International Journal of Clinical Case Reports 2024, Vol.14, No.1, 40-47 http://medscipublisher.com/index.php/ijccr 42 Variant detection is a crucial step in the analysis of genomic data. Identifying and annotating variations in sequencing data helps understand the genetic differences among individuals and populations, as well as genetic variations associated with diseases. Gene annotation is the process of functionally and meaningfully interpreting detected variations. This includes determining the position of the variation on the genome, potential impacts such as protein-coding regions, regulatory elements, or functional non-coding RNA regions, and how they might affect gene and protein functions. In the interpretation of genomic data, functional analysis is a key step in linking genomic variations to biological functions and disease relevance. This can involve using bioinformatics tools and databases to predict and assess the impact of variations, such as predicting protein functions, identifying genetic pathways, and analyzing functional networks. Functional analysis aids in further understanding the influence of genomic variations on individual phenotypes and diseases. Based on the results of the analysis, the interpretation and reporting of genomic data represent the final steps. This involves translating the analysis results into an understandable format, providing detailed interpretation reports to clinical professionals or researchers, explaining the significance of genomic variations, potential health risks, and possible treatment strategies. 1.3 Potential genomic biomarkers and drug response associations Genomic biomarkers refer to specific genetic markers associated with individual genomic variations, used to predict an individual's response to drug treatment. The identification and application of genomic biomarkers can assist physicians in selecting appropriate drug treatment plans on an individualized basis, improving treatment efficacy, reducing adverse reactions, and providing guidance for the practice of personalized medicine. The following are some common examples of genomic biomarkers and their associations with drug responses. Thioguanine is a medication used to treat leukemia and autoimmune diseases (Figure 2). The polymorphism of the TPMT (Thiopurine S-methyltransferase) gene is related to the metabolism of thioguanine. Individuals with TPMT variations that result in reduced enzyme activity may metabolize thioguanine more slowly, leading to potential drug toxicity. Therefore, before administering thioguanine treatment, testing the patient's TPMT gene status can help doctors determine the appropriate dosage, minimizing the risk of adverse reactions to the greatest extent possible. Figure 2 Metabolic process of Azathioprine in vivo CYP2D6 is an enzyme encoded by the human CYP2D6 gene, participating in the metabolism of various drugs. The polymorphism of the CYP2D6 gene can result in differences in drug metabolism capabilities. For example, in patients being treated for hypertension with amlodipine, rapid metabolizers of CYP2D6 may require higher doses to achieve the desired therapeutic effect, while patients carrying CYP2D6 variations may need lower doses to avoid adverse reactions. 2 Personalized Drug Treatment Strategies 2.1 Drug selection and customization Drug selection and customization involve incorporating individual genetic, biological, and clinical characteristics into the process of making decisions about drug treatment. Through personalized drug selection and customization,
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