7 CHAPTER 7 140 and QC was performed on each dataset prior to imputation. Only variants with an imputation quality of ≥ 0.311 and a minor allele count of ≥ 12 were kept for further analysis. For X chromosome analyses males were coded as diploid. Prior to the meta-analysis, the per-study allele labels and allele frequencies were compared with those of the imputation reference panels using EasyQC,11 and removed or reconciled mismatches. The analysis of the Taiwanese cohort was performed separately.8 We first conducted, an inverse variance weighted fixed-effects meta-analysis of European-ancestry cohorts using METAL,12 without genomic control. A total of 14,860,930 variants were present in at least one cohort and included in the meta-analysis, and 5,199,189 (35%) variants were present in all ten cohorts.To identify additional loci we next conducted a secondary trans-ancestry GWAS meta-analysis that also included the East Asian ancestry cohort, using MR-MEGA with default settings,13 which accounts for allelic heterogeneity between ancestries. Of 15,425,163 variants analyzed, 3,792,160 were present in the East Asian cohort. Of these, 3,225,258 (85%) were also present in at least one European cohort. Genome-wide significance was set to p < 5 × 10-8. Due to heterogeneity in allele frequencies and differences in LD structure between European and East Asian populations, which complicates LD modeling, we focused subsequent fine-mapping and functional analyses on data from the European-ancestry GWAS. SNP-based heritability was calculated using LDSC14 after excluding variants that (1) were not present in the HapMap 3 reference panel, (2) explained > 1% of phenotype variation, or variants in LD (r2 > 0.1) with these, and (3) were in the major histocompatibility complex region. Heritability estimates were converted to the liability scale assuming a population prevalence of CH of 0.1%.1 Fine-mapping for significant loci was performed using PICS215 with 1000 Genomes EUR LD reference. Next, a stepwise conditional analysis was performed using FINEMAP16 17 Only biallelic, non-indel variants were included, and a p < 5 × 10−8 was used to define SNPs that were conditionally independent from the lead variant. Candidate gene mapping To prioritize candidate genes for a causal association to CH, five methods were applied: (1) expression quantitative trait locus (eQTL) analysis, (2) transcriptome-wide association (FUSION), (3) finemapping of causal gene sets (FOCUS), (4) association to genetically driven DNAm (MetaMeth), and (5) genes affected by protein-altering variants in high LD with the lead CH variants. eQTL analysis Association between variants and gene expression (cis-eQTL) was estimated based on RNA sequencing and genotype data from 59,327 individuals (Table S4).18 For each CH variant it was tested whether the variant itself, or variants in high LD (r2 ≥ 0.8), associated with one or more top cis-eQTLs, defined as the variant with the lowest p value within a distance of 1 Mb from the gene
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