Crop & Livestock Genomics

Crop & Livestock Genomics Overview

Advanced genomics gives us unprecedented insight into the genetic makeup of crops and livestock. Our work centers on two pivotal areas: exploring genetic diversity and identifying the genes that control important traits.

The process starts by detecting the full range of genetic variants within a population. We then use sophisticated bioinformatics to connect these variants to specific characteristics. This allows us to swiftly decode complex genetic information and accelerate the development of superior agricultural varieties.

Genetic variant exploration refers to the detection of genetic variations within crop and livestock populations. These variations can stem from single - nucleotide polymorphisms (SNPs), copy number variations, structural rearrangements, etc. Such variations naturally exist among different individuals within a population. By integrating bioinformatics with genomic data, we can expedite the discovery of genes that are crucial for enhancing agricultural productivity and quality.

Our Crop & Livestock Genetic Variant Exploration & Trait - Associated Gene ID Service Enhances Your Agricultural Research with:

• Accelerated Trait - Associated Gene Discovery: Leveraging sophisticated machine learning algorithms trained on extensive crop and livestock genomic datasets, we can rapidly screen out potential genes associated with important agricultural traits. This not only shortens the time required for gene discovery but also increases the likelihood of identifying genes with high agricultural value. For example, in crops, we can quickly find genes related to high - yield, disease resistance, or stress tolerance. In livestock, we can identify genes influencing meat tenderness, milk fat content, or fertility.
• Insights into Agricultural Trait Mechanisms: Our approach uncovers novel genetic variants and trait - associated genes that provide deep insights into the underlying mechanisms of complex agricultural traits. This knowledge is vital for developing targeted breeding strategies and improving agricultural production efficiency.
• Research Acceleration Platform: By integrating bioinformatics, machine learning, and genomics, our platform acts as a powerful engine for agricultural research. It enables the rapid discovery of new genes, genetic pathways, and molecular markers for important traits, significantly speeding up the entire research and development cycle.

What Is Crop & Livestock Genomics

At the forefront of agricultural genomics, we specialize in Whole Genome Resequencing (WGR) to decode the full genetic blueprint of your crops and livestock. Our advanced services detect the comprehensive spectrum of genetic variation—from SNPs to structural variants—within any population. This platform is conducive to agricultural advancement.

How We Deliver This Solution

Sample Collection & Preparation

1. Diverse Crop/Livestock Population Recruitment

   First, we will form diverse groups of plants and animals, including wild relatives, local species and commercial species. By analyzing these samples with a broad genetic background, we can precisely identify genetic variations related to important traits, such as drought tolerance of crops or disease resistance of livestock.

   Meanwhile, we consciously include groups from different geographical regions with different phenotypes. This helps us discover genetic markers specific to the population, which can not only support species conservation but also tailor breeding plans for specific environments.

2. Multi-Tissue & Multi-Omics Sampling

   To establish the association between genetic variations and actual traits, when collecting samples, we cover various tissues (such as leaves, roots, muscles, and blood), and simultaneously record environmental data. We adopt a multi-omics approach, integrating genomic, transcriptomic and metabolomic data to comprehensively map the association between genes and traits.

   For instance, when analyzing corn under drought stress, we simultaneously detect the transcriptome data of its root system and soil moisture. Through this kind of correlation analysis, the key genes that respond to drought stress can be accurately identified.

Sequencing

1. Whole Genome Resequencing (WGR)
   • We apply WGR to conduct comprehensive population scans, detecting SNPs, indels, and structural variants across entire genomes. This approach helps uncover rare variants linked to key traits such as yield or meat quality, and can identify selective sweeps within breeding populations. A major application is discovering adaptive alleles in wild relatives—like cold tolerance genes in wild wheat—for introduction into elite varieties.
   • Link: /crop-livestock-genomics/seq/whole-genome-reseq.html
2. Reduced Representation Sequencing (e.g., GBS, RAD-seq)
   • Cost-effective for large populations, these methods target genomic regions with high variability to genotype thousands of individuals. Ideal for GWAS in crops (e.g., mapping QTLs for pest resistance) or parentage analysis in livestock.

     Useful for tracking inbreeding in endangered breeds or validating hybrid vigor in crossbred populations.

