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Most efforts to understand snakebite burden in Nepal have been localized to relatively small areas and focused on humans through epidemiological studies. We present the outcomes of a geospatial analysis of the factors influencing snakebite risk in humans and animals, based on both a national-scale multi-cluster random survey and, environmental, climatic, and socio-economic gridded data for the Terai region of Nepal. The resulting Integrated Nested Laplace Approximation models highlight the importance of poverty as a fundamental risk-increasing factor, augmenting the snakebite odds in humans by 63.9 times. For animals, the minimum temperature of the coldest month was the most influential covariate, increasing the snakebite odds 23.4 times. Several risk hotspots were identified along the Terai, helping to visualize at multiple administrative levels the estimated population numbers exposed to different probability risk thresholds in 1 year. These analyses and findings could be replicable in other countries and for other diseases.

Following domestication, sheep (Ovis aries) have become essential farmed animals across the world through adaptation to a diverse range of environments and varied production systems. Climate-mediated selective pressure has shaped phenotypic variation and has left genetic “footprints” in the genome of breeds raised in different agroecological zones. Unlike numerous studies that have searched for evidence of selection using only population genetics data, here, we conducted an integrated coanalysis of environmental data with single nucleotide polymorphism (SNP) variation. By examining 49,034 SNPs from 32 old, autochthonous sheep breeds that are adapted to a spectrum of different regional climates, we identified 230 SNPs with evidence for selection that is likely due to climate-mediated pressure. Among them, 189 (82%) showed significant correlation (P ≤ 0.05) between allele frequency and climatic variables in a larger set of native populations from a worldwide range of geographic areas and climates. Gene ontology analysis of genes colocated with significant SNPs identified 17 candidates related to GTPase regulator and peptide receptor activities in the biological processes of energy metabolism and endocrine and autoimmune regulation. We also observed high linkage disequilibrium and significant extended haplotype homozygosity for the core haplotype TBC1D12-CH1 of TBC1D12. The global frequency distribution of the core haplotype and allele OAR22_18929579-A showed an apparent geographic pattern and significant (P ≤ 0.05) correlations with climatic variation. Our results imply that adaptations to local climates have shaped the spatial distribution of some variants that are candidates to underpin adaptive variation in sheep.

Background Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation to different environments. As a result, approximately 600 goat breeds have developed worldwide; they differ considerably from one another in terms of phenotypic characteristics and are adapted to a wide range of climatic conditions. In this work, we analyzed the AdaptMap goat dataset, which is composed of data from more than 3000 animals collected worldwide and genotyped with the CaprineSNP50 BeadChip. These animals were partitioned into groups based on geographical area, production uses, available records on solid coat color and environmental variables including the sampling geographical coordinates, to investigate the role of natural and/or artificial selection in shaping the genome of goat breeds. Results Several signatures of selection on different chromosomal regions were detected across the different breeds, sub-geographical clusters, phenotypic and climatic groups. These regions contain genes that are involved in important biological processes, such as milk-, meat- or fiber-related production, coat color, glucose pathway, oxidative stress response, size, and circadian clock differences. Our results confirm previous findings in other species on adaptation to extreme environments and human purposes and provide new genes that could explain some of the differences between goat breeds according to their geographical distribution and adaptation to different environments. Conclusions These analyses of signatures of selection provide a comprehensive first picture of the global domestication process and adaptation of goat breeds and highlight possible genes that may have contributed to the differentiation of this species worldwide.

