Skip to content

Colocalization

Summary Table

NAME CITATION YEAR
CoPheScan Manipur I, Reales G, Sul JH, Shin MK, ...&, Wallace C. (2024) CoPheScan: phenome-wide association studies accounting for linkage disequilibrium Nat. Commun., 15 (1) 5862. doi:10.1038/s41467-024-49990-8. PMID 38997278 2024
Coloc-susie Wallace C. (2021) A more accurate method for colocalisation analysis allowing for multiple causal variants PLoS Genet., 17 (9) e1009440. doi:10.1371/journal.pgen.1009440. PMID 34587156 2021
Coloc Giambartolomei C, Vukcevic D, Schadt EE, Franke L, ...&, Plagnol V. (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics PLoS Genet., 10 (5) e1004383. doi:10.1371/journal.pgen.1004383. PMID 24830394 2014
HyPrColoc Foley CN, Staley JR, Breen PG, Sun BB, ...&, Howson JMM. (2021) A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits Nat. Commun., 12 (1) 764. doi:10.1038/s41467-020-20885-8. PMID 33536417 2021
eCAVIAR Hormozdiari F, van de Bunt M, Segrè AV, Li X, ...&, Eskin E. (2016) Colocalization of GWAS and eQTL signals detects target genes Am. J. Hum. Genet., 99 (6) 1245-1260. doi:10.1016/j.ajhg.2016.10.003. PMID 27866706 2016

CoPheScan

  • NAME : CoPheScan
  • SHORT NAME : CoPheScan
  • FULL NAME : Coloc adapted Phenome-wide Scan
  • URL : https://github.com/ichcha-m/cophescan
  • TITLE : CoPheScan: phenome-wide association studies accounting for linkage disequilibrium
  • DOI : 10.1038/s41467-024-49990-8
  • ABSTRACT : Phenome-wide association studies (PheWAS) facilitate the discovery of associations between a single genetic variant with multiple phenotypes. For variants which impact a specific protein, this can help identify additional therapeutic indications or on-target side effects of intervening on that protein. However, PheWAS is restricted by an inability to distinguish confounding due to linkage disequilibrium (LD) from true pleiotropy. Here we describe CoPheScan (Coloc adapted Phenome-wide Scan), a Bayesian approach that enables an intuitive and systematic exploration of causal associations while simultaneously addressing LD confounding. We demonstrate its performance through simulation, showing considerably better control of false positive rates than a conventional approach not accounting for LD. We used CoPheScan to perform PheWAS of protein-truncating variants and fine-mapped variants from disease and pQTL studies, in 2275 disease phenotypes from the UK Biobank. Our results identify the complexity of known pleiotropic genes such as APOE, and suggest a new causal role for TGM3 in skin cancer.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Manipur I, Reales G, Sul JH, Shin MK, ...&, Wallace C. (2024) CoPheScan: phenome-wide association studies accounting for linkage disequilibrium Nat. Commun., 15 (1) 5862. doi:10.1038/s41467-024-49990-8. PMID 38997278
  • JOURNAL_INFO : Nature communications ; Nat. Commun. ; 2024 ; 15 ; 1 ; 5862
  • PUBMED_LINK : 38997278

Coloc

  • NAME : Coloc
  • SHORT NAME : coloc
  • FULL NAME : coloc
  • URL : https://chr1swallace.github.io/coloc/
  • KEYWORDS : Approximate Bayes Factor (ABF)
  • TITLE : Bayesian test for colocalisation between pairs of genetic association studies using summary statistics
  • DOI : 10.1371/journal.pgen.1004383
  • ABSTRACT : Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.
  • CITATION : Giambartolomei C, Vukcevic D, Schadt EE, Franke L, ...&, Plagnol V. (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics PLoS Genet., 10 (5) e1004383. doi:10.1371/journal.pgen.1004383. PMID 24830394
  • JOURNAL_INFO : PLoS genetics ; PLoS Genet. ; 2014 ; 10 ; 5 ; e1004383
  • PUBMED_LINK : 24830394

