , correspocephala , occurriocephala , correspS. parvus, the divergence time between S. parvus and Sarcocheilichthys caobangensis . Based on the results of the molecular clock test  accomplished by likelihood ratio test in the DAMBE v7.2.43 software  and nucleotide diversity (π), were calculated in the Arlequin v3.5.2.2 software (ΦST), pairwise ΦST, and their significance among the drainages were obtained through 1000 random permutations and the Tamura–Nei model using Arlequin v3.5.2.2. Two genetic differentiation indices, NST and GST, were obtained with 1000 random permutations in the PermutCpSSR v2.0 software . The generation time of S. parvus was set as 2 years  were inferred in BEAST v2.6.6 and visualized in Tracer v1.7.0 , using t 2 years . Therefoyears  project, and a series of hydrological tools were used to create visualized paleo-drainages [for further details, see Due to the maximum sea level drop of about 130 m during the Last Glacial Maximum , paleo-dCyt b sequences  from 235 individuals of S. parvus  ranged from 0.9623 (Qiantang River) to 0.7033 (Han River), while the nucleotide diversity (π) ranged from 0.0304 (Yangtze River) to 0.0013 (Han River) among the distribution drainages of S. parvus , and pairwise ΦST values among the drainages ranged from 0.105 (between Han River and Pearl River) to 0.970  at K = 8 in the SAMOVA analysis. The best grouping arrangement corresponding to K = 8 was as follow. Group 1 (G1): Pearl River  and –24.237 (p = 0.000) for Lineage A, and –1.978 (p = 0.006) and –25.262 (p = 0.000) for Lineage B. The mismatches exhibited multimodal and unimodal distributions for lineages A and B, respectively  flowing into the Poyang Lake, in the apparent absence of physical barriers , displaying strong phylogeographic structure. The splitting of lineages A and B was attributed to geographic isolation, due to the Zhe–Min Uplift acting as a biogeographical barrier before the late Early Pleistocene. Within lineage A, the strong genetic divergence in the Poyang Lake sub-drainage of the middle Yangtze River could be explained by Poyang Lake acting as an ecological barrier, and historical river capture was the main driver of the range expansion of S. parvus from the Yangtze River into the Pearl and Han rivers. Within lineage B, the lack of phylogenetic structure among the lower Yangtze River, Qiantang River, Jiaojiang River, and Ou River may have resulted from paleo-drainage connections or episodic freshwater connections during the eustatic low stand of sea level in the Late Middle–Late Pleistocene. Our results also provide new insight into the planning of management and conservation strategies for preserving the genetic diversity of freshwater fishes in southern China. More phylogeographic studies are needed to draw generalizations on the role of the Zhe–Min Uplift as a biogeographic barrier and Poyang Lake as an ecological barrier in the biogeographic processes of freshwater fishes in southern China.In summary,


Ostrea edulis) is an ecologically and economically important marine bivalve, that has been severely affected by the intracellular parasite Bonamia ostreae. In this study, a flat oyster SNP array  was used to validate previously reported outlier loci for divergent selection associated with B. ostreae exposure in the Northeast Atlantic Area. A total of 134 wild and hatchery individuals from the North Sea, collected in naïve (NV) and long‐term affected (LTA) areas, were analysed. Genetic diversity and differentiation were related to the sampling origin (wild vs. hatchery) when using neutral markers, and to bonamiosis status (NV vs. LTA) when using outlier loci for divergent selection. Two genetic clusters appeared intermingled in all sampling locations when using outlier loci, and their frequency was associated with their bonamiosis status. When both clusters were compared, outlier data sets showed high genetic divergence (FST > 0.25) unlike neutral loci (FST not ≠ 0). Moreover, the cluster associated with LTA samples showed much higher genetic diversity and significant heterozygote excess with outlier loci, but not with neutral data. Most outliers mapped on chromosome 8 (OE‐C8) of the flat oyster genome, supporting a main genomic region underlying resilience to bonamiosis. Furthermore, differentially expressed genes previously reported between NV and LTA strains showed higher mapping density on OE‐C8. A range of relevant immune functions were specifically enriched among genes annotated on OE‐C8, providing hypotheses for resilience mechanisms to an intracellular parasite. The results suggest that marker‐assisted selection could be applied to breed resilient strains of O. edulis to bonamiosis, if lower parasite load and/or higher viability of the LTA genetic cluster following B. ostreae infection is demonstrated.European flat oyster ( Bonamia ostreae is a destructive intracellular parasite transmitted horizontally and putatively also vertically, causing a heavy inflammatory reaction that leads to high mortality following the loss of normal architecture and function in multiple organs  naïve (NV), supposedly to have never been in contact with the parasite and (ii) long‐term affected (LTA), where parasite presence was first reported over 30 years ago and monitored over time to the present  three oyster beds from the North Sea ; (ii) two hatchery batches  from the same area (FAO subarea 27.4 2.2Genomic DNA was extracted from gill tissue using the E.Z.N.A.® Mollusc DNA kit (OMEGA Bio‐Tek) following the manufacturer's recommendations. Briefly, between 30 and 50 mg of fresh gill was treated with a solution containing 20 μl of proteinase K and 700 μl of lysis buffer (CSPL) overnight while gently shaking to mix thoroughly at 65°C. Samples were subjected to RNAse A treatment and to several buffer washes by centrifugation. DNA quantity and purity was measured with a Nanodrop spectrophotometer and DNA concentrations were normalized for genotyping.Samples were shipped to Identigen  for genotyping on a medium‐density Affymetrix Axiom SNP array . After filtering SNPs with <90% call rate, 11,641 were retained, which supports the robustness of the SNP chip platform  and observed (Ho) heterozygosity, and allelic richness (Ar), computed using the rarefaction method. These analyses were performed with the DiveRsity R package v. 1.9 using the ‘basicStats’ function  obtained after 1000 bootstraps iterations with ‘divBasic’ function (DiveRsity R package). A full‐pedigree reconstruction method implemented in Colony v2.0.6.6  between sampling locations was calculated using the StaMPP R package v 1.6.2 with the ‘stamppFst’ function . Global FST was calculated using the R package Genepop with the ‘Fst’ function  in the samples compatible with the data by analysing linkage disequilibrium and Hardy–Weinberg equilibrium following a Bayesian clustering approach. Tests without a priori information were performed using the R package ParallelStructure v 1.0 . To identify the most likely number of clusters, two different K estimators were used: deltaK  and NV  groups (POPGLAG = 1) previously reported , a multivariant method to infer the number of clusters in a sample of genetically related individuals, was applied as a complementary method to infer structure in the samples studied using the Adegenet package function ‘dapc’ in RStudio  was obtained using an exact test for genotyping disequilibrium with Genepop 4.7  between outliers mapping in the same chromosome was estimated using the square of the correlation coefficient Rousset, . LD betwRousset, , conside2.6n = 20; Leitao et al., GOfuncR version 1.14.0 , expected heterozygosity (He) and allelic richness (Ar) were estimated as averages across the 11,641 SNP data set obtained in the North Sea samples after filtering, considering their origin. In wild samples, Ho ranged between 0.264 in NO and 0.275 in OS (mean = 0.269 ± 0.003); He from 0.265 in NO to 0.271 in OS (0.268 ± 0.002); and Ar from 1.893 in WZH to 1.908 in OS and GBR (1.903 ± 0.005) . All sampling locations showed no deviation from panmixia using the whole SNP data set , although the intrapopulation fixation index (FIS) was negative and significant using the 95% range test in the hatchery samples, indicating a slight heterozygote excess. Nearly identical figures were obtained using neutral loci (data not shown).Observed heterozygosity (5) Table . HatcherNe) of 11 for OSH, and 16 breeders, seven males and nine females, and