Genome-wide Association Analysis Service
Overview
Genome-wide association analysis (GWAS) is a method that combines the variation information of individuals in a population with the phenotypic information of traits. Based on the linkage disequilibrium (LD) between genes, the diversity of the phenotype of target traits is statistically correlated with the polymorphism of genes, and finally locates the loci and genes associated with the target traits. It has the advantages of wide material sources, many genetic variations and complete character localization.
Our GWAS solutions are designed to support your population genetics research with:
- Broad Material Sources: Analyze diverse genetic samples.
- Comprehensive Localization: Pinpoint genes and loci accurately.
- Advanced Analysis: Leverage cutting-edge tools for reliable results.
What Is GWAS
GWAS (Genome-Wide Association Study) is a powerful genomics research method used to identify genetic variants associated with complex traits such as disease risk, agronomic traits, drug response, etc. Our GWAS services use high-throughput genotyping (SNP chip) and whole genome sequencing (WGS) technologies, combined with advanced bioinformatics analysis processes, to help researchers and breeders mine key genetic markers to advance precision medicine, agricultural breeding and evolutionary biology research.
How to Measure
1. Sample collection and genotyping
(Sample collection →DNA extraction → Chip/sequencing)
- Sample Type: Human (blood, saliva, tissue); Plants and animals (leaves, seeds, muscle tissue);Microorganism (metagenomic sample)
- Genotyping techniques:
Table1 Genotyping techniques and their characteristics
| Technology | Application scenario | Data volume |
| SNP chip (e.g. Illumina GSA) | Cost-effective and suitable for large sample sizes | 500,000 to 5 million SNP sites |
| Whole Genome Sequencing (WGS) | Full coverage of rare variants | Full genome 30X coverage |
| Targeted sequencing (Panel) | Deep sequencing of specific gene regions | Customize the target area |
- Data Quality Control (QC):
Sample filtering (low quality DNA, gender mismatch sample removal)
SNP filtration (MAF >0.05, deletion rate <5%)
2. Data analysis
- Correlation test method: Univariate analysis (SNP-by-SNP test, such as PLINK's -assoc);Multivariate analysis (considering SNP interactions, such as LASSO regression)
- Significance threshold: Genome level was significant: p < 5×10⁻⁸ (Bonferroni correction);Potential signal: p < 1×10⁻⁵ (independent sample verification required)
- Visualization Tools: Manhattan map: Showing genome-wide salient sites; QQ Plot: Inflation factor (λ) for evaluating statistical tests
3. Meta-analysis.
Population stratification is a common source of false positives in GWAS studies. When population stratification occurs, the conventional method is to analyze the stratified groups independently, and then conduct meta-analysis at last.
Our GWAS Service Workflow
What Can We do
Genomic Data Acquisition: We offer a variety of high-throughput genotyping techniques to select the best solution according to research needs (Illumina Global Screening Array; WGS; Targeted Sequencing; Low-Pass WGS).
Genotype Analysis: We use international standard bioinformatics processes to ensure data quality and reliability.
- SNP Calling & Quality Control (QC)
Filter low-quality data (SNP removal with sequencing depth <10X and MAF<0.01)
Population stratification test (PCA analysis, removing confounding factors)
- Association Testing
Linear regression (LM): for continuous traits (e.g. height, blood pressure)
Logistic regression: for binary traits (e.g., disease vs health)
Mixed linear Model (MLM): corrects for population structure + kinship
- Polygenic Risk Score (PRS, Polygenic Risk Score)
Based on GWAS results, an individual's genetic risk for disease/trait is calculated
Genetic Diversity Metrics:In addition to GWAS core analysis, we also provide population genetics-related indicators to aid evolution and breeding research.
Table2 Genetic Diversity Metrics
| index | Calculation | Biological significance |
| Observed heterozygosity (Ho) | Proportion of heterozygous sites in the sample | Reflect population genetic diversity |
| Expected heterozygosity (He) | The theoretical value of the Hardy-Weinberg equilibrium | Assess whether the group is inbred |
| Allele frequency (AF) | The frequency of a particular SNP in a population | Identify rare/common variants |
| Nucleotide diversity (π) | Average number of differences per base pair | Measure the degree of genomic variation |
Report Generation: We provide standardized + customized analysis reports to facilitate research publication and conversion.
- Standard Report content:
Raw data (VCF/PLINK format)
List of significant association sites (including P-value, OR value, 95%CI)
Functional annotation (gene, pathway, eQTL information)
- Advanced Interpretation Service (optional):
Biological mechanism hypothesis (e.g. potential function of candidate genes)
Experimental validation recommendations (CRISPR editing, cell function experiments)
Integrated analysis across databases (e.g. GWAS Catalog, UK Biobank)
Our Advantages
Ultra-high throughput: supports parallel analysis of millions of samples
Cross-platform compatibility: Integration of chip and WGS
Customized analysis: Adjusting the model according to the research objective (e.g. Mendelian randomization)
Comprehensive Database: Extensive multi-ethnic loci database covering diverse populations
Applications
Crop Improvement & Breeding
Genetic Diversity in Crops: GWAS helps identify yield-related genes (e.g., drought resistance, grain size) by analyzing genetic variation across diverse crop varieties.
