Landscape Genomics
Overview
In the face of climate change and habitat loss, our Conservation & Landscape Genomics service provides the genetic intelligence you need to build resilience.
We start by mapping the genetic basis of adaptation across diverse landscapes. Using genomic data and environmental variables, we identify the precise variants that confer critical traits—from drought tolerance in crops to disease resistance in livestock.
Our Conservation & Landscape Genomics for Adaptive Breeding Service Enhances Your Research and Breeding Efforts with:
- Accelerate Breeding Cycles: We rapidly uncover the genetic basis of adaptive traits, slashing the time needed to develop new, well-adapted varieties. For instance, identifying disease-resistance genes in local livestock can lead to more resilient breeds in a fraction of the time.
- Decipher Trait Inheritance: We provide deep insights into how adaptive traits are inherited—whether for salt tolerance or cold-hardiness. This knowledge is vital for designing breeding strategies that preserve and enhance these traits over the long term.
- Boost Research Efficiency: Our integration of genomics, bioinformatics, and computational modeling acts as a research catalyst. It speeds up the discovery of novel adaptive genes, driving innovation in both conservation and breeding.
What Is Landscape Genomics
Landscape genomics is an interdisciplinary field that bridges ecology, geography, and genetics. It investigates how spatial and environmental heterogeneity—such as variations in climate, soil composition, or altitude—shapes the genetic diversity of populations across a landscape. By analyzing genome-wide patterns in conjunction with environmental data, this approach identifies the specific genes and genetic variants under natural selection from local environmental pressures. Landscape genomics bridges ecology, geography and genetics to test how environmental heterogeneity shapes genomic variation and local adaptation. By coupling genome-wide markers with environmental data, it identifies variants under selection from local pressures and guides conservation and adaptive breeding.
How We Deliver This Solution
Sample Collection & Preparation
- Diverse Germplasm Collection
We support the acquisition of diverse plant and animal materials (e.g., wild relatives, landraces, commercial lines) through sampling-plan design, partner networks and, where required, on-site technical support—subject to local permits and ethical/compliance requirements.
- Multi-Tissue and Multi-Omics Sampling
To link genetic variation with phenotypes and environments, samples can include leaves, roots, seeds/fruit, and—where appropriate—animal tissues or blood under approved protocols. A multi-omics strategy integrating genomic, transcriptomic and proteomic layers can be applied, with environmental covariates (e.g., temperature, soil chemistry) recorded to enable genotype–environment analyses.
A multi - omics strategy integrating genomic, transcriptomic, and proteomic data is used. For instance, when studying rice under flood stress, we collect leaf transcriptome data and measure soil water levels. Through correlation analysis, key genes responding to flood stress can be precisely identified.
Sequencing
- Whole-Genome Resequencing / Low-pass WGS
High or cost-effective low coverage for genome-wide scans, population structure and genotype–environment association.
Efficient discovery and genotyping of thousands of SNPs across many individuals for large-scale landscape studies.
- Targeted Capture Panels (optional)
Focused sequencing of candidate regions/genes associated with adaptation for higher power in validation cohorts.
- Epigenome or Transcriptome Profiling (optional add-ons; species-dependent)
RNA-seq or methylation assays can complement DNA variation to interpret regulatory mechanisms.
Bioinformatics
- Population structure & admixture
PCA/ADMIXTURE; kinship and spatial autocorrelation diagnostics to control confounding.
- Genotype–environment association (GEA)
LFMM/RDA/mixed models to link variants with environmental gradients while accounting for structure and collinearity.
- Isolation by distance / environment / resistance (IBD/IBE/IBR)
Resistance-surface modeling (e.g., least-cost paths, circuit theory) to evaluate how landscape features shape gene flow.
- Selection/outlier scans
FST-based and haplotype-based scans (e.g., XP-CLR, iHS) to prioritize candidate adaptive regions.
- Functional annotation & prioritization
Variant/gene annotation and pathway enrichment to deliver shortlists for marker development and downstream validation.
Service flowchart
Figure 1: How We Deliver This Solution: Landscape Genomics Workflow
Our Advantages
Landscape-Specific Genetic Adaptation Insights
By collecting populations from different environments, sequencing them and conducting bioinformatics analysis, the mechanisms by which various populations adapt to their environments can be discovered. This process identifies crucial adaptive variants—for example, genes that enable fish to survive in different thermal regimes. For example, in fish species living in different river systems with distinct water temperatures and flow rates, we can uncover the genes that enable them to thrive in each specific environment. This knowledge is invaluable for adaptive breeding programs aimed at maintaining or enhancing the ability of species to survive in their native habitats, especially in the face of environmental changes such as climate change and habitat fragmentation.