Ne = 10 for WZS, suggesting a rather balance contribution of breeders. In fact, the average number of individuals per family was 1.474 ± 0.208  for OSH and 2.077 ± 0.380  for WZS. Conversely, wild samples showed no parentage between all specimens analysed in GBR and NO , and only two half‐sibs among the 28 were detected in OS (Ne = 1512), consistent with the higher genetic diversity observed in wild samples.The program Colony estimated a total of 16 breeders, six males and 10 females, and an effective population size   showed lower genetic diversity than those from those long‐term affected by bonamiosis , specially for the 16 outlier data set  Table .3.2FST = 0.0283; Table FST = 0.0003), while the Norwegian (NO) samples were the most divergent (average FST = 0.0169). The two hatchery samples showed the highest pairwise FST value considering the neutral SNP data set (0.0540), but also a high average pairwise FST with respect to the wild ones (0.0356). Lower but not significant (p > 0.05) average pairwise FST values between all sampling locations were observed with the consistent (FST = 0.0253) and suggestive (FST = 0.0159) outliers (Table FST = 0.0283). However, they were higher than the neutral markers when comparing NV (NO and WZH) vs. LTA  sampling locations , although not significant (p > 0.05).Low, but significant, genetic differentiation was observed for most pairwise comparisons between the five North Sea sampling locations with the neutral SNP data set (average pairwise rs Table  regardinK = 1–6 using deltaK and Mean LnP(K) estimators, rendered an optimal K = 3 with the neutral and K = 2 using both outlier SNP data sets. A clear distinction between the hatchery  and wild samples  was observed with the neutral data set .The STRUCTURE analysis without a priori information assessed for t Figure , while tt Figure , the samTo ascertain the correspondence between the ‘orange’ and ‘blue’ clusters with the NV and LTA groups previously reported by Vera et al. , anotherK = 2. A very similar representation was observed with the suggestive outlier data set (data not shown).The genetic structure of flat oyster samples was also tested with DAPC analyses Figure . For theq value) using a threshold of q“orange” > 0.5 and q“blue” ≥ 0.5 to classify the few admixed individuals observed  was almost twofold higher in the ‘orange’ than in the ‘blue’ cluster with both outlier data sets, while the FIS was negative and highly significant in the ‘orange’ cluster, indicating a heterozygote excess, but not in the ‘blue’ one, which conformed to Hardy–Weinberg expectations , while nonsignificant for the neutral . The comparison of the FST distribution between the ‘orange’ and ‘blue’ samples per locus with the three data sets revealed sharp differences between the neutral and outlier data sets, especially for the consistent outliers , especially for the consistent one .Furthermore, s Figure . All neus Figure . Neutral3.3FST between ‘orange’ and ‘blue’ groups (0.182), all consistent outliers showed highly significant LD after Bonferroni correction  detected by Ronza et al.  when coms Figure A and sugp = 0.170). Among them, a block of 1.4 Mb (from 26.3 to 27.7 Mb), close to the proximal end of the 29.44 Mb region, included several genes involved in immune or stress responses, namely genes encoding three collagen‐alpha proteins, calpain, programmed cell death protein 10, mt rRNA methyltransferase 2 and G‐coupled protein coupled receptor Mth2  was very similar to that reported in the Northeast Atlantic using the same SNP data set by Vera et al.  than their wild counterparts, congruent with the estimated effective population size . Anyway, parental contributions were rather evenness both in OSH and WZH, which suggests that the reproduction protocol, at least regarding the maturation condition of parents, seems suitable, and so the main point to be considered in future restocking programs would be the number of batches and progenitors per batch to maintain diversity.Average genetic diversity in wild samples from the North Sea , although related to geographical distance, supporting isolation by distance. Divergence was much higher between hatchery and wild samples (average pairwise FST = 0.0325), as expected due to genetic drift associated to the low number of breeders used for batch production and STRUCTURE analysis depicted a clear differentiation between both groups.Population structure using neutral and outlier loci data sets was evaluated to disentangle genetic differentiation due to demographic factors (genetic drift and migration) from those related to selection due to bonamiosis pressure. As reported by Vera et al. , low genp = 0.054), the orange cluster being more frequent in the LTA samples. The sample size used in our study limits the statistical power of the Fisher's test and in fact, a highly significant association has been detected when sample size increased ; NV sample: 189 individuals (7 ‘orange’/182 ‘blue’); exact test p‐value <0.00001; Kamermans P., in preparation). Furthermore, the ‘orange’ group corresponded to the LTA group reported by Vera et al. (Outlier data sets identified two genetic clusters (‘blue’ and ‘orange’) in the samples from the North Sea that were well defined at the individual level. The proportion of ‘orange’ and ‘blue’ individuals was marginally associated with their bonamiosis status  and the ‘orange’ group showed twice the genetic diversity of the ‘blue’, coupled with a highly significant heterozygote excess, while the ‘blue’ group did not depart from Hardy–Weinberg expectations. This observation might suggest an overdominance mechanism underlying resistance to bonamiosis, but further work should be done for its confirmation. Tolerance or resistance to stress or pathologies has been associated with overdominance in other studies , but their survivorship could not be tested, and we do not know if they are actually more resistant to infection. To disentangle the basis for resilience of orange individuals, it would be necessary to devise a challenge at indoor facilities or alternatively, to gather broad field data with detailed quantitative information on parasite load along with genotyping of the associated genetic markers.‘Orange’ and ‘blue’ groups from the North Sea displayed striking genetic differentiation (Cerastoderma edule), a species with an overlapping distribution and similar life‐history features to flat oyster, Vera et al. (r2 of 0.05, which was significant at distances below 50 kb. The LD detected at OE‐C8 in flat oyster suggests a chromosome reorganization blocking recombination, such as an inversion, that could be confirmed by comparing whole genome long‐read re‐sequencing of ‘orange’ and ‘blue’ samples. Polymorphisms for inversions have been reported to be maintained by natural selection as adaptations to specific environmental conditions by establishing blocks of coadapted genes in a diverse range of species (Fontdevila et al., Vera et al.  reporteda et al.  detectedB. ostreae have been associated with preventing the entrance of the parasite into haemocytes, and with apoptotic processes arresting proliferation of the parasite once it surpasses the external barrier (Gervais et al., Flat oyster responses to 5B. ostreae resilience. This genomic region include genes encoding proteins related to apoptosis and extracellular matrix components, considered key in the response to an intracellular infection. We also detected significant enrichment GO terms involving genes controlling epigenetic marks that could be related to the quick response to bonamiosis observed in LTA oysters. The identified markers at OE‐C8 associated with B. ostreae resilience could aid for recovering its production and ecosystem services throughout the European coast. However, further work is needed to test this association at individual level, to disentangle the components underlying bonamiosis resilience and to confirm the chromosome reorganization suggested at OE‐C8 supporting an overdominance mechanism. Finally, the putative involvement of epigenetic mechanisms on the transgenerational immune memory for a quick response to the parasite is a suggestive hypothesis that deserves further attention.Our data adds new evidence to the divergent selection outliers detected previously by Vera et al.  associatThe authors declare that they have no competing interest.Figures S1–S3Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.


