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

By Gao Wang, Abhishek K. Sarkar, Peter Carbonetto, Matthew Stephens. bioRxiv, 2018

988 citations recorded by the source corpus.

We introduce a simple new approach to variable selection in linear regression, and to quantifying uncertainty in selected variables. 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 Step-wise Selection (IBSS) – which is a Bayesian analogue of traditional 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 show that the IBSS algorithm computes a variational approximation to the posterior distribution under the SuSiE model. Further, this approximate posterior distribution naturally leads to a convenient, novel, way to summarize uncertainty in variable selection, and provides a Credible Set for each selected variable. Our methods are particularly well suited to settings where variables are highly correlated and true effects are very sparse, both of which are characteristics of genetic fine-mapping applications. We demonstrate through numerical experiments that our methods outperform existing methods for this task, and illustrate the methods by fine-mapping genetic variants that influence alternative splicing in human cell-lines. We also discuss both the potential and the challenges for applying these methods to generic variable selection problems.

Subfields: Artificial Intelligence, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d8cfdb075bf76e1ba6279337ac975a1b0d3d55bb

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Citations in the corpus

Plasma proteomic associations with genetics and health in the UK Biobank (2023, methodology): “We investigated evidence of shared genetic associations between variants directly affecting circulating protein expression levels and tissue-level gene expression using the coloc with SuSiE framework 61 .”
Functionally-informed fine-mapping and polygenic localization of complex trait heritability (2019, methodology): “Details of the PolyFun method are provided in Methods; we have released open-source software implementing PolyFun together with SuSiE 21 and FINEMAP 22 .”
Genome-wide enhancer maps link risk variants to disease genes (2021, methodology): “Fine-mapping was performed using the sum of single effects (SuSiE) method 56 , allowing for up to ten causal variants in each region.”
Fine-mapping from summary data with the “Sum of Single Effects” model (2021, methodology): “In recent work, Wang et al [17] introduced a new approach to fine-mapping, SuSiE (short for “SUm of SIngle Effects”), which has several advantages over existing approaches: it is more computationally scalable; and it provides a new, simple way to calculate “credible sets” of putative causal variants”