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Fig. 1 | Genome Biology

Fig. 1

From: Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders

Fig. 1

Study design flowchart. (1) Whole-blood DNA methylation (DNA methylation) data was available for three amyotrophic lateral sclerosis (AUS, KCL and NL), two Parkinson’s disease (SGPD and PEG), and three Alzheimer’s disease (AIBL, ADNI and AddNeuroMed), for which a subset of individuals was diagnosed with mild cognitive impairment (MCI). The MCI patients were not included in analyses, due to lack of power. We also had available two schizophrenia (SCZ1 and SCZ2) and one rheumatoid arthritis cohorts, used to check specificity of results to neurodegenerative disorders. In total, 5551 cases and 4343 controls were available for analyses, after quality control (QC). (2) QC and normalization of DNA methylation data were conducted using the R package meffil [27], which applies an automated estimation of functional normalization parameters that reduces technical variation in DNA methylation levels, thus reducing false positive rates and improving power. (3) To discover differentially methylated positions (DMPs), we applied mixed-linear model-based association studies of DNA methylation for each of the eight available cohorts, using two different methods: MOA and MOMENT [24]. To discover DMPs shared between neurodegenerative disorders, MOMENT results were meta-analyzed, between AUS, KCL, NL, SGPD, PEG, and AIBL cohort. We also found a similar distribution pattern of predicted immune cell-type proportions (CTP) between cases and controls of all disorders. We then attempted to validate our results using out-of-sample classification between disorders—with profile scores derived from CTP and DNA methylation effect sizes—and checking for overlap with GWAS, eQTL, mQTL, and haQTL (xQTLs) signals. Finally, we investigated the relationship between the CTP and DNA methylation-derived scores and blood inflammatory markers in a healthy aging cohort (Lothian Birth Cohort 1936)

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