HOGA1 attains exome-wide significance  for body mass index (BMI). The mRNA expression of HOGA1 is significantly increased in human adipose tissues from obese individuals in the Genotype-Tissue Expression (GTEx) dataset, which supports the genetic association of HOGA1 with BMI. Functional analyses employing cell- and animal model-based approaches were performed to gain insights into the functional relevance of Hoga1 in obesity. Adipogenesis was retarded when Hoga1 was knocked down by siRNA treatment in a mouse 3T3-L1 cell line and a similar inhibitory effect was confirmed in mice with down-regulated Hoga1. Hoga1 antisense oligonucleotide (ASO) treatment reduced body weight, blood lipid level, blood glucose, and adipocyte size in high-fat diet-induced mice. In addition, several lipogenic genes including Srebf1, Scd1, Lp1, and Acaca were down-regulated, while lipolytic genes Cpt1l, Ppara, and Ucp1 were up-regulated. Taken together, HOGA1 is a potential causal gene for obesity as it plays a role in excess body fat development.Although there are a number of discoveries from genome-wide association studies (GWAS) for obesity, it has not been successful in linking GWAS results to biology. We sought to discover causal genes for obesity by conducting functional studies on genes detected from genetic association analysis. Gene-based association analysis of 917 individual exome sequences showed that  Obesity, characterized by increased adipose tissue mass, poses a major unmet public health problem since it is a risk factor for many metabolic diseases and cancer . Excess Obesity is a heritable disease and more than 250 common genetic variants are identified, mainly by genome-wide association studies (GWASs) for body mass index (BMI) over the past decade . HoweverRecently, deep sequencing of all known exons through high-throughput sequencing technology detected low-frequency disease-causing variants . This teA total of 917 subjects from the Korean population were recruited between 2001 and 2014 at Seoul National University Hospital . The stuhttp://broadinstitute.github.io/picard/), and Genome Analysis Toolkit (GATK) software    was emplP-values < 0.05 were considered statistically significant.In 3T3-L1 cell studies, relative gene expression levels measured from three independent experiments are presented as the mean ± standard deviation (SD). Group differences were assessed by the Wilcoxon rank-sum test. Statistical analyses were performed using R software. P-values < 0.05 were considered statistically significant. Statistical analyses were performed using SPSS 12.0 .Results of animal studies from more than three independent experiments are presented as the mean ± standard deviation (SD). Group differences were assessed using one-way analysis of variance (ANOVA), followed by Scheffe’s test. HOGA1 expression data and phenotype information (phs000424.GTEx.v7. p2. c1. GRU) were obtained from the GTEx data portal   obesity classification system. The association between BMI and HOGA1 expression in a total of 848 adipocyte samples was tested by linear regression analysis adjusted for age, sex, and race.g/home/) . HOGA1 eHOGA1 expression was compared between 49 normal and 49 obese individuals using RNA sequencing data of 188 liver tissue samples from the GTEx dataset. Group differences were assessed by the Wilcoxon rank-sum test. Statistical analyses were performed using R software. P-values < 0.05 were considered statistically significant.2 at 37°C. After the 3T3-L1 preadipocytes became 100% confluent, the medium was replaced with a differentiation medium containing 10% FBS , 0.5 mM isobutylmethylxanthine, 1 μM dexamethasone, and 1 μg/ml insulin and cultured for 2 days. The medium was replaced with a differentiation medium every 2 days. On day 7, cells were washed twice with phosphate-buffered saline, fixed with 10% formalin for 20 min at room temperature, and then stained with 0.5% Oil-Red-O in isopropanol for 20 min. Cells were washed with water and images of each dish were taken using an Eclipse TE 2000U inverted microscope with twin CCD cameras .3T3-L1 preadipocytes derived from mouse 3T3 cells were grown in DMEM (Dulbecco’s modified Eagle’s medium) culture medium supplemented with 10% bovine calf serum (BCS) and 1% penicillin in a humidified incubator containing 5% COHoga1 siRNAs and scrambled control siRNA were designed according to GenBank accessions (Hoga1 (100 pmol). siRNA was incubated with Lipofectamine RNAiMAX  in DMEM for 20 min at room temperature before transfection. Six hours after transfection, the medium was replaced with a culture medium and further induced into adipocytes as described above. Differentiated adipocytes were assessed by Oil-Red-O staining and analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) of adipogenic markers.cessions . Two day○C, 55 ± 5% relative humidity, 12 h light–dark cycle). The mice were allowed free access to food and tap water during the experiment. To study the role of Hoga1 in vivo, antisense oligonucleotide (ASO) were used as previously described (Hoga1 were designed for the mouse Hoga1 mRNA sequence and screened to select the most potent ASO (Hoga1 ASO (HFD Hoga1 ASO group), or control ASO (HFD vehicle group) twice per week (25 mg/kg) for 9 weeks. The NCD vehicle group was intraperitoneally injected with control ASO twice per week (25 mg/kg) for 9 weeks. The body weight and food intake were measured every week during animal experiments. All mice were sacrificed by cervical dislocation after overnight fasting at the end of Hoga1 ASO treatment. Fat pads and livers were removed, weighed, and frozen in liquid nitrogen. This study adhered to the Guide for the Care and Use of Laboratory Animals developed by the Institute of Laboratory Animal Resources of the National Research Council. The study protocol was approved by the Institutional Animal Care and Use Committee of the Korea Institute of Science and Technology in Seoul, Korea.Twenty one 5 week-old male C57BL/6J mice  were housed in a controlled environment  and liver obtained from all mice were embedded in tissue-freezing medium . Histological sections (5 μm thickness) of the tissues were cut, deparaffinized in xylene, and rehydrated in an alcohol solution gradient. After fixation, staining with hematoxylin and eosin (H&E) was performed, and adipocyte size was analyzed with an Eclipse TE 2000U inverted microscope with twin CCD cameras . TG, TC, and glucose levels were measured enzymatically using commercial kits . Serum leptin, MCP-1, TNF-α, IL-6, and insulin were quantified using enzyme-linked immunosorbent assay (ELISA) kits . HOMA-IR was calculated using glucose and insulin levels. Each commercial kit was used according to the manufacturer’s instructions.Blood samples were collected by cardiac puncture from all mice, and serum was separated by centrifugation at 7,000 rpm for 10 min and stored at −70Epididymal WAT and liver specimens from all mice (approximately 20 mg each) were homogenized in lysis buffer supplemented with a protease inhibitor cocktail . Western blotting was performed as described in a previously published study . Target Total RNA was isolated from 3T3-L1 cells, livers, or WAT using Trizol reagent . Total RNA was quantified by nanodrop . cDNA was synthesized from 1 µg aliquots of the total RNA in a reaction mixture containing oligo (dT) and reverse transcription premix . qRT-PCR was performed as described previously  using th3T3-L1 cells were purchased from the American Type Culture Collection . Oil-Red-O, 3--2,5-diphenyltetrazolium bromide (MTT), and other reagents were purchased from Sigma-Aldrich . FBS, penicillin/streptomycin and DMEM were purchased from Hyclone . BCS was purchased from Gibco . Antibodies against SREBP-1c, SCD1, FAS, PPARα, PGC-1α, CPT-1L, and β-actin, and HRP-linked anti-rabbit and anti-mouse IgG secondary antibodies were from Cell Signaling Technology  and Santa Cruz Biotechnology , while the antibody against Hoga1 was from Abcam .Genetic association analysis using whole exome sequencing (WES) data from 917 subjects was performed to identify the causal genes for obesity . Among aHOGA1) at exome-wide significance (p < 2.7 × 10–6) with BMI   . 