Genome-wide Association Analysis (GWAS)

As one of the providers of genome-wide association analysis, CD Genomics uses bioinformatics to help you achieve efficient mapping of multiple target trait genes quickly and accurately. Our unique data analysis skills can meet customers' personalized data analysis needs and provide easy-to-interpret data analysis reports.

Introduction of Genome-wide Association Analysis

In genetics, a genome-wide association study (GWAS) is an observational study of a genome-wide set of genetic variants in different individuals to investigate whether any variant is associated with a trait. GWASs typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.

The most common approach of GWA studies is the case-control setup, which compares two large groups of individuals, one healthy control group and one case group affected by a disease, or one conventional trait control group, and one target trait control group. All individuals in each group are genotyped for the majority of commonly known SNPs. The exact number of SNPs depends on the genotyping technology, but are typically one million or more.

Fig 1. An illustration of a Manhattan plot depicting several strongly associated risk loci.

We Can Help Our Clients With

1.Quantitative traits of animals and plants.
2.Human Phenotype Research.
3.Complex disease.
4.Single gene diseaseQTL.

Our Advantages

Identification of novel SNV–trait associations

Discovery of novel biological mechanisms

Diverse clinical applications

Insight into ethnic variation of complex traits

Relevant to low-frequency, rare variants

Identification of novel monogenic and oligogenic disease genes

Relevant to the study of structural variation

Multiple applications beyond gene identification

Straightforward GWAS generation, management and analysis

Easy-to-share and publicly available data

CD Genomics Genome-wide Association Analysis Pipeline

The type of original input data can be Whole Genome Sequencing data (WGS) or Reduced-Representation Genome Sequencing data (RRGS). In addition, in order to obtain better analysis results, the sample size of the original data should be more than 200.

What We Offer

Different input data types (Whole Genome Sequencing data or Reduced-Representation Genome Sequencing data) with different analysis contents.

Whole Genome Sequencing data	Quality control of sequencing data
	Map to reference genome
	SNP and CNV detection and annotation
	Construct phylogenetic tree
	PCA analysis
	Linkage disequilibrium analysis
	Trait association analysis
	Functional annotation of genes in regions related to target traits
	Construction of monomorphic map
Reduced-Representation Genome Sequencing data	Quality control of sequencing data
	Map to reference genome (with reference genome) Align to tag clustering (without reference genome)
	SNP detection and annotation
	Construct phylogenetic tree
	PCA analysis
	Trait association analysis
	Functional annotation of genes in regions related to target traits

If you need any genome-wide association analysis, we will provide appropriate biological information analysis content according to your needs. Please feel free to contact us for details.

How It Works

CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.

CD Genomics has successfully conducted genome-wide association analysis on a variety of diseases and species, combining rich project experience, professional project plan guidance and analysis process, to ensure that the project is carried out accurately and quickly. Please contact us for more information and a detailed quote.

Reference

1. Tam V; et al. Benefits and limitations of genome-wide association studies. Nature Reviews Genetics, 2019(5).467-484