   • Link: /crop-livestock-genomics/seq/reduced-representation.html

Bioinformatics & Functional Analysis

1. Population Structure & Admixture Analysis
   • Using tools like PCA, STRUCTURE, and ADMIXTURE, we characterize genetic diversity and ancestry to delineate subpopulations, trace gene flow, and determine breed composition. These insights directly support conservation planning—for instance, preserving unique alleles in indigenous livestock—and help guide strategic breeding decisions.
   • Tool Example: PLINK for SNP filtering and ADMIXTURE for ancestry inference.
2. Selection Signature Detection
   • We use statistical methods like XP-CLR and iHS to scan genomes for regions that have undergone positive selection. This helps us uncover alleles with proven historical importance—such as those behind increased milk yield in cattle or heat tolerance in poultry. By highlighting these genomic regions under strong selection pressure, we provide breeders with high-value targets to prioritize in their marker-assisted selection programs.
3. Gene-Trait Association (GWAS) & Functional Annotation
   • We link variants to phenotypes (e.g., grain size in rice, wool quality in sheep) using mixed linear models to control for population structure. Results are annotated to prioritize candidate genes (e.g., FLOWERING LOCUS C in crops).
   • Tool Example: GEMMA for GWAS and SnpEff for variant annotation.
4. Polygenic Risk Scoring (PRS) for Breeding
   • Aggregates multiple QTLs into a score to predict breeding value for complex traits (e.g., feed efficiency in pigs). Helps prioritize elite sires/dams or identify underperforming individuals early.
   • Method: Train PRS models using summary statistics from multi-breed GWAS.
5. Pathway & Gene Network Analysis
   • We take the significant variants identified through GWAS and map them onto biological pathways—for instance, those involved in crop stress tolerance or livestock disease resistance. This contextual mapping allows us to identify the key genes that regulate these processes. The most promising of these genes then become high-value candidates for precise intervention using advanced gene-editing or targeted breeding strategies.
   • Tool Example: We typically use platforms like Metascape or Cytoscape to visualize and interpret these complex gene networks.

Service flowchart

Figure 1: How We Deliver This Solution: AI-Enhanced Therapeutic Target Discovery Workflow

Our Advantages

Nutritional Quality Optimization: ML-Assisted Nutrient Gene Screening

Traditional ways of improving nutritional quality mainly focus on post - harvest processing. We use machine-learning models trained on genomic, phenotypic and nutritional datasets to prioritize genes involved in nutrient synthesis and accumulation in crops and livestock. By analyzing genetic variations associated with nutrient levels, such as vitamins, minerals, and proteins, we identify genes that can be targeted for breeding. This helps in developing agricultural products with enhanced nutritional value, meeting the growing demand for healthy and nutritious food in the market.

Scalable High-Throughput Solutions

We have built our own sequencing and analysis platform and carry out production in accordance with standardization. It can effectively handle projects ranging from a single sample to large-scale population studies involving over 10,000 people. An efficient and accurate analysis process lays the foundation for a smooth path in scientific research.

Advanced Bioinformatics Pipelines

Our team has utilized advanced bioinformatics to establish our own sequencing platform and analysis code, ensuring data reproducibility. We can analyze genomic characteristics through various methods to understand the genetic information of crops.

Applications

Breed Improvement & Selection Programs

Genomic sequencing identifies key trait-associated markers (e.g., yield, disease resistance, meat quality), enabling precision breeding in crops and livestock to accelerate genetic gain and reduce generation intervals.

Conservation of Genetic Diversity

By applying population genomics, we can identify critical threats to genetic health—such as bottlenecks, inbreeding, and the loss of unique alleles—in endangered breeds and wild relatives. These insights directly support the development of effective conservation strategies, helping to protect biodiversity and maintain adaptive potential for the future.

Disease Resistance & Health Management

Genome-wide association studies (GWAS) pinpoint genetic variants linked to pathogen resistance (e.g., PRRS in pigs, rust in wheat), supporting development of resilient varieties and reducing reliance on chemicals.

Parentage & Traceability Verification

High-resolution SNP panels validate animal/plant parentage, ensure breed purity, and enable supply chain traceability—critical for premium markets (e.g., organic, heritage breeds) and intellectual property protection.

Demo

Figure 2: Coordinated Transcriptomic and Phenotypic Remodeling in the Pigeon Crop During the Breeding Cycle (Wang, 2023)

Case Study

Long-read sequencing of 111 rice genomes reveals significantly larger pan-genomes.

Journal：Genome Res.

Published：2022

• Challenge
• Solution
• Scientific Achievements

FAQ