Distal eQTL Research Articles

Abstract 14201: Decoding the Genetic Regulation of Cardiac Long Noncoding RNAs in Male and Female Mice Using Integrative Genomic Analysis

Introduction: Long non-coding RNAs (lncRNAs) are increasingly recognized as important regulators of mammalian heart development and potential contributors to human cardiovascular diseases by regulating RNA transcription and processing. However, the genetic regulation lncRNAs and their interactions with mRNAs are poorly understood. Hypothesis: Cardiac lncRNAs are genetically regulated and they differentially modulate mRNA expression in female vs male hearts. Methods: Deep RNA-seq was performed using Illumina HiSeqTM 4000 sequencer protocol. SNP Array was used for mouse genotyping. Weighted Gene Co-expression Network Analysis (WGCNA) was conducted using R. Association analyses were performed using FaST-LMM. Results: To elucidate lncRNA-mediated transcriptional regulation in heart, we performed integrated genomic analysis of RNA-seq derived transcriptome and SNP genotype data from the hybrid mouse diversity panel (HMDP) of adult male and female mice hearts across 100 strains. Using a 5% FDR P value threshold, genome-wide quantitative trait locus (eQTL) analysis detected total of 408513 and 404661 locally associated (within 1 Mb of genomic distance of a given gene) SNP-lncRNA pairs in female vs male heart, respectively, which corresponded to 6977 and 6357 unique lncRNAs in female vs male heart, respectively. After excluding those with distal eQTLs ( P <4.1e-6), 2687 and 2684 cardiac lncRNAs with cis eQTLs were retained. WGNCA revealed distinct lncRNA-mRNA modules. From module-trait correlation analysis, 14 female and 5 male modules correlated with heart weight, body fat, or insulin resistance (Bicor >0.3, P <0.05). By applying eQTL analysis to the module eigengenes, 11 modules were identified with eigengenes QTLs (FDR<0.05). Lastly, a novel SNP-lncRNA-mRNA regulatory circuit involving the mitochondria transcription elongation factor, TSFM, was revealed from our analysis. Conclusions: We present our efforts to investigate lncRNA-mediated regulation of gene expression in female vs male mouse heart across 100 different strains. Our results reveal novel associations on lncRNAs with complex clinical traits and provide resources to facilitate mechanistic exploration of lncRNAs contribution to cardiovascular diseases.

Abstract MP203: Decoding The Cardiac Regulatory Long Noncoding RNAs In Male And Female Mice Using Integrative Multiomics Analysis

Background: Long non-coding RNAs (lncRNAs) are increasingly recognized as important regulators of mammalian heart development and potential contributors to human cardiovascular diseases by regulating RNA transcription, translation and processing. However, the genetic regulation lncRNAs and their interactions with mRNAs are poorly understood. Methods: Deep RNA sequencing was performed following Illumina HiSeqTM 4000 sequencer protocol. SNP Array was used for mice genotyping. For differential gene expression analysis (DGE), DE Seq was used. Weighted gene co-expression network analysis (WGCNA) was performed using R. FaST-LMM was used for expression quantitative trait locus (eQTL) analysis. Results: To elucidate lncRNA-mediated transcriptional regulation in heart, we performed integrated multiomics analysis of RNA-seq derived transcriptome and SNP genotype data from the hybrid mouse diversity panel (HMDP) of adult male and female mice hearts across 100 strains. We observed significant variability in cardiac lncRNAs expression across the 100 strains. Using a 5% FDR P value threshold, genome-wide eQTL detected total of408513 and 404661 locally associated (within 1 Mb of genomic distance up- or down-stream of a given gene)SNP-lncRNA pairs in female vs male heart, respectively, which corresponded to 2687 unique lncRNAs in female and 2684 unique lncRNAs in males after excluding those with distal eQTLs (at P <4.1e-6). Genome-wide correlation analysis revealed 2794 local and 1233 distal uniquely co-regulated lncRNA-mRNA pairs ( P< 1e-8).Global WGNCA and module-trait correlation analysis identified 14 female and 5 male lncRNA-mRNA networks(Bicor>0.3) that exhibited significant correlation with heart weight, body fat, or insulin resistance. By applying eQTL analysis to the module eigengenes, we identified 11 unique module eigengenes with eQTLs (FDR<0.05). Conclusions: This study represents the first effort to investigate lncRNA-mediated regulation of gene expression in male and female mouse heart across 100 different strains. The results reveal novel associations of lncRNAs with complex clinical traits and provide resources to facilitate mechanistic exploration of the lncRNAs function in cardiovascular disease models.

Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast.

Causal gene networks model the flow of information within a cell. Reconstructing causal networks from omics data is challenging because correlation does not imply causation. When genomics and transcriptomics data from a segregating population are combined, genomic variants can be used to orient the direction of causality between gene expression traits. Instrumental variable methods use a local expression quantitative trait locus (eQTL) as a randomized instrument for a gene's expression level, and assign target genes based on distal eQTL associations. Mediation-based methods additionally require that distal eQTL associations are mediated by the source gene. A detailed comparison between these methods has not yet been conducted, due to the lack of a standardized implementation of different methods, the limited sample size of most multi-omics datasets, and the absence of ground-truth networks for most organisms. Here we used Findr, a software package providing uniform implementations of instrumental variable, mediation, and coexpression-based methods, a recent dataset of 1012 segregants from a cross between two budding yeast strains, and the Yeastract database of known transcriptional interactions to compare causal gene network inference methods. We found that causal inference methods result in a significant overlap with the ground-truth, whereas coexpression did not perform better than random. A subsampling analysis revealed that the performance of mediation saturates at large sample sizes, due to a loss of sensitivity when residual correlations become significant. Instrumental variable methods on the other hand contain false positive predictions, due to genomic linkage between eQTL instruments. Instrumental variable and mediation-based methods also have complementary roles for identifying causal genes underlying transcriptional hotspots. Instrumental variable methods correctly predicted STB5 targets for a hotspot centred on the transcription factor STB5, whereas mediation failed due to Stb5p auto-regulating its own expression. Mediation suggests a new candidate gene, DNM1, for a hotspot on Chr XII, whereas instrumental variable methods could not distinguish between multiple genes located within the hotspot. In conclusion, causal inference from genomics and transcriptomics data is a powerful approach for reconstructing causal gene networks, which could be further improved by the development of methods to control for residual correlations in mediation analyses, and for genomic linkage and pleiotropic effects from transcriptional hotspots in instrumental variable analyses.

TreeMap: a structured approach to fine mapping of eQTL variants.

MotivationExpression quantitative trait loci (eQTL) harbor genetic variants modulating gene transcription. Fine mapping of regulatory variants at these loci is a daunting task due to the juxtaposition of causal and linked variants at a locus as well as the likelihood of interactions among multiple variants. This problem is exacerbated in genes with multiple cis-acting eQTL, where superimposed effects of adjacent loci further distort the association signals.ResultsWe developed a novel algorithm, TreeMap, that identifies putative causal variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. Guided by the hierarchical structure of linkage disequilibrium, TreeMap performs an organized search for individual and multiple causal variants. Via extensive simulations, we show that TreeMap detects co-regulating variants more accurately than current methods. Furthermore, its high computational efficiency enables genome-wide analysis of long-range eQTL. We applied TreeMap to GTEx data of brain hippocampus samples and transverse colon samples to search for eQTL in gene bodies and in 4 Mbps gene-flanking regions, discovering numerous distal eQTL. Furthermore, we found concordant distal eQTL that were present in both brain and colon samples, implying long-range regulation of gene expression.Availability and implementationTreeMap is available as an R package enabled for parallel processing at https://github.com/liliulab/treemap.Supplementary information Supplementary data are available at Bioinformatics online.

Combined GWAS and eQTL analysis uncovers a genetic regulatory network orchestrating the initiation of secondary cell wall development in cotton.

