GENOME-WIDE ASSOCIATION ANALYSIS FOR IDENTIFYING GENETIC RISK FACTORS IN COMPLEX DISEASES

Abstract

Complex diseases like diabetes, cardiovascular disorders and cancer develop due to a combination of multiple genetic and environmental factors and their diagnosis and treatment proves to be extremely difficult. Genetic risk factors that are linked to these diseases need to be identified to gain an insight into the disease processes and a better disease early detection strategy. Nonetheless, the conventional genetic techniques are not usually able to capture the complicated structure of such diseases because of the limitations on resolution and sample size. In this study, a Genome-Wide Association Study (GWAS) is to be utilised in order to provide a systematic testing of the genetic variations in the entire genome, and determination of significant genetic interactions between single nucleotide polymorphisms (SNPs) and complex diseases. Standard quality control was done with large-scale genomic data, and statistical analysis performed on logistic regression and association tests. A large significance level was used to guarantee that the identified variants were of high reliability. The analysis showed that there were several statistically significant SNPs related to the vulnerability of the disease, which identified the areas of high importance of the genomic regions and the candidate genes in case of disease development. The results can be useful in understanding the genetic architecture of complex diseases and have confirmed that GWAS is effective in risk loci detection. To conclude, GWAS is an effective approach to discover genetic risk factors to allow early diagnosis and facilitate the creation of precision medicine strategies based on the indicators of unique genetic compositions.