Browse
Submit Data
Databases
API
Help

Dataset Information

30 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Precise and Cost-Effective Nanopore Sequencing for Post-GWAS Fine-Mapping and Causal Variant Identification.

ABSTRACT: Fine-mapping of interesting loci discovered by genome-wide association study (GWAS) is mandatory to pinpoint causal variants. Traditionally, this fine-mapping is completed through increasing the genotyping density at candidate loci, for which imputation is the current standard approach. Although imputation is a useful technique, it has a number of limitations that impede accuracy. In this work, we describe the development of a precise and cost-effective Nanopore sequencing-based pipeline that provides comprehensive and accurate information at candidate loci to identify potential causal single-nucleotide polymorphisms (SNPs). We demonstrate the utility of this technique via the fine-mapping of a GWAS positive hit comprising a synonymous SNP that is associated with doxorubicin-induced cardiotoxicity. In this work, we provide a proof of principle for the application of Nanopore sequencing in post-GWAS fine-mapping and pinpointing of potential causal SNPs with a minimal cost of just ~$10/100 kb/sample.

SUBMITTER: Magdy T

PROVIDER: S-EPMC7096756 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Evaluating the Performance of Fine-Mapping Strategies at Common Variant GWAS Loci.

Project description:The growing availability of high-quality genomic annotation has increased the potential for mechanistic insights when the specific variants driving common genome-wide association signals are accurately localized. A range of fine-mapping strategies have been advocated, and specific successes reported, but the overall performance of such approaches, in the face of the extensive linkage disequilibrium that characterizes the human genome, is not well understood. Using simulations based on sequence data from the 1000 Genomes Project, we quantify the extent to which fine-mapping, here conducted using an approximate Bayesian approach, can be expected to lead to useful improvements in causal variant localization. We show that resolution is highly variable between loci, and that performance is severely degraded as the statistical power to detect association is reduced. We confirm that, where causal variants are shared between ancestry groups, further improvements in performance can be obtained in a trans-ethnic fine-mapping design. Finally, using empirical data from a recently published genome-wide association study for ankylosing spondylitis, we provide empirical confirmation of the behaviour of the approximate Bayesian approach and demonstrate that seven of twenty-six loci can be fine-mapped to fewer than ten variants.

| S-EPMC4583479 | biostudies-literature

Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification.

Project description:Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website.

| S-EPMC3738448 | biostudies-literature

Fine-scale mapping of chromosome 9q22.33 identifies candidate causal variant in ovarian cancer

Project description: Not available

| S-EPMC10874173 | biostudies-literature

A catalog of GWAS fine-mapping efforts in autoimmune disease.

Project description:Genome-wide association studies (GWASs) have enabled unbiased identification of genetic loci contributing to common complex diseases. Because GWAS loci often harbor many variants and genes, it remains a major challenge to move from GWASs' statistical associations to the identification of causal variants and genes that underlie these association signals. Researchers have applied many statistical and functional fine-mapping strategies to prioritize genetic variants and genes as potential candidates. There is no gold standard in fine-mapping approaches, but consistent results across different approaches can improve confidence in the fine-mapping findings. Here, we combined text mining with a systematic review and formed a catalog of 85 studies with evidence of fine mapping for at least one autoimmune GWAS locus. Across all fine-mapping studies, we compiled 230 GWAS loci with allelic heterogeneity estimates and predictions of causal variants and trait-relevant genes. These 230 loci included 455 combinations of locus-by-disease association signals with 15 autoimmune diseases. Using these estimates, we assessed the probability of mediating disease risk associations across genes in GWAS loci and identified robust signals of causal disease biology. We predict that this comprehensive catalog of GWAS fine-mapping efforts in autoimmune disease will greatly help distill the plethora of information in the field and inform therapeutic strategies.

| S-EPMC8059376 | biostudies-literature

Fine Mapping of a Major Backfat QTL Reveals a Causal Regulatory Variant Affecting the <i>CCND2</i> Gene.

Project description:Backfat is an important trait in pork production, and it has been included in the breeding objectives of genetic companies for decades. Although adipose tissue is a good energy storage, excessive fat results in reduced efficiency and economical losses. A large QTL for backfat thickness on chromosome 5 is still segregating in different commercial pig breeds. We fine mapped this QTL region using a genome-wide association analysis (GWAS) with 133,358 genotyped animals from five commercial populations (Landrace, Pietrain, Large White, Synthetic, and Duroc) imputed to the porcine 660K SNP chip. The lead SNP was located at 5:66103958 (G/A) within the third intron of the CCND2 gene, with the G allele associated with more backfat, while the A allele is associated with less backfat. We further phased the QTL region to discover a core haplotype of five SNPs associated with low backfat across three breeds. Linkage disequilibrium analysis using whole-genome sequence data revealed three candidate causal variants within intronic regions and downstream of the CCND2 gene, including the lead SNP. We evaluated the association of the lead SNP with the expression of the genes in the QTL region (including CCND2) in a large cohort of 100 crossbred samples, sequenced in four different tissues (lung, spleen, liver, muscle). Results show that the A allele increases the expression of CCND2 in an additive way in three out of four tissues. Our findings indicate that the causal variant for this QTL region is a regulatory variant within the third intron of the CCND2 gene affecting the expression of CCND2.

| S-EPMC9180923 | biostudies-literature

Identifying causal variants by fine mapping across multiple studies.

Project description:Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of "fine mapping" methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).

| S-EPMC8491908 | biostudies-literature

Genome-wide fine-mapping improves identification of causal variants.

Project description:Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn's disease. Overall, we analyse 599 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.

| S-EPMC11275676 | biostudies-literature

Single-Cell Epigenomics and Functional Fine-Mapping of Atherosclerosis GWAS Loci.

Project description:[Figure: see text].

| S-EPMC8260472 | biostudies-literature

Identification of a Potential Regulatory Variant for Colorectal Cancer Risk Mapping to Chromosome 5q31.1: A Post-GWAS Study.

Project description:Large-scale genome-wide association studies (GWAS) have established chromosome 5q31.1 as a susceptibility locus for colorectal cancer (CRC), which was still lack of causal genetic variants. We searched potentially regulatory single nucleotide polymorphisms (SNPs) in the overlap region between linkage disequilibrium (LD) block of 5q31.1 and regulatory elements predicted by histone modifications, then tested their association with CRC via a case-control study. Among three candidate common variants, we found rs17716310 conferred significantly (heterozygous model: OR = 1.273, 95% confidence interval (95%CI) = 1.016-1.595, P = 0.036) and marginally (dominant model: OR = 1.238, 95%CI = 1.000-1.532, P = 0.050) increase risk for CRC in a Chinese population including 695 cases and 709 controls. This variation was suggested to be regulatory altering the activity of enhancer that control PITX1 expression. Using epigenetic information such as chromatin immunoprecipitation-sequencing (ChIP-seq) data might help researchers to interpret the results of GWAS and locate causal variants for diseases in post-GWAS era.

| S-EPMC4575091 | biostudies-literature

Genetic and epigenetic fine mapping of causal autoimmune disease variants.

Project description:Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4(+) T-cell subsets, regulatory T cells, CD8(+) T cells, B cells, and monocytes. We find that ∼90% of causal variants are non-coding, with ∼60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10-20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.

| S-EPMC4336207 | 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