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The Impact of Stability Considerations on Genetic Fine-Mapping

Fine-mapping methods, which aim to identify genetic variants responsible for complex traits following genetic association studies, typically assume that sufficient adjustments for confounding within the association study cohort have been made, e.g., through regressing out the top principal component...

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Autores principales: Aw, Alan, Jin, Lionel Chentian, Ioannidis, Nilah, Song, Yun S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120703/
https://www.ncbi.nlm.nih.gov/pubmed/37090514
http://dx.doi.org/10.1101/2023.04.11.536456
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author Aw, Alan
Jin, Lionel Chentian
Ioannidis, Nilah
Song, Yun S.
author_facet Aw, Alan
Jin, Lionel Chentian
Ioannidis, Nilah
Song, Yun S.
author_sort Aw, Alan
collection PubMed
description Fine-mapping methods, which aim to identify genetic variants responsible for complex traits following genetic association studies, typically assume that sufficient adjustments for confounding within the association study cohort have been made, e.g., through regressing out the top principal components (i.e., residualization). Despite its widespread use, however, residualization may not completely remove all sources of confounding. Here, we propose a complementary stability-guided approach that does not rely on residualization, which identifies consistently fine-mapped variants across different genetic backgrounds or environments. We demonstrate the utility of this approach by applying it to fine-map eQTLs in the GEUVADIS data. Using 378 different functional annotations of the human genome, including recent deep learning-based annotations (e.g., Enformer), we compare enrichments of these annotations among variants for which the stability and traditional residualization-based fine-mapping approaches agree against those for which they disagree, and find that the stability approach enhances the power of traditional fine-mapping methods in identifying variants with functional impact. Finally, in cases where the two approaches report distinct variants, our approach identifies variants comparably enriched for functional annotations. Our findings suggest that the stability principle, as a conceptually simple device, complements existing approaches to fine-mapping, reinforcing recent advocacy of evaluating cross-population and cross-environment portability of biological findings. To support visualization and interpretation of our results, we provide a Shiny app, available at: https://alan-aw.shinyapps.io/stability_v0/.
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spelling pubmed-101207032023-04-22 The Impact of Stability Considerations on Genetic Fine-Mapping Aw, Alan Jin, Lionel Chentian Ioannidis, Nilah Song, Yun S. bioRxiv Article Fine-mapping methods, which aim to identify genetic variants responsible for complex traits following genetic association studies, typically assume that sufficient adjustments for confounding within the association study cohort have been made, e.g., through regressing out the top principal components (i.e., residualization). Despite its widespread use, however, residualization may not completely remove all sources of confounding. Here, we propose a complementary stability-guided approach that does not rely on residualization, which identifies consistently fine-mapped variants across different genetic backgrounds or environments. We demonstrate the utility of this approach by applying it to fine-map eQTLs in the GEUVADIS data. Using 378 different functional annotations of the human genome, including recent deep learning-based annotations (e.g., Enformer), we compare enrichments of these annotations among variants for which the stability and traditional residualization-based fine-mapping approaches agree against those for which they disagree, and find that the stability approach enhances the power of traditional fine-mapping methods in identifying variants with functional impact. Finally, in cases where the two approaches report distinct variants, our approach identifies variants comparably enriched for functional annotations. Our findings suggest that the stability principle, as a conceptually simple device, complements existing approaches to fine-mapping, reinforcing recent advocacy of evaluating cross-population and cross-environment portability of biological findings. To support visualization and interpretation of our results, we provide a Shiny app, available at: https://alan-aw.shinyapps.io/stability_v0/. Cold Spring Harbor Laboratory 2023-04-13 /pmc/articles/PMC10120703/ /pubmed/37090514 http://dx.doi.org/10.1101/2023.04.11.536456 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Aw, Alan
Jin, Lionel Chentian
Ioannidis, Nilah
Song, Yun S.
The Impact of Stability Considerations on Genetic Fine-Mapping
title The Impact of Stability Considerations on Genetic Fine-Mapping
title_full The Impact of Stability Considerations on Genetic Fine-Mapping
title_fullStr The Impact of Stability Considerations on Genetic Fine-Mapping
title_full_unstemmed The Impact of Stability Considerations on Genetic Fine-Mapping
title_short The Impact of Stability Considerations on Genetic Fine-Mapping
title_sort impact of stability considerations on genetic fine-mapping
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120703/
https://www.ncbi.nlm.nih.gov/pubmed/37090514
http://dx.doi.org/10.1101/2023.04.11.536456
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