Causal Inference Test Research Articles

Integrative analysis identified mediation effects of lncRNAs on the correlations between methylation and mRNA.

The aim of this study was to construct DNA methylation-lncRNA-mRNA interaction trios in peripheral blood mononuclear cells. We first conducted eQTL analyses using genome-wide methylation, lncRNA and mRNA expression data from 43 Chinese females. Next, causal inference test (CIT) was used to detect the lncRNA mediation effects on methylation and mRNA. Methylation-lncRNA cis-eQTL analysis identified 11 significant cis-methylation-lncRNA pairs. Combined with the results from the next lncRNA-mRNA eQTL and methylation-mRNA eQTL analyses, the 11 significant pairs and their corresponding 11,204 target e-mRNAs formed 12,245 trios. Further CIT identified six lncRNAs as mediators in regulating the corresponding pairs between methylation and mRNA. This study detected lncRNAs with mediation effects on the correlations between DNA methylations and a large number of mRNAs.

Investigation of parent-of-origin effects induced by fenofibrate treatment on triglycerides levels

BackgroundGenome-wide association studies performed on triglycerides (TGs) have not accounted for epigenetic mechanisms that may partially explain trait heritability.ResultsParent-of-origin (POO) effect association analyses using an agnostic approach or a candidate approach were performed for pretreatment TG levels, posttreatment TG levels, and pre- and posttreatment TG-level differences in the real GAW20 family data set. We detected 22 genetic variants with suggestive POO effects with at least 1 phenotype (P ≤ 10− 5). We evaluated the association of these 22 significant genetic variants showing POO effects with close DNA methylation probes associated with TGs. A total of 18 DNA methylation probes located in the vicinity of the 22 SNPs were associated with at least 1 phenotype and 6 SNP-probe pairs were associated with DNA methylation probes at the nominal level of P < 0.05, among which 1 pair presented evidence of POO effect. Our analyses identified a paternal effect of SNP rs301621 on the difference between pre- and posttreatment TG levels (P = 1.2 × 10− 5). This same SNP showed evidence for a maternal effect on methylation levels of a nearby probe (cg10206250; P = 0.01). Using a causal inference test we established that the observed POO effect of rs301621 was not mediated by DNA methylation at cg10206250.ConclusionsWe performed POO effect association analyses of SNPs with TGs, as well as association analyses of SNPs with DNA methylation probes. These analyses, which were followed by a causal inference test, established that the paternal effect at the SNP rs301621 is induced by treatment and is not mediated by methylation level at cg10206250.

Systematic analysis reveals long noncoding RNAs regulating neighboring transcription factors in human cancers

Long noncoding RNAs (lncRNAs) are proposed to play essential roles in regulating gene transcription. Moreover, a subset has been implicated in modulating the expression of the nearby loci. Here we systematically evaluated the relationship between lncRNAs and their neighboring genes based on transcriptome expression profiles from 4900 samples across 12 cancer types. Our findings reveal that lncRNAs, especially those of high syntenic conservation across species, are spatially correlated with transcription factors across the genome. Combining the methods of conservation, co-expression, and causal inference test, we identified a list of 28 lncRNA/TF regulatory pairs across 12 TCGA cancer types, and 19 of which were further confirmed in additional cancer cell lines. Several of these pairs, including PTV1/MYC and GATA6-AS1/GATA6, show prior evidence of regulatory relationships. Other candidates such as LINC00261/FOXA2 and PITRM1-AS1/KLF6 were novel. Our study highlights the significant roles of lncRNAs in tumorigenesis and provides a comprehensive overview of lncRNA regulation on its neighboring TF genes in human cancers.

Genetic regulation of IL1RL1 methylation and IL1RL1-a protein levels in asthma.

