Incorporating summary statistics into VAMP framework
Al Depope*, Jakub Bajzik*, Marco Mondelli and Matthew R. Robinson
We first adapt gVAMP to the summary statistics setup in order to propose a novel method called summary gVAMP (sgVAMP). We demonstrate that compared to other popular summary statistics methods, sgVAMP achieves state-of-the-art out-of-sample prediction accuracy across several traits in the UK biobank using the 2.17 million SNP set. Secondly, we extend sgVAMP to a multi-cohort setting, which allows for the joint estimation of shared and population-specific signals across multiple ancestries.
Joint modelling of whole genome sequence data for human height via approximate message passing
Al Depope*, Jakub Bajzik, Marco Mondelli and Matthew R. Robinson
We develop a new algorithmic paradigm based on approximate message passing, gVAMP, to directly fine-map whole-genome sequence (WGS) variants and gene burden scores, conditional on all other measured DNA variation genome-wide. We find that the genetic architecture of height inferred from WGS data differs from that inferred from imputed single nucleotide polymorphism (SNP) variants:common variant associations from imputed SNP data are allocated to WGS variants of lower frequency, and there is a stronger relationship of effect size and variant frequency.
Epigenome-wide association studies using approximate message passing
Jakub Bajzik*, Al Depope, Daniel L. McCartney, Markus J. Bauer, Riccardo E. Marioni, Marco Mondelli and Matthew R. Robinson
We develop gVAMPomi, approximate message passing-based paradigm, and apply it to the largest human methylation dataset generated to date, the Generation Scotland study. We find 92 CpG probes whose effects are significantly associated with traits, conditional on all other CpG probes, representing a significant increase over 37 CpG probes discovered by baseline MCMC approach.
Detection of age-specific genetic effects for age-at-onset complex traits
Sven Erik Ojavee* and Al Depope*
We develop an extension of the time-to-event MCMC software called BayesW to two and three epochs by imposing statistical model and deriving Gibbs updates for interaction parameters and epoch-specific parameters.