Background Goat populations that are characterized within the AdaptMap project cover a large part of the worldwide distribution of this species and provide the opportunity to assess their diversity at a global scale. We analysed genome-wide 50 K single nucleotide polymorphism (SNP) data from 144 populations to describe the global patterns of molecular variation, compare them to those observed in other livestock species, and identify the drivers that led to the current distribution of goats. Results A high degree of genetic variability exists among the goat populations studied. Our results highlight a strong partitioning of molecular diversity between and within continents. Three major gene pools correspond to goats from Europe, Africa and West Asia. Dissection of sub-structures disclosed regional gene pools, which reflect the main post-domestication migration routes. We also identified several exchanges, mainly in African populations, and which often involve admixed and cosmopolitan breeds. Extensive gene flow has taken place within specific areas (e.g., south Europe, Morocco and Mali-Burkina Faso-Nigeria), whereas elsewhere isolation due to geographical barriers (e.g., seas or mountains) or human management has decreased local gene flows. Conclusions After domestication in the Fertile Crescent in the early Neolithic era (ca. 12,000 YBP), domestic goats that already carried differentiated gene pools spread to Europe, Africa and Asia. The spread of these populations determined the major genomic background of the continental populations, which currently have a more marked subdivision than that observed in other ruminant livestock species. Subsequently, further diversification occurred at the regional level due to geographical and reproductive isolation, which was accompanied by additional migrations and/or importations, the traces of which are still detectable today. The effects of breed formation were clearly detected, particularly in Central and North Europe. Overall, our results highlight a remarkable diversity that occurs at the global scale and is locally partitioned and often affected by introgression from cosmopolitan breeds. These findings support the importance of long-term preservation of goat diversity, and provide a useful framework for investigating adaptive introgression, directing genetic improvement and choosing breeding targets.

Coral reefs are suffering a major decline due to the environmental constraints imposed by climate change. Over the last 20 years, three major coral bleaching events occurred in concomitance with anomalous heatwaves, provoking a severe loss of coral cover worldwide. The conservation strategies for preserving reefs, as they are implemented now, cannot cope with global climatic shifts. Consequently, researchers are advocating for preservation networks to be set‐up to reinforce coral adaptive potential. However, the main obstacle to this implementation is that studies on coral adaption are usually hard to generalize at the scale of a reef system. Here, we study the relationships between genotype frequencies and environmental characteristics of the sea (seascape genomics), in combination with connectivity analysis, to investigate the adaptive potential of a flagship coral species of the Ryukyu Archipelago (Japan). By associating genotype frequencies with descriptors of historical environmental conditions, we discovered six genomic regions hosting polymorphisms that might promote resistance against heat stress. Remarkably, annotations of genes in these regions were consistent with molecular roles associated with heat responses. Furthermore, we combined information on genetic and spatial distances between reefs to predict connectivity at a regional scale. The combination of these results portrayed the adaptive potential of this population: we were able to identify reefs carrying potential heat stress adapted genotypes and to understand how they disperse to neighbouring reefs. This information was summarized by objective, quantifiable and mappable indices covering the whole region, which can be extremely useful for future prioritization of reefs in conservation planning. This framework is transferable to any coral species on any reef system and therefore represents a valuable tool for empowering preservation efforts dedicated to the protection of coral reefs in warming oceans.

Though Switzerland has one of the highest life expectancies in the world, this global indicator may mask significant disparities at a local level. The present study used a spatial cluster detection approach based on individual death records to investigate the geographical footprint of life expectancy inequalities in the state of Geneva, Switzerland. Individual-level mortality data (n = 22,751) were obtained from Geneva’s official death notices (2009–2016). We measured life expectancy inequalities using the years of potential life lost or gained (YPLLG) metric, defined as the difference between an individual’s age at death and their life expectancy at birth. We assessed the spatial dependence of YPLLG across the state of Geneva using spatial autocorrelation statistics (Local Moran’s I). To ensure the robustness of the patterns discovered, we ran the analyses for ten random subsets of 10,000 individuals taken from the 22,751 deceased. We also repeated the spatial analysis for YPLLG before and after controlling for individual-level and neighborhood-level covariates. The results showed that YPLLG was not randomly distributed across the state of Geneva. The ten random subsets revealed no significant difference with the geographic footprint of YPLLG and the population characteristics within Local Moran cluster types, suggesting robustness for the observed spatial structure. The proportion of women, the proportion of Swiss, the neighborhood median income, and the neighborhood median age were all significantly lower for populations in low YPLLG clusters when compared to populations in high YPLLG clusters. After controlling for individual-level and neighborhood-level covariates, we observed a reduction of 43% and 39% in the size of low and high YPLLG clusters, respectively. To our knowledge, this is the first study in Switzerland using spatial cluster detection methods to investigate inequalities in life expectancy at a local scale and based on individual data. We identified clear geographic footprints of YPLLG, which may support further investigations and guide future public health interventions at the local level.