Interleukin-1 receptor-like 1 (IL1RL1) is an important asthma gene. (Epi)genetic regulation of IL1RL1 protein expression has not been established. We assessed the association between IL1RL1 single nucleotide polymorphisms (SNPs), IL1RL1 methylation and serum IL1RL1-a protein levels, and aimed to identify causal pathways in asthma.Associations of IL1RL1 SNPs with asthma were determined in the Dutch Asthma Genome-wide Association Study cohort and three European birth cohorts, BAMSE (Children/Barn, Allergy, Milieu, Stockholm, an Epidemiological survey), INMA (Infancia y Medio Ambiente) and PIAMA (Prevention and Incidence of Asthma and Mite Allergy), participating in the Mechanisms of the Development of Allergy study. We performed blood DNA IL1RL1 methylation quantitative trait locus (QTL) analysis (n=496) and (epi)genome-wide protein QTL analysis on serum IL1RL1-a levels (n=1462). We investigated the association of IL1RL1 CpG methylation with asthma (n=632) and IL1RL1-a levels (n=548), with subsequent causal inference testing. Finally, we determined the association of IL1RL1-a levels with asthma and its clinical characteristics (n=1101).IL1RL1 asthma-risk SNPs strongly associated with IL1RL1 methylation (rs1420101; p=3.7×10-16) and serum IL1RL1-a levels (p=2.8×10-56). IL1RL1 methylation was not associated with asthma or IL1RL1-a levels. IL1RL1-a levels negatively correlated with blood eosinophil counts, whereas there was no association between IL1RL1-a levels and asthma.In conclusion, asthma-associated IL1RL1 SNPs strongly regulate IL1RL1 methylation and serum IL1RL1-a levels, yet neither these IL1RL1-methylation CpG sites nor IL1RL1-a levels are associated with asthma.

Mediation analysis of alcohol consumption, DNA methylation, and epithelial ovarian cancer.

Epigenetic factors and consumption of alcohol, which suppresses DNA methylation, may influence the development and progression of epithelial ovarian cancer (EOC). However, there is a lack of understanding whether these factors interact to affect the EOC risk. In this study, we aimed to gain insight into this relationship by identifying leukocyte-derived DNA methylation markers acting as potential mediators of alcohol-associated EOC. We implemented a causal inference test (CIT) and the VanderWeele and Vansteelandt multiple mediator model to examine CpG sites that mediate the association between alcohol consumption and EOC risk. We modified one step of the CIT by adopting a high-dimensional inference procedure. The data were based on 196 cases and 202 age-matched controls from the Mayo Clinic Ovarian Cancer Case-Control Study. Implementation of the CIT test revealed two CpG sites (cg09358725, cg11016563), which represent potential mediators of the relationship between alcohol consumption and EOC case-control status. Implementation of the VanderWeele and Vansteelandt multiple mediator model further revealed that these two CpGs were the key mediators. Decreased methylation at both CpGs was more common in cases who drank alcohol at the time of enrollment vs. those who did not. cg11016563 resides in TRPC6 which has been previously shown to be overexpressed in EOC. These findings suggest two CpGs may serve as novel biomarkers for EOC susceptibility.

Orienting the causal relationship between imprecisely measured traits using GWAS summary data.

Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.

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

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

Association Between a CCL17 Genetic Variant and Risk of Coronary Artery Disease in a Chinese Han Population.

In the present study we investigated the effects of genetic variations in the C-C motif chemokine ligand 17 (CCL17) gene on serum CCL17 levels and risk of coronary artery disease (CAD).Methods and Results:A case-control study was conducted to determine causal inferences amongCCL17single-nucleotide polymorphisms (SNPs), serum CCL17 levels, and risk of CAD. Luciferase assays, electrophoretic mobility shift assays (EMSA), and allele-specific quantitative chromatin immunoprecipitation (ChIP) assays were used to assess the function of the SNPs. In all, 947 participants (794 with CAD, 153 without CAD) were included in the study. The T allele in rs223828, located in intron of theCCL17gene, was associated with increased serum CCL17 levels as well as increased CAD risk. A causal inference test using mediation analysis suggested that rs223828 had a significant indirect casual effect on the increased risk of CAD mediated via serum CCL17 levels. Luciferase assays confirmed that the rs223828T allele enhancesCCL17promoter activity. Protein-DNA binding studies using EMSA and allele-specific quantitative ChIP assays indicated preferential activator protein-1 (AP-1) complex formation and recruitment with the rs223828 T allele compared with the C allele. We propose that theCCL17SNP rs223828 is associated with increased risk of CAD, and that this site may be a potential AP-1 binding site.

