Synthesizing environmental, epidemiological and vector and parasite genetic data to assist decision making for disease elimination

We present a framework for identifying when conditions are favourable for transmission of vector-borne diseases between communities by incorporating predicted disease prevalence mapping with landscape analysis of sociological, environmental and host/parasite genetic data. We explored the relationship between environmental features and gene flow of a filarial parasite of humans, Onchocerca volvulus, and its vector, blackflies in the genus Simulium. We generated a baseline microfilarial prevalence map from point estimates from 47 locations in the ecological transition separating the savannah and forest in Ghana, where transmission of O. volvulus persists despite onchocerciasis control efforts. We generated movement suitability maps based on environmental correlates with mitochondrial population structure of 164 parasites from 15 communities and 93 vectors from only four sampling sites, and compared these to the baseline prevalence map. Parasite genetic distance between sampling locations was significantly associated with elevation (r = .793, p = .005) and soil moisture (r = .507, p = .002), while vector genetic distance was associated with soil moisture (r = .788, p = .0417) and precipitation (r = .835, p = .0417). The correlation between baseline prevalence and parasite resistance surface maps was stronger than that between prevalence and vector resistance surface maps. The centre of the study area had high prevalence and suitability for parasite and vector gene flow, potentially contributing to persistent transmission and suggesting the importance of re-evaluating transmission zone boundaries. With suitably dense sampling, this framework can help delineate transmission zones for onchocerciasis and would be translatable to other vector-borne diseases.

Genomic Signatures of Local Adaptation under High Gene Flow in Lumpfish-Implications for Broodstock Provenance Sourcing and Larval Production

Aquaculture of the lumpfish (Cyclopterus lumpus L.) has become a large, lucrative industry owing to the escalating demand for "cleaner fish" to minimise sea lice infestations in Atlantic salmon mariculture farms. We used over 10K genome-wide single nucleotide polymorphisms (SNPs) to investigate the spatial patterns of genomic variation in the lumpfish along the coast of Norway and across the North Atlantic. Moreover, we applied three genome scans for outliers and two genotype-environment association tests to assess the signatures and patterns of local adaptation under extensive gene flow. With our 'global' sampling regime, we found two major genetic groups of lumpfish, i.e., the western and eastern Atlantic. Regionally in Norway, we found marginal evidence of population structure, where the population genomic analysis revealed a small portion of individuals with a different genetic ancestry. Nevertheless, we found strong support for local adaption under high gene flow in the Norwegian lumpfish and identified over 380 high-confidence environment-associated loci linked to gene sets with a key role in biological processes associated with environmental pressures and embryonic development. Our results bridge population genetic/genomics studies with seascape genomics studies and will facilitate genome-enabled monitoring of the genetic impacts of escapees and allow for genetic-informed broodstock selection and management in Norway.

Landscape Genetics for Brazilian Equines

Optimization of DNA collection for National gene bank and conservation programs requires information on spatial and genetic distribution of animals countrywide. The relationship between genetic and geographic distances were examined in 8 Brazilian horse breeds (Baixadeiro, Crioulo, Campeiro, Lavradeiro, Marajoara, Mangalarga Marchador, Pantaneiro and Puruca) using Single Nucleotide Polymorphism markers and collection point locations. Mantel correlations, Genetic Landscape Shape Interpolation, Allelic Aggregation Index Analyses and Spatial autocorrelation tests indicated a nonrandom distribution of horses throughout the country. Minimum collection distances for the national Gene Bank should be 530km, with clear divisions seen in genetic structure of horse populations in both North/South and East/West directions. Comparing Pantaneiro and North/Northeastern breeds, physical distance is not necessarily the defining factor for genetic differentiation. This should be considered when sampling these local breeds. These data can help optimise GenBank collection routines and conservation strategies for these breeds.

