Data from: Joint variable selection for omic biomarkers in time-to-event data

The incidence of the vast majority of neurodegenerative, cancer, and
metabolic diseases generally increases exponentially with age. In
large-scale biobanks, linking time-to-diagnosis information in electronic
health records to multiple genomic (“multiomics”) measures has the
potential to reveal the genes and biological pathways involved in the
disease onset and progression. To date, association testing has commonly
been conducted by testing one variable at a time, which ignores
correlation structure and increases the risk of false discoveries. To
address these issues, we introduce a novel fully parametric Bayesian
computational method, vampW, based on the Vector Approximate Message
Passing framework applied to a Weibull model. vampW jointly models
correlated features, provides joint association testing via joint
Posterior Inclusion Probabilities (PIPs), and incorporates prior
knowledge. Here, we report PIPs obtained from the analysis of 53,018 UK
Biobank participants across 24 disease outcomes. Using a 95% PIP
threshold, vampW identifies 219 protein-disease associations. After
correcting protein levels for exponential age effects in addition to
linear age and sex correction, vampW identifies 1,308 associations. The
findings replicate in independent cohorts using different measurement
technologies, within data from Iceland and a novel Generation Scotland
proteomics dataset.