This literature review “Genotyping to Identify the Candidate Genes of Type 2 Diabetes” reflects upon sources representing the latest scientific advances in the study of hereditary and environmental factors predisposing to the occurrence of type 2 diabetes…
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A discussed sample was genotyped on an Affymetrix 100K SNP (Single Nucleotide Polymorphism) array and was tested for connection with incident diabetes and 6 quantitative traits associated with diabetes.
The replication sample comprised of 1,465 discrete FHS offspring which was drawn from a plated set of DNAs where only a single person from individual pedigree was selected. An added sample of 251 offspring which were also genotyped and examined in the 100K array helped to test the degree of concordance between the 100K GWA scan and follow-up genotyping platforms. The quality control filters generated 66543 SNPs for the investigation process.
Two methods were used to prioritize SNPs that are strongly associated with type 2 diabetes or allied traits. The first strategy used the ranking of P values derived from GEE or FBAT models and chose those SNPs with P<0.01. The other strategy chose SNPs allied with multiply related traits. The 763 SNPs prioritized were analyzed for in silico reproduction in 3 other 100K and a 500K GWA datasets.
The statistical analysis for quantitative traits employed the GEE and FBAT models to examine the correlation between age and alleles. The incident Type II diabetes was tested through the Cox Model which examined the hazard ratio with respect to SNPs against the risk of new diabetes cases. Martingale residuals tested from a sex-adjusted model that showed high negative value implying early onset of diabetes and high positive value implying no diabetes at a later stage with follow-up.
The results showed that the 100k scans gave no credible in silico replication. 78 SNPs showed association with traits related to diabetes in the 100K GWA scan and 25 showed the same in 500K GWA analysis. The FHS 100K GWA scan is a valuable tool for follow-up of genetic correlations with diabetes-linked quantitative traits. To find out new diabetes genes, one would need bigger samples and a denser array along with potential replication strategies.
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