The candidate gene approach to conducting genetic association studies focuses on associations between genetic variation within pre-specified genes of interest and phenotypes or disease states. This is in contrast to genome-wide association studies (GWAS), which scan the entire genome for common genetic variation. Candidate genes are most often selected for study based on a priori knowledge of the gene’s biological functional impact on the trait or disease in question. The rationale behind focusing on allelic variation in specific, biologically relevant regions of the genome is that certain mutations will directly impact the function of the gene in question, and lead to the phenotype or disease state being investigated. This approach usually uses the case-control study design to try to answer the question, “Is one allele of a candidate gene more frequently seen in subjects with the disease than in subjects without the disease?” 
Selecting a candidate gene
Suitable candidate genes are generally selected based on known biological, physiological, or functional relevance to the disease in question. This approach is limited by its reliance on existing knowledge about known or theoretical biology of disease. However, more recently developed molecular tools are allowing insight into disease mechanisms and pinpointing potential regions of interest in the genome. Genome-wide association studies and quantitative trait locus (QTL) mapping examine common variation across the entire genome, and as such can detect a new region of interest that is in or near a potential candidate gene. Microarray data allow researchers to examine differential gene expression between cases and controls, and can help pinpoint new potential genes of interest In addition, the availability of genetic information through online databases enables researchers to mine existing data and web-based resources for new candidate gene targets.
Many online databases are available to research genes across species. Gene is one such database that allows access to information about phenotypes, pathways, and variations of many genes across species.
Prior to the candidate-gene approach
Before the candidate-gene approach was fully developed, various other methods were used to identify genes linked to disease-states. These methods studied genetic linkage and positional cloning through the use of a genetic screen, and were effective at identifying relative risk genes in Mendelian diseases. However, these methods are not as beneficial when studying complex diseases for several reasons:
1. Complex diseases tend to vary in both age of onset and severity. This can be due to variation in penetrance and expressivity. For most human diseases, variable expressivity of the disease phenotype is the norm. This makes choosing one specific age group or phenotypic marker more difficult to select for study.
2. The origins of complex disease involve many biological pathways, some of which may differ between disease phenotypes.
3. Most importantly, complex diseases often illustrate genetic heterogeneity – multiple genes can be found that interact and produce one disease state. Oftentimes, each single gene is partially responsible for the phenotype produced and overall risk for the disorder.
Despite the drawbacks of linkage analysis studies, they are nevertheless useful in preliminary studies to isolate genes linked to disease.
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