Enrichment of Cis-Regulatory Gene Expression SNPs and Methylation Quantitative Trait Loci Among Bipolar Disorder Susceptibility Variants

Department of Medicine, University of Chicago, Chicago, IL, USA.
Molecular Psychiatry (Impact Factor: 14.5). 01/2012; 18(3). DOI: 10.1038/mp.2011.174
Source: PubMed


We conducted a systematic study of top susceptibility variants from a genome-wide association (GWA) study of bipolar disorder to gain insight into the functional consequences of genetic variation influencing disease risk. We report here the results of experiments to explore the effects of these susceptibility variants on DNA methylation and mRNA expression in human cerebellum samples. Among the top susceptibility variants, we identified an enrichment of cis regulatory loci on mRNA expression (eQTLs), and a significant excess of quantitative trait loci for DNA CpG methylation, hereafter referred to as methylation quantitative trait loci (mQTLs). Bipolar disorder susceptibility variants that cis regulate both cerebellar expression and methylation of the same gene are a very small proportion of bipolar disorder susceptibility variants. This finding suggests that mQTLs and eQTLs provide orthogonal ways of functionally annotating genetic variation within the context of studies of pathophysiology in brain. No lymphocyte mQTL enrichment was found, suggesting that mQTL enrichment was specific to the cerebellum, in contrast to eQTLs. Separately, we found that using mQTL information to restrict the number of single-nucleotide polymorphisms studied enhances our ability to detect a significant association. With this restriction a priori informed by the observed functional enrichment, we identified a significant association (rs12618769, P(bonferroni)<0.05) from two other GWA studies (TGen+GAIN; 2191 cases and 1434 controls) of bipolar disorder, which we replicated in an independent GWA study (WTCCC). Collectively, our findings highlight the importance of integrating functional annotation of genetic variants for gene expression and DNA methylation to advance the biological understanding of bipolar disorder.Molecular Psychiatry advance online publication, 3 January 2012; doi:10.1038/mp.2011.174.

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Available from: William Bradford Lawson, Mar 11, 2014
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    • "A genome-wide association analysis of SNPs with DNA methylation was conducted in the combined cohort of controls and cases to identify mQTL, as has been done in other previous QTL studies (Liu et al., 2010; Zhang et al., 2010; Gamazon et al., 2013). In the cis-analysis (cis defined as within 1 Mb of a CpG site), 36,366 SNP-CpG pairs were significantly correlated (Bonferroni corrected p < 0.05) (Supplementary Table S4). "
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    ABSTRACT: Background: Schizophrenia is a complex psychiatric disorder with a lifetime morbidity rate of 0.5–1.0%. The pathophysiology of schizophrenia still remains obscure. Accumulating evidence indicates that DNA methylation, which is the addition of a methyl group to the cytosine in a CpG dinucleotide, might play an important role in the pathogenesis of schizophrenia. Methods: To gain further insight into the molecular mechanisms underlying schizophrenia, a genome-wide DNA methylation profiling (27,578 CpG dinucleotides spanning 14,495 genes) of the human dorsolateral prefrontal cortex (DLPFC) was conducted in a large cohort (n = 216) of well characterized specimens from individuals with schizophrenia and non-psychiatric controls, combined with an analysis of genetic variance at ~880,000 SNPs. Results: Aberrant DNA methylation in schizophrenia was identified at 107 CpG sites at 5% Bonferroni correction (p < 1.99 × 10−6). Of these significantly altered sites, hyper-DNA methylation was observed at 79 sites (73.8%), mostly in the CpG islands (CGIs) and in the regions flanking CGIs (CGI: 31 sites; CGI shore: 35 sites; CGI shelf: 3 sites). Furthermore, a large number of cis-methylation quantitative trait loci (mQTL) were identified, including associations with risk SNPs implicated in schizophrenia. Conclusions: These results suggest that altered DNA methylation might be involved in the pathophysiology and/or treatment of schizophrenia, and that a combination of epigenetic and genetic approaches will be useful to understanding the molecular mechanism of this complex disorder.
    Frontiers in Genetics 08/2014; 5:280. DOI:10.3389/fgene.2014.00280
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    • "In light of genome-wide association studies (GWAS), meQTL facilitate the interpretation of non-coding genetic variability and their association to phenotypic differences (Freedman et al., 2011; Hernandez and Singleton, 2012; Kilpinen and Dermitzakis, 2012). Recent studies have given an outlook of the potential of integrative genome-epigenome studies for the meaningful interpretation of genetic risk alleles (Gamazon et al., 2013; Scherf et al., 2013). Further, genotype–epitype associations guided the interpretation of physiological traits, such as natural human variation (Heyn et al., 2013) or aging (Bell et al., 2012). "
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    ABSTRACT: With rapid advances in sequencing technologies, we are undergoing a paradigm shift from hypothesis- to data-driven research. Genome-wide profiling efforts have given informative insights into biological processes; however, considering the wealth of variation, the major challenge still remains in their meaningful interpretation. In particular sequence variation in non-coding contexts is often challenging to interpret. Here, data integration approaches for the identification of functional genetic variability represent a possible solution. Exemplary, functional linkage analysis integrating genotype and expression data determined regulatory quantitative trait loci and proposed causal relationships. In addition to gene expression, epigenetic regulation and specifically DNA methylation was established as highly valuable surrogate mark for functional variance of the genetic code. Epigenetic modification has served as powerful mediator trait to elucidate mechanisms forming phenotypes in health and disease. Particularly, integrative studies of genetic and DNA methylation data have been able to guide interpretation strategies of risk genotypes, but also proved their value for physiological traits, such as natural human variation and aging. This Review seeks to illustrate the power of data integration in the genomic era exemplified by DNA methylation quantitative trait loci. However, the model is further extendable to virtually all traceable molecular traits.
    Frontiers in Genetics 05/2014; 5:113. DOI:10.3389/fgene.2014.00113
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    • "We found that the Holm-significant meQTLs identified in PONS were enriched (empirical p-value = .019) for SNP associations from the GWAS. When we performed a similar analysis using a looser definition of meQTLs (p < .001), we saw enrichment in FCTX (p < .0001), TCTX (p = .048), and PONS (p = .005), though not CRBLM, as observed in [9]. "
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    ABSTRACT: Individual genotypes at specific loci can result in different patterns of DNA methylation. These methylation quantitative trait loci (meQTLs) influence methylation across extended genomic regions and may underlie direct SNP associations or gene-environment interactions. We hypothesized that the detection of meQTLs varies with ancestral population, developmental stage, and tissue type. We explored this by analyzing seven datasets that varied by ancestry (African American vs. Caucasian), developmental stage (neonate vs. adult), and tissue type (blood vs. four regions of postmortem brain) with genome-wide DNA methylation and SNP data. We tested for meQTLs by constructing linear regression models of methylation levels at each CpG site on SNP genotypes within 50 kb under an additive model controlling for multiple tests. Most meQTLs mapped to intronic regions, although a limited number appeared to occur in synonymous or nonsynonymous coding SNPs. We saw significant overlap of meQTLs between ancestral groups, developmental stages, and tissue types, with the highest rates of overlap within the four brain regions. Compared with a random group of SNPs with comparable frequencies, meQTLs were more likely to be 1) represented among the most associated SNPs in the WTCCC bipolar disorder results and 2) located in microRNA binding sites. These data give us insight into how SNPs impact gene regulation and support the notion that peripheral blood may be a reliable correlate of physiological processes in other tissues.
    BMC Genomics 02/2014; 15(1):145. DOI:10.1186/1471-2164-15-145 · 3.99 Impact Factor
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