0.0058)  using RNg/home/) . Linear HOGA1 (Hoga1) was first examined during adipocyte differentiation of 3T3-L1 cells derived from mouse 3T3 cells. Although 3T3-L1 cells have a fibroblast-like morphology, the cells display an adipocyte-like phenotype under appropriate differentiation conditions (Hoga1 expression was observed in response to adipogenic stimulation at the late stage together with the induced gene expression pattern of peroxisome proliferator-activated receptor gamma (Pparg), a key regulator of adipocyte differentiation  twice a week. After 9 weeks of treatment, Hoga1 mRNA and protein levels in the epididymal white adipose tissue (eWAT) increased compared to those in the normocaloric diet (NCD) mice. This increase in Hoga1 mRNA and protein levels in the eWAT dropped to that of NCD mice when HFD-induced mice were treated with Hoga1 ASO  (Hoga1 is associated with adipogenesis.C57BL/6J mice induced with a high-fat diet (HFD) were randomly divided and intraperitoneally injected with a control oligonucleotide (vehicle) or with oga1 ASO . Howeverced mice . Similar= 0.297) . These rHoga1 ASO (25 mg/kg) suppressed the HFD-induced weight gain  (47%), and leptin (38%), whereas total cholesterol (TC) in the serum did not significantly change  levels in HFD-induced mice  than in HFD-induced mice treated with scrambled control ASO (HFD vehicle) (Hoga1 down-regulation ameliorates adipose tissue inflammation in obese mice.Obesity induces chronic inflammation in adipose tissues . We test in eWAT . Serum cvehicle) . This suHoga1 in adipogenesis. The levels of proteins   (Ppara), carnitine palmitoyl transferase 1 (Cpt1a), Ppar-gamma coactivator 1 alpha (Pgc1α), and uncoupling 1 (Ucp1) were increased in the Hoga1 ASO-treated group. These results indicate that Hoga1 may enhance and suppress the expression of lipogenic and lipolytic genes, respectively.The expression levels of genes involved in lipid metabolism in eWAT were measured to gain insights into the molecular mechanism of e [Fas])  and mRNA Srebf1)  involved Srebf1)  and mRNA Srebf1)  levels op-values , with exome-wide significance (p = 7.07 × 10–7) for BMI  reduced Hoga1 expression in eWAT, (ii) decreased adipocyte size and the expression of lipogenic key factors in the eWAT, and (iii) decreased inflammatory cytokine levels that are highly associated with metabolic dysregulation in tissue and serum. Weight loss in obesity ameliorates inflammatory factors  protein interactions need to be elucidated to understand the molecular mechanism exerted by HOGA1 in adipogenesis.Dysregulation of lipid metabolism is one of the major pathogenesis of obesity . CirculaModulating the proliferation or differentiation of adipocytes may yield promising drug targets . However


For the emission magnitudes of 78 C40 cities, we find good correlations between the GPC and EDGAR (R2 = 0.80) and the GPC and ODIAC (R2 = 0.72). Regionally, African cities show the largest variability in the three emission estimates. For the emission trends, the standard deviation of the differences is ±4.7% yr−1 for EDGAR vs. GPC and is ±3.9% yr−1 for ODIAC vs. GPC: a factor of ∼2 larger than the trends that many C40 cities pledged . To examine the source of discrepancies in the emission datasets, we assess the impact of spatial resolutions of EDGAR (0.1°) and ODIAC (1 km) on estimating varying-sized cities’ emissions. Our analysis shows that the coarser resolution of EDGAR can artificially decrease emissions by 13% for cities smaller than 1000 km2. We find that data quality of emission factors (EFs) used in GPC inventories vary regionally: the highest quality for European and North American and the lowest for African and Latin American cities. Our study indicates that the following items should be prioritized to reduce the discrepancies between the two emission calculation methods: (a) implementing local-specific/up-to-date EFs in GPC inventories, (b) keeping the global power plant database current, and (c) incorporating satellite-derived CO2 datasets (i.e. NASA OCO-3).Under the leadership of the C40 Cities Climate Leadership Group (C40), approximately 1100 global cities have signed to reach net-zero emissions by 2050. Accurate greenhouse gas emission calculations at the city-scale have become critical. This study forms a bridge between the two emission calculation methods: (a) the city-scale accounting used by C40 cities—the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) and (b) the global-scale gridded datasets used by the research community—the Emission Database for Global Atmospheric Research (EDGAR) and Open‐Source Data Inventory for Anthropogenic CO Urban emissions could increase as the global population percentage living in cities is expected to increase from 55% in 2018 to 68% in 2050 U.N. . Under tet alet alet alet alThe Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) is a guideline tool for cities to estimate annual GHG emissions  . We expand on the previous literature by Chen et al  and global gridded emission datasets (EDGAR and ODIAC) in estimating citywide CO2.2.1.www.c40knowledgehub.org/s/article/C40-cities-greenhouse-gas-emissions-interactive-dashboard) (C40 2 (FFCO2) emissions by applying a scaling factor of 0.982 to the total fossil-fuel GHG emissions from the Dashboard data (figure S1). See text S1 for detailed methods for isolating FFCO2 emissions from each CO2 emissions dataset.The C40 GHG Dashboard compiles the GPC GHG emission inventories reported by 82 C40 cities (46 countries) to CDP. The emission accounting years for these inventories vary by city, ranging from 1990 to 2020. The C40 GHG Dashboard data are obtained from the C40 knowledge hub website (ard) C40 . We isol2 emissions estimates are primarily based upon energy data from the International Energy Agency and national-specific EFs . These annual CO2 emissions by sub-sectors are spatially distributed into 0.1° grids globally using ∼300 spatial proxies (i.e. population/urban settlements (JRC GHSL), point source distributions , and line sources (OpenStreetMap) , available for the 1970–2018 