Coloc-susie

  • NAME : Coloc-susie
  • SHORT NAME : Coloc-susie
  • FULL NAME : Coloc-susie
  • URL : https://chr1swallace.github.io/coloc/articles/a06_SuSiE.html
  • KEYWORDS : Approximate Bayes Factor (ABF), Sum of Single Effects (SuSiE)
  • TITLE : A more accurate method for colocalisation analysis allowing for multiple causal variants
  • DOI : 10.1371/journal.pgen.1009440
  • ABSTRACT : In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes the simplifying assumption that only a single causal variant exists for any given trait in any genomic region. Here, we examine the potential of the recently proposed Sum of Single Effects (SuSiE) regression framework, which can be used for fine-mapping genetic signals, for use with coloc. SuSiE is a novel approach that allows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered. We show this results in more accurate coloc inference than other proposals to adapt coloc for multiple causal variants based on conditioning. We therefore recommend that coloc be used in combination with SuSiE to optimise accuracy of colocalisation analyses when multiple causal variants exist.
  • COPYRIGHT : http://creativecommons.org/licenses/by/4.0/
  • CITATION : Wallace C. (2021) A more accurate method for colocalisation analysis allowing for multiple causal variants PLoS Genet., 17 (9) e1009440. doi:10.1371/journal.pgen.1009440. PMID 34587156
  • JOURNAL_INFO : PLoS genetics ; PLoS Genet. ; 2021 ; 17 ; 9 ; e1009440
  • PUBMED_LINK : 34587156

HyPrColoc

  • NAME : HyPrColoc
  • SHORT NAME : HyPrColoc
  • FULL NAME : Hypothesis Prioritisation for multi-trait Colocalization
  • URL : https://github.com/cnfoley/hyprcoloc
  • KEYWORDS : multiple traits,
  • TITLE : A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits
  • DOI : 10.1038/s41467-020-20885-8
  • ABSTRACT : Genome-wide association studies (GWAS) have identified thousands of genomic regions affecting complex diseases. The next challenge is to elucidate the causal genes and mechanisms involved. One approach is to use statistical colocalization to assess shared genetic aetiology across multiple related traits (e.g. molecular traits, metabolic pathways and complex diseases) to identify causal pathways, prioritize causal variants and evaluate pleiotropy. We propose HyPrColoc (Hypothesis Prioritisation for multi-trait Colocalization), an efficient deterministic Bayesian algorithm using GWAS summary statistics that can detect colocalization across vast numbers of traits simultaneously (e.g. 100 traits can be jointly analysed in around 1 s). We perform a genome-wide multi-trait colocalization analysis of coronary heart disease (CHD) and fourteen related traits, identifying 43 regions in which CHD colocalized with ≥1 trait, including 5 previously unknown CHD loci. Across the 43 loci, we further integrate gene and protein expression quantitative trait loci to identify candidate causal genes.
  • CITATION : Foley CN, Staley JR, Breen PG, Sun BB, ...&, Howson JMM. (2021) A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits Nat. Commun., 12 (1) 764. doi:10.1038/s41467-020-20885-8. PMID 33536417
  • JOURNAL_INFO : Nature communications ; Nat. Commun. ; 2021 ; 12 ; 1 ; 764
  • PUBMED_LINK : 33536417

eCAVIAR

  • NAME : eCAVIAR
  • SHORT NAME : eCAVIAR
  • FULL NAME : eQTL and GWAS Causal Variant Identification in Associated Regions
  • URL : https://github.com/fhormoz/caviar
  • TITLE : Colocalization of GWAS and eQTL signals detects target genes
  • DOI : 10.1016/j.ajhg.2016.10.003
  • ABSTRACT : The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci.
  • COPYRIGHT : http://www.elsevier.com/open-access/userlicense/1.0/
  • CITATION : Hormozdiari F, van de Bunt M, Segrè AV, Li X, ...&, Eskin E. (2016) Colocalization of GWAS and eQTL signals detects target genes Am. J. Hum. Genet., 99 (6) 1245-1260. doi:10.1016/j.ajhg.2016.10.003. PMID 27866706
  • JOURNAL_INFO : The American Journal of Human Genetics ; Am. J. Hum. Genet. ; 2016 ; 99 ; 6 ; 1245-1260
  • PUBMED_LINK : 27866706