Multi‑Omics Integration
Overview
We provide multi-omics integration solutions purpose-built for population genetics. By jointly analyzing genome variation, gene regulation and molecular pathways, we identify key loci, superior alleles and regulatory mechanisms underlying yield, quality and stress resistance (or other target traits). The outputs—validated markers, candidate genes and predictive models—help prioritize lines and design more efficient breeding strategies.
Our multi-omics integration service offers the following core advantages for your research and development:
- Deep germplasm mining: Leveraging curated public and proprietary datasets plus your project's data, we uncover rare alleles and hard-to-detect variants that matter for target traits.
- Efficient breeding decision-support: Through robust markers and multi-omics predictive models, we enable earlier, higher-throughput screening—shortening cycles and reducing field trial load.
- Customized trait packages: Solutions are tailored by species, environment and program goals (e.g., yield, grain/fruit quality, biotic/abiotic resistance), with transparent deliverables.
- Faster research milestones: With a scalable analysis platform and expert interpretation, programs reach go/no-go decisions sooner and focus resources where impact is highest.
What Is Multi-Omics Integration
Multi-Omics Integration refers to the integration of multi-level biomolecular data such as genomics, transcriptomics, proteomics, and metabolomics. Through normalization, correlation analysis, and biological function analysis, it reveals the intermolecular collaborative regulatory mechanisms. This technology can systematically analyze complex biological processes, providing key basis for research insights, drug development and biomarker research.
How We Deliver This Solution
Sample Collection & Preparation
- Tailored sampling plan: We design sampling strategies based on study goals (e.g., population structure, adaptation, complex traits), covering cohort size, geographic distribution, and multi-omics sample types (e.g., leaf/tissue, seed/grain, and other species-appropriate tissues/cells), with standardized metadata collection to support downstream integration.
- Standardized collection and stabilization: We provide SOPs and sampling kits to standardize collection and stabilization across omics layers (DNA/RNA/protein/metabolites). Samples are aliquoted by type and preserved with appropriate stabilizers or low-temperature storage to protect molecular integrity. Required sample amounts are as follows:
Plant tissue: ≥1 g
Animal tissue: ≥0.5 g
Mammalian whole blood: ≥1 mL
Whole blood with nucleated RBCs (e.g., birds, fish): ≥0.5 mL
Cultured cells: ≥5 × 10⁶ cells (log-phase growth recommended; adherent cells must be trypsinized)
Nucleic acid samples: Total amount ≥300 ng, concentration ≥10 ng/μL
- Quality control and data integrity: Each batch undergoes integrity/purity/concentration checks appropriate to the material (OD260/280 between 1.8–2.0). From labeling and metadata capture to cold-chain logistics, standardized procedures help ensure traceability and data consistency prior to laboratory processing.
Sequencing
1. Whole Genome Resequencing for Population Genomics
Description: We use whole-genome sequencing to comprehensively capture single nucleotide polymorphisms, insertions and deletions, structural variations and copy number variations in samples without the need for pre-set sites. Compared with chip or exon sequencing, WGS can discover more rare variations and functional sites in non-coding regions.
Application: This method serves as the foundation for constructing high-resolution population genetic maps, conducting detailed population structure analyses, and performing genome-wide association analyses. In disease research, we utilize WGS to discover novel genetic markers associated with complex diseases and trace the origin and spread history of specific pathogenic alleles in the population.
Link: /pop-genomics/seq/whole-genome-resequencing.html
2. Multi-Omics Layer Sequencing for Functional Insights
Description: We apply a multi-omics sequencing strategy to further sequence the transcriptome and epigenome (such as methylation) beyond the genome. This enables us to link genetic variations with gene expression regulation and epigenetic status, thereby revealing the intermediate mechanism from genotype to phenotype.
Application: Multi-omics sequencing enables us to go beyond simple genetic associations and gain a deeper understanding of the molecular regulatory mechanisms of traits. In environmental adaptability research, we utilize this method to reveal how specific genetic mutations ultimately drive phenotypic adaptation by regulating gene expression (transcriptome) or being influenced by environmental factors (epigenome).
Link: /pop-genomics/seq/dna-methylation-array.html
Bioinformatics
1. Multi-omics data quality control and precise genotyping
Firstly, we conduct strict quality control on the original sequencing data, including the elimination of linkers and low-quality sequences, sequencing error correction, and assessment of sample correlations at the population level to identify potentially confounding samples. Subsequently, through a precise sequence alignment and variation identification process compared with international authoritative databases, a high-quality population genetic variation dataset was obtained.
2. Analysis of population genetic structure and evolutionary history
Based on high-quality genetic variation data, we use models such as principal component analysis and ADMIXTURE to analyze the fine genetic structure of the population you are studying. Furthermore, we detected the signals left by natural selection on the genome through allelic spectrum analysis, linkage disequilibrium patterns and selection sweep analysis, and inferred the mixed history of the population and changes in the effective population size.