period, are obtained from the JRC EDGAR website (https://edgar.jrc.ec.europa.eu/dataset_ghg60).EDGAR’s annual fossil CO2 emission estimates from the Carbon Dioxide Information Analysis Center .ODIAC downscales national fossil CO2.2.2 emissions from the five sectors—residential, commercial, industrial, energy, and transportation—from the GPC inventory, EDGAR, and ODIAC, respectively. Four C40 cities—Bangkok, Dakar, Hong Kong, and Phoenix—are excluded due to missing emissions data for one or more subsectors in the GPC inventories. See text S1 for detailed methods for isolating FFCO2 emissions from each CO2 emissions dataset.We align the three datasets—GPC inventory, EDGAR, and ODIAC—by sector, time, and spatial area to enable comparisons between them. For the sectoral alignment, we include scope 1 FFCOFor the temporal alignment of annual emission estimates, we choose the most recent accounting year with estimates from all three emissions datasets available for each city. Of the 82 C40 cities with self-reported inventories, 74 cities have inventories for years between 2014 and 2018, which align with the years available from EDGAR version 6.0 and ODIAC version 2020b. Four cities—Rotterdam, Bengaluru, Mumbai, and Washington, DC—have their first available GPC inventory for 2019, and we use EDGAR emissions for 2018 for the comparison. For the temporal alignment of emission trend estimates, we use the earliest and latest accounting years available before 2019 for each city. Of the 82 C40 cities, 39 cities have submitted at least two complete inventories between 2000 and 2018.N = 55), broader metropolitan area (N = 14), province/district/state/county (N = 6), and others . For the spatial alignments, we aggregate CO2 emissions from EDGAR and ODIAC by sampling grids within the same geographical boundary used to construct the GPC inventory of each city  and scope 2 (i.e. emissions occurring due to the use of grid-supplied energy) emissions from stationary energy and transportation sectors, and scope 1 and scope 3 (i.e. emissions occurring outside the city due to activities within the city) emissions from the waste sector  is 57% for Africa, 77% for Central East Asia, 51% for South and West Asia, 84% for Europe, 76% for Latin America, 70% for North America, and 49% for South and West Asia cities, with the global mean of 69%. For the nine cities—Abidjan, Amsterdam, Barcelona, Buenos Aires, Copenhagen, Rio de Janeiro, Rotterdam, Venice, and Yokohama—the BASIC emissions are smaller than the FFCO2 emissions as these cities are net exporters of electricity toward surrounding areas.On average, the percentage of FFCO2) of FFCO2 are emitted from 78 C40 cities annually   to citywide FFCO2 emissions, the residential/commercial/industrial sector contributes 32% (1σ: ±17%), and the transportation sector contributes 51% (1σ: ±22%). Regionally, cities in Europe have the largest energy sector contribution to CO2 emissions in this subset of cities (33 ± 30%), and cities in North America have the lowest energy sector contribution (7 ± 10%). Transportation contributes the most to FFCO2 emissions in cities in Latin America (65 ± 18%) and the least in Central East Asian cities (32 ± 24%).Based on GPC inventories, 890 million tons MtCO of FFCO22 emissions  is 14.7 million (14 312 km2) for Chengdu and 0.25 million (26 km2) for the city of Sydney (Note: the Sydney metropolis’ covers 5.2 million people in 12 368 km2). Figure 2 emission for Sydney is 13.4 MtCO2 km−2 (citywide 0.3 MtCO2), greater than Tokyo’s per-area emissions of 10.8 MtCO2 km−2 (citywide: 23.6 MtCO2), highlighting the importance of using the matching geographical boundary for the emission comparison study. Cities with power plants within their geographical boundary tend to have larger scope 1 FFCO2 emissions (i.e. Rotterdam). In this study, we do not consider emissions occurring due to the import/export of electricity.Socioeconomic factors such as population, area, and population density show scaling relationships with citywide COllion 14 12 km2 fo3.1.2.2 emissions in GPC inventories for the 78 C40 cities compared to emission estimates from the two global gridded emission datasets: EDGAR and ODIAC. Accounting years of the GPC inventories chosen for comparison range from 2014 to 2019 (median year: 2018). EDGAR’s estimates of FFCO2 emissions show a good correlation with GPC inventory estimates, with an R2 value of 0.80 and the slope of ordinary least squares (OLS) fit as 1.09. Regionally, cities in North America show the highest R2 value of 0.95, followed by cities in Europe (R2 = 0.94). Across 78 cities, the mean and standard deviation of the relative difference between EDGAR and GPC inventory estimates are −25 ± 53% (Relative difference = (EDGAR − GPC)/mean × 100). African cities show the largest relative difference (−70 ± 84%). For Latin American cities, EDGAR emissions tend to be consistently smaller than GPC inventory emissions: 11 of 12 Latin American cities show EDGAR emissions lower than GPC inventory emissions. Only Salvador, Brazil, shows EDGAR emissions greater than GPC emissions by 13%.Figure R2 of 0.72 and the slope of OLS fit as 1.08. Regionally, European cities show the highest R2 of 0.91. Across all 78 cities, the mean and standard deviation of the relative difference between ODIAC and GPC inventory estimates are 12 ± 62%. For six cities in South and West Asia, ODIAC emission estimates are consistently larger than the GPC inventory emission estimates (RD = 82 ± 36%). African cities show a relative difference of −11 ± 111%, the largest variability across all regions.ODIAC shows a good correlation with GPC inventory estimates for the 78 C40 cities, with an 3.2.3.2.1.2 emissions between the earliest and latest accounting years for which GPC emissions inventories are available. For the 46 C40 cities with more than one accounting year, the temporal gaps between the two accounting years range from 1 to 18 years (median: 5 years). Of the 46 C40 cities, 25 cities show declines in FFCO2 emissions over time. New Orleans, USA, shows the fastest decline rate of −13.1% yr−1 between 2005 and 2018. The rapid decline of CO2 emissions in New Orleans results from the 2016 closure of the Michoud power plant . The transportation sector emissions have declined for 26 out of 46 cities since the first accounting years. For the residential/commercial/industrial sector, 26 out of 46 cities show declines in emissions. Energy sector emissions have declined in 19 cities and increased in another 19, while the remaining 8 cities have no in-city energy sector CO2 emissions.Figure lant EPA . The med3.2.2.2 emissions for 39 C40 cities, estimated from the GPC inventory, EDGAR, and ODIAC. For the comparison analysis, we calculate the trends in emissions using the emissions for two accounting years as followings: /CO2,Earliest/Δyears × 100, unit: % yr−1). The comparison analysis excludes seven cities listed in figure Gridded Dataset − TrendGPC) is 0.5 ± 4.7% yr−1 for EDGAR vs. GPC and −0.3 ± 3.9% yr−1 for ODIAC vs. GPC.Figure −1 for EDGAR vs. GPC and −1.0% yr−1 for ODIAC vs. GPC. However, the trend estimates for Latin