Consequences of the Last Glacial Period on the Genetic Diversity of Southeast Asians

The last glacial period (LGP) promoted a loss of genetic diversity in Paleolithic populations of modern humans from diverse regions of the world by range contractions and habitat fragmentation. However, this period also provided some currently submersed lands, such as the Sunda shelf in Southeast Asia (SEA), that could have favored the expansion of our species. Concerning the latter, still little is known about the influence of the lowering sea level on the genetic diversity of current SEA populations. Here, we applied approximate Bayesian computation, based on extensive spatially explicit computer simulations, to evaluate the fitting of mtDNA data from diverse SEA populations with alternative evolutionary scenarios that consider and ignore the LGP and migration through long-distance dispersal (LDD). We found that both the LGP and migration through LDD should be taken into consideration to explain the currently observed genetic diversity in these populations and supported a rapid expansion of first populations throughout SEA. We also found that temporarily available lands caused by the low sea level of the LGP provided additional resources and migration corridors that favored genetic diversity. We conclude that migration through LDD and temporarily available lands during the LGP should be considered to properly understand and model the first expansions of modern humans.

Characterizing the spatial distributions of soil biota at a legacy base metal mine using environmental DNA

Characterizing the distribution of biota in response to contaminants is a critical element of site risk assessments. In this study we investigated the spatial distributions of biota and soil chemistry data in surface soil from Sunny Corner, a legacy base metal sulfide mine, Australia. Our results showed that copper (Cu), zinc (Zn), arsenic (As) and lead (Pb) in the surface soil exceeded Australian national soil quality guidelines and posed risks to the environment. Environmental (e)DNA metabarcoding of prokaryote and eukaryote composition confirmed the suggestion of environmental risk posed by these elements collectively explaining 72.9 % and 60.5 % of the total variation in the composition of soil prokaryotes and eukaryotes, respectively. Prokaryotic taxa from the phyla Gemmatimonadetes, Verrucomicrobia and Deinococcus-Thermus showed similar spatial patterns to As and Pb, and were positively correlated. Eukaryotic taxa from the phylum Chlorophyta had similar positive correlations with As and Pb in the soil. In contrast, Amoebozoa and Cercozoa, were sensitive to metals and metalloids, having higher relative abundances in soils with lower concentrations of contaminants. Our study shows that metabarcoding is a promising ecological approach for rapid, large scale assessment of contaminated and potentially impacted sites.

Seascape genomics reveals candidate molecular targets of heat stress adaptation in three coral species

Anomalous heat waves are causing a major decline of hard corals around the world and threatening the persistence of coral reefs. There are, however, reefs that have been exposed to recurrent thermal stress over the years and whose corals appear to have been tolerant against heat. One of the mechanisms that could explain this phenomenon is local adaptation, but the underlying molecular mechanisms are poorly known. In this work, we applied a seascape genomics approach to study heat stress adaptation in three coral species of New Caledonia (southwestern Pacific) and to uncover the molecular actors potentially involved. We used remote sensing data to characterize the environmental trends across the reef system, and sampled corals living at the most contrasted sites. These samples underwent next generation sequencing to reveal single nucleotide polymorphisms (SNPs), frequencies of which were associated with heat stress gradients. As these SNPs might underpin an adaptive role, we characterized the functional roles of the genes located in their genomic region. In each of the studied species, we found heat stress-associated SNPs located in proximity of genes involved in pathways well known to contribute to the cellular responses against heat, such as protein folding, oxidative stress homeostasis, inflammatory and apoptotic pathways, and DNA damage-repair. In some cases, the same candidate molecular targets of heat stress adaptation recurred among species. Together, these results underline the relevance and the power of the seascape genomics approach for the discovery of adaptive traits that could allow corals to persist across wider thermal ranges.

Sampling strategy optimization to increase statistical power in landscape genomics: A simulation-based approach

