Computational protocol: Association between CETP, MLXIPL, and TOMM40 polymorphisms and serum lipid levels in a Latvian population

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Protocol publication

[…] The normal distributions of all quantitative variables were tested with the two most important parameters, the mean value and its standard deviation (SD) (), and with the Shapiro–Wilk test. None of the lipid levels were normally distributed according to the Shapiro–Wilk test, even after various transformations. Therefore, to assess the influence of the covariates, we used linear regression, applying less-stringent normality criteria: the 68–95–99.7 rule or the three-sigma rule, according to which about 68% of values should fit within an interval of one SD, 95% in two SDs, and 99.7% in three SDs. Among all the variables tested, the TG levels were not normally distributed, so they were log transformed for further statistical analysis. We applied a linear regression analysis with and without the covariates (age, sex, BMI, and glucose levels), and tested epistasis and Hardy–Weinberg equilibrium with the PLINK v2.050 software (http://pngu.mgh.harvard.edu/purcell/plink/) (). The Bonferroni correction was used to calculate the significance level (0.05/139 = 3.5 × 10− 4). To calculate the joint effects, all SNPs in genes with more than one nominally associated SNP were divided into haploblocks using HapMap data and Haploview software v4.2 (, ), and one SNP was chosen from each haploblock. The joint effect analyses were performed with the SPSS v13.0 software, using a one-sample t test. The association analysis of haplotypes was performed by PLINK toolset. A gene-by-gene interaction analysis was performed with the PLINK v2.050 software and GMDR software Beta 0.9 (http://sourceforge.net/projects/gmdr/) (). Imputation was performed with the IMPUTE2 v2.2.2 software (https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#home) (, ) for loci containing more than five SNPs. As reference haplotypes, we used the 1000 Genomes Phase I integrated variant set. The imputation region was set based on the coordinates of the first- and last-tested SNP at each locus. The SNPTEST v2.4.1 software was used to calculate the association between the imputed SNPs and the four lipid parameters (https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html#Download_and_Compilation) (). To accommodate uncertain genotypes, we used the –method threshold option, with a threshold value of 0.9. The additive model for association studies was chosen to calculate the P values. The Bonferroni threshold was calculated as follows: 0.05/number of our genotyped SNPs (n = 139) at all loci. Statistical power was calculated with the Quanto v1.2.4 software (). The minor allele frequencies (MAFs) of our genotyped SNPs ranged from 0.020 to 0.474. Taking into account this range of MAFs, our study had sufficient power (80%) to detect beta coefficients in the following range for each of the parameters: increased TC, 0.50–0.15; increased LDL–cholesterol, 0.45–0.15; reduced HDL–cholesterol, 0.200; and increased TG, 0.30–0.10. […]

Pipeline specifications

Software tools PLINK, Haploview, GMDR, IMPUTE, SNPTEST
Application GWAS
Diseases Coronary Artery Disease, Hypercholesterolemia, Dyslipidemias, Atherosclerosis
Chemicals Cholesterol, Triglycerides