Association Mapping Approach into Type 2 Diabetes Using Biomarkers and Clinical Data

Abstract

The global growth in the incidence of Type 2 Diabetes (T2D) has become a major international health concern. As such, understanding the etiology of Type 2 Diabetes is vital. This paper investigates a variety of statistical methodologies at various level of complexity to analyze genotype data and identify biomarkers that show evidence of increased susceptibility to T2D and related traits. A critical overview of several selected statistical methods for population-based association mapping particularly case-control genetic association analysis is presented. A discussion on a dataset accessed in this paper that includes 3435 female subjects for cases and controls with genotype information across 879071 Single Nucleotide Polymorphism (SNPs) is presented. Quality control steps into the dataset through pre-processing phase are performed to remove samples and markers that failed the quality control test. Association analysis is discussed to address which statistical method is appropriate for the dataset. Our genetic association analysis produced promising results and indicated that Allelic association test showed one SNP above the genome-wide significance threshold of $5 \times 10^{ - 8}$ which is rs10519107 $\left( {{\text{Odds }}\,{\text{Ratio}}\, \left( {\text{OR}} \right) = 0.7409, \,{\text{P}} - {\text{Value }}({\text{P}}) = 1.813 \times 10^{ - 9} } \right)$. While there are several SNPs above the suggestive association threshold of $5 \times 10^{ - 6}$, these SNPs should be considered for further investigation. Furthermore, Logistic Regression analysis adjusted for multiple confounder factors indicated that none of the genotyped SNPs had passed genome-wide significance threshold of $5 \times 10^{ - 8 }$. Nevertheless, four SNPs (rs10519107, rs4368343, rs6848779, rs11729955) have passed suggestive association threshold.