An alternative approach to multiple testing for methylation QTL mapping reduces the proportion of falsely identified CpGs

Systemic interindividual epigenetic variation in humans is associated with transposable elements and under strong genetic control

BACKGROUND

Genetic variants can modulate phenotypic outcomes via epigenetic intermediates, for example at methylation quantitative trait loci (mQTL). We present the first large-scale assessment of mQTL at human genomic regions selected for interindividual variation in CpG methylation, which we call correlated regions of systemic interindividual variation (CoRSIVs). These can be assayed in blood DNA and do not reflect interindividual variation in cellular composition.

RESULTS

We use target-capture bisulfite sequencing to assess DNA methylation at 4086 CoRSIVs in multiple tissues from each of 188 donors in the NIH Gene-Tissue Expression (GTEx) program. At CoRSIVs, DNA methylation in peripheral blood correlates with methylation and gene expression in internal organs. We also discover unprecedented mQTL at these regions. Genetic influences on CoRSIV methylation are extremely strong (median R²=0.76), cumulatively comprising over 70-fold more human mQTL than detected in the most powerful previous study. Moreover, mQTL beta coefficients at CoRSIVs are highly skewed (i.e., the major allele predicts higher methylation). Both surprising findings are independently validated in a cohort of 47 non-GTEx individuals. Genomic regions flanking CoRSIVs show long-range enrichments for LINE-1 and LTR transposable elements; the skewed beta coefficients may therefore reflect evolutionary selection of genetic variants that promote their methylation and silencing. Analyses of GWAS summary statistics show that mQTL polymorphisms at CoRSIVs are associated with metabolic and other classes of disease.

CONCLUSIONS

A focus on systemic interindividual epigenetic variants, clearly enhanced in mQTL content, should likewise benefit studies attempting to link human epigenetic variation to the risk of disease.

Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability between 40 and 50%. DNA methylation patterns can serve as proxies of (past) exposures and disease progression, as well as providing a potential mechanism that mediates genetic or environmental risk. Here, we present a blood-based epigenome-wide association study meta-analysis in 9706 samples passing stringent quality control (6763 patients, 2943 controls). We identified a total of 45 differentially methylated positions (DMPs) annotated to 42 genes, which are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity. We then tested 39 DNA methylation-based proxies of putative ALS risk factors and found that high-density lipoprotein cholesterol, body mass index, white blood cell proportions, and alcohol intake were independently associated with ALS. Integration of these results with our latest genome-wide association study showed that cholesterol biosynthesis was potentially causally related to ALS. Last, DNA methylation at several DMPs and blood cell proportion estimates derived from DNA methylation data were associated with survival rate in patients, suggesting that they might represent indicators of underlying disease processes potentially amenable to therapeutic interventions.

Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference

BACKGROUND

DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data.

RESULTS

By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g., NFKBIE, CDCA7(L), and NLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation.

CONCLUSION

We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation.

IMAGE: high-powered detection of genetic effects on DNA methylation using integrated methylation QTL mapping and allele-specific analysis

Identifying genetic variants that are associated with methylation variation-an analysis commonly referred to as methylation quantitative trait locus (mQTL) mapping-is important for understanding the epigenetic mechanisms underlying genotype-trait associations. Here, we develop a statistical method, IMAGE, for mQTL mapping in sequencing-based methylation studies. IMAGE properly accounts for the count nature of bisulfite sequencing data and incorporates allele-specific methylation patterns from heterozygous individuals to enable more powerful mQTL discovery. We compare IMAGE with existing approaches through extensive simulation. We also apply IMAGE to analyze two bisulfite sequencing studies, in which IMAGE identifies more mQTL than existing approaches.

Autosomal genetic variation is associated with DNA methylation in regions variably escaping X-chromosome inactivation

X-chromosome inactivation (XCI), i.e., the inactivation of one of the female X chromosomes, restores equal expression of X-chromosomal genes between females and males. However, ~10% of genes show variable degrees of escape from XCI between females, although little is known about the causes of variable XCI. Using a discovery data-set of 1867 females and 1398 males and a replication sample of 3351 females, we show that genetic variation at three autosomal loci is associated with female-specific changes in X-chromosome methylation. Through cis-eQTL expression analysis, we map these loci to the genes SMCHD1/METTL4, TRIM6/HBG2, and ZSCAN9. Low-expression alleles of the loci are predominantly associated with mild hypomethylation of CpG islands near genes known to variably escape XCI, implicating the autosomal genes in variable XCI. Together, these results suggest a genetic basis for variable escape from XCI and highlight the potential of a population genomics approach to identify genes involved in XCI.

Genome-wide integrative analysis identified SNP-miRNA-mRNA interaction networks in peripheral blood mononuclear cells

AIM

To detect SNP-miRNA-mRNA interaction networks and to elucidate miRNA-mediated regulation effects on mRNA expression.