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

Identifying causal genetic variants and understanding their mechanisms of effect on traits remains a challenge in genome-wide association studies (GWASs). In particular, how genetic variants (i.e., trans-eQTLs) affect expression of remote genes (i.e., trans-eGenes) remains unknown. We hypothesized that some trans-eQTLs regulate expression of distant genes by altering the expression of nearby genes (cis-eGenes). Using published GWAS datasets with 39,165 single-nucleotide polymorphisms (SNPs) associated with 1,960 traits, we explored whole blood gene expression associations of trait-associated SNPs in 5,257 individuals from the Framingham Heart Study. We identified 2,350 trans-eQTLs (at p < 10-7); more than 80% of them were found to have cis-associated eGenes. Mediation testing suggested that for 35% of trans-eQTL-trans-eGene pairs in different chromosomes and 90% pairs in the same chromosome, the disease-associated SNP may alter expression of the trans-eGene via cis-eGene expression. In addition, we identified 13 trans-eQTL hotspots, affecting from ten to hundreds of genes, suggesting the existence of master genetic regulators. Using causal inference testing, we searched causal variants across eight cardiometabolic traits (BMI, systolic and diastolic blood pressure, LDL cholesterol, HDL cholesterol, total cholesterol, triglycerides, and fasting blood glucose) and identified several cis-eGenes (ALDH2 for systolic and diastolic blood pressure, MCM6 and DARS for total cholesterol, and TRIB1 for triglycerides) that were causal mediators for the corresponding traits, as well as examples of trans-mediators (TAGAP for LDL cholesterol). The finding of extensive evidence of genome-wide mediation effects suggests a critical role of cryptic gene regulation underlying many disease traits.

Integrative multi-omics analysis revealed SNP-lncRNA-mRNA (SLM) networks in human peripheral blood mononuclear cells.

Long non-coding RNAs (lncRNAs) serve as important controller of cellular functions via regulating RNA transcription, degradation and translation. However, what are the regulation patterns of lncRNAs on downstream mRNA and how the upstream genetic variants regulate lncRNAs are largely unknown. We first performed a comprehensive expression quantitative trait locus (eQTL) analysis (MatrixeQTL package, R) using genome-wide lncRNA expression and SNP genotype data from human peripheral blood mononuclear cells (PBMCs) of 43 unrelated individuals. Subsequently, multi-omics integrative network analysis was applied to construct SNP-lncRNA-mRNA (SLM) interaction networks. The causal inference test (CIT) was used to identify lncRNA-mediated (epi-) genetic regulation on mRNA expressions. Our eQTL analysis detected 707 pairs of cis-effect associations (p<5.64E-06) and 6657 trans-effect associations (p<3.51E-08), respectively. We also found that top significant cis-eSNPs were enriched around the lncRNA transcription start site regions, and that enrichment patterns of cis-eSNPs differs among different lncRNA sizes (small, medium and large).The constructed SLM interaction networks (1 primary networks and four small separate networks) showed various complex interaction patterns. Especially, the in-depth CIT detected 50 significant lncRNA-mediated SLM trios, and some hotspots (e.g., SNPs: rs926370, rs7716167 and rs16880521; lncRNAs: HIT000061975 and ENST00000579057.1). This study represents the first effort of dissecting the SLM interaction patterns in PBMCs by multi-omics integrative network analysis and causal inference test for clearing the regulation chain. The results provide novel insights into the regulation patterns of lncRNA, and may facilitate investigations of PBMC-related immune physiological process and immunological diseases in the future.

Identification of a Putative Quantitative Trait Gene for Resistance to Obesity in Mice Using Transcriptome Analysis and Causal Inference Tests.