3. Multi-omics integration and functional mechanism analysis
This is our core strength. We integrate genomic, transcriptomic, and epigenomic data, and utilize advanced statistical methods—including multi-omics Mendelian randomization—to perform causal inference–oriented analyses when key assumptions are satisfied. This approach helps explore potential causal relationships within the "genetic variation–molecular phenotype–macroscopic trait" framework. Ultimately, through functional enrichment analysis and visualizations, we clearly highlight key genes, pathways, and regulatory logic associated with the target traits.
Service flowchart
Figure 1: How We Deliver This Solution: Multi-Omics Integration Workflow
Our Advantages
Comprehensive Data Coverage
We integrate multi-layered omics data, including genomics, transcriptomics, proteomics, and metabolomics, ensuring a holistic view of biological systems.
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
Complex disease research: In fields such as cardiovascular diseases and neurodegenerative diseases, multi-omics integration can capture complementary information of diseases at different molecular levels, revealing biological associations that cannot be captured by a single omics. Dynamic monitoring of proteomic and metabolomic data can reflect the real-time status of drug absorption/metabolism.
New target identification: Jointly modeling host and microbiome multi-omics can uncover mechanisms that influence metabolism, immunity and behavior—informing hypothesis generation for future interventions.
Biomarker discovery: Through module-level interpretation mechanisms, multi-omics integration can identify disease-related functional modules, which may become candidates for new drug development or therapeutic targets. For instance, the GREMI framework helps researchers identify disease-related functional modules through module-level interpretation mechanisms, promoting the discovery of biomarkers.
Demo
Figure 2: Comparison of PCS, PCA and LD feature extraction methods on seven different models (Zhao, 2025)
Case Study
Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits.
Journal: Genome Biology
Published: 2020
GWAS loci often pinpoint associated variants but not the underlying biological mechanisms. In practice, the same locus may show context-specific regulatory effects (e.g., tumor vs. normal tissue), and LD can confound interpretation. A robust multi-omics strategy is needed to connect trait-associated SNPs to molecular readouts across tissues/conditions while controlling LD.
- Multi-layer QTL integration: Jointly integrate GWAS summary statistics with eQTL/meQTL/pQTL summary statistics from trait-relevant tissues and conditions to evaluate coordinated SNP effects across omics layers.
- Posterior-probability prioritization: Enumerate association patterns and assign posterior probabilities to prioritize SNP–gene–omics configurations with high confidence for follow-up interpretation.
- LD-aware conditional analysis: Perform conditional association analysis within loci by adjusting for lead SNPs, reducing spurious multi-omics signals driven by LD rather than shared causal effects.
- Pleiotropy discovery + replication: Extend the same framework to detect pleiotropic variants across multiple complex traits and validate with independent GWAS resources when available.
- Mechanistic interpretation of susceptibility loci: In a breast cancer application, the study integrated BCAC GWAS with TCGA tumor multi-omics QTLs and GTEx normal-tissue eQTLs, enabling LD-aware mechanistic interpretation of GWAS hits.
- Multi-omics "cascading effects" signals quantified: After conditional analysis at an 80% probability cutoff, 52/158 GWAS SNPs were associated with ≥1 omics trait; 26 with ≥2; 9 with ≥3; and 1 with all 4 omics traits (GWAS + multiple omics layers), illustrating how multi-omics integration refines functional prioritization.
- Pleiotropy detection with replication evidence: For height vs. BMI, 32/683 candidate SNPs were detected as associated with both traits; 27/32 replicated in UK Biobank under a Bonferroni threshold, showing integration can surface pleiotropic signals even when one trait alone is sub–genome-wide significant.
Figure 3: Primo workflow for integrating GWAS and multi-omics QTL summary statistics with LD-aware conditional analysis.
FAQs
By integrating cell-type–resolved omics and spatial transcriptomics techniques, the impact of cell type heterogeneity on phenotypes in the population is analyzed. Integrate ancient DNA and multi-omics data to reconstruct the population evolution map in deep time. Biologists, statisticians, and computational biologists collaborate to develop new algorithms (such as deep learning-driven cross-omics causal inference). Promote the multi-omics expansion of open databases (such as UK Biobank and GNOMAD) to facilitate cross-population research.
References:
- Zhao L, Tang P, Luo J, et al. Genomic prediction with NetGP based on gene network and multi-omics data in plants. Plant Biotechnol J. 2025 Apr;23(4):1190-1201. doi: 10.1111/pbi.14577.
- Li D, Geng Z, Xia S, et al. Integrative multi-omics analysis reveals genetic and heterotic contributions to male fertility and yield in potato. Nat Commun. 2024 Oct 5;15(1):8652. doi: 10.1038/s41467-024-53044-4.
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