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. P.; Magi, R.; Wielscher, M.; Hassan, S.; van Duijn, C. M.; Sebert, S.; Jarvelin, M.-R.; Kaakinen, M. A.; Prokopenko, I.

doi:10.1101/656918

bioRxiv · 10.1101/656918

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. P.·Magi, R.·Wielscher, M.·Hassan, S.·van Duijn, C. M.·Sebert, S.·Jarvelin, M.-R.·Kaakinen, M. A.·Prokopenko, I.

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Abstract

BackgroundMulti-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).\n\nResultsWe 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<1x10-7 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).\n\nIn Model 1, we detected epigenome-wide significant association in the MP-EWAS meta-analysis at cg13708645 (chr12:121,974,305; P=1.2x10-8) 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=2.3x10-7) annotated to C3orf58 with strongest effect on FI in single-trait analysis and multi-phenotype effect on FI and WHI within Model 1.\n\nWe 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.\n\nConclusionsWe implemented MP-EWAS in methylSCOPA and demonstrated its enhanced power over single-trait EWAS for correlated phenotypes in large-scale data.

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Draisma, H., Liu, J., Pupko, I., Demirkan, A., Balkhiyarova, Z., Morris, A. P., Magi, R., Wielscher, M., Hassan, S., van Duijn, C. M., Sebert, S., Jarvelin, M.-R., Kaakinen, M. A., Prokopenko, I.. 2019-06-03. methylSCOPA and META-methylSCOPA: software for the analysis and aggregation of epigenome-wide association studies of multiple correlated phenotypes. https://doi.org/10.1101/656918

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