Abundant Quantitative Trait Loci Exist for DNA Methylation and Gene Expression in Human Brain

By J. Gibbs, M. P. van der Brug, D. Hernandez, B. Traynor, M. Nalls, Shiao-lin Lai, S. Arepalli, Allissa Dillman, Ian Rafferty, J. Troncoso and 6 more. PLoS Genetics, 2010

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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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/febeed04fa38393ad6a6072953f624e22b865458

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Citations in the corpus

Annotation of functional variation in personal genomes using RegulomeDB (2012, methodology): “We also include a large collection of eQTL (Myers et al. 2007; Stranger et al. 2007; Schadt et al. 2008; Veyrieras et al. 2008; Dimas et al. 2009; Gibbs et al. 2010; Montgomery et al. 2010; Pickrell et al. 2010; Zeller et al. 2010), dsQTL (Degner et al. 2012), and ChIP-exo (Rhee and Pugh 2011) data.”
Deep learning for computational biology (2016, neutral): “…been applied to identify regulatory variants that affect gene expression levels (Montgomery et al , 2010; Pickrell et al , 2010), DNA methylation (Gibbs et al , 2010; Bell et al , 2011), histone marks (Grubert et al , 2015; Waszak et al , 2015) and proteome variation (Vincent et al , 2010;…”
Common variants in ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease (2011, positive): “We used data from two published expression quantitative trait loci (eQTL) datasets (derived from lymphoblastoid cell lines 17 and brain 18 ) to determine if E u r op e P M C F und e r s A u t ho r M a nu s c r i p t s E u r op e P M C F und e r s A u t ho r M a nu s c r i p t s rs3764650 is…”
Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia (2017, neutral): “2341385, 66810; CACNA1C; CACNA1C-IT3; (2) 29916839, 11550; TMTC1; (1) 92243186, 14324; / 0 109884367, 1328396; FAM222A; FAM222A-AS1; GIT2; GLTP; GPN3; HVCN1; IFT81; KCTD10; MIR4497; MMAB; MVK; (26) 117662712, 283134; KSR2; NOS1; (2) 123460719, 286243; ARL6IP4; C12orf65; CDK2AP1; LOC100507091; MIR430”
An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease (2016, background): “DNA methylation data of the cerebellum, frontal cortex, pons, and temporal cortex regions from up to 148 neurologically normal participants of European ancestry [76].”
Epigenetic modifications and human disease (2010, background): “Not only do the different regions of an organ present different expression patterns, but the same cell type has different transcriptional regulation depending on its localization in the orga”
Epigenome-Wide Scans Identify Differentially Methylated Regions for Age and Age-Related Phenotypes in a Healthy Ageing Population (2012, background): “We observed that 10% of previously reported eQTLs in LCLs also had significant meQTL signals in whole blood, suggesting shared mechanisms of methylation and gene-expression regulation in a small proportion of genes, which is consistent with previous findings [1,3,5].”
Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, background): “…tissues) have been reported by correlating the genome-wide SNP data with tissue-specific DNA methylome data (Bell et al., 2011; Drong et al., 2013; Gibbs et al., 2010; Grundberg et al., 2013; Gutierrez-Arcelus et al., 2013; Heyn et al., 2013; Heyn et al., 2014; Shi et al., 2014; A. K. Smith et…”
Beyond GWASs: illuminating the dark road from association to function. (2013, background): “Most studies to date have found that correlations with cis-genotypes are more frequent, but there is also some evidence of transregulation cross multiple tissues.(98) Risk-associated SNPs can also disrupt the structural organization of chromatin in the nucleus.”
Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia (2016, methodology): “Table 1: Overlaps and differences between CMC and other publicly available eQTL resources * Best eQTL per probeset reported ** Best eQTL per gene reported FDR < 5% used to define eQTL in all cohorts. eQTL for Brain Cloud, HBCC, HBTRC, NIH and UKBEC were computed as described in the supplement. eQTL ”