Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ABSTRACT:

Motivation

Expression 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.

Results

We 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 implementation

TreeMap 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.

SUBMITTER: Liu L

PROVIDER: S-EPMC8150140 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Leveraging allelic imbalance to refine fine-mapping for eQTL studies.

Project description:Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allelic imbalance (AIM) that measures imbalance in gene expression on two chromosomes of a diploid organism. In this work, we develop a novel statistical method that leverages both AIM and total expression data to detect causal variants that regulate gene expression. We illustrate through simulations and application to 10 tissues of the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. Across all tissues and genes, our method achieves a median reduction rate of 11% in the number of putative causal variants. We use chromatin state data from the Roadmap Epigenomics Consortium to show that the putative causal variants identified by our method are enriched for active regions of the genome, providing orthogonal support that our method identifies causal variants with increased specificity.

| S-EPMC6952111 | biostudies-literature

Fine-mapping within eQTL credible intervals by expression CROP-seq.

Project description:The majority of genome-wide association study (GWAS)-identified SNPs are located in noncoding regions of genes and are likely to influence disease risk and phenotypes by affecting gene expression. Since credible intervals responsible for genome-wide associations typically consist of ≥100 variants with similar statistical support, experimental methods are needed to fine map causal variants. We report here a moderate-throughput approach to identifying regulatory GWAS variants, expression CROP-seq, which consists of multiplex CRISPR-Cas9 genome editing combined with single-cell RNAseq to measure perturbation in transcript abundance. Mutations were induced in the HL60/S4 myeloid cell line nearby 57 SNPs in three genes, two of which, rs2251039 and rs35675666, significantly altered CISD1 and PARK7 expression, respectively, with strong replication and validation in single-cell clones. The sites overlap with chromatin accessibility peaks and define causal variants for inflammatory bowel disease at the two loci. This relatively inexpensive approach should be scalable for broad surveys and is also implementable for the fine mapping of individual genes.

| S-EPMC7334875 | biostudies-literature

An eQTL mapping approach reveals that rare variants in the SEMA5A regulatory network impact autism risk.

Project description:To date, genome-wide single nucleotide polymorphism (SNP) and copy number variant (CNV) association studies of autism spectrum disorders (ASDs) have led to promising signals but not to easily interpretable or translatable results. Our own genome-wide association study (GWAS) showed significant association to an intergenic SNP near Semaphorin 5A (SEMA5A) and provided evidence for reduced expression of the same gene. In a novel GWAS follow-up approach, we map an expression regulatory pathway for a GWAS candidate gene, SEMA5A, in silico by using population expression and genotype data sets. We find that the SEMA5A regulatory network significantly overlaps rare autism-specific CNVs. The SEMA5A regulatory network includes previous autism candidate genes and regions, including MACROD2, A2BP1, MCPH1, MAST4, CDH8, CADM1, FOXP1, AUTS2, MBD5, 7q21, 20p, USH2A, KIRREL3, DBF4B and RELN, among others. Our results provide: (i) a novel data-derived network implicated in autism, (ii) evidence that the same pathway seeded by an initial SNP association shows association with rare genetic variation in ASDs, (iii) a potential mechanism of action and interpretation for the previous autism candidate genes and genetic variants that fall in this network, and (iv) a novel approach that can be applied to other candidate genes for complex genetic disorders. We take a step towards better understanding of the significance of SEMA5A pathways in autism that can guide interpretation of many other genetic results in ASDs.

| S-EPMC3690972 | biostudies-literature

Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision.

Project description:Statistical fine-mapping prioritizes putative causal variants from a large number of candidate variants, and is widely used in expression quantitative loci (eQTLs) studies. In eQTL fine-mapping, the existence of causal variants for gene expression is not guaranteed, since the genetic heritability of gene expression explained by nearby (cis-) variants is limited. Here we introduce a refined fine-mapping algorithm, named Knockoff-Finemap combination (KFc). KFc estimates the probability that the causal variant(s) exist in the cis-window of a gene through construction of knockoff genotypes (i.e. a set of synthetic genotypes that resembles the original genotypes), and uses it to adjust the posterior inclusion probabilities (PIPs). Utilizing simulated gene expression data, we show that KFc results in calibrated PIP distribution with improved precision. When applied to gene expression data of 465 genotyped samples from the Japan COVID-19 Task Force (JCTF), KFc resulted in significant enrichment of a functional score as well as reporter assay hits in the top PIP bins. When combined with functional priors derived from an external fine-mapping study (GTEx), KFc resulted in a significantly higher proportion of hematopoietic trait putative causal variants in the top PIP bins. Our work presents improvements in the precision of a major fine-mapping algorithm.

| S-EPMC10616627 | biostudies-literature

Fine-mapping within eQTL Credible Intervals by E-CROPseq

Project description:Precise identification of causal variants within credible intervals of eQTL associations is needed to identify regulatory GWAS variants. We show that CROPseq, namely multiplex CRISPR-Cas9 genome editing combined with single cell RNAseq, is a viable strategy for fine mapping regulatory SNPs. Mutations were induced nearby 67 SNPs in three genes, two of which, rs2251039 and rs17523802, significantly altered CISD1 and PARK7 expression, respectively, and overlap with chromatin accessibility peaks.