The cotton fibre serves as a valuable experimental system to study cell wall synthesis in plants, but our understanding of the genetic regulation of this process during fibre development remains limited. We performed a genome-wide association study (GWAS) and identified 28 genetic loci associated with fibre quality in allotetraploid cotton. To investigate the regulatory roles of these loci, we sequenced fibre transcriptomes of 251 cotton accessions and identified 15330 expression quantitative trait loci (eQTL). Analysis of local eQTL and GWAS data prioritised 13 likely causal genes for differential fibre quality in a transcriptome-wide association study (TWAS). Characterisation of distal eQTL revealed unequal genetic regulation patterns between two subgenomes, highlighted by an eQTL hotspot (Hot216) that established a genome-wide genetic network regulating the expression of 962 genes. The primary regulatory role of Hot216, and specifically the gene encoding a KIP-related protein, was found to be the transcriptional regulation of genes responsible for cell wall synthesis, which contributes to fibre length by modulating the developmental transition from rapid cell elongation to secondary cell wall synthesis. This study uncovered the genetic regulation of fibre-cell development and revealed the molecular basis of the temporal modulation of secondary cell wall synthesis during plant cell elongation.

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.

Prevalent function of genome loci associated with development of multiple sclerosis as revealed by GWAS and eQTL analysis

We studied genome distribution of single-nucleotide polymorphisms (SNP) associated with development of multiple sclerosis and identified genome segments enriched in such polymorphisms. Some SNPs observed in identified segments are also local or distal eQTLs (expression quantitative trait loci) for a number of genes expressed in the blood or the nervous system. We analyzed lists of genes expression of which depends on these eQTLs, separately for the blood and the nervous system, and identified GO functions overrepresented in such gene lists. An antigen processing and presentation via MHC class II appeared to be the main gene functions either in the blood or in the nervous system. We identified a set of SNPs genetically linked with at least three SNPs associated with multiple sclerosis in GWAS, which includes eQTLs for all overrepresented functions. These SNPs and genes are located in a rather short locus on chromosome 14 presumably containing IGHG genes. SNPs from this genome segment affect expression of the HLA-DOB, HLA-DQA1, HLA-DQA2, and HLA-DQB1 genes both in the blood and in the nervous system. The results we obtained made it possible to suggest the mechanisms of multiple sclerosis development.

Functional Architectures of Local and Distal Regulation of Gene Expression in Multiple Human Tissues

Genetic variants that modulate gene expression levels play an important role in the etiology of human diseases and complex traits. Although large-scale eQTL mapping studies routinely identify many local eQTLs, the molecular mechanisms by which genetic variants regulate expression remain unclear, particularly for distal eQTLs, which these studies are not well powered to detect. Here, we leveraged all variants (not just those that pass stringent significance thresholds) to analyze the functional architecture of local and distal regulation of gene expression in 15 human tissues by employing an extension of stratified LD-score regression that produces robust results in simulations. The top enriched functional categories in local regulation of peripheral-blood gene expression included coding regions (11.41×), conserved regions (4.67×), and four histone marks (p < 5× 10-5 for all enrichments); local enrichments were similar across the 15 tissues. We also observed substantial enrichments for distal regulation of peripheral-blood gene expression: coding regions (4.47×), conserved regions (4.51×), and two histone marks (p < 3× 10-7 for all enrichments). Analyses of the genetic correlation of gene expression across tissues confirmed that local regulation of gene expression is largely shared across tissues but that distal regulation is highly tissue specific. Our results elucidate the functional components of the genetic architecture of local and distal regulation of gene expression.

