Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain.
- Authors
- Gibbs, J Raphael; van der Brug, Marcel P; Hernandez, Dena G; Traynor, Bryan J; Nalls, Michael A; Lai, Shiao-Lin; Arepalli, Sampath; Dillman, Allissa; Rafferty, Ian P; Troncoso, Juan; Johnson, Robert; Zielke, H Ronald; Ferrucci, Luigi; Longo, Dan L; Cookson, Mark R; Singleton, Andrew B
- Year
- 2010
- Journal
- PLoS genetics
- PMID
- 20485568
- DOI
- 10.1371/journal.pgen.1000952
- PMCID
- PMC2869317
A fundamental challenge in the post-genome era is to understand and annotate the consequences of genetic variation, particularly within the context of human tissues. We present a set of integrated experiments that investigate the effects of common genetic variability on DNA methylation and mRNA expression in four human brain regions each from 150 individuals (600 samples total). We find an abundance of genetic cis regulation of mRNA expression and show for the first time abundant quantitative trait loci for DNA CpG methylation across the genome. We show peak enrichment for cis expression QTLs to be approximately 68,000 bp away from individual transcription start sites; however, the peak enrichment for cis CpG methylation QTLs is located much closer, only 45 bp from the CpG site in question. We observe that the largest magnitude quantitative trait loci occur across distinct brain tissues. Our analyses reveal that CpG methylation quantitative trait loci are more likely to occur for CpG sites outside of islands. Lastly, we show that while we can observe individual QTLs that appear to affect both the level of a transcript and a physically close CpG methylation site, these are quite rare. We believe these data, which we have made publicly available, will provide a critical step toward understanding the biological effects of genetic variation.
Analysis of CpG methylation and mRNA measures across four human brain regions.(A,B), unsupervised cluster analysis of CpG methylation levels at autosomal loci and mRNA expression levels. Data arising from each brain region are colored accordingly and demonstrate consistent separation of cerebellum, pons and cortical samples, with separation of frontal and temporal cortex samples using mRNA transcript levels. (C,D) Tissue based pairwise comparisons of CpG methylation and mRNA expression. The analyses in these figures used cleaned data. Histograms show the distribution of CpG methylation levels and mRNA expression levels for each tissue. Scatter plots are direct comparison of the level of each detected transcript or methylation level in each tissue pair. Notably frontal cortex and temporal cortex show the most similar patterns of CpG methylation and expression; conversely, all comparisons against cerebellar tissue show this tissue to have the most distinct patterns for all measures.
Quantitative trait loci (QTL) for CpG methylation (methQTL), mRNA expression (eQTL) in four brain regions.(A,B), methQTL and eQTL respectively, where the Y-axis represents the physical location of the QTL SNP and the X-axis the physical location of the QTL probe (CpG site or mRNA). The size of each point reflects the relative strength of the association (R2). (A) The excess of points along the diagonal illustrates that detected methQTL are predominantly cis acting; but that trans methQTLs with high R2 values exist across multiple tissues. (B) Shows the majority of eQTL loci are in cis and that the in cis effect sizes are larger that those observed in trans. (CβG) cis methQTL results showing a symmetric association between methylation level and variants on both sides of the CpG site in all four brain regions. (HβL) cis eQTL results showing a symmetric association between expression level and variants on both sides of the transcription start site (TSS, corrected for strand) in all four brain regions. G and L show the average p value for significant methQTL or eQTL across regions. For both methQTL and eQTL the more significant cis QTLs tended to be both closer to the transcriptions start site or CpG methylation site and common across tissue regions tested.
Peak enrichment for QTLs.Plots showing the large cis enrichment of significantly identified QTLs over trans. For the paper and result tables cis has been defined as SNPs and probes (CpG and mRNA) that are Β±1 Mb of each other. At this annotated definition of cis the enrichment of results are on the right of these two plots x-axis, where the plots are all four brain tissue regions together. The left plot is for CpGs and the right for mRNA. At 1 Mb the enrichment of cis to trans for CpG QTLs is 4,427-fold and for mRNA is 7,255-fold. These plots show how this enrichment changes when considering other distances as a threshold for cis, the X-axis are these other distances (non-linear) at 50 distances that are four/fifths the size of the next larger distance. The plots are based on the proportions of significant cis and trans SNP/Probe pairings from the possible pairings at different distances.