2019-11-28 | GSE140553 | GEO

Constraints on eQTL Fine Mapping in the Presence of Multisite Local Regulation of Gene Expression.

Project description:Expression quantitative trait locus (eQTL) detection has emerged as an important tool for unraveling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and the magnitude of effects. We describe a series of simulation studies designed to evaluate the impact of linkage disequilibrium (LD) on the fine mapping of causal variants with typical eQTL effect sizes. In the presence of multisite regulation, even though between 80 and 90% of modeled eSNPs associate with normally distributed traits, up to 10% of all secondary signals could be statistical artifacts, and at least 5% but up to one-quarter of credible intervals of SNPs within r2 > 0.8 of the peak may not even include a causal site. The Bayesian methods eCAVIAR and DAP (Deterministic Approximation of Posteriors) provide only modest improvement in resolution. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine mapping of causal variants needs to be adjusted for multisite influences, as conditional estimates can be highly biased by interference among linked sites, but ultimately experimental verification of individual effects is needed. Presumably similar conclusions apply not just to eQTL mapping, but to multisite influences on fine mapping of most types of quantitative trait.

| S-EPMC5555460 | biostudies-literature

Trans-eQTL mapping in gene sets identifies network effects of genetic variants.

Project description:Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.

| S-EPMC11019359 | biostudies-literature

Fine-mapping CASP8 risk variants in breast cancer.

Project description:BACKGROUND:Multiple genome-wide and candidate gene association studies have been conducted in search of common risk variants for breast cancer. Recent large meta analyses, consolidating evidence from these studies, have been consistent in highlighting the caspase-8 (CASP8) gene as important in this regard. To define a risk haplotype and map the CASP8 gene region with respect to underlying susceptibility variant/s, we screened four genes in the CASP8 region on 2q33-q34 for breast cancer risk. METHODS:Two independent data sets from the United Kingdom and the United States, including 3,888 breast cancer cases and controls, were genotyped for 45 tagging single nucleotide polymorphisms (tSNP) in the expanded CASP8 region. SNP and haplotype association tests were carried out using Monte Carlo-based methods. RESULTS:We identified a three-SNP haplotype across rs3834129, rs6723097, and rs3817578 that was significantly associated with breast cancer (P < 5 × 10(-6)), with a dominant risk ratio and 95% CI of 1.28 (1.21-1.35) and frequency of 0.29 in controls. Evidence for this risk haplotype was extremely consistent across the two study sites and also consistent with previous data. CONCLUSION:This three-SNP risk haplotype represents the best characterization so far of the chromosome upon which the susceptibility variant resides. IMPACT:Characterization of the risk haplotype provides a strong foundation for resequencing efforts to identify the underlying risk variant, which may prove useful for individual-level risk prediction, and provide novel insights into breast carcinogenesis.

| S-EPMC3253962 | biostudies-literature

An integrative approach for fine-mapping chromatin interactions.

Project description:MotivationChromatin interactions play an important role in genome architecture and gene regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g. 5-25 kb), which is substantially coarser than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions.ResultsTo predict the sources of Hi-C-identified interactions at a high resolution (e.g. 100 bp), we developed a computational method that integrates data from DNase-seq and ChIP-seq of TFs and histone marks. Our method, χ-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. χ-CNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show χ-CNN predictions enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological significance. χ-CNN provides an approach for analyzing important aspects of genome architecture and gene regulation at a higher resolution than previously possible.Availability and implementationχ-CNN software is available on GitHub (https://github.com/ernstlab/X-CNN).Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC7425030 | biostudies-literature

A statistical approach to fine-mapping for the identification of potential causal variants related to human intelligence.

Project description:Genome-wide association studies (GWASs) have identified >20 genetic loci associated with human intelligence. However, due to correlations between the trait-associated SNPs, only a few of the loci are confirmed to have a true biological effect. In order to distinguish the SNPs that have a causal effect on human intelligence, we must eliminate the noise from the high degree of linkage disequilibrium that persists throughout the genome. In this study, we apply a novel PAINTOR fine-mapping method, which uses a Bayesian approach to determine the SNPs with the highest probability of causality. This technique incorporates the GWAS summary statistics, linkage disequilibrium structure, and functional annotations to compute the posterior probability of causality for all SNPs in the GWAS-associated regions. We found five SNPs (rs6002620, rs41352752, rs6568547, rs138592330, and rs28371699) with a high probability of causality, three of which have posterior probabilities >0.60. The SNP rs6002620 (NDUFA6), which is involved in mitochondrial function, has the highest likelihood of causality. These findings provide important insight into the genetic determinants contributing to human intelligence.

| S-EPMC6712985 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data