Computational protocol: A genome-wide association meta-analysis on lipoprotein (a) concentrations adjusted for apolipoprotein (a) isoforms[S]

Similar protocols

Protocol publication

[…] Because the distribution of the Lp(a) concentrations were highly skewed, an inverse-normal transformation was applied to the measured Lp(a) concentrations. In each study, each SNP was tested for association with these inverse-normal transformed Lp(a) concentrations in an additive genetic model using linear regression, adjusting for age and sex (model 1). In addition, a second model, adjusted for age, sex, and the apo(a) isoform that was predominantly expressed in the immunoblot (model 2), was tested. To obtain interpretable effect estimates, linear regression was also performed on the original scale of Lp(a) for both models. Genome-wide analysis in the FamHS study was done using a linear mixed model accounting for familial dependencies described by a pedigree-based kinship matrix.For the meta-analysis of all GWASs, the software, METASOFT (), was used for all imputed SNPs that met imputation and quality control criteria and were present in at least two studies (∼9.2 M SNPs). Details on quality control, filtering criteria, and the meta-analysis approach are provided in the supplemental Materials and Methods and supplemental Fig. S1. Gender-stratified models were also applied for both models, followed by a t-test on effect differences between men and women (). Pairwise linkage disequilibrium (LD) was evaluated using SNiPA with 1000 Genomes, phase1v3 data (). [...] To detect independently associated SNPs, a conditional stepwise analysis was performed using the program, GCTA [version 1.24.7 ()]. For each locus with at least one P value <5 × 10−8, the SNP with the lowest P value was taken as the lead SNP. It was planned to include all SNPs within a region ±500 kb surrounding the lead SNP in the conditional analysis. Because genome-wide significant SNPs were also found outside of this range for the LPA gene region, the conditional analysis was extended to a range of 1.76 Mb. GCTA uses the summary statistics of the meta-analysis plus one reference population for LD calculation. As reference population, a combined genotype dataset of KORA F3 and KORA F4 was used (n = 6,002). By default, the lead SNP was included in the model first. Then, all SNPs in the included gene region were tested for association in addition to the already included SNPs in a stepwise manner. Using all independently associated SNPs from model 1, an unweighted, as well as weighted, SNP-score was derived. The unweighted SNP-score corresponded to the number of Lp(a)-increasing alleles. For weighting, β estimates on inverse-normal transformed Lp(a) values from the joint model of all included SNPs were taken. [...] In addition to the analysis of single SNP effects, a gene-based scan was performed using meta-analysis results from both models (with and without adjusting for isoforms) using the software, KGG version 3.5 (). Gene regions were defined as the gene ±20 kb according to the RefGene database. Using this definition, 66.5% of all available SNPs were included. For the gene-based analysis, the extended Simes test (GATES) was used as implemented in KGG (). To adjust for multiple testing, the Bonferroni method was applied on the number of tested genes (25,128 genes, which resulted in a significance level of 1.99 × 10−6). To calculate LD between the SNPs, the 1000G phase1v3 Reference was used. In addition to the hypothesis-free gene-based test, 21 candidate genes from literature were tested for association (supplemental Materials and Methods) using a Bonferroni significance P value of 0.05/21 = 0.0024. [...] The combined dataset of both KORA studies (n = 6,002) was used to estimate the genomic heritability, which is the proportion of phenotypic variance explained by all tested SNPs (). In addition, the proportion of variance explained by individual SNPs was calculated with data from both KORA studies using the software GCTA (v.1.24.7) (). In the FamHS study, the proportion of the additive (polygenic) variance on the phenotypic variance, the narrow-sense heritability h2, was estimated using GenABEL’s polygenic function, taking the kinship matrix into account. This narrow-sense heritability thus also includes the variance explained by unmeasured SNP effects and other factors (e.g., CNVs). […]

Pipeline specifications

Software tools METASOFT, SNiPA, GCTA, KGG, GenABEL
Databases RefGene
Application GWAS
Diseases Coronary Artery Disease