It is still challenging to identify causal genes governing obesity. Pbwg1.5, a quantitative trait locus (QTL) for resistance to obesity, was previously discovered from wild Mus musculus castaneus mice and was fine-mapped to a 2.1-Mb genomic region of mouse chromosome 2, where no known gene with an effect on white adipose tissue (WAT) has been reported. The aim of this study was to identify a strong candidate gene for Pbwg1.5 by an integration approach of transcriptome analysis (RNA-sequencing followed by real-time PCR analysis) and the causal inference test (CIT), a statistical method to infer causal relationships between diplotypes, gene expression and trait values. Body weight, body composition and biochemical traits were measured in F2 mice obtained from an intercross between the C57BL/6JJcl strain and a congenic strain carrying Pbwg1.5 on the C57BL/6JJcl background. The F2 mice showed significant diplotype differences in 12 traits including body weight, WAT weight and serum cholesterol/triglyceride levels. The transcriptome analysis revealed that Ly75, Pla2r1, Fap and Gca genes were differentially expressed in the liver and that Fap, Ifih1 and Grb14 were differentially expressed in WAT. However, CITs indicated statistical evidence that only the liver Ly75 gene mediated between genotype and WAT. Ly75 expression was negatively associated with WAT weight. The results suggested that Ly75 is a putative quantitative trait gene for the obesity-resistant Pbwg1.5 QTL discovered from the wild M. m. castaneus mouse. The finding provides a novel insight into a better understanding of the genetic basis for prevention of obesity.

Epigenome-wide association data implicates DNA methylation-mediated genetic risk in psoriasis.

BackgroundPsoriasis is a chronic inflammatory skin disease characterized by epidermal hyperproliferation and altered keratinocyte differentiation and inflammation and is caused by the interplay of genetic and environmental factors. Previous studies have revealed that DNA methylation (DNAm) and genetic makers are closely associated with psoriasis, and strong evidences have shown that DNAm can be controlled by genetic factors, which attracted us to evaluate the relationship among DNAm, genetic makers, and disease status.MethodsWe utilized the genome-wide methylation data of psoriatic skin (PP, N = 114) and unaffected control skin (NN, N = 62) tissue samples in our previous study, and we performed whole-genome genotyping with peripheral blood of the same samples to evaluate the underlying genetic effect on skin DNA methylation. Causal inference test (CIT) was used to assess whether DNAm regulate genetic variation and gain a better understanding of the epigenetic basis of psoriasis susceptibility.ResultsWe identified 129 SNP-CpG pairs achieving the significant association threshold, which constituted 28 unique methylation quantitative trait loci (MethQTL) and 34 unique CpGs. There are 18 SNPs were associated with psoriasis at a Bonferoni-corrected P < 0.05, and these 18 SNPs formed 93 SNP-CpG pairs with 17 unique CpG sites. We found that 11 of 93 SNP-CpG pairs, composed of 5 unique SNPs and 3 CpG sites, presented a methylation-mediated relationship between SNPs and psoriasis. The 3 CpG sites were located on the body of C1orf106, the TSS1500 promoter region of DMBX1 and the body of SIK3.ConclusionsThis study revealed that DNAm of some genes can be controlled by genetic factors and also mediate risk variation for psoriasis in Chinese Han population and provided novel molecular insights into the pathogenesis of psoriasis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-016-0297-z) contains supplementary material, which is available to authorized users.

A Genome-Wide mQTL Analysis in Human Adipose Tissue Identifies Genetic Variants Associated with DNA Methylation, Gene Expression and Metabolic Traits

Little is known about the extent to which interactions between genetics and epigenetics may affect the risk of complex metabolic diseases and/or their intermediary phenotypes. We performed a genome-wide DNA methylation quantitative trait locus (mQTL) analysis in human adipose tissue of 119 men, where 592,794 single nucleotide polymorphisms (SNPs) were related to DNA methylation of 477,891 CpG sites, covering 99% of RefSeq genes. SNPs in significant mQTLs were further related to gene expression in adipose tissue and obesity related traits. We found 101,911 SNP-CpG pairs (mQTLs) in cis and 5,342 SNP-CpG pairs in trans showing significant associations between genotype and DNA methylation in adipose tissue after correction for multiple testing, where cis is defined as distance less than 500 kb between a SNP and CpG site. These mQTLs include reported obesity, lipid and type 2 diabetes loci, e.g. ADCY3/POMC, APOA5, CETP, FADS2, GCKR, SORT1 and LEPR. Significant mQTLs were overrepresented in intergenic regions meanwhile underrepresented in promoter regions and CpG islands. We further identified 635 SNPs in significant cis-mQTLs associated with expression of 86 genes in adipose tissue including CHRNA5, G6PC2, GPX7, RPL27A, THNSL2 and ZFP57. SNPs in significant mQTLs were also associated with body mass index (BMI), lipid traits and glucose and insulin levels in our study cohort and public available consortia data. Importantly, the Causal Inference Test (CIT) demonstrates how genetic variants mediate their effects on metabolic traits (e.g. BMI, cholesterol, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c) and homeostatic model assessment of insulin resistance (HOMA-IR)) via altered DNA methylation in human adipose tissue. This study identifies genome-wide interactions between genetic and epigenetic variation in both cis and trans positions influencing gene expression in adipose tissue and in vivo (dys)metabolic traits associated with the development of obesity and diabetes.

