24 research outputs found
Eliciting Informative Priors by Modelling Expert Decision Making
This article introduces a new method for eliciting prior distributions from
experts. The method models an expert decision-making process to infer a prior
probability distribution for a rare event . More specifically, assuming
there exists a decision-making process closely related to which forms a
decision , where a history of decisions have been collected. By modelling
the data observed to make the historic decisions, using a Bayesian model, an
analyst can infer a distribution for the parameters of the random variable .
This distribution can be used to approximate the prior distribution for the
parameters of the random variable for event . This method is novel in the
field of prior elicitation and has the potential of improving upon current
methods by using real-life decision-making processes, that can carry real-life
consequences, and, because it does not require an expert to have statistical
knowledge. Future decision making can be improved upon using this method, as it
highlights variables that are impacting the decision making process. An
application for eliciting a prior distribution of recidivism, for an
individual, is used to explain this method further
Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans
Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have
fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in
25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16
regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of
correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP,
while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in
Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium
(LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region.
Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant
enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the
refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa,
an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of
PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent
signals within the same regio