This guest post is by Vincent Plagnol on his group’s paper Bayesian test for co-localisation between pairs of genetic association studies using summary statistics, arXived here. This has been cross-posted from the Plagnol Lab web site.

In this paper we want to answer the following question: given two genetic association studies both showing some association signal at a locus, how likely is it that the same variant is responsible for both associations?

We care about this because a shared causal variant is likely to imply an etiological link between the traits being considered. An obvious application consists of comparing a gene expression study and a disease trait. If one can show that the same variant is affecting both measurements, then it is very likely that the expression of this gene is affecting disease pathogenesis. It also provides information about the tissue type where the effect is mediated. This is a key information to inform a drug design process.

Previous work that led to this manuscript

A while back, I started a discussion with my colleague (and co-author on this manuscript) Eric Schadt about the involvement of a gene name RPS26 in type 1 diabetes. We came up with tests of co-localisation, which were later improved by my colleague (and co-author as well) Chris Wallace, based in Cambridge. These tests are somewhat dated now. The earliest version considered situations with very small number of SNPs, and was not well suited for densely typed regions, in particular as a result of imputation procedures.

This SNP density problem can be overcome to some extent, and Chris Wallace discusses how to do this here. However, a more fundamental issue is the Bayesian/frequentist difference. These earlier tests were testing the null hypothesis of a shared causal variant. Failing to reject the null could be the result of either a lack of power, or a true shared causal variant. In this newer Bayesian framework, the probability of each scenario is computed, including the “lack of power” case. It then becomes easier to interpret the outcome of the test. The tests are about to be released in the latest version of the coloc package (which is maintained by Chris Wallace).

In this latest paper, the underlying model is closely linked to the one proposed by Matthew Stephens and colleagues in a recent PLoS Genetics paper. However, co-localisation was more a side story in this paper, whereas it is the central point of our work. In particular, we show that it is possible to use single SNP P-values to obtain a very good approximation of the correct answer. As discussed below, this has important practical applications.

Another closely related work is the software Sherlock. Sherlock also uses P-values, and also tries to match a gene expression dataset with another GWAS. However, Sherlock does not really perform a co-localisation test but rather a general matching between a gene and a GWAS. In particular, in the Sherlock framework, only the variants significantly associated with gene expression contribute to the final test statistic. In contrast, a variant flat for the expression trait but strongly associated with disease provides strong support against co-localisation. Our work incorporates this information, by adding support to the “distinct association peaks” scenario.

A warning about the interpretation

As always in statistics, correlation does not imply causality. And what we quantify here are correlations. We can find very strong evidence that the same variant is affecting two traits, but what we cannot conclude without doubt is that the two traits (say, expression of a gene and disease outcome) are causally related. It may be likely, but we are not testing this.

An illustration of the complexity of this is the commonly observed case where a single variant (or haplotype) appears to affect the expression of a group of genes in the same chromosome region. Our test may, in such a situation, provide strong evidence of co-localisation for several of these genes with a disease GWAS. However, most of the time the expression of a single of these genes will actually causally affect the disease trait of interest. It does not mean that the test is wrong but one just has to understand what it is actually testing. Precisely, two traits affected by the same causal variant may suggest a causal link between both, but it does not have to be the case.

Two limitations of this approach

There are two additional limitations to mention. One is that the causal variant should be typed or imputed. We use simulations to show that if this is not the case, the behaviour of the test becomes very conservative.

A second issue is the presence of more than one association for the same trait at a locus. If both associations have approximately the same level of significance, the test can misbehave. In addition, identifying co-localisation with the secondary association requires conditional P-values. We give a nice example of this in the paper. However, if only P-values are available (which is key for what we want to do), this requires using approximate methods. Things are much easier if the genotype level data are available and a proper conditional regression can be implemented.

Why it is important to use summary statistics

Data sharing is always a contentious issue in human genetics. I am incredibly frustrated by the lack of willingness displayed by some groups to share data, even though the claim is that they do. It is a topic for another post. Eric Schadt has been extremely helpful by sharing the liver gene expression dataset with us, but this is a rather uncommon behaviour. In most cases, data are hidden between various “regulations” and “data access committees” that rarely meet and extensively delay the process of data sharing.

Given this frustration, being able to base tests on P-values makes it much easier to interact with other groups and share data. The success of large scale meta-analyses is an example of this. This is why we worked out the statistics so that P-values alone are sufficient to derive the probabilities for each scenario.

A practical implication is that it becomes possible to build a web-based server that will take P-values uploaded by users, compare these P-values with a set of GWAS datasets stored on the server (typically expression studies but perhaps other data types) and return statistics about the overlapping association signals.

We have initiated that process and the coloc server is now live (http://coloc.cs.ucl.ac.uk/coloc/), with a lot of help from the Computer Science department at UCL. We have only loaded the liver dataset that we used in this preprint as of now, but we are in the process of adding a brain gene expression study, led by my colleagues Mike Weale, John Hardy and Mina Ryten. We very much welcome collaborations, and if other datasets, for gene expression or any other relevant traits, are available, we would love to collaborate and incorporate these data into our server.

From genome-wide to “phenome-wide”

What we really want to do with this tool in the near future is mine dozens of GWAS studies using single variant P-values summary data, and search for connections that have been missed by previous investigators. Perhaps there are lipid traits that can be linked to neurodegenerative conditions, like the well known APOE result? Perhaps some T cell genes have an unexpected effect on a cardiovascular trait? Obviously these are not likely events but the genome-wide analysis of many association studies is likely to show several results of this type. The idea is to not only work genome-wide but also “phenome-wide”, comparing as many pairs of traits as possible. Again, this is definitely a collaborative work and we would be excited if we could bring more datasets to make these comparisons more powerful. So don’t hesitate to get in touch.