Comparison of QTLs for CpG methylation and mRNA expression across tissue regions.Any QTL that passed our threshold for significance in at least one tissue was included in the Ternary plots. The color of the points in the ternary plots reflects the cumulative R2 value for all tissues tested within each plot. Points toward the center indicate an equal R2 value across the three regions under investigation. Points toward the corner of a plot indicate a high R2 in one of the three tissues; points toward the edges of the plot indicate a high R2 in two of the three tissues. (AβH) Comparing methQTL in every three-way combination of the the four tissues for cis (AβD) and trans (EβH). (IβP) eQTL comparisons across each group of three tissues for cis and trans effects. Notably the cumulative R2 is generally higher for cis compared to trans loci across both methQTL and eQTL. Green circles highlight a cluster of relatively high cumulative R2 values driven primarily by the observed R2 within cerebellar tissue. These points were revealed to be a cis eQTL involving 20 SNPs and two neighboring transcripts, PPAPDC1A and C10orf85. (QβT) Boxplots show expression level plotted against genotype for one of these eQTL SNP-transcript pairs (SNP rs2182513 and PPAPDC1A) and illustrate that this is a tissue specific QTL limited to the cerebellum.
Intersection of QTLs for CpG and mRNA traits.Plots shown for each tissue region; cerebellum, frontal cortex, pons and temporal cortex. Per tissue for every pairing of CpG site and mRNA transcript where the CpG was within a CpG island and within 1Mb of the mRNA TSS and both the CpG and mRNA have a significant cis QTL; i.e. a triplet is formed between SNP, mRNA and CpG, where the SNP was significantly correlated in cis with either the CpG or mRNA of the CpG-mRNA cis pairs under consideration. For these triplets we plotted the eQTL R2 (X axis) and the methQTL R2 (Y axis). The R2 values are shifted into one of four quadrants (without changing the effect size) based on the positive and negative combinations of correlations for the eQTLs and methQTLs. A positive correlation with mRNA means that the level of expression is increased with the minor allele of the SNP and a negative correlation means that the mRNA expression level is decreased with the minor allele of the SNP. For CpGs a positive correlation implies that the level of methylation is increased at a CpG site with the minor allele and a negative correlation implies that the level of methylation is decreased with the minor allele. The top-left quadrant contains negative eQTL and positive methQTL correlations. The top-right quadrant contains eQTLs and methQTLs where the correlation was positive in both. The bottom-left quadrant contains eQTLs and methQTLs where the correlation was negative in both. The bottom-right quadrant contains positive eQTL and negative methQTL correlations. The triplets are plotted as circles where the radius of the circle represent the R2 value between the CpG site and mRNA transcript. The color of circle represents the distance between the CpG site and the mRNA TSS, where red indicates that the CpG site is close and upstream to the mRNA TSS; blue indicates that the CpG site is close to the mRNA TSS but downstream; and black indicates that the CpG site is farther from the mRNA TSS both up or downstream. Triplets that fall along the main diagonals are those where a significant methQTL and eQTL are present; the CpG and mRNA pairs for these triplets are closer together and have a larger R2 between the CpG and mRNA. Triplets that are on or close to the X axis are those where there is a significant eQTL but a lack of signal for the methQTL, likewise the items that are on or close to the Y axis are those with a significant methQTL and a lack of signal for the eQTL.
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| Genetic-epigenetic interactions in cis: a major focus in the post-GWAS era. | Do C et al. | β | 2017 | β |
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| Integrative Approaches to Understanding the Pathogenic Role of Genetic Variation in Rheumatic Diseases. | Laufer VA et al. | β | 2017 | β |
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| Parkinson disease polygenic risk score is associated with Parkinson disease status and age at onset but not with alpha-synuclein cerebrospinal fluid levels. | Ibanez L et al. | β | 2017 | β |
| Protocol for the EMPHASIS study; epigenetic mechanisms linking maternal pre-conceptional nutrition and children's health in India and Sub-Saharan Africa. | Chandak GR et al. | β | 2017 | β |
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| Review: DNA methylation and alcohol use disorders: Progress and challenges. | Zhang H et al. | β | 2017 | β |
| Risk alleles of genes with monoallelic expression are enriched in gain-of-function variants and depleted in loss-of-function variants for neurodevelopmental disorders. | Savova V et al. | β | 2017 | β |
| RL-SKAT: An Exact and Efficient Score Test for Heritability and Set Tests. | Schweiger R et al. | β | 2017 | β |
| Role of DNA methylation at the placental RTL1 gene locus in type 1 diabetes. | Belot MP et al. | β | 2017 | β |
