Trait Enhancement
Overview
Trait enhancement via population genomics does not rely on random crossing and long-cycle field selection alone. Instead, it integrates large-scale sequencing, high-resolution genotype–phenotype association and genomic prediction to pinpoint the key loci and superior alleles that control yield, quality and stress resistance.
CD Genomics focuses on discovering these genomic targets, developing high-confidence molecular markers, and building predictive models to accelerate your breeding pipelines, without directly performing gene editing or transgenic operations.
Our Advanced Trait Enhancement Service Elevates Your Research and Development with:
Unparalleled Genetic Diversity Access: Leverage our vast, globally-sourced germplasm banks and genomic databases to identify novel genes and alleles inaccessible through conventional means.
Streamlined Breeding Pipeline: Accelerate your breeding cycles by integrating our proprietary molecular markers for high-throughput, early-stage selection, drastically reducing time and field trial costs.
Customized Trait Optimization: Recognizing that different organisms and environments require unique trait enhancements, we offer tailored solutions that address specific challenges. Whether it's enhancing the nutritional content of crops, improving the meat quality in livestock, or developing plants with enhanced resistance to pests and diseases, our service can be customized to meet your exact needs.
Accelerated Development Timelines: By leveraging our advanced genetic tools and expertise, we can significantly shorten the development cycle for new, enhanced varieties. This means faster time-to-market for your products, giving you a competitive edge in the industry.
What Is Trait Enhancement
Trait enhancement is a targeted scientific discipline focused on improving specific characteristics in organisms by combining genetic variation, phenotypic data and modern analytical tools. In a population genomics–driven framework, trait enhancement does not primarily rely on directly altering genomes in the laboratory. Instead, it aims to identify naturally occurring alleles, haplotypes and loci that underlie desirable phenotypes, and to use molecular markers and genomic prediction models to assemble these superior alleles through breeding.
In our solutions, trait enhancement is powered by genome-wide association studies, selection scans, haplotype-based analyses and genomic prediction, which together enable the rational design of breeding strategies for higher yield, stress resistance and improved quality.
How We Deliver This Solution
Germplasm & Phenotyping
Global Germplasm Bank: We own and manage a diverse live germplasm bank covering cultivated species, local varieties and their wild relatives. This provides us with an unparalleled breadth of genetic diversity and is the source for uncovering new genes.
High-throughput precise phenotypic analysis: We utilize automated field equipment, unmanned aerial vehicle (UAV) remote sensing, and indoor imaging systems to collect precise phenotypic data related to yield, biological stress (such as diseases), and abiotic stress (such as drought) on a large scale and non-destructively throughout the entire growth cycle of crops.
Genotype-phenotypic association bridging: By combining massive and multi-dimensional phenotypic data with whole-genome sequencing data, we can establish a strong genotype-phenotypic association, thereby precisely revealing the genetic loci that control complex traits and providing a key basis for subsequent marker development and precise screening.
Sequencing
- Whole Genome Resequencing for Population Genomics
Description: By conducting whole-genome sequencing on hundreds to thousands of individuals in a population, a complete map of genetic variations can be drawn. This technology can precisely detect single nucleotide polymorphisms, insertions/deletions and structural variations across the entire genome, providing core data support for genome-wide association analysis, selective clearance analysis and genomic prediction, and helping researchers systematically discover all potential genetic loci that control complex traits.
Application: It is the foundation for genome-wide association studies (GWAS), selective sweep analysis, and genomic prediction, enabling the discovery of all potential genetic loci controlling complex traits.
- Genotyping-by-Sequencing (GBS) for Large Populations
Description: A cost-effective reduced-representation sequencing technique that discovers and genotypes a high density of SNPs across a large number of individuals.
Application: Ideal for genomic selection and large-scale population genetic studies in breeding programs where genotyping thousands of lines is required for traits like yield and abiotic stress tolerance.
Bioinformatics
- Population Genomics and Selection Signature Analysis
We utilize tools like ADMIXTURE to understand population structure and history. Then, we apply statistical methods (e.g., XP-EHH, Fst) to scan genomes for regions that have been under natural or artificial selection. This identifies valuable genomic segments, such as those for drought adaptation in maize, providing direct targets for trait introgression.
- Gene-Trait Association Mining (GWAS)
We correlate genome-wide genetic markers with high-quality phenotypic data using models that correct for population structure. This pinpoints specific genomic loci significantly associated with target traits, for example, identifying a novel gene associated with seed size in soybean.
- Haplotype-Based Gene Discovery and Prioritization
We move beyond single markers to analyze haplotype blocks. By identifying shared haplotype regions among superior individuals, we can more accurately pinpoint causal genes and their superior alleles (haplotypes), significantly enhancing the efficiency of gene cloning and marker development for breeding.
Service flowchart
Figure 1: How We Deliver This Solution: Trait Enhancement via Pop 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.
