Agrigenomic Trait Mapping
Agrigenomic Trait Mapping Overview
Our advanced sequencing and analysis platforms uncover the genetic basis of key agricultural traits. We leverage artificial intelligence to decode complex genomic data, rapidly connecting genetic markers to trait performance. This enables us to predict how plants and livestock will express these crucial characteristics, accelerating the development of more resilient and productive varieties for modern agriculture. We use advanced sequencing to map the genes behind important agricultural traits like yield, disease resistance, and stress tolerance. This process, known as agrigenomic trait mapping, reveals how specific genes influence a plant's or animal's characteristics.
Our Agrigenomic Trait Mapping Service Enhances Your Agricultural Research with:
- Precise Trait-Gene Association: Using sophisticated statistical models and AI algorithms, we can accurately map the genetic regions associated with specific agricultural traits. This helps breeders to focus on the most relevant genes during the breeding process, improving the efficiency of trait selection.
- Accelerated Breeding Programs: By quickly identifying the genetic basis of desirable traits, our service enables breeders to shorten the breeding cycle. This means that new and improved crop and livestock varieties can be developed and introduced to the market faster, meeting the growing demand for high-quality agricultural products.
- Insights into Trait Inheritance: Our approach provides in-depth knowledge about how traits are inherited across generations. This information is essential for designing effective breeding strategies to maintain and enhance the desired traits in future generations.
- Research Efficiency Boost: The integration of AI, bioinformatics, and genomics in our service acts as a powerful accelerator for agricultural research. It facilitates the rapid discovery of novel genetic markers and genes associated with important agricultural traits, driving innovation in the agricultural sector.
What Is Agrigenomic Trait Mapping
Agricultural genomic trait mapping is an important research field in modern agriculture. Studying agricultural genomes can help researchers identify gene loci related to yield, quality and other aspects. By analyzing the genetic markers in the genome, it reveals how specific genes affect trait expression. By leveraging genomic data, accelerate the breeding of high-yield varieties and enhance agricultural productivity and sustainability.
How We Deliver This Solution
Sample Collection & Preparation
1. Diverse Germplasm Collection
We gather a wide-ranging collection of plant and animal germplasm, encompassing wild types, local landraces, and commercial cultivars/breeds. Analyzing these samples with diverse genetic backgrounds enables accurate identification of genetic variations related to crucial traits, like disease resistance in plants or milk production in animals.
Simultaneously, we include samples from different geographical areas with distinct phenotypes. This aids in discovering population-specific genetic markers, which can assist in germplasm conservation and design breed - improvement programs for specific ecological niches.
2. Multi-Tissue and Multi-Omics Sampling
To establish the link between genetic variations and phenotypic traits, during sample collection, we sample multiple tissues (e.g., leaves, fruits, muscle, and milk). We also record environmental factors. A multi-omics strategy, integrating genomic, transcriptomic, and proteomic data, is employed to comprehensively map gene-trait associations.
For example, when studying rice under flood stress, we simultaneously collect leaf transcriptome data and measure soil water levels. Through such correlation analysis, key genes responding to flood stress can be precisely pinpointed.
Sequencing
1. DNA Methylation Microarray for Population Genetics
Description: This method focuses on analyzing DNA methylation patterns across a population. DNA methylation acts as an epigenetic "switch" that can turn genes on or off without changing the underlying DNA sequence. Using microarray technology, we can scan this methylation status across thousands of genomic regions in a population. This enables us to explore how epigenetic variation contributes to observable differences in traits-such as why some individuals are more susceptible to disease, grow faster, or better tolerate stress. For example, in crops, changes in DNA methylation patterns can be associated with responses to environmental stresses like drought or salinity.
Application: Helps in understanding the role of epigenetics in population-level trait variation, which can be crucial for breeding programs aiming to enhance adaptability and resilience.
2. Whole Exome Sequencing for Population Genetics
Description: Whole exome sequencing focuses on sequencing the protein-coding regions of the genome, known as the exome. Although the exome represents only a small fraction of the genome (about 1-2%), it contains a large proportion of the genetic variations that are likely to have functional effects.
Application: In population genetics, whole exome sequencing can be used to identify genetic variants associated with traits in a more targeted and cost-effective way compared to whole genome sequencing. It is useful for studying the genetic basis of diseases and economically important traits in crops and livestock. For example, in cattle, whole exome sequencing can help in identifying mutations in genes related to milk production or fertility.
Bioinformatics
1. Population Structure and Admixture Analysis
Population Structure and Admixture Analysis allow us to identify distinct subpopulations, trace historical gene flow, and accurately determine breed composition. The findings directly support real-world decisions—from conserving unique alleles in local livestock to guiding strategic crossbreeding.
Example Tools: We typically use PLINK for quality control and SNP filtering, and ADMIXTURE for robust ancestry inference.
2. Selection Signature Detection
By applying statistical methods such as XP-CLR and iHS, we scan genomes for regions that have been under positive selection. This process uncovers alleles with proven historical value—like those that enhance egg production in poultry or drought tolerance in crops.
3. Gene - Trait Association (GWAS) and Functional Annotation
We associate variants with phenotypes (e.g., fiber length in cotton, meat tenderness in cattle) using mixed linear models to account for population structure. The results are annotated to prioritize candidate genes (e.g., VERNALIZATION genes in crops).
Tool Example: GEMMA for GWAS and SnpEff for variant annotation.