Cit: hypothesis testing software for mediation analysis in genomic applications

The challenges of successfully applying causal inference methods include: (i) satisfying underlying assumptions, (ii) limitations in data/models accommodated by the software and (iii) low power of common multiple testing approaches. The causal inference test (CIT) is based on hypothesis testing rather than estimation, allowing the testable assumptions to be evaluated in the determination of statistical significance. A user-friendly software package provides P-values and optionally permutation-based FDR estimates (q-values) for potential mediators. It can handle single and multiple binary and continuous instrumental variables, binary or continuous outcome variables and adjustment covariates. Also, the permutation-based FDR option provides a non-parametric implementation. Simulation studies demonstrate the validity of the cit package and show a substantial advantage of permutation-based FDR over other common multiple testing strategies. The cit open-source R package is freely available from the CRAN website (https://cran.r-project.org/web/packages/cit/index.html) with embedded C ++ code that utilizes the GNU Scientific Library, also freely available (http://www.gnu.org/software/gsl/). joshua.millstein@usc.edu Supplementary data are available at Bioinformatics online.

A Genome-Wide Association Study of a Biomarker of Nicotine Metabolism.

Individuals with fast nicotine metabolism typically smoke more and thus have a greater risk for smoking-induced diseases. Further, the efficacy of smoking cessation pharmacotherapy is dependent on the rate of nicotine metabolism. Our objective was to use nicotine metabolite ratio (NMR), an established biomarker of nicotine metabolism rate, in a genome-wide association study (GWAS) to identify novel genetic variants influencing nicotine metabolism. A heritability estimate of 0.81 (95% CI 0.70–0.88) was obtained for NMR using monozygotic and dizygotic twins of the FinnTwin cohort. We performed a GWAS in cotinine-verified current smokers of three Finnish cohorts (FinnTwin, Young Finns Study, FINRISK2007), followed by a meta-analysis of 1518 subjects, and annotated the genome-wide significant SNPs with methylation quantitative loci (meQTL) analyses. We detected association on 19q13 with 719 SNPs exceeding genome-wide significance within a 4.2 Mb region. The strongest evidence for association emerged for CYP2A6 (min p = 5.77E-86, in intron 4), the main metabolic enzyme for nicotine. Other interesting genes with genome-wide significant signals included CYP2B6, CYP2A7, EGLN2, and NUMBL. Conditional analyses revealed three independent signals on 19q13, all located within or in the immediate vicinity of CYP2A6. A genetic risk score constructed using the independent signals showed association with smoking quantity (p = 0.0019) in two independent Finnish samples. Our meQTL results showed that methylation values of 16 CpG sites within the region are affected by genotypes of the genome-wide significant SNPs, and according to causal inference test, for some of the SNPs the effect on NMR is mediated through methylation. To our knowledge, this is the first GWAS on NMR. Our results enclose three independent novel signals on 19q13.2. The detected CYP2A6 variants explain a strikingly large fraction of variance (up to 31%) in NMR in these study samples. Further, we provide evidence for plausible epigenetic mechanisms influencing NMR.

Layered genetic control of DNA methylation and gene expression: a locus of multiple sclerosis in healthy individuals.