American cities showed significant variability: the standard deviation of the difference is ±6.0% yr−1 for EDGAR vs. GPC and ±4.5% yr−1 for ODIAC vs. GPC, both values being the second largest following African cities .Two African cities—Johannesburg and Tshwane, South Africa—show the largest discrepancies in the trend estimates, having opposite directional changes. For Latin American cities, the trend estimates showed overall good agreement with a mean difference of 0.3% yr3.3.3.3.1.2 emissions as follows. First, ODIAC’s 1 km grid cells are spatially aggregated into 0.1° grid cells. Then, citywide FFCO2 emissions for 78 C40 cities are estimated from 0.1° gridded ODIAC using the same area scaling approach used for EDGAR , −2.6% for cities between 1000 km2 and 3000 km2 (N = 21), and −0.7% for cities larger than 3000 km2 (N = 10). Boston, USA , the coefficient of variation (CV) is calculated for each of the 78 C40 cities . Figure N when aggregated to citywide sum emissions. Cities in North America showed the best goodness-of-fit statistic with the chi-squared (2), was lowest for European cities (383%) and highest for African cities (1373%).As a measure of variability among three citywide FFCO3.3.3.2 emissions increased by 12.3% yr−1 (1.0 MtCO2) between 2014 and 2018, and the power plant emissions account for two third of the increase (0.64 MtCO2) .For Amman, Jordan, we found inconsistent accounting of the two gas-fired power plants—Amman East Power Plant (400 MW) and Levant Power Plant (241 MW)—which generate 8% of Jordan’s electricity demand , and the power sector induced a decline of 34% .The Michoud power plant in New Orleans, which accounted for one-third of citywide FFCO2016 EPA . The New2016 EPA . Accordi% figure . Meanwhi3.3.4.The GPC inventories submitted through the City Inventory Reporting and Information System include a spreadsheet documenting EFs. Figure 4.4.1.2 emission is −25 ± 53% between EDGAR and GPC inventory and 12 ± 62% between ODIAC and GPC inventory. Among the seven global regions, cities in Africa showed the largest variability in the relative difference . This result is consistent with Chen et al , Sao Paulo (+43%), and Beijing (+40%). Our analysis shows that ODIAC’s FFCO2 emissions are higher than the GPC inventories for Cape Town (+91%) and Sao Paulo (+31%). Meanwhile, EDGAR’s estimates are lower than the GPC inventories for Cape Town (−25%) and Sao Paulo (−60%).For 78 C40 cities, the relative difference in annual citywide FFCOen et al , who repl  and ±3.9% yr−1 (ODIAC vs. GPC) are greater than the rates of emission reduction that many C40 cities have pledged: i.e. net zero by 2050 from the baseline year of 2010 (−2.5% yr−1). This finding suggests that emission estimates from global gridded emission datasets must be significantly improved for city-level policy applications, such as emission mitigation progress tracking. The following sections discuss three primary sources of uncertainty—spatial disaggregation, point emission sources, and EFs.For 39 C40 cities, the difference of trend estimates is 0.5 ± 4.7% yr4.2.2 emission studies given its 1 km spatial resolution . For the cities between 1000 km2 and 3000 km2, relatively small underestimations are expected (−2.6%) when using EDGAR. For the cities smaller than 1000 km2, we recommend careful investigation of emission spatial gradients before using EDGAR, as significant bias can be introduced (−63% (Tel Aviv) ∼ +183% (Boston)).Figure 4.3.et al (σ) for CDIAC’s FFCO2 emitting grid cell over the annual timescale, and Oda et al  tend to decrease with the increasing geographical area of cities. When a regression model of s = 14), and African cities showed the highest standard error (s = 55). The regional variation in the standard error is induced by regional biases in (a) spatial proxies used for disaggregating national emissions (i.e. population settlements for EDGAR and nighttime light intensity for ODIAC), (b) point emission source error, (c) EFs used in GPC inventory, and (d) uncertainty in national FFCO2 emissions: Andres et al (2 emissions ranges from a few percent (i.e. 3%–5% for the U.S.) to ∼50% for countries with poorly maintained statistical infrastructures of energy data.Andres et al  reportedda et al  reportedes et al  reportedA relatively higher standard error for a given region implies that the spatial disaggregation error has a relatively small impact on the region, and the other sources of uncertainty—national emissions, point sources, and EFs—have large contributions to the variability in emission estimates. We discuss the impact of discrepancies in EFs and point sources in the following sections.4.4.−1, ODIAC: 2.3% yr−1, GPC inventory: 2.6% yr−1). The case study of New Orleans, USA, reveals that EDGAR and ODIAC have some point sources that are not keeping up with the up-to-date operation status, inducing significant error in the trend estimates .Two cities—New Orleans, USA, and Amman, Jordan—show the largest discrepancies in the emission trend estimates. The case study of Amman, Jordan, shows inconsistent accounting of power plants between GPC inventory, EDGAR, and ODIAC lead to large variability in the trend estimates  provides operation status, emissions, and geolocations of individual power-generating facilities EPA . The CAM4.5.s = 55), while North American (s = 14) and European cities (s = 18) show the lowest standard error.The average reference years of EFs documented in GPC inventories are 2007 for African cities, 2010 for Latin American cities, and 2016 for North American and European cities. International scale EFs, such as the ones from IPCC 2006, account for 87% of entire EFs documented in African cities’ GPC inventory, while that only account for 4% in North American cities’ inventories. The availability of local-specific/up-to-date EFs across global regions correlates with the emissions comparison results: the African cities show the largest standard error for the CV regression , can provide independent citywide emission estimates . The level of agreement between ODIAC and GPC inventory varies by region: African cities show the largest variability between ODIAC and GPC. ODIAC (1 km resolution) and EDGAR (0.1° resolution) tend to show better agreement toward GPC inventory for larger cities. We recommend using EDGAR for cities larger than 1000 km2. For emission trend estimates, our study shows that neither the current version of ODIAC2020b nor EDGARv60 are suitable for tracking citywide emission mitigation progress at a policy-relevant scale.Our study shows that ODIAC could be used to provide first-order estimates for citywide annual FFCOThe data quality of EFs used in GPC inventories varies by region, with the highest quality for European and North American and the lowest for African and Latin American cities. Updating local-specific and up-to-date EFs, which would require investments in city-scale data-collecting systems, will narrow the discrepancies in emission estimates. We also acknowledge that emerging satellite remote sensing data (i.e. NASA OCO-3) could be utilized to bridge gaps between various emission estimation methods.


