GWAS Analysis

Introduction

Genome-wide association analysis (GWAS) is an important method widely used to find genes associated with complex genetic diseases. Many pathogenic mutants have been found through genetic research, and these mutants refer to the mutation sites on the chromosomes. Genome-wide association analysis attempts to find the mutation sites on the chromosomes and study the association of these mutation sites with diseases or other traits. At present, more than 4,000 GWAS studies have been carried out worldwide, and more than 100,000 genes related to various diseases (such as cancer, hypertension, type II diabetes, rheumatoid arthritis, etc.) and important physiological traits have been discovered. Genome-wide association analysis uses millions of single-nucleotide polymorphisms (SNPs) in the genome as molecular genetic markers, and uses regression analysis methods to conduct control analysis or correlation analysis at the genome-wide level. Genome-wide association analysis is an analysis strategy for discovering genetic variation that affects complex traits through association analysis.

An Example of GWAS Analysis Manhattan Plot

Fig 1. The Manhattan plot for the genome-wide association study (GWAS) of obesity (BMR) in the Korean female subjects. (Myoungsook, et al. 2016)

• This plot is based on -log₁₀ (P-value) from GWAS and imputation analysis against chromosome position.
• Each color represents a different chromosome.
• Blue horizontal line indicates the suggestive association threshold, P = 1 × 10^-4.

Applications

The main applications of Genome-wide association analysis in biomedical are:

• Basic biomedical research: Clinical diagnosis-including biomarkers, disease mechanism, disease classification, personalized treatment, and other research directions.
• Drug research: Biomedicine research on drug target, drug development, etc.

What We Offer

As one of the global biological information analysis service providers, CD Genomics provides established, cost-efficient, and rapid turnaround analysis services for GWAS analysis for researchers, aiming to help you understand the relationship between disease or individual phenotype and genotype. CD Genomics provides different software such as BWA, samtools, GATK, GEMMA, Plink, Admixture, and Tassel to perform whole GWAS analysis for customers to meet the analysis needs of different research directions. In addition, we can use various formats of data for analysis such as raw data files, or other intermediate data formats. You only need to provide us with your original data, including phenotype data and genetic data. We will be responsible for all the follow-up matters of the project, and finally provide you with a complete and easy-to-interpret analysis report.

Data Analysis Technical Route

Fig 2. Flow chart showing GWAS analysis.

An Example of GWAS Analysis Process

Different software has different analysis schemes. Here is a brief description of the general process of GATK software for GWAS analysis:

1. Import data, and control and filter the quality of the original data.

2. Use BAW to build index and compare.

3. Use samtools for format conversion.

4. GATK mutation detection, detecting and filtering SNPs.

5. Perform association analysis on traits and phenotypes, and make charts, such as Manhattan charts.

Data Ready

Before data analysis, the first thing is to get your data ready. For GWAS analysis, the raw input data includes phenotypic data and genetic data, which can be obtained from the following channels:

To process data more efficiently, we prefer to receive data files in the raw format, but we can also accept pre-normalized files. More importantly, there are currently many databases related to different diseases or SNPs. We are able to provide services for obtaining and mining data from available databases. Alternatively, if you do not currently have the genetic input data, CD Genomics can also provide you with a variety of sequencing services based on its rich sequencing experience. If you have any questions about the data analysis cycle, analysis content and price, please click online inquiry.

What's More

Biomedical-Bioinformatics, as a division of CD Genomics, provides a one-stop genome-wide association analysis service for biomedical researchers. With years of experience in biological data analysis, CD Genomics' analysis engineers will provide you with the most appropriate analysis strategy based on your data, and generate high-quality results and charts that can be used for publication. For more detailed information, such as sequencing services and other data analysis services, please feel free to contact us.

References

1. Leone M A, et al. Association of Genetic Markers with CSF Oligoclonal Bands in Multiple Sclerosis Patients[J]. Plos One, 2013, 8.
2. Myoungsook, et al. Genome-wide association study for the interaction between BMR and BMI in obese Korean women including overweight [J]. Nutrition Research & Practice, 2016. Feb; 10(1): 115-124.

* For research use only. Not for use in clinical diagnosis or treatment of humans or animals.