Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics

By C. Giambartolomei, Damjan Vukcevic, Eric E. Schadt, L. Franke, A. Hingorani, Chris Wallace, V. Plagnol. PLoS Genetics, 2013

3,608 citations recorded by the source corpus.

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.

Subfields: Cardiology and Cardiovascular Medicine, Epidemiology, Genetics

View Paper: https://www.semanticscholar.org/paper/8dc7bb4e015b7d5e210ae7add0742ea92c9c40c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

In Epidemiology

In Genetics

Citations in the corpus

Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “The R/coloc (25) package uses summary data for the SNPs in a region and estimates the posterior probability of shared genetic factors by evaluating the similarity of effect size patterns across the region.”
Fine-mapping from summary data with the “Sum of Single Effects” model (2021, methodology): “The SER is particularly convenient because posterior computations are analytically tractible [9]; consequently, despite its clear limitations, the SER has been widely used [2,23–25].”
The power of genetic diversity in genome-wide association studies of lipids (2021, methodology): “Expression quantitative trait loci colocalization was performed using coloc 61 Field-specific reporting Please select the one below that is the best fit for your research.”
A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits (2019, background): “For, example, H(2,1) denotes the set of hypotheses in 19”
The MR-Base platform supports systematic causal inference across the human phenome (2018, methodology): “…to support other methods for the investigation of complex trait genetic architecture, such as fine-mapping ( Benner et al., 2016 ), colocalisation ( Zhu et al., 2016 ; Newcombe et al., 2016 ; Giambartolomei et al., 2014 ) and polygenic risk prediction ( Dudbridge, 2013 ; Euesden et al., 2015 ).”
The druggable genome and support for target identification and validation in drug development (2016, background): “Localisation of causal genes could also be aided by evidence on the effect of genetic variants on the RNA transcription, on the activity or concentration of proteins and metabolites, combining new proteomic and metabolomics technologies that are scalable to large population studies (56, 57) with sta”
Detection and interpretation of shared genetic influences on 42 human traits (2016, positive): “To do this, we developed a statistical model (extending that used by Giambartolomei et al. 38 ) to estimate the probability that a given genomic region either 1) contains a genetic variant that influences the first trait, 2) contains a genetic variant that influences the second trait, 3) contains a ”
Genomic dissection of bipolar disorder and schizophrenia including 28 subphenotypes (2018, background): “We sought to identify additional disease-specific loci by comprehensively incorporating expression information with association results to perform fine-mapping and identify novel variants (Gamazon et al., 2015; Giambartolomei et al., 2014; Gusev et al., 2016; He et al., 2013).”