An increasing number of studies are using landscape genomics to investigate local adaptation in wild and domestic populations. Implementation of this approach requires the sampling phase to consider the complexity of environmental settings and the burden of logistical constraints. These important aspects are often underestimated in the literature dedicated to sampling strategies. In this study, we computed simulated genomic data sets to run against actual environmental data in order to trial landscape genomics experiments under distinct sampling strategies. These strategies differed by design approach (to enhance environmental and/or geographical representativeness at study sites), number of sampling locations and sample sizes. We then evaluated how these elements affected statistical performances (power and false discoveries) under two antithetical demographic scenarios. Our results highlight the importance of selecting an appropriate sample size, which should be modified based on the demographic characteristics of the studied population. For species with limited dispersal, sample sizes above 200 units are generally sufficient to detect most adaptive signals, while in random mating populations this threshold should be increased to 400 units. Furthermore, we describe a design approach that maximizes both environmental and geographical representativeness of sampling sites and show how it systematically outperforms random or regular sampling schemes. Finally, we show that although having more sampling locations (between 40 and 50 sites) increase statistical power and reduce false discovery rate, similar results can be achieved with a moderate number of sites (20 sites). Overall, this study provides valuable guidelines for optimizing sampling strategies for landscape genomics experiments.

Rapid identification and interpretation of gene-environment associations using the new R.SamBada landscape genomics pipeline

samβada is a genome–environment association software, designed to search for signatures of local adaptation. However, pre‐ and postprocessing of data can be labour‐intensive, preventing wider uptake of the method. We have now developed R.SamBada, an r‐package providing a pipeline for landscape genomic analysis based on samβada, spanning from the retrieval of environmental conditions at sampling locations to gene annotation using the Ensembl genome browser. As a result, R.SamBada standardizes the landscape genomics pipeline and eases the search for candidate genes of local adaptation, enhancing reproducibility of landscape genomic studies. The efficiency and power of the pipeline is illustrated using two examples: sheep populations from Morocco with no evident population structure and Lidia cattle from Spain displaying population substructuring. In both cases, R.SamBada enabled rapid identification and interpretation of candidate genes, which are further discussed in the light of local adaptation. The package is available in the r CRAN package repository and on GitHub (github.com/SolangeD/R.SamBada).

Selecting among Alternative Scenarios of Human Evolution by Simulated Genetic Gradients

Selecting among alternative scenarios of human evolution is nowadays a common methodology to investigate the history of our species. This strategy is usually based on computer simulations of genetic data under different evolutionary scenarios, followed by a fitting of the simulated data with the real data. A recent trend in the investigation of ancestral evolutionary processes of modern humans is the application of genetic gradients as a measure of fitting, since evolutionary processes such as range expansions, range contractions, and population admixture (among others) can lead to different genetic gradients. In addition, this strategy allows the analysis of the genetic causes of the observed genetic gradients. Here, we review recent findings on the selection among alternative scenarios of human evolution based on simulated genetic gradients, including pros and cons. First, we describe common methodologies to simulate genetic gradients and apply them to select among alternative scenarios of human evolution. Next, we review previous studies on the influence of range expansions, population admixture, last glacial period, and migration with long-distance dispersal on genetic gradients for some regions of the world. Finally, we discuss this analytical approach, including technical limitations, required improvements, and advice. Although here we focus on human evolution, this approach could be extended to study other species.

Navigating the Interface Between Landscape Genetics and Landscape Genomics

As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used.

Multiscale landscape genomic models to detect signatures of selection in the alpine plant Biscutella laevigata

Plant species are known to adapt locally to their environment, particularly in mountainous areas where conditions can vary drastically over short distances. The climate of such landscapes being largely influenced by topography, using fine-scale models to evaluate environmental heterogeneity may help detecting adaptation to micro-habitats. Here, we applied a multiscale landscape genomic approach to detect evidence of local adaptation in the alpine plant Biscutella laevigata. The two gene pools identified, experiencing limited gene flow along a 1-km ridge, were different in regard to several habitat features derived from a very high resolution (VHR) digital elevation model (DEM). A correlative approach detected signatures of selection along environmental gradients such as altitude, wind exposure, and solar radiation, indicating adaptive pressures likely driven by fine-scale topography. Using a large panel of DEM-derived variables as ecologically relevant proxies, our results highlighted the critical role of spatial resolution. These high-resolution multiscale variables indeed indicate that the robustness of associations between genetic loci and environmental features depends on spatial parameters that are poorly documented. We argue that the scale issue is critical in landscape genomics and that multiscale ecological variables are key to improve our understanding of local adaptation in highly heterogeneous landscapes.