Pan‑Genome & Adaptive Breeding
Pan‑Genome & Adaptive Breeding Overview
Our cutting-edge approach fuses pan-genome analysis with landscape genomics. We begin by constructing a species' pan-genome via high-throughput sequencing, which captures the entire gene set—from core genes shared by all to variable genes present in specific populations. Through the integration of advanced bioinformatics and artificial intelligence, we analyze how these genes interact with environmental factors. This enables us to identify the key genetic elements that contribute to adaptability, providing a foundation for breeding more resilient crops and livestock. Landscape genomics analysis then helps us understand how these genetic variations are distributed across different geographical and environmental landscapes.
Our Pan - Genome & Landscape Genomics–Driven Adaptive Breeding Solution Offers the Following Advantages for Your Agricultural Research:
- Comprehensive Genetic Diversity Assessment: Standard genomes give you a single reference; our pan-genome delivers the complete picture. We uncover the entire set of genes within a species, including rare and adaptive alleles hidden in specific populations. This allows you to strategically select parents that maximize genetic diversity for complex traits like climate resilience, future-proofing your breeding pipeline.
- Environment-Specific Trait Prediction: This genetic blueprint is then connected to the real world via AI-Powered Trait Prediction. By modeling gene-environment interactions, we can accurately forecast how varieties will perform in target environments, enabling the design of locally-optimized crops and livestock.
- Accelerated Adaptive Breeding Cycles: Traditional breeding is slow and uncertain. Our approach identifies the genetic keys to adaptation upfront, allowing you to screen seedlings for the most valuable genes. This targeted selection slashes R&D timelines, enabling you to bring hardier, higher-yielding varieties to market in half the time, and stay ahead of environmental changes.
- Preservation of Genetic Resources: Our approach also helps in the conservation of genetic resources. By understanding the genetic diversity within a species and its distribution across different landscapes, we can identify populations with unique genetic traits that may be at risk of extinction. This information can be used to develop conservation strategies, such as in - situ and ex - situ conservation programs, to preserve these valuable genetic resources for future breeding efforts.
- Enhanced Research Collaboration and Innovation: Our platform integrates pan-genome sequencing, landscape genomics, and AI to create a shared space for innovation. It does more than just store data—it actively connects researchers, breeders, and farmers. We built a collaborative platform that brings together pan-genome sequencing, landscape genomics, and AI. By integrating these diverse datasets, the platform enables a more complete, systems-level understanding that drives meaningful innovation across the entire agricultural community. This collaborative environment also drives innovation in the agricultural sector, as researchers can build on each other's findings to develop new and improved breeding strategies and technologies.
What Is Pan‑Genome & Adaptive Breeding
Pan-genome research captures all genes in a species, while landscape genomics examines how genes interact with environments. For instance, in wheat breeding, researchers use pan-genome data to identify beneficial genes, and landscape genomics to understand their adaptation to different climates. This approach accelerates the development of resilient, high-yielding wheat varieties, enhancing food security.
How We Deliver This Solution
1. Sample Collection & Preparation
- Diverse Sampling: Collect from wild relatives, landraces, and modern cultivars. Priority is given to harsh environments (e.g., drought-prone, high-salinity) as they are reservoirs for adaptive alleles.
- Contextual Data: Record GPS, climate, and soil data at each collection point. This environmental context is essential for meaningful genomic analysis.
- Quality-Assured DNA: Extract DNA using proven methods (e.g., CTAB for plants). Quality control is non-negotiable—we select samples with high molecular weight and purity to ensure sequencing success.
2. Sequencing
- Short-Read Sequencing (Illumina):
Perform whole-genome sequencing (WGS) at 30–50× coverage to detect SNPs and small indels.
Use paired-end (150–250 bp) reads for accurate variant calling and assembly scaffolding.
- Long-Read Sequencing (PacBio/Oxford Nanopore):
Generate HiFi reads (PacBio) or ultra-long Nanopore reads (≥50 kb) to resolve repetitive regions, gene families, and structural variants (SVs).
Target coverage of 20–30× for haplotype-resolved assemblies.
- Targeted Sequencing (Amplicon/Exome):
Enrich candidate genes under selection (e.g., heat-shock proteins, drought-responsive transcription factors) using hybridization capture or PCR.
Combine with metabolomic/proteomic data to validate functional links.
- Spatial Sequencing (Georeferenced WGS):
Integrate GPS coordinates with sequencing data to map allele frequencies across landscapes, enabling landscape genomics analyses.
3. Bioinformatics
- Pan-Genome Construction:
Use tools like Panaroo or PGAP to assemble core (shared) and accessory (variable) genomes from multiple individuals.
Identify presence/absence variants (PAVs) and gene gain/loss events linked to environmental adaptation.
- Variant Calling & Annotation:
We detect SNPs and indels with high accuracy using GATK, then annotate them with SnpEff to pinpoint the most impactful mutations—especially those in regulatory regions that can control gene expression.
- Landscape Genomics Analysis:
Apply redundancy analysis (RDA) or latent factor mixed models (LFMM) to correlate genetic variants with environmental variables.
Identify loci under selection (e.g., FST outliers, XP-CLR) and validate with gene ontology (GO) enrichment.