DNA methylation may contribute to the etiology of complex genetic disorders through its impact on genome integrity and gene expression; it is modulated by DNA-sequence variants, named methylation quantitative trait loci (meQTLs). Most meQTLs influence methylation of a few CpG dinucleotides within short genomic regions (<3 kb). Here, we identified a layered genetic control of DNA methylation at numerous CpGs across a long 300 kb genomic region. This control involved a single long-range meQTL and multiple local meQTLs. The long-range meQTL explained up to 75% of variance in methylation of CpGs located over extended areas of the 300 kb region. The meQTL was identified in four samples (P = 2.8 × 10(-17), 3.1 × 10(-31), 4.0 × 10(-71)and 5.2 × 10(-199)), comprising a total of 2796 individuals. The long-range meQTL was strongly associated not only with DNA methylation but also with mRNA expression of several genes within the 300 kb region (P = 7.1 × 10(-18)-1.0 × 10(-123)). The associations of the meQTL with gene expression became attenuated when adjusted for DNA methylation (causal inference test: P = 2.4 × 10(-13)-7.1 × 10(-20)), indicating coordinated regulation of DNA methylation and gene expression. Further, the long-range meQTL was found to be in linkage disequilibrium with the most replicated locus of multiple sclerosis, a disease affecting primarily the brain white matter. In middle-aged adults free of the disease, we observed that the risk allele was associated with subtle structural properties of the brain white matter found in multiple sclerosis (P = 0.02). In summary, we identified a long-range meQTL that controls methylation and expression of several genes and may be involved in increasing brain vulnerability to multiple sclerosis.

Genome-wide associations between genetic and epigenetic variation influence mRNA expression and insulin secretion in human pancreatic islets.

Genetic and epigenetic mechanisms may interact and together affect biological processes and disease development. However, most previous studies have investigated genetic and epigenetic mechanisms independently, and studies examining their interactions throughout the human genome are lacking. To identify genetic loci that interact with the epigenome, we performed the first genome-wide DNA methylation quantitative trait locus (mQTL) analysis in human pancreatic islets. We related 574,553 single nucleotide polymorphisms (SNPs) with genome-wide DNA methylation data of 468,787 CpG sites targeting 99% of RefSeq genes in islets from 89 donors. We identified 67,438 SNP-CpG pairs in cis, corresponding to 36,783 SNPs (6.4% of tested SNPs) and 11,735 CpG sites (2.5% of tested CpGs), and 2,562 significant SNP-CpG pairs in trans, corresponding to 1,465 SNPs (0.3% of tested SNPs) and 383 CpG sites (0.08% of tested CpGs), showing significant associations after correction for multiple testing. These include reported diabetes loci, e.g. ADCY5, KCNJ11, HLA-DQA1, INS, PDX1 and GRB10. CpGs of significant cis-mQTLs were overrepresented in the gene body and outside of CpG islands. Follow-up analyses further identified mQTLs associated with gene expression and insulin secretion in human islets. Causal inference test (CIT) identified SNP-CpG pairs where DNA methylation in human islets is the potential mediator of the genetic association with gene expression or insulin secretion. Functional analyses further demonstrated that identified candidate genes (GPX7, GSTT1 and SNX19) directly affect key biological processes such as proliferation and apoptosis in pancreatic β-cells. Finally, we found direct correlations between DNA methylation of 22,773 (4.9%) CpGs with mRNA expression of 4,876 genes, where 90% of the correlations were negative when CpGs were located in the region surrounding transcription start site. Our study demonstrates for the first time how genome-wide genetic and epigenetic variation interacts to influence gene expression, islet function and potential diabetes risk in humans.

Abstract 260: Integrative genomic analysis identifies epigenetic marks that mediate genetic risk for epithelial ovarian cancer

Abstract Both genetic and epigenetic factors influence the development and progression of epithelial ovarian cancer (EOC). However, there is an incomplete understanding of the interrelationship between these factors and whether they interact to impact disease risk. Given that the development of ovarian cancer is complex, we aimed to identify DNA methylation marks that are candidates for mediating ovarian cancer genetic risk. We used 214 cases and 214 age-matched controls from the Mayo Clinic Ovarian Cancer Study. Pretreatment, blood-derived DNA was profiled for genome-wide methylation (Illumina Infinium HumanMethylation27 BeadArray) and single nucleotide polymorphisms (SNPs, Illumina Infinium HD Human610-Quad BeadArray). We employed a three-step filtering procedure, followed by the Causal Inference Test (CIT), to distinguish CpG sites that mediate genetic risk, from those that are consequential or independently acted on by genotype. Controlling for the estimated distribution of immune cells and other key covariates, we identified 1,993 out of 25,926 (7.7%) CpGs that were significantly differentially methylated between cases and controls (FDR, q &amp;lt; 0.05). The relationship between methylation and case-control status among these 1,993 CpGs was found to be highly consistent with the results of an independent study. Implementation of the CIT test revealed 13 CpG/SNP pairs, comprising 13 unique CpGs and 17 unique SNPs, which represent potential methylation-mediated relationships between genotype and EOC risk. These findings provide additional insight into EOC etiology and may serve as novel biomarkers for EOC susceptibility. Citation Format: Devin C. Koestler, Prabhakar Chalise, Mine S. Cicek, Julie M. Cunningham, Sebastian Armasu, Melissa C. Larson, Jeremy Chien, Matthew Block, Kimberly R. Kalli, Thomas A. Sellers, Ellen L. Goode, Brooke L. Fridley. Integrative genomic analysis identifies epigenetic marks that mediate genetic risk for epithelial ovarian cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 260. doi:10.1158/1538-7445.AM2014-260

