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

Hussain, A and Abdulaimma, B and Fergus, P and Al-Jumeily, D and Aday Curbelo Montañez, C and Hind, J and Radi, N Association Mapping Approach into Type 2 Diabetes using Biomarkers and Clinical Data. In: International Conference on Intelligent Computing, 07 August 2017 - 10 August 2017, Liverpool, UK. (Accepted)

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Abstract

The global growth in incidence of Type 2 Diabetes (T2D) has become a major international health concern. As such, understanding the aetiology of Type 2 Diabetes is vital. This paper investigates a variety of statistical method-ologies at various level of complexity to analyse genotype data and identify bi-omarkers that show evidence of increase 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 pre-sented. 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 can be appropriate to the dataset. Our genetic association analysis produces promising results and indicated that Allelic asso-ciation test showed one SNP above the genome-wide significance threshold of 5×10−8 which is rs10519107 (Odds Ratio (OR)=0.7409,P−Value (P)=1.813×10−9), While, there are several SNPs above the suggestive association threshold of 5×10−6 these SNPs could worth further investigation. Furthermore, Logistic Regression analysis adjusted for multiple confounder factors indicated that none of the genotyped SNPs has passed genome-wide significance threshold of 5×10−8 . Nevertheless, four SNPs (rs10519107, rs4368343, rs6848779, rs11729955) have passed suggestive association threshold.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > R Medicine (General)
Divisions: Computer Science
Date Deposited: 08 May 2017 11:28
Last Modified: 10 Aug 2017 23:50
URI: http://researchonline.ljmu.ac.uk/id/eprint/6374

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