| Single nucleotide polymorphism rs3124599 in Notch1 is associated with the risk of lung cancer in northeast Chinese non-smoking females. | Quan X et al. | β | 2017 | β |
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| A genome-wide association study in multiple system atrophy. | Sailer A et al. | β | 2016 | β |
| A Genome-Wide mQTL Analysis in Human Adipose Tissue Identifies Genetic Variants Associated with DNA Methylation, Gene Expression and Metabolic Traits. | Volkov P et al. | β | 2016 | β |
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| AlzBase: an Integrative Database for Gene Dysregulation in Alzheimer's Disease. | Bai Z et al. | β | 2016 | β |
| Alzheimer's Disease Risk Polymorphisms Regulate Gene Expression in the ZCWPW1 and the CELF1 Loci. | Karch CM et al. | β | 2016 | β |
| Amnion as a surrogate tissue reporter of the effects of maternal preeclampsia on the fetus. | Suzuki M et al. | β | 2016 | β |
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| Ancient Haplotypes at the 15q24.2 Microdeletion Region Are Linked to Brain Expression of MAN2C1 and Children's Intelligence. | CΓ‘ceres A et al. | β | 2016 | β |
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| A Polymorphic Antioxidant Response Element Links NRF2/sMAF Binding to Enhanced MAPT Expression and Reduced Risk of Parkinsonian Disorders. | Wang X et al. | β | 2016 | β |
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| A Systematic Investigation into Aging Related Genes in Brain and Their Relationship with Alzheimer's Disease. | Meng G et al. | β | 2016 | β |
| CACNA1C hypermethylation is associated with bipolar disorder. | Starnawska A et al. | β | 2016 | β |
| Capture Hi-C identifies a novel causal gene, IL20RA, in the pan-autoimmune genetic susceptibility region 6q23. | McGovern A et al. | β | 2016 | β |
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| Epigenetic Research in Neuropsychiatric Disorders: the "Tissue Issue". | Bakulski KM et al. | β | 2016 | β |
| Epigenome-wide Association Studies and the Interpretation of Disease -Omics. | Birney E et al. | β | 2016 | β |
| eSNPO: An eQTL-based SNP Ontology and SNP functional enrichment analysis platform. | Li J et al. | β | 2016 | β |
| Evidence for genetic regulation of mRNA expression of the dosage-sensitive gene retinoic acid induced-1 (RAI1) in human brain. | Chen L et al. | β | 2016 | β |
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| Familial resemblances in blood leukocyte DNA methylation levels. | Tremblay BL et al. | β | 2016 | β |
| Fine-Mapping of Common Genetic Variants Associated with Colorectal Tumor Risk Identified Potential Functional Variants. | Du M et al. | β | 2016 | β |
| Gene, Environment and Methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment. | Pan H et al. | β | 2016 | β |
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| Genetic and environmental impacts on DNA methylation levels in twins. | Yet I et al. | β | 2016 | β |
| Genetic and transcriptional analysis of human host response to healthy gut microbiota. | Richards AL et al. | β | 2016 | β |
| Genetic sources of population epigenomic variation. | Taudt A et al. | β | 2016 | β |
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| Genome-wide DNA methylation profiling in the superior temporal gyrus reveals epigenetic signatures associated with Alzheimer's disease. | Watson CT et al. | β | 2016 | β |
| Genome-wide epigenomic profiling for biomarker discovery. | Dirks RA et al. | β | 2016 | β |
| Genome-wide significant schizophrenia risk variation on chromosome 10q24 is associated with altered cis-regulation of BORCS7, AS3MT, and NT5C2 in the human brain. | Duarte RRR et al. | β | 2016 | β |
| GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation. | Lu Q et al. | β | 2016 | β |
| HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. | Ward LD et al. | β | 2016 | β |
| Heritable DNA Methylation in CD4+ Cells among Complex Families Displays Genetic and Non-Genetic Effects. | Day K et al. | β | 2016 | β |
| IGF1R as a Key Target in High Risk, Metastatic Medulloblastoma. | Svalina MN et al. | β | 2016 | β |
| Imputation of DNA Methylation Levels in the Brain Implicates a Risk Factor for Parkinson's Disease. | Rawlik K et al. | β | 2016 | β |
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| Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. | Hannon E et al. | β | 2016 | β |
| Mutation Rate Variation is a Primary Determinant of the Distribution of Allele Frequencies in Humans. | Harpak A et al. | β | 2016 | β |
| Neo-cartilage engineered from primary chondrocytes is epigenetically similar to autologous cartilage, in contrast to using mesenchymal stem cells. | Bomer N et al. | β | 2016 | β |
| Nutritional Influence on Epigenetic Marks and Effect on Livestock Production. | Murdoch BM et al. | β | 2016 | β |
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| A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. | Nikpay M et al. | β | 2015 | β |
| Adding 'epi-' to behaviour genetics: implications for animal domestication. | Jensen P | β | 2015 | β |
| Alterations in global DNA methylation and hydroxymethylation are not detected in Alzheimer's disease. | Lashley T et al. | β | 2015 | β |
| A multiancestry study identifies novel genetic associations with CHRNA5 methylation in human brain and risk of nicotine dependence. | Hancock DB et al. | β | 2015 | β |