MATERIALS & METHODS

In human peripheral blood mononuclear cells of 43 females, SNP-miRNA-mRNA interaction networks were established through an integrative analysis. Then causal inference test was followed to detect miRNA-mediated effects on mRNA expressions.

RESULTS

About 167 trios corresponding to 56 SNPs, 20 miRNAs and 47 target-mRNAs have the SNP-miRNA-mRNA interactions, but only 22 trios have miRNA-mediated effects between SNP and mRNA. For the three miRNAs (hsa-miR-222-3p, hsa-miR-181b-5p and hsa-miR-106b-5p), each mediates at least four correlations between SNP and mRNA. The mRNAs in interaction play an important role in energy metabolism, cellular and tissue homeostasis.

CONCLUSION

This study represents the first effort of constructing an integrative interaction network of SNP-miRNA-mRNA and miRNA-mediated regulatory effects that provide helpful clues for future investigations of peripheral blood mononuclear cell-related physiological process and immunological diseases.

Genetic-epigenetic interactions in cis: a major focus in the post-GWAS era

Studies on genetic-epigenetic interactions, including the mapping of methylation quantitative trait loci (mQTLs) and haplotype-dependent allele-specific DNA methylation (hap-ASM), have become a major focus in the post-genome-wide-association-study (GWAS) era. Such maps can nominate regulatory sequence variants that underlie GWAS signals for common diseases, ranging from neuropsychiatric disorders to cancers. Conversely, mQTLs need to be filtered out when searching for non-genetic effects in epigenome-wide association studies (EWAS). Sequence variants in CCCTC-binding factor (CTCF) and transcription factor binding sites have been mechanistically linked to mQTLs and hap-ASM. Identifying these sites can point to disease-associated transcriptional pathways, with implications for targeted treatment and prevention.

Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

BACKGROUND

Epigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages.

RESULTS

Here, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile. aVMPs are enriched at polycomb repressed regions and, accordingly, methylation at those positions is associated with the expression of genes encoding components of polycomb repressive complex 2 (PRC2) in trans. Further analysis revealed trans-associations for 1816 aVMPs with an additional 854 genes. These trans-associated aVMPs are characterized by either an age-related gain of methylation at CpG islands marked by PRC2 or a loss of methylation at enhancers. This distinct pattern extends to other tissues and multiple cancer types. Finally, genes associated with aVMPs in trans whose expression is variably upregulated with age (733 genes) play a key role in DNA repair and apoptosis, whereas downregulated aVMP-associated genes (121 genes) are mapped to defined pathways in cellular metabolism.

CONCLUSIONS

Our results link age-related changes in DNA methylation to fundamental mechanisms that are thought to drive human ageing.

Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network

Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biological background, 1 PPDT-Module and 22 PCOS potential drug targets were identified, 21 of which were verified by literatures to be associated with the pathogenesis of PCOS. 42 drugs targeting to 13 PCOS potential drug targets were investigated experimentally or clinically for PCOS. Evaluated by independent datasets, the whole PPDT-Module and 22 PCOS potential drug targets could not only reveal the drug response, but also distinguish the statuses between normal and disease. Our identified PPDT-Module and PCOS potential drug targets would shed light on the treatment of PCOS. And our approach would provide valuable insights to research on the pathogenesis and drug response of other diseases.

Decoding the non-coding genome: elucidating genetic risk outside the coding genome

Current evidence emerging from genome-wide association studies indicates that the genetic underpinnings of complex traits are likely attributable to genetic variation that changes gene expression, rather than (or in combination with) variation that changes protein-coding sequences. This is particularly compelling with respect to psychiatric disorders, as genetic changes in regulatory regions may result in differential transcriptional responses to developmental cues and environmental/psychosocial stressors. Until recently, however, the link between transcriptional regulation and psychiatric genetic risk has been understudied. Multiple obstacles have contributed to the paucity of research in this area, including challenges in identifying the positions of remote (distal from the promoter) regulatory elements (e.g. enhancers) and their target genes and the underrepresentation of neural cell types and brain tissues in epigenome projects - the availability of high-quality brain tissues for epigenetic and transcriptome profiling, particularly for the adolescent and developing brain, has been limited. Further challenges have arisen in the prediction and testing of the functional impact of DNA variation with respect to multiple aspects of transcriptional control, including regulatory-element interaction (e.g. between enhancers and promoters), transcription factor binding and DNA methylation. Further, the brain has uncommon DNA-methylation marks with unique genomic distributions not found in other tissues - current evidence suggests the involvement of non-CG methylation and 5-hydroxymethylation in neurodevelopmental processes but much remains unknown. We review here knowledge gaps as well as both technological and resource obstacles that will need to be overcome in order to elucidate the involvement of brain-relevant gene-regulatory variants in genetic risk for psychiatric disorders.