With the recent advance in high throughput genome sequencing technologies, coupled with better computational approaches, a large amount of “omics” data have been generated to understand the factors underlying biology and genetic architecture of complex traits and hence, can accelerate genetic gain in aquaculture species. This Research Topic attempted to present and discuss innovative approaches and recent advances in the field of genetics and “omics” to provide solutions to enhance aquaculture breeding. The book included 30 articles (three review and 27 research articles), which are grouped in 15 different themes or topics : Control of reproduction is the first step before initiating captive breeding programs for any aquaculture species. In efforts to develop a new genetic and stock enhancement program for black sea cucumber Holothuria scabra, Chieu et al. identified the neurohormone relaxin-like gonad-stimulating peptide (RGP) and a recombinant RGP was produced to induce spawning in this species. Spawned oocytes were fertilized successfully, larvae settled and developed into juveniles. These results demonstrated the roles of neuropeptides involved in sea cucumber reproduction that can provide a new artificial breeding approach in sea cucumber aquaculture.Theme 2 (Genetic diversity of founder stocks): Assessing genetic diversity of founder stocks is another important step to form a base population with large genetic variability to ensure long term response to selection. In this regard, Hamilton et al. used a new generation of DNA markers  to analyse sibship relationship among founder stocks of rohu carp Labeo rohita. Guo et al. also attempted to develop new sets of SNPs to examine genetic diversity of yellow perch Perca favescens. Results from these studies demonstrated that DNA markers can assist the identification and recruitment of genetically diverse stocks for selective breeding programs.Theme 3 (Genetic basis of production traits): Once the base population is established, it is necessary to know genetic parameters for key traits of interest, to guide the breeding goal and response to selection. In Pacific whiteleg shrimp Liptopaneus vannamei, Trang et al. analysed a pedigree comprising 15,000 individuals and reported there are heritable genetic variation for disease resistance to white sport syndrome virus, WSSV . There were also strong and positive genetic correlations of WSSV resistance between different infection periods. The genetic associations between WSSV resistance and growth traits, albeit negative, were not significant. Improving the definition and recording of target traits is also important. In Turbot Philasterides dicentrarchi, Saura et al. used genotype data  derived from genotyping by sequencing platforms to understand genomic architecture of disease resistance, resilience and tolerance. The SNP heritabilities for these traits were moderate to high, indicating that selection to improve disease resistance can be effective in aquaculture species.Theme 4 (Designing breeding programs): Genetic selection has significantly improved productivity and quality of several aquaculture species. In this book, Khang et al. reported an 8-years breeding program for improved growth and survival in a marine finfish, Asian seabass Lates calcarifer. In addition, they used the data collected over 8 years to estimate genetic parameters for body traits and survival during growth trajectory and investigated the effect of genotype by culture system (tank vs sea cage) interaction to optimise the design and conduct of the breeding program for this species. In addition to growth and survival, Vandeputte et al. demonstrated that selection for carcass weight also increased genetic gain for fillet yield by 1.16% per generation in rainbow trout Oncorhynchus mykiss. The selection programs in both Asian seabass and rainbow trout have based on pedigree and phenotype information. In breeding programs for two main species farmed in New Zealand: Chinook (king) salmon Oncorhynchus tshawytscha and green shell mussel Perna canaliculus, new high through-put sequencing platforms  are being used to generate genomic resources to incorporate into breeding selection schemes for these species .Theme 5 : Broadening the breeding objectives in genetic programs for aquaculture species is essential to maximise productivity and revenue of aquaculture enterprises. In this regard, Knap and Kause proposed two new measures to improve efficiency of food utilisation, i.e., protein to lipid ratio and residual feed intake. Prchal et al. also attempted recording morphological measurements by 2D digitalization and ultrasound imagery to predict fillet yield in common carp Cyprinus carpio. While the results of both studies are encouraging, benefit-cost analysis of these technologies should be evaluated in practical breeding programs in fish species, because recording protein (lipid) content or individual feed intake as well as carcass traits using ultrasound tomography are laborious and expensive.Theme 6 (Monitoring of breeding programs): Assessment of genetic programs should be frequently conducted to monitor the progress of the selected population and to refine breeding technologies. Barría et al. used a 200K high-density SNP array to estimate population size of 43 (10 generations ago) to 325 (139 generations ago) in Coho salmon Oncorhynchus kisutch. In closed breeding nucleus, the population size should be at least 50 to maintain an acceptable level of inbreeding below 1% per generation. The effective population size is also related to genetic diversity and adaptation and evolution of captive populations.Theme 7 (Next generation sequencing and marker development): The advent of next generation sequencing enables the simultaneous genotyping of hundreds of thousands, or millions of DNA markers used for genetic and genomic studies in various aquaculture species. Well-designed SNP panels can support breeding programme design and operation in multiple ways. One example is, through whole genome resequencing, approximately 30 million SNPs were identified on the rainbow trout genome and functional annotation based on the genome position of each SNP, which provide an important source to design new high-density SNP arrays . This approach was used to develop a SNP genotyping tool for parentage assignment and grouping of hybrids animals from their parents in blacklip Haliotos rubra and greenlip H. laevigata abalone .Theme 8 : With the abundance of SNPs markers, two studies in this Special Issue attempted to develop genetic linkage maps for two important fish species. First, Joshi et al. developed a 58k SNP-array and a high-density linkage map for Nile tilapia Oreochromis niloticus. The linkage map was integrated with the O niloticus genome to produce an integrated physical and genetic linkage map comprising 40,186 SNPs distributed across 22 linkage groups. Second, Nguyen et al. also developed a first high-density linkage map involving 4,000 SNPs distributed across 24 linkage groups for yellowtail kingfish Seriola lalandi. Results from these results revealed a general pattern of recombination across the genome was notably different between the sexes, with female recombination rates being higher across many regions of the genome.Theme 9 : In addition to polymorphic DNA markers generated from high through-put genome sequencing, Salem et al. used a 50k transcribed gene SNP-chip derived from RNA-seq data and identified several QTL regions explaining up to 28.4% of the additive genetic variance for muscle yield in rainbow trout O. mykiss, with 46 SNPs clustered into 23 annotated genes on chromosome 14 or 49 SNPs clustered into 16 annotated genes on chromosome 16. Examples of the annotated genes in these QTL regions are fibroblast growth factor-binding protein-1(FGFBP1) which had a single nonsynonymous SNP found in a window that explained 12.24% of the additive genetic variance or the cysteine/serine-rich nuclear protein 2 (CSRNP2) or the citrate synthase (CS). However, the CS assay showed no association between any of the SNP genotypes and CS activity. The author suggested an important role of mitochondrial functions to muscle growth as reported in earlier studies in human. In yellowtail kingfish, Nguyen et al. used 13,898 SNPs generated by genotyping by sequencing platforms and reported that there were six SNPs significantly associated with growth. The significant SNPs are located in (near) genes with known functions linked with signal transduction, cytoskeleton, membrane channels and ions transport. However, these SNPs explained only a limited proportion of the additive genetic variance, only 3.58%. Results from these studies suggest that growth and quantitative traits are involved with complex processes controlled by multiple genes and gene families. Functional characterization of the significant markers deserves