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Metabolite Genome-Wide Association Study (mGWAS) Service

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  • Case Study

Metabolomics is an integral component of systems biology, focusing on the qualitative and quantitative analysis of all small-molecule metabolites within an organism. It seeks to establish the relative associations between metabolites and physiological or pathological changes. The subjects of this research are typically small molecules with a molecular weight of less than 1,000 Daltons.

Metabolites, often regarded as the most direct reflection of an organism's phenotype, are considered the bridge connecting genotype and phenotype. Changes in metabolite levels can directly unveil gene functionality, providing a more effective insight into biochemical and molecular mechanisms. Understanding the synthesis, regulation, and physiological functions of metabolites is of paramount importance.

With advancements in mass spectrometry and nuclear magnetic resonance platforms, and the development of genomic sequencing technologies, metabolomics has combined forces with genomics to form Metabolome Genome-Wide Association Studies (mGWAS). This approach allows for a faster and more accurate revelation of the genetic mechanisms behind species' phenotypes.

What is mGWAS?

mGWAS utilizes genotype data acquired through whole-genome sequencing techniques and combines it with metabolome data obtained through methods like mass spectrometry for a comprehensive genome-wide association analysis based on metabolomics. This approach aids in the identification of candidate genes that regulate metabolites, the exploration of related metabolic pathways governing phenotypes, and a deeper understanding of the genetic mechanisms controlling metabolite synthesis.

Currently, mGWAS based on metabolites has found extensive applications in the study of both animals and plants. By combining metabolomics with genomic genetic analyses, thousands of candidate loci or genes have been identified. Beyond elucidating the genetic mechanisms of plant metabolism, mGWAS also contributes to functional genomics research. Increasingly, researchers are employing mGWAS to uncover the genetic and biochemical foundations of animal and plant metabolites, providing fresh insights into the study of disease mechanisms. mGWAS is currently utilized in the investigation of various diseases (such as cancer, obesity, cardiovascular diseases, diabetes, depression, and Alzheimer's disease) and the genetic mechanisms of both animals and plants (such as tomatoes, cotton, and maize).

Building upon the strengths of two technical service projects at Creative Proteomics, mGWAS technology services have become a distinctive offering from Creative Proteomics, receiving acclaim from a broad community of research users.

Technical Principle of mGWAS

mGWAS employs small-molecule compounds as representatives of metabolic traits or certain agricultural productivity traits. Given the substantial variations in the types and quantities of metabolites among different varieties and tissues, second-generation sequencing technology is used to obtain genotype data from population materials. This data is then combined with metabolome data to conduct a genome-wide association analysis based on metabolites. This approach is conducive to the simultaneous identification of candidate genes that regulate metabolites, exploration of related metabolic pathways governing traits such as yield, quality, and environmental responses, and a deeper understanding of the genetic mechanisms that control plant metabolite synthesis. It effectively bridges the gap between genomics and phenotypes.


Full Spectrum Metabolomics + Resequencing (mGWAS)Conducts the association analysis between metabolomics data from a cohort as the phenotype and the genome.Diverse metabolite types and contents exhibit significant variations among different varieties or individuals, with rich data (over a thousand metabolites).More phenotype data lead to the localization of more genes. Quantifiable phenotypes result in more accurate quantitative analysis, pinpointing SNP locations more precisely. Large data size enhances the likelihood of detecting rare SNP loci.
Traditional Phenotype + Resequencing (GWAS)Gathers a variety of sample phenotype information from a cohort and conducts phenotype-genome association analysis.Traditional phenotypes encompass fewer types and are challenging to quantify, being significantly affected by the environment.Relatively fewer genes are identified, and the localization effect is weaker. Multiple genes can be associated simultaneously, making it difficult to distinguish the primary effective gene.


Metabolite Genome-Wide Association Study (mGWAS) Service

Service Content

Creative Proteomics possesses high-performance sequencing and mass spectrometry platforms, as well as a high-throughput sequencing (NGS) field with multiple products, offering a flexible and intelligent delivery platform. We provide comprehensive scientific services covering genomics, transcriptomics, epigenomics, translatomics, single-cell omics, proteomics, metabolomics, and more. We serve researchers with an all-in-one solution from sample preparation to mGWAS analysis, along with multi-omics joint analyses, such as GWAS, TWAS, eGWAS, to meet both standardized and personalized analytical requirements of our clients.

