Hopp til innhold
Close

Get alerts of updates about «CeFH Biostatistical seminar: presentation of "A simple new approach to variable selection in regression, with application to genetic fine-mapping" by Wang et al.»

How often would you like to receive alerts from fhi.no? (This affects all your alerts)
Do you also want alerts about:

The email address you register will only be used to send you these alerts. You can cancel and delete your email address at any time by following the link in the alerts you receive.
Read more about the privacy policy for fhi.no

You have subscribed to alerts about:

  • CeFH Biostatistical seminar: presentation of "A simple new approach to variable selection in regression, with application to genetic fine-mapping" by Wang et al.

Oops, something went wrong...

... contact nettredaksjon@fhi.no.

... reload the page and try again-

Event

CeFH Biostatistical seminar: presentation of "A simple new approach to variable selection in regression, with application to genetic fine-mapping" by Wang et al.

Presentation by William Denault, Centre for Fertility and Health and Department of Genetic and Bioinformatics at the Norwegian Institute of Public Health, and University of Bergen

Presentation by William Denault, Centre for Fertility and Health and Department of Genetic and Bioinformatics at the Norwegian Institute of Public Health, and University of Bergen


5. Dec | 2019

11:00-12:00
Seminar
Marcus Thranes gate 2, meeting room 202, 2nd floor

William Denault PhD student at the Centre for Fertility and Health and Department of Genetics and Bioinformatics at the Norwegian Institute of Public Health, and University of Bergen, will present the following paper:

 

A simple new approach to variable selection in regression, with application to genetic fine-mapping

By Wang et al

https://www.biorxiv.org/content/10.1101/501114v2

 

Abstract

We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model – the “Sum of Single Effects” (SuSiE) model – which comes from writing the sparse vector of regression coefficients as a sum of “single-effect” vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure – Iterative Bayesian Stepwise Selection (IBSS) – which is a Bayesian analogue of stepwise selection methods. IBSS shares the computational simplicity and speed of traditional stepwise methods, but instead of selecting a single variable at each step, IBSS computes a distribution on variables that captures uncertainty in which variable to select. We provide a formal justification of this intuitive algorithm by showing that it optimizes a variational approximation to the posterior distribution under the SuSiE model. Further, this approximate posterior distribution naturally yields convenient novel summaries of uncertainty in variable selection, providing a Credible Set of variables for each selection. Our methods are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse, both of which are characteristics of genetic fine-mapping applications. We demonstrate through numerical experiments that our methods outper-form existing methods for this task, and illustrate their application to fine-mapping genetic variants influencing alternative splicing in human cell-lines. We also discuss the potential and challenges for applying these methods to generic variable selection problems.