Single nucleotide polymorphism data analysis using binary logistics regression model based on HapMap data.

Genome-wide association study (GWAS) can be used to determine which genes are associated with certain traits or diseases. This research is to identify which SNP (Single Nucleotide Polymorphism) is associated with the presence of certain diseases or traits. The method used is the Logistics Regression model. Based on the statistical value of Logistics Regression, it was obtained that the p−value for rejecting or accepting H0 was that SNP was not associated with the presence of a particular disease or trait with a significance level of α = 0.05. In making the decision, H0 is rejected or accepted, the significance level limit using Bonferroni correction and the program default significance level is also used. The significant SNP results from the three levels of significance level α = 0.05, the Bonferroni correction results and the R program default are 4213 SNP, 2211 SNPs, and 334 SNPs. Using these results, the genes associated with the Europa or Yaruba races can be determined. [ABSTRACT FROM AUTHOR]