Project NameTesting ContentData Analysis
Untargeted MetabolomicsComprehensive profiling of metabolites
  • Univariate and multivariate data analysis of the metabolome
  • GWAS data analysis
  • Integrative analysis of GWAS and metabolite quantitative traits (mQTL) to identify susceptibility loci related to metabolites
  • Exploration of genetic variant sites related to metabolic pathways and discovery of regulatory genes, among others
Untargeted LipidomicsComprehensive profiling of lipids
Targeted MetabolomicsSpecific target metabolites
GWASGWAS Technical Service

Service Advantage

Comprehensive Metabolomics Technologies: Our advanced metabolomics technologies cover a broad spectrum of metabolomic profiling, including non-targeted metabolomics (with a capability to detect up to 1000 different metabolites), lipidomics, and various targeted metabolomics approaches. This diverse range of techniques caters to the specific requirements of a wide range of research projects.

Advantages of mGWAS Approach: In mGWAS studies, metabolites serve as quantitative traits for conducting association analyses with genomic variation. The advantages of mGWAS stem from the high number of detectable metabolites (ranging from 300 to 1000 different metabolites), significant variations in their concentrations (ranging from 10 to 10,000-fold differences), and the strong genetic effects they exhibit (explaining over 40% of the variability). As a result, the outcomes of mGWAS surpass those achieved using traditional phenotypic traits.

Sample Requirements

GWAS SampleSample TypeSample RequirementsSample Storage/Transport Conditions
gDNA SampleConcentration >50ng/ul; Total amount >1ug; OD260/280 between 1.7-2.0; No severe degradationStore at -20°C, avoid repeated freeze-thaw cycles/Low-temperature transportation with ice packs
Blood SampleVolume >0.5ml; Recommended to use EDTA anticoagulant tube; Avoid hemolysis

Saliva/SwabSaliva volume >1ml; Swabs >2 cotton swabs; Fresh samples preferred

Tissue/Cell SampleTissue sample >100mg; Cell sample >1×10

Metabolomics SampleSample TypeSample RequirementsSample Storage/Transport Conditions
Serum or Plasma Sample200 μl/sample; No contamination; No hemolysis; Avoid repeated freeze-thaw cyclesAfter collection, store at -80°C, ship on dry ice
Urine Sample200 μl/sample; No contamination; Avoid repeated freeze-thaw cycles

Cerebrospinal Fluid300 μl/sample; Avoid repeated freeze-thaw cycles

Tissue100 mg/sample; No contamination; Avoid repeated freeze-thaw cycles

Stool (Human)5-10 g/sample; No contamination; Avoid repeated freeze-thaw cycles

Multi-omics Analyses of 398 Foxtail Millet Accessions Revealing Genomic Regions Associated with Domestication, Metabolite Traits, and Anti-inflammatory Effects

Journal: Molecular Plant

Impact Factor: 21.949

Published: 2022

Foxtail millet, derived from the domestication of wild green foxtail grass, is valued not only for its high nutritional content but also for its medicinal properties. However, the genetic mechanisms underlying foxtail millet remain unclear. In this study, a total of 398 foxtail millet natural populations were employed for a multi-omics joint analysis, including whole-genome resequencing, transcriptomics, and metabolomics. Several hundreds of metabolites associated with natural variations were identified, revealing significant differences in metabolite natural variations and genetic structures among different foxtail millet subgroups.

Through combined analyses of mGWAS (metabolome genome-wide association study) and TWAS (transcriptome-wide association study), the selection of genes associated with foxtail millet yellow grain traits was found to be responsible for alterations in the levels of metabolites such as carotenoids. In vitro cell inflammation tests demonstrated that 83 metabolites in foxtail millet possess anti-inflammatory properties. This research elucidates the genetic mechanisms driving targeted changes in metabolites during the domestication of foxtail millet, laying the genetic foundation for enhancing its nutritional value.

Combined analysis of mGWAS and TWAS identifies metabolites and genes related to seed coat colorCombined analysis of mGWAS and TWAS identifies metabolites and genes related to seed coat color


  1. Liang X, Liu S, Wang T, Li F, Cheng J, Lai J, Qin F, Li Z, Wang X, Jiang C. (2021) Metabolomics-driven gene mining and genetic improvement of tolerance to salt-induced osmotic stress in maize. New Phytol. 230, 2355-2370.
  2. Li X., Gao J., Song J., Guo K., Hou S., Wang X., He Q., Zhang Y., Zhang Y., Yang Y., Tang J., Wang H., Persson S., Huang M., Xu L., Zhong L., Li D., Liu Y., Wu H., Diao X., Chen P., Wang X., and Han Y. (2022). Multi-omics analyses of 398 foxtail millet accessions reveal genomic regions associated with domestication, metabolite traits, and anti-inflammatory effects. Mol. Plant. 15, 1367–1383.
  3. Zhang Y, Shen Q, Leng L, Zhang D, Chen S, Shi Y, Ning Z, Chen S. Incipient diploidization of the medicinal plant Perilla within 10,000 years. Nat Commun. 2021 Sep 17;12(1):5508.
* For Research Use Only. Not for use in diagnostic procedures.
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