The evolutionary basis of domestication has been a longstanding question and its genetic architecture is becoming more tractable as more domestic species become genome-enabled. Before becoming established worldwide, sheep and goats were domesticated in the fertile crescent 10,500 years before present (YBP) where their wild relatives remain. Here we sequence the genomes of wild Asiatic mouflon and Bezoar ibex in the sheep and goat domestication center and compare their genomes with that of domestics from local, traditional, and improved breeds. Among the genomic regions carrying selective sweeps differentiating domestic breeds from wild populations, which are associated among others to genes involved in nervous system, immunity and productivity traits, 20 are common to Capra and Ovis. The patterns of selection vary between species, suggesting that while common targets of selection related to domestication and improvement exist, different solutions have arisen to achieve similar phenotypic end-points within these closely related livestock species.

As anomalous heat waves are causing the widespread decline of coral reefs worldwide, there is an urgent need to identify coral populations tolerant to thermal stress. Heat stress adaptive potential is the degree of tolerance expected from evolutionary processes and, for a given reef, depends on the arrival of propagules from reefs exposed to recurrent thermal stress. For this reason, assessing spatial patterns of thermal adaptation and reef connectivity is of paramount importance to inform conservation strategies. In this work, we applied a seascape genomics framework to characterize the spatial patterns of thermal adaptation and connectivity for coral reefs of New Caledonia (Southern Pacific). In this approach, remote sensing of seascape conditions was combined with genomic data from three coral species. For every reef of the region, we computed a probability of heat stress adaptation, and two indices forecasting inbound and outbound connectivity. We then compared our indicators to field survey data, and observed that decrease of coral cover after heat stress was lower at reefs predicted with high probability of adaptation and inbound connectivity. Last, we discussed how these indicators can be used to inform local conservation strategies and preserve the adaptive potential of New Caledonian reefs.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non‐model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype–environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole‐genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high‐resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine‐grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

Evidence suggests that the built environment can influence the intensity of physical activity. However, despite the importance of the geographic context, most of the studies do not consider the spatial framework of this association. We aimed to assess individual spatial dependence of objectively measured moderate and vigorous physical activity (MVPA) and describe the characteristics of the built environment among spatial clusters of MVPA. Cross-sectional data from the second follow-up (2014-2017) of CoLaus|PsyCoLaus, a longitudinal population-based study of the Lausanne area (Switzerland), was used to objectively measure MVPA using accelerometers. Local Moran's I was used to assess the spatial dependence of MVPA and detect geographic clusters of low and high MVPA. Additionally, the characteristics of the built environment observed in the clusters based on raw MVPA and MVPA adjusted for socioeconomic and demographic factors were compared. Data from 1,889 participants (median age 63, 55% women) were used. The geographic distribution of MVPA and the characteristics of the built environment among clusters were similar for raw and adjusted MVPA. In the adjusted model, we found a low concentration of individuals within spatial clusters of high MVPA (median: 38.5mins; 3% of the studied population) and low MVPA (median: 10.9 mins; 2% of the studied population). Yet, clear differences were found in both models between clusters regarding the built environment; high MVPA clusters were located in areas where specific compositions of the built environment favor physical activity. Our results suggest the built environment may influence local spatial patterns of MVPA independently of socioeconomic and demographic factors. Interventions in the built environment should be considered to promote physically active behaviors in urban areas.