methylSCOPA and META-methylSCOPA: software for the analysis and aggregation of epigenome-wide association studies of multiple correlated phenotypes
Draisma H., Liu J., Pupko I., Demirkan A., Balkhiyarova Z., Morris A., Magi R., Wielscher M., Hassan S., van Duijn C., Sebert S., Jarvelin M-R., Kaakinen M., Prokopenko I.
Background Multi-phenotype genome-wide association studies (MP-GWAS) of correlated traits have greater power to detect genotype–phenotype associations than single-trait GWAS. However, no multi-phenotype analysis method exists for epigenome-wide association studies (EWAS). Results We extended the SCOPA approach developed by us to “methylSCOPA” software in C++ by ‘reversely’ regressing DNA hyper/hypo-methylation information on a linear combination of phenotypes. We evaluated two models of association between DNA methylation and fasting glucose (FG) and insulin (FI) levels: Model 1, including FG, FI, and three measured potential confounders (body mass index [BMI], fasting serum triglyceride levels [TG], and waist/hip ratio [WHR]), and Model 2, including FG and FI corrected for the effects of BMI, TG, and WHR. Both models were additionally corrected for participant sex and smoking status (current/ever/never). We meta-analyzed the cohort-specific MP-EWAS results with our novel software META-methylSCOPA, mapped genomic locations to CGCh37/hg19, and adopted P <1×10 −7 to denote epigenome-wide significance. We used the Illumina Infinium HumanMethylation450K BeadChip array data from the Northern Finland Birth Cohorts (NFBC) 1966/1986. We quality-controlled the data, regressed out the effects of measured potential confounders, and normalized the methylation signal intensity and FI data. The MP-EWAS included data for 643/457 individuals from NFBC1966 and NFBC1986, respectively (total N=1,100). In Model 1, we detected epigenome-wide significant association in the MP-EWAS meta-analysis at cg13708645 (chr12:121,974,305; P =1.2×10 −8 ) within KDM2B gene. Single-trait effects within KDM2B were on FI, BMI, and WHR. Model with effect on BMI and WHR showed the strongest association at this locus, while effect on FI in single-phenotype analysis was driven by the effect of adiposity. In Model 2, the strongest association was at cg05063096 (chr3:143,689,810; P =2.3×10 −7 ) annotated to C3orf58 with strongest effect on FI in single-trait analysis and multi-phenotype effect on FI and WHI within Model 1. We characterized the effects of established EWAS loci for diabetes and its risk factors and detected suggestive (p<0.01) associations at six markers including PHGDH, TXNIP, SLC7A11, CPT1A, MYO5C and ABCG1 , through the dissection of the multi-phenotype effects in Model 1. Conclusions We implemented MP-EWAS in methylSCOPA and demonstrated its enhanced power over single-trait EWAS for correlated phenotypes in large-scale data.