The key difference between candidate gene and GWAS is that candidate gene approach investigates the genetic variation within a small number of pre-specified genes of interest while GWAS investigates the entire genome for a common genetic variation behind a particular disease condition.
Candidate gene approach and genome-wide association studies (GWAS) are two methods that are important to detect genetic susceptibility to diseases. Candidate gene approach is based on a pre-specified small number of genes while GWAS is based on testing the entire genome. Hence, candidate gene approach requires prior knowledge of genes with relevance to the disease, unlike GWAS.
CONTENTS
1. Overview and Key Difference
2. What is Candidate Gene
3. What is GWAS
4. Similarities Between Candidate Gene and GWAS
5. Side by Side Comparison – Candidate Gene vs GWAS in Tabular Form
6. Summary
What is Candidate Gene?
Candidate gene approach is one of the techniques that investigates the association between genetic variation within pre-specified genes of interest and phenotypes or disease states. In this approach, it is necessary to have prior knowledge of the gene’s biological functional impact on the trait or disease in question. Based on this knowledge, a small number of genes are selected and analysed for genetic variation.
Figure 01: Candidate Gene Approach
Selection of the genes is made based on the biological, physiological and functional relevance to the disease in question in this approach. This approach is often designed as a case-control study.
What is GWAS?
GWAS stands for Genome-Wide Association Study. It also refers to whole-genome association studies. These studies focus mainly on observational studies. They analyse the genetic variants of different individuals usually associated with a specific trait. The whole genome is important for GWAS analysis.
GWAS is an important tool in the analysis of single nucleotide polymorphisms associated with various disease conditions. It is a comparative study of the different single nucleotide polymorphisms across a wide population. The study sample of GWAS is very high; hence, it also takes the format of a cross-sectional cohort study.
Figure 02: GWAS
The first GWAS study took place with regard to myocardial infarction and analysing the genes associated with myocardial infarction. At present, GWAS plays an important role in determining the genetic background of complex diseases with unknown aetiology.
What are the Similarities Between Candidate Gene and GWAS?
- Both candidate gene approach and GWAS are techniques that analyse the genetic association between genotype and phenotype of the disease.
- Both approaches help to understand the genetic basis of susceptibility to disease.
What is the Difference Between Candidate Gene and GWAS?
Candidate gene approach is based on the candidate genes or pre-specified genes, while GWAS is based on the entire genome. So, this is the key difference between candidate gene and GWAS. Also, in candidate gene approach, the selection of genes is necessary while it is not needed in GWAS.
Moreover, candidate gene approach requires prior knowledge on the genes relevant to the disease, while it is not necessary for GWAS.
Below infographic provides a more detailed comparison related to the difference between candidate gene and GWAS.
Summary – Candidate Gene vs GWAS
Candidate gene and GWAS are two gene association studies. Candidate gene approach focuses on the genetic variation associated with the disease within a small number of pre-specified genes. In contrast, GWAS investigates the genetic variation associated with the disease within the entire genome. So, this is the key difference between candidate gene and GWAS. Both methods are important in understanding the genetic basis of susceptibility to disease.
Reference:
1. “Candidate Gene – An Overview | Sciencedirect Topics”. Sciencedirect.Com, 2020, Available here.
2. “Candidate Gene”. En.Wikipedia.Org, 2020, Available here.
Image Courtesy:
1. “Ranking-Candidate-Disease-Genes-from-Gene-Expression-and-Protein-Interaction-A-Katz-Centrality-pone.0024306.g001” By Zhao J, Yang T, Huang Y, Holme P – Image file from Zhao J, Yang T, Huang Y, Holme P (2011). “Ranking Candidate Disease Genes from Gene Expression and Protein Interaction: A Katz-Centrality Based Approach”. PLOS ONE. DOI:10.1371/journal.pone.0024306. PMID 21912686. PMC: 3166320 (CC BY 3.0) via Commons Wikimedia
2. “Manhattan Plot” By M. Kamran Ikram et al – Ikram MK et al (2010) Four Novel Loci (19q13, 6q24, 12q24, and 5q14) Influence the Microcirculation In Vivo. PLoS Genet. 2010 Oct 28;6(10):e1001184. doi:10.1371/journal.pgen.1001184.g001 (CC BY 2.5) via Commons Wikimedia
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