Enhanced Data Pre-processing for the Identification of Alzheimer's Disease-Associated SNPs

Abstract

Alzheimers Disease (AD) is a complex neurodegenerative disorder that has gained significant attention in scientific research, particularly since the Human Genome Project. Based on twin studies that utilize the resemblance of Alzheimers disease risk between pairs of twins, it has been found that the overall heritability of the disease is estimated at 0.58. When shared environmental factors are taken into account, the maximum heritability reaches 0.79. This suggests that approximately 58-79% of the susceptibility to late-onset Alzheimers disease can be attributed to genetic factors [4]. In 2022, it is estimated that AD will affect over 50 million people worldwide, and its economic burden exceeds a trillion US dollars per year. One promising approach is Genome-Wide Association Studies (GWAS), which allow the identification of genetic variants associated with AD susceptibility. Of particular interest are Single Nucleotide Polymorphisms (SNPs), which represent variations in a single nucleotide base in the DNA sequence. In this study, we investigated the association between SNPs and AD susceptibility by applying various quality control (QC) parameters during data pre-processing and rank the SNP associations through mixed linear models-based GWAS implemented in BLUPF90. Our findings indicate that the identified SNPs are located in regions already associated with Alzheimers Disease, including non-coding regions. We also investigated the impact of incorporating demographic data into our models. However, the results indicated that the inclusion of such data did not yield any benefits for the model. This study highlights the importance of GWAS in identifying potential genetic risk factors for AD and underscores the need for further research to gain a better understanding of the complex genetic mechanisms underlying this debilitating disease.