further study to elucidate their biological functions underlying growth and complex traits in kingfish as well as other aquaculture species.Theme 10 (Genomic selection): Genomic selection is of practical significance in aquaculture species especially for traits that are difficult to measure  or for those with low heritability . Palaiokostas et al. showed that genomic prediction of breeding values using 12,311 SNPs derived from Restricted site associated DNA (RAD) sequencing outperformed pedigree-based prediction by 18% for early growth in juvenile common carp C. carpio. Similarly, the superiority in the prediction accuracy for growth-related traits were from 25 to 30% in Pacific oyster when a medium density SNP array (23k SNPs) was used . The prediction accuracy for resilience to Philasterides dicentrarchi, a parasite causing scuticociliatosis in Turbot was 0.46, which was also higher than the estimate obtained from the pedigree, 0.41 . The genomic prediction accuracy varies from 0.16 to 0.83 (median = 0.60) for various disease survival traits in aquatic animal species, which open opportunities to select for quality traits  in highly economic value species, such as Pearl oyster Pinctada maxima .Theme 11 (Marker- or gene-assisted selection): In addition to genomic selection, which is based on genome-wide marker information, two studies in this Issue attempted to identify genes (variants) with moderate to large effects to enable gene- or marker-assisted selection (MAS) in rainbow trout O. mykiss and Atlantic salmon Salmo salar. Liu et al. incorporated 36 SNPs  into the selection program for Bacterial cold water disease (BCWD), caused by Flavobacterium psychrophilum in rainbow trout, and demonstrated that accuracy of MAS was around 0.50, which was equal or greater than the accuracy of the conventional family-based selection in the same generation. Another successful example of MAS is for IPN (Infectious Pancreatic Necrosis) resistance in Atlantic salmon in Norway and the United Kingdom .Theme 12 (Reference genome assembly): An essential genomic tool is a sequenced and assembled genome for a species of interest. Shin et al. constructed the first draft genome, with a size of 829.3 Mb, using the 90X PacBio sequence, for red sea bream of Sparidae family. Additionally, Gan et al. used Nanopore long reads (∼30x coverage) and applied hybrid meta-assembly approach to assemble the first high-quality genome assembly for Blacklip abalone. The authors also annotated the genome and explored the genome for heat-shock proteins (HSPs), a highly conserved protein family whose synthesis has been previously linked to thermal stress in Haliotis species. To date, the assembled teleost genomes is around a hundred, but it will likely double in coming years through Vertebrate Genome Project. Fully assembled and annotated genomes will assist future selective breeding and fundamental genomic and evolutionary studies for important aquaculture species.Theme 13 (Understanding immune response): Boison et al. performed whole transcriptome profiling of normal and infected fish to understand molecular mechanisms underlying genetic resistance to Amoebic gill disease (AGD), caused by the infection of the protozoan Neoparamoeba perurans, a primary gill disease of farmed Atlantic salmon  around the globe. The study identified three main QTL regions on chromosomes 4, 9 and 13 linked with AGD resistance in this species. The QTL region on chromosomes 4 harbours members of the cadherin gene family, the region on chromosome 9 associated with another member of this gene, i.e., protocadherin Fat 4, and the region on chromosome 9 covering interleukin-18 (IL18) binding protein, a gene playing a crucial role in inhibiting proinflammatory effect of cytokine IL18. In Maraena whitefish (Coregonus maraena), Rebl et al. performed microarray-based transcriptome analysis of three main tissues  and detected 112 differentially expressed genes related to thermal stress induced by gradual or acute temperature rise in these tissues. Examples of genes that are upregulated after heat shock include HSP40, HSP70 and HSP90; their functional homologs, co-chaperons and stress-signal transducers are conserved across different species. Furthermore, Tsakogiannis et al. reported male and female-specific genes and their pathways that can improve efforts to control phenotypic sex in gilthead seabream.Theme 14 (Genome editing): Genome (gene) editing has some potential applications to enhance aquaculture breeding, albeit there remains some controversy in the potential use of this technology. In this Issue, Chen et al. established gene transfer and gene editing techniques to shorten sexual maturation time in Sturgeon species, which normally range from 6 to 12 years in males and 10—18 years in females. The authors reported that the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 gave better results than transcription activator-like effector nucleases (TALENs) in terms of embryo survival rate. The study also successfully edited ntl (no tail) gene and produced sturgeon fries with phenotypes similar to those observed after ntl knockdown using morpholino technology in zebrafish.Theme 15 (Genomic tools): Hollenback and Jonhston and Guppy et al. presented the state of current “omic” technologies used in genetic selection programs for mollusc and Penaeids, respectively. The reviews covered the current literature in genomics, transcriptomics and to a lesser extent, proteomics as well as genetic methods such as linkage maps, QTL analysis, genome-wide association studies, genomic prediction and functional genomics. However, the “genomic tools” lack important components of emerging areas of research, namely metagenomics, epigenomics etc, which display potential applications to enhance aquaculture breeding, especially for traits which are difficult to measure  or for those which are largely influenced by genotype × environment interaction. These areas of research merits further research to optimise genetic improvement programs for aquatic animal species.In summary, this Special Issue (book) presented a systematic and integrated approach for the design and conduct of genetic enhancement programs, from reproduction biology to assessment of genetic diversity to recruit founder stocks to form base populations, successful implementation of selective breeding programs, as well as the application of gene- (marker-) assisted selection and the incorporation of new “omics” resources to accelerate genetic progress made in selective breeding programs for aquaculture species. To date, genetic improvements in aquaculture species have been based mainly on the application of quantitative genetics theories and conventional selective breeding approach. Genome-based selection has emerged as a new paradigm in genetic enhancement programs for both terrestrial and aquatic animal species. In coming years, we are expecting to see more the application of other omics resources  in practical selective breeding programs for aquaculture species of economic importance.Finally, we would like to thank all the authors, reviewers and editors who have made this Research Topic a success.


Preconceptional genetic diagnosis help couples of genetic disorders carrier risk making an informed reproductive decision. The risk is considerably higher for consanguineous couples. Premarital screening can also offers a crucial health assessment of soon-to-be married couples with genetic risk factors based on specific family history. However, such approach is not usually easy to manage in conservative societies, particularly when the affected family refuse to deliver the necessary information about the genetic condition considered as a taboo.Here, we addressed the psychological benefits of preconceptional and premarital genetic diagnosis through a retrospective study about the preconceptional diagnosis inquiries in our genetic counselling.In order to assess requests for autosomal recessive disorders during ten years of our genetic counselling activity at the medical university of Sfax, we reviewed 2500 medical files.Three couples were recorded for genetic preconceptional diagnosis. Another couple was documented for seeking a premarital screening for an unknown neuropathy before wedding engagement decision. This single case was referred to us because of a familial history of a severe neuropathy that was noted in the offspring of a shared cousin. The couple was unable to bring us more information about the genetic condition because of the familial repugnance.Although our study is limited at the genetic level, it could be socially interesting because it showed the negative attitudes of the general population towards the genetic conditions and the familial responsiveness, as well as the reticence of physicians towards genetic preconceptional and premarital carrier diagnosis.No significant relationships.