Service flowchart
Figure 1: How We Deliver This Solution: AI-Enhanced Therapeutic Target Discovery Workflow
Our Advantages
Multi-Trait Genetic Mapping
We analyze multiple traits together to provide a complete view of genetic architecture. For example, we can identify genomic regions (QTLs) that jointly affect grain size, protein content, and drought tolerance in crops. Through fine-mapping, we narrow these regions down to specific genes, helping breeders focus on the most promising targets and develop varieties with multiple improved traits more efficiently.
Personalized Breeding Strategy Formulation Based on Genomic Selection
Our agrigenomic trait mapping service enables the formulation of personalized breeding strategies tailored to the specific goals and requirements of different agricultural projects. Genomic sequencing technology can predict the breeding value of individual species and provide breeders with detailed breeding population information about the genetic potential of each plant or animal.
Applications
Disease Resistance
Controlling disease is critical for both animal welfare and agricultural economics. Through agrigenomic trait mapping, we can now identify the specific genetic variants that make some animals naturally more resistant to common illnesses. In dairy herds, this means selecting for cattle with a genetic predisposition against mastitis, reducing incidence rates and enhancing productivity. For pork producers, it means breeding pigs with inherent resistance to devastating viruses like PRRS. This strategy allows us to strengthen livestock populations from within, decreasing dependence on broad-spectrum treatments and building a more resilient agricultural future.
Endangered Species Protection
Agrigenomic trait mapping can be applied to the conservation of endangered agricultural-related species. By mapping the genetic diversity of endangered plant and animal species, conservationists can identify populations with unique genetic traits and develop strategies to preserve them.
Demo
Figure 2: Phylogenetic tree of vertebrates with genomic imprinting research. The figure shows the clades in which the existence of imprinting has been tested. (Hubert, 2024)
Case Study
Development and validation of PCR marker array for molecular selection towards spring, vernalization-independent and winter, vernalization-responsive ecotypes of white lupin (Lupinus albus L.).
Journal: Sci Rep.
Published：2025
While white lupin is a nutritious and sustainable legume with growing global interest, its wider adoption in temperate climates is hindered by a key challenge: controlling flowering time in response to vernalization.
The species has two main ecotypes:
- Winter-type: Requires prolonged cold (vernalization) to flower, making it suitable for autumn sowing in milder regions but vulnerable to frost.
- Spring-type: Has low vernalization needs, enabling spring sowing in colder areas, but often flowers too late to avoid summer drought.
Breeders struggle to develop varieties that flower at the right time for different growing regions because:
- Vernalization response is complex, involving multiple genetic loci.
- Existing molecular markers could not reliably predict extreme early or late flowering across diverse genetic backgrounds.
- This lack of predictive tools slows down the breeding of locally adapted, high-yielding cultivars.
To address this, we implemented a marker-assisted selection strategy to precisely breed for optimal flowering time.
Our approach combined advanced genomic resources with practical tool development:
- Developed a Versatile PCR Array: Consolidated these markers into a single, efficient platform for high-throughput genotyping.
- Leveraged Existing Genomic Resources: Used the available high-density linkage map, reference genomes, and pangenome to inform our marker design.
- Converted GWAS Findings into Practical Tools: We translated significant markers from a recent Genome-Wide Association Study (GWAS)—including SNPs and presence/absence variants—into versatile, cost-effective PCR-based markers (e.g., CAPS, dCAPS, allele-specific PCR).
We successfully created and validated a powerful molecular toolkit for white lupin breeding:
- A Comprehensive PCR Marker Array: Developed a robust set of PCR markers that cover multiple genetic loci controlling flowering time, including both newly discovered and previously known regions.
- Enabled Precision Breeding: The array allows breeders to efficiently select for both spring-type (early flowering, low vernalization need) and winter-type (vernalization-responsive) ideotypes, including accessions with rare, extreme early- or late-flowering alleles.
- Bridged Genomics and Field Application: By transforming complex genomic data into a user-friendly selection tool, we have significantly accelerated the development of white lupin varieties tailored for specific climates and sowing seasons.
Figure 3: Correlation heatmap reporting Spearman rank correlation coefficients for each trait vs white lupin linkage map QTL PCR marker comparison.
FAQ
This technology is transformative because it moves breeding from a predominantly phenotype-driven process to a data-driven, predictive science. Traditionally, breeders had to wait for plants to mature or animals to grow to see the outcome of their crosses, which was time-consuming and often influenced by environmental factors. Agrigenomic trait mapping allows us to predict the potential of an individual at a very early stage, for instance, by analyzing the DNA of a seedling. This dramatically shortens the breeding cycle, increases selection accuracy, and enables the stacking of multiple desirable genes—for example, combining genes for disease resistance and superior nutritional quality—into a single elite variety or breed much faster than was ever possible before.
References
- Hubert JN, Perret M, Riquet J, et al. Livestock species as emerging models for genomic imprinting. Front Cell Dev Biol. 2024 Feb 15;12:1348036. doi: 10.3389/fcell.2024.1348036.
- Surma A, Książkiewicz M, Bielski W, et al. Development and validation of PCR marker array for molecular selection towards spring, vernalization-independent and winter, vernalization-responsive ecotypes of white lupin (Lupinus albus L.). Sci Rep. 2025 Jan 21;15(1):2659. doi: 10.1038/s41598-025-86482-1.
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