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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.〔Zhu M, Zhao S. Candidate Gene Identification Approach: Progress and Challenges. Int J Biol Sci 2007; 3(7):420-427. ()〕 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. The great variability between organisms can sometimes make it difficult to distinguish normal variation in SNP from a candidate gene from disease-associated variation. In weaving through the large amounts of data, there are several other factors that can help lead to the most probable variant. These factors include priorities in SNPs, relative risk of functional change in genes, and linkage disequilibrium among SNPs.〔 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. * When examining functionality between genes in pathways, the (Gene Ontology Consortium ) can help map these relationships. The GO Project describes gene products in three different ways via a species-independent manner: biological processes, cellular components, and molecular functions. Using this information can further a priori knowledge of a pathway and thus help to choose the most likely candidate gene involved. * (Topp Gene ) is another useful database that allows users to prioritize candidate genes using functional annotations or network analysis. ToppGene aids researchers in selecting a subset of likely candidate genes from larger sets of candidate genes, likely discovered through high-throughput genome technologies. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「candidate gene」の詳細全文を読む スポンサード リンク
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