M58 - Cross-Tissue EQTL Enrichment of Associations In Schizophrenia

Background In the era of Big Data, genome-wide association studies (GWASs) have been the main instrument to better our understanding of the genetics behind human traits. The schizophrenia GWAS by the psychiatric genomics consortium (PGC) identified many loci plausibly involved in the etiology of the disorder [Ripke et al., 2014]. However, the picture appears to be far from complete [Lichtenstein, 2009; Polderman, 2015; Gejman, 2010; Lee, 2012]. Judging by the number of non-coding variants identified by GWASs of schizophrenia and other complex traits, many causal variants are likely to affect the regulation of the genes involved rather than severely disrupt their function. Expression quantitative trait loci (eQTLs) are genomic loci that contribute to variation in levels of mRNA. It is known [Bacanu, 2014] that many eQTLs are associated with schizophrenia, as well as other phenotypes [Nicolae, 2010]. Methods Given known pleiotropy between schizophrenia and other traits, we looked for association enrichment of eQTLs in tissues not obviously relevant to psychiatric disorders (blood, adipose, LCL, and skin [Buil, 2015]). In order to consolidate any evidence of tissue-specificity, we further looked for differential enrichment in different ENCODE functional annotation categories. We used summary p-value statistics from schizophrenia as well as body mass index, Crohn's disease, height, systolic blood pressure and type II diabetes GWASs. We matched the eQTLs to SNP sets with similar minor allele frequencies (MAF) and distances from their respective transcription start sites (TSS), and no evidence of association with expression levels of any genes. We further subdivided eQTLs into proximal and distal eQTLs, according to their position relative to the TSS or to their affiliation to promoters, and we annotated them according to their ENCODE designation. We visualized the enrichment in fold enrichment plots, screened it using Fisher tests and assessed its extent by regressing the squared association z-score against SNP affiliations and LD-score. Results Significant enrichment was seen in adipose (beta=0.24, p=2.2E-06), LCL (beta=0.27, p=4.0E-08) and skin (beta=0.23, p=2.7E-06) tissues. The enrichment was as widespread as that observed in another highly polygenic trait such as height, and more widespread than in traits such as body mass index (adipose and skin) or blood pressure (adipose). The schizophrenia association extent does not appear to depend on the eQTLs' distance from the TSS nor from their explicit affiliation to specific functional elements, as defined by ENCODE, suggesting that risk alleles for schizophrenia could frequently act in many different tissue contexts. Discussion Our results are in line with the notion that schizophrenia is a disorder involving many tissues and known pleiotropy with cardiovascular and immune diseases back our findings. The statistical power of the underlying GWAS does play a role in the detection of association enrichment of the sort we sought. Polygenicity, however, is also a major factor, as exemplified by the results for height, body mass index and blood pressure. In summary, we find that looking for functional correlates of schizophrenia risk loci in non-obvious tissues is productive, and this could be because of either pleiotropy or increased effectiveness of variants that work in many different environmental contexts. This suggests the utility of large, single tissue eQTL experiments in the study of schizophrenia in addition to smaller, multiple tissue approaches.

Abstract 17027: Expression Quantitative Trait Locus (eQTL) Map for Genetic Variants Associated With Cardiovascular Traits in Human Heart

Introduction: To identify genetic variants that may influence disease susceptibility by modulating gene expression, this study investigated associations between genetic variants associated with cardiovascular traits and gene expression in heart tissue. Methods: In left ventricle from 102 heart donors with no diagnosed heart disease and 100 heart transplant patients (explanted failed hearts), genotyping was performed with Illumina CardioMetabo Chips (covering ~200,000 variants associated with cardiovascular and metabolic traits). Gene expression profiles for each tissue sample were generated with Affymetrix Human Gene ST 1.0 microarrays. Associations between genotype and gene expression (expression quantitative trait loci, eQTLs) were tested with an additive genetic model, adjusted for multiple comparisons. For each gene, proximal (cis) and distal (trans) eQTL effects were tested by analyzing variants &lt;1Mb and &gt;1Mb from the transcription start site, respectively. Results: After quality control filtering, 30,412 genes were tested for association with 54,908 variants in donors and 56,976 variants in transplant patients. In donors, 275 genes with cis-eQTL effects and 100 genes with trans-eQTL effects were identified (adjusted p&lt;0.05, effect size: 0.1-1.8). In patients, 244 genes with cis-eQTL effects and 101 genes with trans-eQTL effects were identified (adjusted p&lt;0.05, effect size: 0.1-1.3). Fewer than half the genes with cis-eQTL effects (n=115) were shared by donors and transplant patients. Conclusions: This study provides the first eQTL map of disease-associated variants in established heart disease. These data may help identify functional non-coding variants that contribute to susceptibility to disease. Figure: Upper panel, Manhattan plot of genes with cis-eQTL (dark blue) and trans-eQTL (light blue) effects in heart donors (top) and transplant patients (bottom). Lower panel, example cis-eQTL shared by donors and transplant patients.

Novel distal eQTL analysis demonstrates effect of population genetic architecture on detecting and interpreting associations.