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| Ancestry dependent DNA methylation and influence of maternal nutrition. | Mozhui K et al. | β | 2015 | β |
| An ImmunoChip study of multiple sclerosis risk in African Americans. | Isobe N et al. | β | 2015 | β |
| An integrative analysis of regional gene expression profiles in the human brain. | Myers EM et al. | β | 2015 | β |
| A pooling-based approach to mapping genetic variants associated with DNA methylation. | Kaplow IM et al. | β | 2015 | β |
| Assessment of the genetic basis of rosacea by genome-wide association study. | Chang ALS et al. | β | 2015 | β |
| Association between perinatal methylation of the neuronal differentiation regulator HES1 and later childhood neurocognitive function and behaviour. | Lillycrop KA et al. | β | 2015 | β |
| Association of Brain DNA methylation in SORL1, ABCA7, HLA-DRB5, SLC24A4, and BIN1 with pathological diagnosis of Alzheimer disease. | Yu L et al. | β | 2015 | β |
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| Development and the epigenome: the 'synapse' of gene-environment interplay. | Boyce WT et al. | β | 2015 | β |
| DNA methylation changes in epithelial ovarian cancer histotypes. | Earp MA et al. | β | 2015 | β |
| Effect of genetic ancestry on leukocyte global DNA methylation in cancer patients. | Cappetta M et al. | β | 2015 | β |
| Enrichment of inflammatory bowel disease and colorectal cancer risk variants in colon expression quantitative trait loci. | Hulur I et al. | β | 2015 | β |
| Epigenetics and the burden of noncommunicable disease: a paucity of research in Africa. | Hobbs A et al. | β | 2015 | β |
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| Genome-wide association study of neocortical Lewy-related pathology. | Peuralinna T et al. | β | 2015 | β |
| Genome-wide DNA methylome analysis reveals epigenetically dysregulated non-coding RNAs in human breast cancer. | Li Y et al. | β | 2015 | β |
| Genome-wide Meta-analysis on the Sense of Smell Among US Older Adults. | Dong J et al. | β | 2015 | β |
| Genome-wide methylation analysis identifies differentially methylated CpG loci associated with severe obesity in childhood. | Huang RC et al. | β | 2015 | β |
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| Genomic modulators of gene expression in human neutrophils. | Naranbhai V et al. | β | 2015 | β |
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| Hypermethylation of repeat expanded C9orf72 is a clinical and molecular disease modifier. | Russ J et al. | β | 2015 | β |
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| Implication of a Chromosome 15q15.2 Locus in Regulating UBR1 and Predisposing Smokers to MGMT Methylation in Lung. | Leng S et al. | β | 2015 | β |
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| Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. | Hannon E et al. | β | 2015 | β |
| Inter-individual variability contrasts with regional homogeneity in the human brain DNA methylome. | Illingworth RS et al. | β | 2015 | β |
| Layered genetic control of DNA methylation and gene expression: a locus of multiple sclerosis in healthy individuals. | Shin J et al. | β | 2015 | β |
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| Long-range epigenetic regulation is conferred by genetic variation located at thousands of independent loci. | Lemire M et al. | β | 2015 | β |
| Many obesity-associated SNPs strongly associate with DNA methylation changes at proximal promoters and enhancers. | Voisin S et al. | β | 2015 | β |
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| Novel epigenetic determinants of type 2 diabetes in Mexican-American families. | Kulkarni H et al. | β | 2015 | β |
| Parkinson's disease correlates with promoter methylation in the Ξ±-synuclein gene. | PihlstrΓΈm L et al. | β | 2015 | β |
| Polymorphisms involving gain or loss of CpG sites are significantly enriched in trait-associated SNPs. | Zhou D et al. | β | 2015 | β |
| Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements. | Zhang W et al. | β | 2015 | β |
| QuASAR: quantitative allele-specific analysis of reads. | Harvey CT et al. | β | 2015 | β |
| RNA sequencing of transcriptomes in human brain regions: protein-coding and non-coding RNAs, isoforms and alleles. | Webb A et al. | β | 2015 | β |
| Robust Linear Models for Cis-eQTL Analysis. | Rantalainen M et al. | β | 2015 | β |
| Role of mecp2 in experience-dependent epigenetic programming. | Zimmermann CA et al. | β | 2015 | β |
| Systematic Integration of Brain eQTL and GWAS Identifies ZNF323 as a Novel Schizophrenia Risk Gene and Suggests Recent Positive Selection Based on Compensatory Advantage on Pulmonary Function. | Luo XJ et al. | β | 2015 | β |
| Systems mapping for hematopoietic progenitor cell heterogeneity. | Zhou L et al. | β | 2015 | β |
| The APOE Gene is Differentially Methylated in Alzheimer's Disease. | Foraker J et al. | β | 2015 | β |
| The cerebellum ages slowly according to the epigenetic clock. | Horvath S et al. | β | 2015 | β |
| The complex pattern of epigenomic variation between natural yeast strains at single-nucleosome resolution. | Filleton F et al. | β | 2015 | β |
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