Mitochondrial genome-wide analysis of nuclear DNA methylation quantitative trait loci

Abstract

Mitochondria have a complex communication network with the surrounding cell and can alter nuclear DNA methylation (DNAm). Variation in the mitochondrial DNA (mtDNA) has also been linked to differential DNAm. Genome-wide association studies have identified numerous DNAm quantitative trait loci, but these studies have not examined the mitochondrial genome. Herein, we quantified nuclear DNAm from blood and conducted a mitochondrial genome-wide association study of DNAm, with an additional emphasis on sex- and prediabetes-specific heterogeneity. We used the Young Finns Study (n = 926) with sequenced mtDNA genotypes as a discovery sample and sought replication in the Ludwigshafen Risk and Cardiovascular Health study (n = 2317). We identified numerous significant associations in the discovery phase (P < 10−9), but they were not replicated when accounting for multiple testing. In total, 27 associations were nominally replicated with a P < 0.05. The replication analysis presented no evidence of sex- or prediabetes-specific heterogeneity. The 27 associations were included in a joint meta-analysis of the two cohorts, and 19 DNAm sites associated with mtDNA variants, while four other sites showed haplogroup associations. An expression quantitative trait methylation analysis was performed for the identified DNAm sites, pinpointing two statistically significant associations. This study provides evidence of a mitochondrial genetic control of nuclear DNAm with little evidence found for sex- and prediabetes-specific effects. The lack of a comparable mtDNA data set for replication is a limitation in our study and further studies are needed to validate our results.

Methylation status of VTRNA2-1/nc886 is stable across human populations, monozygotic twin pairs and in majority of somatic tissues

“…To analyse the methylation of nc886 across different tissues, 17 datasets were utilized. These included a dataset consisting of 30 tissues from a 112-year-old female (56), as well as datasets consisting of different brain regions ((57), GSE134379), adipose tissue (54,58), muscle (46,(55)(56)(57), GSE142141, GSE171140), liver (58), buccal swabs (52,61), skin (62), sperm (63,64) and placenta (65)(66)(67)(68). All utilized datasets are described in detail in Supplementary Table 1.…”

“…Datasets GSE61454, GSE71678, GSE134379, and GSE157896 were downloaded as raw idat-files, extracted with minfi package function read.metharray.exp and quantile normalized with default settings (80). For DILGOM (44), FTC (54,60), ERMA (60), KORA (11,45), LURIC (46), SATSA (47) and YFS (11), the processing of DNA methylation data has been described in detail in previous publications referenced here. For all datasets, information on sample material, Illumina array type (Illumina Infinium MethylationEPIC or Methylation450K BeadChip), and the used processing methods are provided in Supplementary Table 1.…”

“…For the population analysis, we included 32 datasets, with the number of individuals ranging from 131 to 2711 (total n = 30 347). In these datasets, the DNA methylation data was available from blood (11,(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48), separated peripheral blood cells (49)(50)(51), blood spots (19), umbilical cord buffy coats (52), foetal cord tissue (53) or buccal swabs (52). Association of zygosity (MZ vs DZ pairs) with nc886 methylation was analysed in 5 datasets (36,47,48,54,55).…”

Methylation status of VTRNA2-1 / nc886 is stable across populations, monozygotic twin pairs and in majority of tissues

“…For the population analysis, 32 datasets were included, with the number of individuals ranging from 131 to 2711 (total n = 30,347). In these datasets, the DNA methylation data was available from blood [11,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48], separated peripheral blood cells [49][50][51], blood spots [20], umbilical cord buffy coats [52], fetal cord tissue [53] or buccal swabs [52]. Association of zygosity (MZ vs DZ pairs) with nc886 methylation was analyzed in five datasets [36,47,48,54,55].…”

“…To analyze the methylation of nc886 across different tissues, 17 datasets were used. These included a dataset consisting of 30 tissues from a 112-year-old female [57], as well as datasets consisting of different brain regions [58] (GSE134379), adipose tissue [54,59], muscle [46,55,56,57,60], (GSE142141, GSE171140), liver [59], buccal swabs [52,62], skin [63], sperm [64,65] and placenta [66][67][68][69]. All datasets are described in detail in Supplementary Table 1.…”