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. 2014 Nov 15;23(22):5893-905.
doi: 10.1093/hmg/ddu313. Epub 2014 Jun 18.

Linking the genetic architecture of cytosine modifications with human complex traits

Xu Zhang  1 , Erika L Moen  2 , Cong Liu  3 , Wenbo Mu  3 , Eric R Gamazon  4 , Shannon M Delaney  5 , Claudia Wing  5 , Lucy A Godley  6 , M Eileen Dolan  6 , Wei Zhang  7
Affiliations

Linking the genetic architecture of cytosine modifications with human complex traits

Xu Zhang et al. Hum Mol Genet. .

Abstract

Interindividual variation in cytosine modifications could contribute to heterogeneity in disease risks and other complex traits. We assessed the genetic architecture of cytosine modifications at 283,540 CpG sites in lymphoblastoid cell lines (LCLs) derived from independent samples of European and African descent. Our study suggests that cytosine modification variation was primarily controlled in local by single major modification quantitative trait locus (mQTL) and additional minor loci. Local genetic epistasis was detectable for a small proportion of CpG sites, which were enriched by more than 9-fold for CpG sites mapped to population-specific mQTL. Genetically dependent CpG sites whose modification levels negatively (repressive sites) or positively (facilitative sites) correlated with gene expression levels significantly co-localized with transcription factor binding, with the repressive sites predominantly associated with active promoters whereas the facilitative sites rarely at active promoters. Genetically independent repressive or facilitative sites preferentially modulated gene expression variation by influencing local chromatin accessibility, with the facilitative sites primarily antagonizing H3K27me3 and H3K9me3 deposition. In comparison with expression quantitative trait loci (eQTL), mQTL detected from LCLs were enriched in associations for a broader range of disease categories including chronic inflammatory, autoimmune and psychiatric disorders, suggesting that cytosine modification variation, while possesses a degree of cell linage specificity, is more stably inherited over development than gene expression variation. About 11% of unique single-nucleotide polymorphisms reported in the Genome-Wide Association Study Catalog were annotated, 78% as mQTL and 31% as eQTL in LCLs, which covered 37% of the investigated diseases/traits and provided insights to the biological mechanisms.

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Figures

Figure 1.
Figure 1.
Genetic architecture of cytosine modifications. (A and B) For associations detected in whole-genome scan, the chromosomal positions of CpG sites were plotted against the positions of SNPs for CEU (A) and YRI (B). (C) For associations detected in ±1 Mb scan, r2 was plotted against the relative distance from SNPs to CpG sites for CEU (upper panel) and YRI (lower panel). SNPs were pruned by linkage disequilibrium (r2 > 0.3) for presentation. The ±100 kb regions of CpG sites were marked by orange vertical lines. (D) For CpG sites mapped to multiple additive mQTL (≥2), the adjusted association r2 of multiple mQTL was plotted against that of the most significant mQTL, for CEU (blue) and YRI (orange). (E) An example of local genetic epistasis showed the mean (points) and standard deviation (vertical lines) of M-values at cg00680696 according to genotypes at rs1050816 (x-axis) and rs4674390 (colored by black, blue and orange).
Figure 2.
Figure 2.
Genetic and epigenetic dissection of gene expression variation. (A) mQTL were enriched for eQTL. For genes containing mQTL within the ±100 kb regions, P-value distribution of associations between expression levels and mQTL (black) were compared with that between expression levels and all SNPs within the regions (gray). (B) Distribution of relative positions between CpG sites and the correlated genes for genetically dependent CpG-gene pairs. TSS: transcriptional start site; TES: transcriptional end site. (C) Epigenetic regulation of gene expression. For cytosine modification-gene expression correlations detected at 5% FDR, the association r2 between gene expression levels and residual cytosine modification levels was compared with the association r2 between gene expression levels and unadjusted cytosine modification levels. The majority of correlations, after adjusted for genetic variations, remained to be significant. (D) Distribution of relative positions between CpG sites and the correlated genes for genetically independent CpG-gene pairs. In (B) and (D), gray bars represent counts and step-wise lines represent proportions.
Figure 3.
Figure 3.
A flowchart of analyses that define genetically dependent and genetically independent CpG-gene expression correlation. mQTL: SNPs whose genotypes associate with cytosine modification levels. meQTL: mQTL whose genotypes associate with gene expression levels. Genetically dependent CpG-gene expression correlation: cytosine modification and gene expression traits that are mapped to meQTL and that themselves are correlated at P < 0.05; the corresponding CpG site was referred to as ‘genetically dependent CpG site.’ Genetically independent CpG-gene expression correlation: cytosine modification and gene expression traits that are correlated at genome-wide significance, which remains significant after adjusting for genetic variation; the corresponding CpG site was referred to as ‘genetically independent CpG site.’ Facilitative CpG site: genetically dependent or genetically independent CpG site whose cytosine modification levels positively correlate with gene expression levels. Repressive CpG site: genetically dependent or genetically independent CpG site whose modification levels negatively correlate with gene expression levels.
Figure 4.
Figure 4.
Genetic and non-genetic gene regulation mediated through cytosine modifications. Co-localization of genetically dependent (A and B) and genetically independent (C and D) CpG sites detected in the CEU samples with transcription factor binding sites (A and C) and histone markers (B and D). Facilitative sites were plotted in upper panels and repressive sites were plotted in lower panels. Counts of co-localization were represented by bars, for each type of peaks that exceeded 95% percentile of the corresponding null distributions. The aggregated counts of co-localization with transcription factor binding sites were colored by orange, while counts of co-localization with H3K4me3, H3K36me3, H3K9me3 and H3K27me3 peaks were colored by black, red, green and blue, respectively. Total counts of the repressive or facilitative sites were displayed as step-wise lines.
Figure 5.
Figure 5.
Annotation of GWAS loci by mQTL and eQTL. Enrichment of mQTL (A) and eQTL (B) for GWAS SNPs. The null distributions of the number of random SNPs that overlapped with GWAS SNPs were displayed as histograms. The red points mark the number of mQTL and eQTL that overlapped with GWAS SNPs. (C) Enrichment of mQTL and eQTL in GWAS disease categories. The null distributions of the number of random GWAS SNPs annotated as mQTL (left sides) and eQTL (right sides) were displayed as gray points, with darkness representing density of points. The red and green points mark the numbers of GWAS SNPs annotated as mQTL or eQTL for the given disease categories, respectively. Cardiovas.: cardiovascular; neuro.: neurological; psychia.: psychiatric; inflamm.: inflammatory.
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