Combating Agricultural Threats: Studying weed/pest genetic variation helps develop targeted control strategies. Pathogen GWAS identifies virulence factors, aiding in disease-resistant crop development.
Human Disease Research
Disease susceptibility: GWAS has been used to identify genetic variants associated with a variety of complex diseases such as diabetes, cardiovascular disease, cancer, psychiatric disorders, autoimmune diseases, and more.
Drug response: By analyzing the relationship between drug response and genetic variation, GWAS can help predict an individual's response to a specific drug, thereby guiding personalized medicine.
Animal science
Livestock breeding: GWAS is used to identify genetic variations related to growth rate, meat quality, milk yield, disease resistance and other traits of livestock (such as cattle, pigs, chickens, etc.) to optimize breeding programs.
Wildlife conservation: Study the adaptation, evolutionary history and population genetic structure of wild animals to provide the basis for conservation biology.
Behavioral genetics
Complex behavioral traits: GWAS is used to study genetic variation associated with human behavior (such as cognitive abilities, personality traits, mental disorders, etc.), revealing the genetic basis of behavior.
Social behavior: In animal models, GWAS can be used to study genetic variants associated with social behavior, aggression, reproductive behavior, and more.
Evolutionary biology
Adaptive evolution:GWAS can be used to identify genetic variation associated with species adaptation (e.g., plateau adaptation, extreme environment adaptation), revealing the mechanism of natural selection.
Population differentiation: By comparing the genomes of different populations, GWAS can study evolutionary processes such as population differentiation, gene flow, and genetic drift.
Case Study
Genome-Wide Association Analysis Identifies LILRB2 Gene for Pathological Myopia
Journal: Adv Sci (Weinh)
Published: 2024
Pathological Myopia (PM) is a blinding eye disease characterized by excessive elongation of the eye axis and degeneration of the fundus. Its genetic mechanism is complex, involving the interaction of multiple genes and environmental factors. Genome-wide association analysis (GWAS) is an unbiased approach to genetics that scans genome-wide genetic variation to identify loci associated with complex diseases or traits. Through GWAS analysis, this study aims to reveal the genetic susceptibility genes of pathological myopia and provide new molecular targets for the pathogenesis and early intervention of the disease.
GWAS
A total of 2119 PM patients with an axial length (AL) greater than 26.0 mm in both eyes were enrolled in this study. To maximize the detection power, the patients with an AL greater than 26.0 mm in both eyes were enrolled in the first stage of GWAS screening. For the discovery stage, 806 cases and 2591 controls and 764939 SNPs passed the quality-control criteria for further statistical analysis. Locus 19q13.42 showed a significant association with PM by GWAS (with adjusted P values below 5 × 10−8) (Figure 1A). We chose three SNPs (rs7247538, rs13345069, and rs367070) located in 19q13.42 for replication (Figure 1B). Two reported myopia SNPs located in 15q25.1 (rs4778879 and rs939658) were also included in the replication stage as they nearly reached genome-wide significance (Figure 1C). Among them, rs367070 located in the intron of LILRA3 (19q13.42) showed the strongest association (P = 2.63 × 10−22; odds ratio [OR]Ｇ = 0.68) in the meta-analysis (Table 3 ).
Fig1 Genome-wide association analysis (GWAS)-associated regions from the discovery stage. A) Manhattan plots of the GWAS discovery stage for PM. Each plot shows −log10-transformed P values for all SNPs. SNPs in locus 19q13.42 passed the genome-wide significance (P < 5 × 10−8). The orange suggestive line is at 1 × 10−5, and the blue suggestive line is at 5 × 10−8. B) LocusZoom plots of regional association of LILRA3/LILRB2 (19q13.42) in PM in the discovery stage. C) LocusZoom plots of the regional association of RASGRF on 15q25.1 in PM in the discovery stage.
Table 3 Association results for the five loci identified in this study.
FAQs
In principle, the more samples the better. At present, GWAS products suggest that WGS needs more than 200 samples, GBS needs more than 300 samples, and the third generation technology can reduce the sample size as appropriate.
In the absence of a reference genome, SNPS can be detected by clustering using simplified genome sequencing (RAD-seq or GBS). The SNPS obtained by clustering detection can be used for whole-gene association analysis, but due to the lack of genomic annotation information, gene annotation cannot be further conducted after the association sites are found.
Demo Template
Image reference: DOI: https://doi.org/10.4093/dmj.2021.0375
Quantile-quantile (Q-Q) plot and Manhattan plot for the genetic association
LocusZoom plot of suggestive SNP×time association in meta-analysis
Variants with suggestive evidence of SNP time association
References
- Wang A, Jiang Y, et al. Genome-wide association study-based identification genes influencing agronomic traits in rice (Oryza sativa L.). Genomics. 2021. https://doi.org/10.1016/j.ygeno.2021.03.016.
- Barrio-Hernandez I, Beltrao P. Network analysis of genome-wide association studies for drug target prioritisation. Curr Opin Chem Biol. 2022. doi: 10.1016/j.cbpa.2022.102206.
- Jiang L, Huang L, et al. Genome-Wide Association Analysis Identifies LILRB2 Gene for Pathological Myopia. Adv Sci (Weinh). 2024. doi: 10.1002/advs.202308968.
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