Insight into Environment-specific genetic adaptation
By conducting genomic analysis on populations from different environmental sources, we can reveal the genetic mechanisms by which species adapt to specific environments such as drought and high temperatures, providing unique targets for breeding highly adaptable varieties.
Customized trait solutions
Based on different species, environments and market demands, we offer tailor-made trait optimization solutions, precisely aggregating superior alleles to achieve targeted breeding of "design-oriented" varieties.
Applications
Precisely enhance yield and quality: Analyze the genetic networks that control the components of yield (such as the number of panicles and grain weight) and nutritional qualities (such as protein and oil content), achieve the coordinated improvement of these complex traits, and cultivate new high-yield and high-quality varieties.
Customized crop design: Based on specific processing requirements, market taste preferences or local planting environments, precisely aggregate superior alleles and tailor-make "design-oriented" crop varieties.
Uncovering recessive superior genes: Efficiently identify valuable genes lost in modern breeding from wild relatives and local varieties (such as high nutrient utilization rate and unique flavor), and broaden the genetic basis of breeding germplasm.
Prospective climate-adaptive breeding: By using genomic prediction models, genetic combinations that may perform better under future climate scenarios are screened out to cultivate crop varieties that can adapt to future environments in advance, ensuring food security.
Demo
Figure 2: High-res mapping in chickpea found loci linked to DYI. Analysis of 222 ILs via phylogenetic tree, PCA, LD decay, GWAS, etc. was done. (Thakro, 2023)
Case Study
Comparative and population genomics of buckwheat species reveal key determinants of flavor and fertility.
Journal:Mol Plant.
Published：2023
- Limited genomic resources: As an important economic crop, the high-quality genomic resources of common buckwheat are scarce, which hinders the research on its molecular genetics, breeding, evolution and domestication.
- Lower yield and flavor differences: Common buckwheat has a lower per-unit yield compared to Tartary buckwheat due to its self - incompatibility and flower morphology-based cross - pollination limitations. Also, its flavonoid content, which affects flavor, is lower than that of Tartary buckwheat, though it is generally considered tastier.
- Understanding genetic basis: There was a lack of in-depth understanding of the genetic basis for common buckwheat's wider environmental adaptability and more pleasant flavor compared to Tartary buckwheat, as well as the genes responsible for its yield, fertility, and flavor - related traits.
- Genome sequencing and assembly: Report a high-quality reference genome of F. esculentum var. homotropicum, a self-pollinated variant of common buckwheat, and use it as a representative for common buckwheat research.
- Comparative genomic analysis: Conduct comparative genomic analysis between the assembled genome of F. esculentum var. homotropicum and the Tartary buckwheat genome to explore the genetic basis of common buckwheat's characteristics.
- Genome resequencing and population genomics: Present genome resequencing data from 572 common buckwheat germplasm accessions and use population genomic analysis to identify genes related to environmental adaptability between different cultivated groups.
- Key gene mining: By integrating multi-year agronomic trait and flavor metabolite data, we successfully identified a series of candidate genes that regulate yield, fertility, and flavor formation through genome-wide association studies (GWAS).
- Genome resource development: Successfully assembled a high-quality reference genome of F. esculentum var. homotropicum, providing a valuable resource for common buckwheat research.
- Genetic basis insights: Gained insights into the genetic basis of common buckwheat's wider environmental adaptability and more pleasant flavor compared to Tartary buckwheat through comparative genomic analysis.
- Gene identification: Identified a series of candidate genes likely responsible for yield, fertility, and flavor in common buckwheat through population genomic analysis and GWAS, which will accelerate the breeding process of common buckwheat.
Figure 3: A simplified representation of the biosynthetic pathway of rutin metabolism. The expression of each gene in different tissues (root, stem, leaf, flower, and seed) of F. esculentum var. homotropicum is presented in terms of FPKM.
FAQ
A: The time varies depending on the crop and its characteristics. Thanks to our efficient platform, the 10 to 12 years required by traditional methods can usually be shortened to 5 to 7 years, and the gene discovery stage of complex traits can be completed within 1 to 2 years.
A: The confidentiality and ownership of customer data and germplasm resources are the cornerstone of our cooperation. We adopt strict legal agreements, physical isolation and information security management systems to ensure that your exclusive assets are completely yours.
References
- Thakro V, Malik N, Basu U, et al. A superior gene allele involved in abscisic acid signaling enhances drought tolerance and yield in chickpea. Plant Physiol. 2023 Mar 17;191(3):1884-1912. doi: 10.1093/plphys/kiac550.
- Zhang K, He Y, Lu X, et al. Comparative and population genomics of buckwheat species reveal key determinants of flavor and fertility. Mol Plant. 2023 Sep 4;16(9):1427-1444. doi: 10.1016/j.molp.2023.08.013.
Related
Services
Download