- BreedinG Tools Integration:
Develop genomic prediction models (e.g., GBLUP, BayesR) incorporating pan-genome variants and landscape genomics signatures.
Use TALENs, zinc-finger nucleases (ZFNs), or oligonucleotide-directed mutagenesis (ODM) for precise editing of adaptive alleles identified in wild relatives.
- Data Visualization & Sharing:
Visualize pan-genomes as graph-based assemblies (e.g., Bandage) and map adaptive loci onto geographic heatmaps.
Deposit data in public repositories (NCBI, EBI) to facilitate global breeding efforts.
Service flowchart
Figure 1: How We Deliver This Solution: Pan‑Genome & Landscape Genomics–Driven Adaptive Breeding Solution
Our Advantages
Unveil Gene Functions via Pan-Genome
A single reference genome can't capture a species' full genetic diversity. That's why we build a pan-genome—a complete collection of genes from many individuals across the species. This approach allows us to discover rare and novel genes that are often missed, revealing new sources of traits like stress tolerance and efficient nutrient use. It essentially gives breeders a much larger toolkit to work with.
Applications
1. Enhancing Crop Resilience to Climate Extremes
By constructing pangenomes, we can identify the core genes and structural variations that grant crops tolerance to stresses like drought and salinity. We further combine this with landscape genomics, which correlates genetic data with environmental conditions to find adaptive traits in the wild. In blueberries and cranberries, for example, this combined approach has been key. The pangenome points to essential genes for cold hardiness, while landscape analysis reveals which genomic regions help plants adapt to local temperatures. Together, they enable us to develop robust varieties that maintain stable yields despite climate fluctuations.
2. Accelerating Conservation and Restoration of Endangered Species
Conservation is no longer just about protecting habitats—it's about actively ensuring species can genetically adapt to change. We use pangenomes to understand a species' full genetic potential and landscape genomics to locate populations with critical adaptive traits, like heat-resistant corals.
3. Optimizing Livestock Performance in Diverse Environments
This platform focuses on the impact of different environments on the production performance of livestock, delves deeply into the mechanisms of action of factors such as temperature, humidity, and light, aiming to explore targeted optimization strategies to enhance the growth efficiency and health level of livestock and achieve efficient and sustainable breeding.
Demo
Figure 2: Selection signatures of SNPs in European beef and dairy cattle. (Dai, 2025)
Case Study
Identification of candidate genes for reproductive traits in Xinjiang sheep breeds based on genomic structural variation.
Journal: Front Vet Sci
Published：2025
Improving reproductive efficiency is the core driving force of the sheep industry economy, with the number of lambs born alone contributing 70-90% of the output value. However, the low yield caused by seasonal estrus (such as Kazakh sheep and Yunnan semi-fine wool sheep, which mostly have only one litter) has become a prominent bottleneck for the development of the industry. Although there are excellent germplasms such as Hu sheep that have multiple litters and are in heat all year round, their overall reproductive performance still needs to be improved. At present, its genetic research foundation is weak, especially lacking systematic exploration of loci such as structural variations (SV).
To systematically identify structural variants (SVs) associated with reproduction, we analyzed whole-genome sequences from 73 ewes across seven breeds using multiple tools (Delly, Manta, Lumpy). Our pipeline integrated the selection of high-frequency SVs, functional annotation, and screening of trait-linked candidate genes, culminating in experimental validation.
This work builds upon foundational SV research in sheep. Previous studies by Liang et al. and Yang et al. demonstrated that SVs can influence gene expression underlying adaptation and key production traits, even revealing convergent evolution. Li et al. further linked SVs to tail morphology. This study contributes to understanding Kazakh sheep's reproductive trait genetic mechanisms and provides a theoretical framework for sheep genetic research and molecular breeding improvements.
Figure 3: Manhattan plot for the correlation analysis of structural variations among different sheep breeds
FAQ
Adaptive Breeding is a modern breeding strategy that aims to develop new crop varieties or animal breeds specifically designed to thrive in current and future target environments, particularly under climate change-induced stresses (e.g., drought, heat, new pathogens). It moves beyond simply maximizing yield in ideal conditions to focus on resilience, stability, and environmental adaptation. It leverages genomic tools to rapidly incorporate beneficial alleles that confer these adaptive traits.
Landscape genomics connects the dots between DNA and the environment. By overlaying genetic data with geographic and climate information, we can create a map of natural selection. This shows us exactly which genes help Ethiopian chickens survive drought, or enable barley to thrive in the cold Tibetan plateau. It's a powerful way to mine existing seed banks for useful traits, turning preserved samples into practical breeding solutions by linking genetic markers to real-world adaptation.
References
- Dai S, Zhao P, Li W, et al. Global Pangenome Analysis Highlights the Critical Role of Structural Variants in Cattle Improvement and Identifies a Unique Event as a Novel Enhancer in IGFBP7+ Cells. Mol Biol Evol. 2025 Sep 1;42(9):msaf205. doi: 10.1093/molbev/msaf205
- Li J, Yiming S, Zheng W, et al. Identification of candidate genes for reproductive traits in Xinjiang sheep breeds based on genomic structural variation. Front Vet Sci. 2025 Jun 6;12:1551293. doi: 10.3389/fvets.2025.1551293.
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