Mapping expression quantitative trait loci (eQTL) has identified genetic variants associated with transcription rates and has provided insight into genotype–phenotype associations obtained from genome-wide association studies (GWAS). Traditional eQTL mapping methods present significant challenges for the multiple-testing burden, resulting in a limited ability to detect eQTL that reside distal to the affected gene. To overcome this, we developed a novel eQTL testing approach, “network-based, large-scale identification of distal eQTL” (NetLIFT), which performs eQTL testing based on the pairwise conditional dependencies between genes’ expression levels. When applied to existing data from yeast segregants, NetLIFT replicated most previously identified distal eQTL and identified 46% more genes with distal effects compared to local effects. In liver data from mouse lines derived through the Collaborative Cross project, NetLIFT detected 5744 genes with local eQTL while 3322 genes had distal eQTL. This analysis revealed founder-of-origin effects for a subset of local eQTL that may contribute to previously described phenotypic differences in metabolic traits. In human lymphoblastoid cell lines, NetLIFT was able to detect 1274 transcripts with distal eQTL that had not been reported in previous studies, while 2483 transcripts with local eQTL were identified. In all species, we found no enrichment for transcription factors facilitating eQTL associations; instead, we found that most trans-acting factors were annotated for metabolic function, suggesting that genetic variation may indirectly regulate multigene pathways by targeting key components of feedback processes within regulatory networks. Furthermore, the unique genetic history of each population appears to influence the detection of genes with local and distal eQTL.

Novel Distal eQTL Analysis Demonstrates Effect of Population Genetic Architecture on Detecting and Interpreting Associations

Novel Distal eQTL Analysis Demonstrates Effect of Population Genetic Architecture on Detecting and Interpreting Associations

Higher-order chromatin domains link eQTLs with the expression of far-away genes.

Distal expression quantitative trait loci (distal eQTLs) are genetic mutations that affect the expression of genes genomically far away. However, the mechanisms that cause a distal eQTL to modulate gene expression are not yet clear. Recent high-resolution chromosome conformation capture experiments along with a growing database of eQTLs provide an opportunity to understand the spatial mechanisms influencing distal eQTL associations on a genome-wide scale. We test the hypothesis that spatial proximity contributes to eQTL-gene regulation in the context of the higher-order domain structure of chromatin as determined from recent Hi-C chromosome conformation experiments. This analysis suggests that the large-scale topology of chromatin is coupled with eQTL associations by providing evidence that eQTLs are in general spatially close to their target genes, occur often around topological domain boundaries and preferentially associate with genes across domains. We also find that within-domain eQTLs that overlap with regulatory elements such as promoters and enhancers are spatially more close than the overall set of within-domain eQTLs, suggesting that spatial proximity derived from the domain structure in chromatin plays an important role in the regulation of gene expression.

Expression Quantitative Trait Loci: Replication, Tissue- and Sex-Specificity in Mice

By treating the transcript abundance as a quantitative trait, gene expression can be mapped to local or distant genomic regions relative to the gene encoding the transcript. Local expression quantitative trait loci (eQTL) generally act in cis (that is, control the expression of only the contiguous structural gene), whereas distal eQTL act in trans. Distal eQTL are more difficult to identify with certainty due to the fact that significant thresholds are very high since all regions of the genome must be tested, and confounding factors such as batch effects can produce false positives. Here, we compare findings from two large genetic crosses between mouse strains C3H/HeJ and C57BL/6J to evaluate the reliability of distal eQTL detection, including "hotspots" influencing the expression of multiple genes in trans. We found that >63% of local eQTL and >18% of distal eQTL were replicable at a threshold of LOD > 4.3 between crosses and 76% of local and >24% of distal eQTL at a threshold of LOD > 6. Additionally, at LOD > 4.3 four tissues studied (adipose, brain, liver, and muscle) exhibited >50% preservation of local eQTL and >17% preservation of distal eQTL. We observed replicated distal eQTL hotspots between the crosses on chromosomes 9 and 17. Finally, >69% of local eQTL and >10% of distal eQTL were preserved in most tissues between sexes. We conclude that most local eQTL are highly replicable between mouse crosses, tissues, and sex as compared to distal eQTL, which exhibited modest replicability.