PALM : a powerful and adaptive latent model for prioritizing risk variants with functional annotations
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Original language | English |
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Article number | btad068 |
Journal / Publication | Bioinformatics |
Volume | 39 |
Issue number | 2 |
Online published | 6 Feb 2023 |
Publication status | Published - Feb 2023 |
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DOI | DOI |
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Publisher's Copyright Statement
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85148773508&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(225b297a-6044-41b5-9351-db4db78a9b06).html |
Abstract
MOTIVATION: The findings from genome-wide association studies (GWASs) have greatly helped us to understand the genetic basis of human complex traits and diseases. Despite the tremendous progress, much effects are still needed to address several major challenges arising in GWAS. First, most GWAS hits are located in the non-coding region of human genome, and thus their biological functions largely remain unknown. Second, due to the polygenicity of human complex traits and diseases, many genetic risk variants with weak or moderate effects have not been identified yet.
RESULTS: To address the above challenges, we propose a powerful and adaptive latent model (PALM) to integrate cell-type/tissue-specific functional annotations with GWAS summary statistics. Unlike existing methods, which are mainly based on linear models, PALM leverages a tree ensemble to adaptively characterize non-linear relationship between functional annotations and the association status of genetic variants. To make PALM scalable to millions of variants and hundreds of functional annotations, we develop a functional gradient-based expectation-maximization algorithm, to fit the tree-based non-linear model in a stable manner. Through comprehensive simulation studies, we show that PALM not only controls false discovery rate well, but also improves statistical power of identifying risk variants. We also apply PALM to integrate summary statistics of 30 GWASs with 127 cell type/tissue-specific functional annotations. The results indicate that PALM can identify more risk variants as well as rank the importance of functional annotations, yielding better interpretation of GWAS results.
AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/YangLabHKUST/PALM.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2023. Published by Oxford University Press.
RESULTS: To address the above challenges, we propose a powerful and adaptive latent model (PALM) to integrate cell-type/tissue-specific functional annotations with GWAS summary statistics. Unlike existing methods, which are mainly based on linear models, PALM leverages a tree ensemble to adaptively characterize non-linear relationship between functional annotations and the association status of genetic variants. To make PALM scalable to millions of variants and hundreds of functional annotations, we develop a functional gradient-based expectation-maximization algorithm, to fit the tree-based non-linear model in a stable manner. Through comprehensive simulation studies, we show that PALM not only controls false discovery rate well, but also improves statistical power of identifying risk variants. We also apply PALM to integrate summary statistics of 30 GWASs with 127 cell type/tissue-specific functional annotations. The results indicate that PALM can identify more risk variants as well as rank the importance of functional annotations, yielding better interpretation of GWAS results.
AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/YangLabHKUST/PALM.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2023. Published by Oxford University Press.
Research Area(s)
Citation Format(s)
PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations. / Yu, Xinyi; Xiao, Jiashun; Cai, Mingxuan et al.
In: Bioinformatics, Vol. 39, No. 2, btad068, 02.2023.
In: Bioinformatics, Vol. 39, No. 2, btad068, 02.2023.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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