# colocPropTest

An R package for proportionality testing for colocalisation.

Colocalisation is the occurence of two traits sharing a causal
variant in a given genetic region. One popular method for inferring
whether two traits have a colocalising signal is “coloc” [1]. This is
based on fine mapping the summary statistics for two traits, and
performing some joint analysis. However, fine mapping can be inaccurate
when information varies between SNPs (eg due to varying sample size),
particularly in large samples, and this inaccuracy can propogate to
coloc.

This motivated us to consider an older method of testing
colocalisation, based on testing proportionality of regression
coefficients between two traits [3,4]. There were a number of issues
with this approach which motivated the development of coloc:

it tests the null hypothesis that coefficients are proportional,
and thus is difficult to interpret because failure to reject can either
correspond to colocalisation, or lack of power

the test is based on the coefficients from a joint multi-SNP
model, which can be hard to reconstruct accurately from the marginal
test statistics typically available

the degrees of freedom of the test is n-1, where n is the number
of SNPs, which can lead to lack of power

Here we solve those issues by

only advising to conduct the test when standard evidence points
to convincing association with both traits (eg minimum p values \(< 10^{-8}\))

using marginal coefficients directly, with the LD between the
variants used to infer the covariance of those test statistics

performing many tests of pairs of variants rather than one test
of n variants, and using false discovery rates to infer whether any of
those tests were true rejections of the null of colocalisation

## References

Giambartolomei, C. et al. Bayesian Test for Colocalisation
between Pairs of Genetic Association Studies Using Summary Statistics.
PLOS Genet. 10, e1004383 (2014).

Kanai, M. et al. Meta-analysis fine-mapping is often
miscalibrated at single-variant resolution. Cell Genomics 2, 100210
(2022).

Plagnol, V., Smyth, D. J., Todd, J. A. & Clayton, D. G.
Statistical independence of the colocalized association signals for type
1 diabetes and RPS26 gene expression on chromosome 12q13. Biostatistics
10, 327–334 (2009).

Wallace, C. Statistical Testing of Shared Genetic Control for
Potentially Related Traits. Genet. Epidemiol. 37, 802–813
(2013).