  Integration of data from genome-wide single nucleotide polymorphism (SNP)
association studies of different traits should allow researchers to disentangle
the genetics of potentially related traits within individually associated
regions. Formal statistical colocalisation testing of individual regions, which
requires selection of a set of SNPs summarizing the association in a region. We
show that the SNP selection method greatly affects type 1 error rates, with
published studies having used methods expected to result in substantially
inflated type 1 error rates. We show that either avoiding variable selection
and instead testing the most informative principal components or integrating
over variable selection using Bayesian model averaging can lead to correct
control of type 1 error rates. Application to data from Graves' disease and
Hashimoto's thyroiditis reveals a common genetic signature across seven regions
shared between the diseases, and indicates that in five of six regions
associated with Graves' disease and not Hashimoto's thyroiditis, this more
likely reflects genuine absence of association with the latter rather than lack
of power. Our examination, by simulation, of the performance of colocalisation
tests and associated software will foster more widespread adoption of formal
colocalisation testing. Given the increasing availability of large expression
and genetic association data sets from disease-relevant tissue and purified
cell populations, coupled with identification of regulatory sequences by
projects such as ENCODE, colocalisation analysis has the potential to reveal
both shared genetic signatures of related traits and causal disease genes and
tissues.
