Hidden Photons in Extra Dimensions

Additional U(1) gauge symmetries and corresponding vector bosons, called hidden photons, interacting with the regular photon via kinetic mixing are well motivated in extensions of the Standard Model. Such extensions often exhibit extra spatial dimensions. In this note we investigate the effects of hidden photons living in extra dimensions. In four dimensions such a hidden photon is only detectable if it has a mass or if there exists additional matter charged under it. We note that in extra dimensions suitable masses for hidden photons are automatically present in form of the Kaluza-Klein tower.Comment: 5 pages, 4 figures; Proceedings of the 9th Patras Workshop on Axions, WIMPs and WISPs, Mainz, June 24-28, 201

Hidden photons with Kaluza-Klein towers

One of the simplest extensions of the Standard Model (SM) is an extra U(1) gauge group under which SM matter does not carry any charge. The associated boson -- the hidden photon -- then interacts via kinetic mixing with the ordinary photon. Such hidden photons arise naturally in UV extensions such as string theory, often accompanied by the presence of extra spatial dimensions. In this note we investigate a toy scenario where the hidden photon extends into these extra dimensions. Interaction via kinetic mixing is observable only if the hidden photon is massive. In four dimensions this mass needs to be generated via a Higgs or Stueckelberg mechanism. However, in a situation with compactified extra dimensions there automatically exist massive Kaluza-Klein modes which make the interaction observable. We present phenomenological constraints for our toy model. This example demonstrates that the additional particles arising in a more complete theory can have significant effects on the phenomenology.Comment: 20 pages, 3 figure

A more accurate method for colocalisation analysis allowing for multiple causal variants.

In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes the simplifying assumption that only a single causal variant exists for any given trait in any genomic region. Here, we examine the potential of the recently proposed Sum of Single Effects (SuSiE) regression framework, which can be used for fine-mapping genetic signals, for use with coloc. SuSiE is a novel approach that allows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered. We show this results in more accurate coloc inference than other proposals to adapt coloc for multiple causal variants based on conditioning. We therefore recommend that coloc be used in combination with SuSiE to optimise accuracy of colocalisation analyses when multiple causal variants exist

Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses.

Horizontal integration of summary statistics from different GWAS traits can be used to evaluate evidence for their shared genetic causality. One popular method to do this is a Bayesian method, coloc, which is attractive in requiring only GWAS summary statistics and no linkage disequilibrium estimates and is now being used routinely to perform thousands of comparisons between traits. Here we show that while most users do not adjust default software values, misspecification of prior parameters can substantially alter posterior inference. We suggest data driven methods to derive sensible prior values, and demonstrate how sensitivity analysis can be used to assess robustness of posterior inference. The flexibility of coloc comes at the expense of an unrealistic assumption of a single causal variant per trait. This assumption can be relaxed by stepwise conditioning, but this requires external software and an LD matrix aligned to study alleles. We have now implemented conditioning within coloc, and propose a new alternative method, masking, that does not require LD and approximates conditioning when causal variants are independent. Importantly, masking can be used in combination with conditioning where allelically aligned LD estimates are available for only a single trait. We have implemented these developments in a new version of coloc which we hope will enable more informed choice of priors and overcome the restriction of the single causal variant assumptions in coloc analysis