Integrative genomic analysis identifies epigenetic marks that mediate genetic risk for epithelial ovarian cancer

BackgroundBoth genetic and epigenetic factors influence the development and progression of epithelial ovarian cancer (EOC). However, there is an incomplete understanding of the interrelationship between these factors and the extent to which they interact to impact disease risk. In the present study, we aimed to gain insight into this relationship by identifying DNA methylation marks that are candidate mediators of ovarian cancer genetic risk.MethodsWe used 214 cases and 214 age-matched controls from the Mayo Clinic Ovarian Cancer Study. Pretreatment, blood-derived DNA was profiled for genome-wide methylation (Illumina Infinium HumanMethylation27 BeadArray) and single nucleotide polymorphisms (SNPs, Illumina Infinium HD Human610-Quad BeadArray). The Causal Inference Test (CIT) was implemented to distinguish CpG sites that mediate genetic risk, from those that are consequential or independently acted on by genotype.ResultsControlling for the estimated distribution of immune cells and other key covariates, our initial epigenome-wide association analysis revealed 1,993 significantly differentially methylated CpGs that between cases and controls (FDR, q < 0.05). The relationship between methylation and case-control status for these 1,993 CpGs was found to be highly consistent with the results of previously published, independent study that consisted of peripheral blood DNA methylation signatures in 131 pretreatment cases and 274 controls. Implementation of the CIT test revealed 17 CpG/SNP pairs, comprising 13 unique CpGs and 17 unique SNPs, which represent potential methylation-mediated relationships between genotype and EOC risk. Of these 13 CpGs, several are associated with immune related genes and genes that have been previously shown to exhibit altered expression in the context of cancer.ConclusionsThese findings provide additional insight into EOC etiology and may serve as novel biomarkers for EOC susceptibility.

Identification of Causal Genes, Networks, and Transcriptional Regulators of REM Sleep and Wake

Sleep-wake traits are well-known to be under substantial genetic control, but the specific genes and gene networks underlying primary sleep-wake traits have largely eluded identification using conventional approaches, especially in mammals. Thus, the aim of this study was to use systems genetics and statistical approaches to uncover the genetic networks underlying 2 primary sleep traits in the mouse: 24-h duration of REM sleep and wake. Genome-wide RNA expression data from 3 tissues (anterior cortex, hypothalamus, thalamus/midbrain) were used in conjunction with high-density genotyping to identify candidate causal genes and networks mediating the effects of 2 QTL regulating the 24-h duration of REM sleep and one regulating the 24-h duration of wake. Basic sleep research laboratory. Male [C57BL/6J × (BALB/cByJ × C57BL/6J*) F1] N(2) mice (n = 283). None. The genetic variation of a mouse N2 mapping cross was leveraged against sleep-state phenotypic variation as well as quantitative gene expression measurement in key brain regions using integrative genomics approaches to uncover multiple causal sleep-state regulatory genes, including several surprising novel candidates, which interact as components of networks that modulate REM sleep and wake. In particular, it was discovered that a core network module, consisting of 20 genes, involved in the regulation of REM sleep duration is conserved across the cortex, hypothalamus, and thalamus. A novel application of a formal causal inference test was also used to identify those genes directly regulating sleep via control of expression. Systems genetics approaches reveal novel candidate genes, complex networks and specific transcriptional regulators of REM sleep and wake duration in mammals.