A simple new approach to variable selection in regression, with application to genetic fine mapping
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Abstract
Summary 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’ model, called ‘SuSiE’—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 SuSiE. 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 outperform existing methods for this task, and we 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.
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We applied a Bayesian statistical fine-mapping method (SuSiE) that analyzes both NIM and MH SNPs in the region surrounding an associated NIM to exclude settings where the association signal at an NIM might be driven by LD with a non-introgressed variant.
“To exclude settings where the association signal at an NIM might be driven by LD with a non-introgressed variant, we applied a Bayesian statistical fine-mapping method (SuSiE, Wang et al., 2020) that analyzes both NIM and MH SNPs in the region surrounding an associated NIM to output a set of SNPs that can explain the association signal at the region.”
From The lingering effects of Neanderthal introgression on human complex traits (2023) · cites this paper as Wang et al., 2020
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Sex differences in brain protein expression and disease
Wingo, A. P., Liu, Y., Gerasimov, E. S., Vattathil, S. M., Liu, J., Cutler, D. J., Epstein, M. P., Blokland, G. A. M., Thambisetty, M., Troncoso, J. C., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02509-y · Cited by 94 · see citing works
The distribution of common-variant effect sizes
O’Connor, L. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00901-3 · Cited by 81 · see citing works
Shade, L. M. P., Katsumata, Y., Abner, E. L., Aung, K. Z., Claas, S. A., Qiao, Q., Heberle, B. A., Brandon, J. A., Page, M. L., Hohman, T. J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01939-9 · Cited by 72 · see citing works
The lingering effects of Neanderthal introgression on human complex traits
Wei, X., Robles, C. R., Pazokitoroudi, A., Ganna, A., Gusev, A., Durvasula, A., Gazal, S., Loh, P.-R., Reich, D., & Sankararaman, S. (2023) · eLife · doi:10.7554/elife.80757 · Cited by 43 · see citing works
“Our pipeline starts with a subset of significantly associated NIMs that are relatively independent (p<10) followed by the application of a statistical finemapping method (SuSiE) within the 200 kb window around each NIM signal (Wang et al., 2020) and additional post- processing to obtain a set of NIMs that have an incr…”
MethodShared genetic architectures of educational attainment in East Asian and European populations
Chen, T.-T., Kim, J., Lam, M., Chuang, Y.-F., Chiu, Y.-L., Lin, S.-C., Jung, S.-H., Kim, B., Kim, S., Cho, C., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01781-9 · Cited by 18 · see citing works
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Meta-analysis of ACE inhibitor-induced angioedema identifies novel risk locus.
Mathey, C. M., Maj, C., Eriksson, N., Krebs, K., Westmeier, J., David, F. S., Koromina, M., Scheer, A. B., Szabo, N., Wedi, B., et al. (2024) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2023.11.921 · Cited by 15 · see citing works
Cross-ancestry comparison of aptamer and antibody protein measures
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Genetic determinants of plasma protein levels in the Estonian population
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Genetic drivers of progression in Alzheimer’s disease are distinct from disease risk
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Effects of introgressed Neanderthal alleles on present-day brain morphology
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- Authors
- Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Stephens, Matthew
- Venue
- Journal of the Royal Statistical Society Series B (Statistical Methodology)
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-07-10
- Volume
- 82
- Issue
- 5
- Pages
- 1273-1300
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- PMC
- PMC10201948
- OpenAlex
- W3042068575
- Cited by
- 1,121
- References
- 86
- Citations by year
- 2026: 292 · 2025: 241 · 2024: 231 · 2023: 162 · 2022: 98 · 2021: 64 · 2020: 28 · 2019: 4
- FWCI
- 66.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Gordon and Betty Moore Foundation (GBMF4559)
- University of Chicago
- National Institutes of Health (HG002585)
- National Human Genome Research Institute (R56HG002585, R01HG002585)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- feature selection, linear regression, Susie, variational approximation, credible sets, correlated variables, genetic fine-mapping, alternative splicing, genetic variants
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