  Genome-Wide Association Studies (GWAS) help identify genetic variations in
people with diseases such as Parkinson's disease (PD), which are less common in
those without the disease. Thus, GWAS data can be used to identify genetic
variations associated with the disease. Feature selection and machine learning
approaches can be used to analyze GWAS data and identify potential disease
biomarkers. However, GWAS studies have technical variations that affect the
reproducibility of identified biomarkers, such as differences in genotyping
platforms and selection criteria for individuals to be genotyped. To address
this issue, we collected five GWAS datasets from the database of Genotypes and
Phenotypes (dbGaP) and explored several data integration strategies. We
evaluated the agreement among different strategies in terms of the Single
Nucleotide Polymorphisms (SNPs) that were identified as potential PD
biomarkers. Our results showed a low concordance of biomarkers discovered using
different datasets or integration strategies. However, we identified fifty SNPs
that were identified at least twice, which could potentially serve as novel PD
biomarkers. These SNPs are indirectly linked to PD in the literature but have
not been directly associated with PD before. These findings open up new
potential avenues of investigation.