High-Throughput Genomic Data Analysis for Efficient Decision-Making
Our platform combines high-throughput sequencing with advanced bioinformatics to rapidly identify adaptive genetic markers. For example, when breeding grassland plants, we can quickly screen thousands of individuals for drought tolerance genes. This speed and precision enable faster, more informed breeding decisions, significantly accelerating the development of well-adapted varieties.
Applications
Predicting Species Responses to Climate Shifts
As climates change, understanding which populations can adapt—and which risk extinction—becomes critical. By analyzing genotype-environment associations, we can forecast how species like alpine plants or cold-water fish may respond to rising temperatures. For example, identifying populations carrying alleles associated with heat tolerance enables targeted conservation, while models of gene flow under future climate scenarios help design ecological corridors to facilitate natural migration.
Multi-omics data fusion
By integrating genomic, transcriptomic, epigenomic and metabolomic data, the molecular mechanisms of gene-environment interactions are analyzed. For instance, transcriptome sequencing can reveal the gene expression regulatory network of crops under drought stress, guiding gene editing breeding.
Optimization of agricultural ecosystem services
Biodiversity-friendly design: By analyzing the correlation between crop flower trait genes and pollinator visit rates, cultivate highly attractive varieties and enhance farmland pollination services.
Root system function and soil health: Study the interaction between crop root genotypes and soil types as well as microbiota, screen deep-rooted or highly efficient nutrient absorption varieties, and enhance soil carbon sequestration and nutrient cycling capabilities.
Demo
Figure 2: Cline width and center location vary across species and across chromosomes. X-axis shows distance (in degrees longitude) along the sampled area. Y-axis shows the projected cline from population assignments of 0–1 in each taxon (panel) and each chromosome (lines). Genomes are given by thick dashed lines. (Provost, 2022)
Case Study
Comparative genomics reveals the high diversity and adaptation strategies of Polaromonas from polar environments.
Journal:BMC Genomics.
Published：2025
- Cold environment stress: Low-temperature environments impose stresses like freezing, desiccation, and high osmolarity on microorganisms, affecting their survival.
- Incomplete understanding of microbial response: The response of microbes in cold environments to climate warming is not fully understood, especially for Polaromonas, which is important in the cryosphere carbon cycle and may create a positive feedback effect on climate change.
- Limited knowledge of Polaromonas: Microbial pure-culture research has limited coverage of Polaromonas diversity, and the lack of sufficient global-scale genome analysis restricts understanding of its ecological role and potential applications.
- Leveraging high - throughput sequencing: With the development of high-throughput sequencing technology, the turnover time and cost of metagenomic sequencing have decreased, enabling the discovery of numerous draft genomes (MAGs) of previously uncultured Polaromonas through genome-centered metagenomics.
- Conducting comparative genomic analysis: Comparative genomic analysis was carried out using genomic data from NCBI. 202 Polaromonas genomes were retrieved from public databases, and their phylogenetic diversity was examined to understand the evolution and adaptation of Polaromonas in cold environments.
- Identified a unique ecological clade: A unique ecological clade from the polar region was identified in the phylogenetic tree.
- Revealed genomic differences: Comparative genomic analysis showed that Polaromonas species from polar regions had larger genome sizes with greater gene density compared to a reference group from non-polar regions.
- Discovered evolutionary patterns: Two distinct evolutionary patterns were observed in the polar-environment-only clade of Polaromonas.
- Found key adaptation factor: Betaine was found to be crucial for the cold adaptation of Polaromonas, improving the understanding of its metabolic capabilities, habitat adaptations, and evolutionary processes in polar and high-altitude ecosystems.
Figure 3: Heatmap showing ANI analysis of 8 genomes from the polar group and 3 closely related genomes from the nonpolar region. The ANI values greater than 95% are shown in the figure. Various species are distinguished by color in the figure, and the labels for nonpolar genomes are black.
FAQ
The time frame for seeing results can vary significantly depending on the species, the complexity of the traits being targeted, and the breeding goals. For some relatively simple traits in fast-growing organisms like certain crops, results may be visible within a few breeding cycles, which could take a few years. However, for more complex traits in long-lived species such as trees or large mammals, it may take several decades to develop varieties or populations with significantly improved adaptive traits. The use of advanced genomic technologies can speed up the process to some extent, but the biological nature of the organisms still plays a crucial role.
References
- Provost K, Shue SY, Forcellati M, et al. The Genomic Landscapes of Desert Birds Form over Multiple Time Scales. Mol Biol Evol. 2022 Oct 7;39(10):msac200. doi: 10.1093/molbev/msac200.
- Du Y, He C, Lloyd KG, et al. Comparative genomics reveals the high diversity and adaptation strategies of Polaromonas from polar environments. BMC Genomics. 2025 Mar 14;26(1):248. doi: 10.1186/s12864-025-11410-6.
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