Computational protocol: Meta-analysis of gene–environment-wide association scans accounting for education level identifies additional loci for refractive error

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[…] Detailed information on the genotyping platforms and QC procedures for each study is provided in and . Each study applied stringent QC filters for GWAS. In general, duplicate DNA samples, individuals with low call rate (<95%), gender mismatch or ethnic outliers were excluded. SNPs were excluded if low genotyping call rate (>5% missingness), monomorphic SNPs, with minor allele frequency (MAF) <1% or in Hardy–Weinberg disequilibrium (P<10−6). After QC filtering, the array genotypes of each study were imputed using the 1000 Genomes Project data as reference panels (build 37, phase 1 release, March 2012) with the software Minimac or IMPUTE2 (ref. ). Approximately six million SNPs that passed imputation quality thresholds (MACH: r2>0.5 or IMPUTE info score >0.5) and with MAF ≥5% were eligible for the meta-analysis (). [...] We adopted the JMA approach, to simultaneously test both SNP main effects and SNP × education interactions for spherical equivalent with a fixed-effect model, using SNP and SNP × education regression coefficients (βSNP and βSNP × education, respectively) and a β's covariance matrix from each study. A Wald's statistic, following a χ2-distribution with two degrees of freedom, was used to test the joint significance of the βSNP and βSNP × education. The JMA was performed with METAL, as previously described by Manning et al.. A Cochran's Q-test was used to assess heterogeneity of the β-coefficients across studies for the SNP and interaction effects. To test for interaction between the SNP and education, we conducted a secondary meta-analysis of the SNP × education interaction effects for spherical equivalent (βSNP × education, one degree of freedom), with a fixed-effects model using inverse-variance weighting in METAL; this is a traditional meta-analysis to investigate SNP × education interactions per se. Effects and s.e. of the SNP effect on spherical equivalent in the lower education group (βSNP) and higher education group (βSNP+βSNP × education) were derived from the JMA output. We used the P-value of 5 × 10−8 as a significant threshold for JMA test. For the SNP and SNP × education effects for the identified top loci, the P-value threshold for significance was set at 0.0055=0.05/9 (9 index SNPs underlying analyses).We performed a meta-regression to explore sources of heterogeneity in our meta-analysis for three loci showing G × E interactions (R package ‘metafor'). Meta-regression included the following study-specific variables as covariates: study sample size, proportion of individuals in the higher education group, average spherical equivalent, education main effects on spherical equivalent (higher education level versus lower), ethnicity (Asian versus European), study design (independent samples versus family-based studies), study year and average age. Meta-regression was also conducted to test the fold changes of the interaction β-coefficients in Asians versus Europeans for the 39 known myopia loci.The study-specific genomic control inflation factors λgc for the joint test for SNP and interaction terms ranged from 1.009 to 1.125 with an average of 1.019 (), calculated by the ratio of the observed median χ2 divided by the expected median of the 2df χ2-distribution (1.382). Genomic control correction was applied to each individual study. For studies of small sample sizes (n<500) with λgc >1.05, we further, before starting the meta-analysis, excluded SNPs showing significant joint P-value <1 × 10−5 but neither the SNP nor SNP × education effects supported such an association. Quantile–quantile plots showed only modest inflation of the test statistics in the JMA test (Europeans: λgc=1.081; Asians: λgc=1.053; Combined: λgc=1.092; ), similar to previous GEWIS studies with comparable sample sizes. We excluded a small number of markers in the meta-analysis with Phet<0.0001. The λgc for the SNP × education interaction term in the individual studies ranged from 1.01 to 1.08, indicating little evidence of test statistic inflation on SNP × education effect for each study. [...] The coordinates and variant identifiers are reported on the NCBI B37 (hg19) genome build and annotated using UCSC Genome Browser. We identified variants within each of the LD blocks (r2≥0.8) in European and Asian populations of the 1000 Genomes Project (100 kb flanking the top SNP at each locus), to apply functional annotations of transcription regulation using HaploReg and Encyclopedia of DNA Elements data. We also generated funcational association and co-expression network using GeneMANIA, to determine whether the disease-related genes identified in this study and previous GWAS are functionally connected.To assess gene expression in human tissues, we examined the Ocular Tissue Database and the EyeSAGE database. The estimated gene and exome-level abundances are available online (see URL). Normalization of gene expression used the Probe Logarithmic Intensity Error method with genomic control-background correction. Relationships between genotype and cis regulation of gene expression levels () were assessed using expression quantitative trait locus associations in multiple human tissues from UK samples, as well as gene expression profiles obtained from GTExPortal database. […]

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