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Simultaneous detection of novel genes and SNPs by adaptive p-value combination
Combining SNP p-values from GWAS summary data is a promising strategy for detecting novel genetic factors. Existing statistical methods for the p-value-based SNP-set testing confront two challenges. First, the statistical power of different methods depends on unknown patterns of genetic effects that...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9713700/ https://www.ncbi.nlm.nih.gov/pubmed/36468009 http://dx.doi.org/10.3389/fgene.2022.1009428 |
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author | Chen, Xiaohui Zhang, Hong Liu, Ming Deng, Hong-Wen Wu, Zheyang |
author_facet | Chen, Xiaohui Zhang, Hong Liu, Ming Deng, Hong-Wen Wu, Zheyang |
author_sort | Chen, Xiaohui |
collection | PubMed |
description | Combining SNP p-values from GWAS summary data is a promising strategy for detecting novel genetic factors. Existing statistical methods for the p-value-based SNP-set testing confront two challenges. First, the statistical power of different methods depends on unknown patterns of genetic effects that could drastically vary over different SNP sets. Second, they do not identify which SNPs primarily contribute to the global association of the whole set. We propose a new signal-adaptive analysis pipeline to address these challenges using the omnibus thresholding Fisher’s method (oTFisher). The oTFisher remains robustly powerful over various patterns of genetic effects. Its adaptive thresholding can be applied to estimate important SNPs contributing to the overall significance of the given SNP set. We develop efficient calculation algorithms to control the type I error rate, which accounts for the linkage disequilibrium among SNPs. Extensive simulations show that the oTFisher has robustly high power and provides a higher balanced accuracy in screening SNPs than the traditional Bonferroni and FDR procedures. We applied the oTFisher to study the genetic association of genes and haplotype blocks of the bone density-related traits using the summary data of the Genetic Factors for Osteoporosis Consortium. The oTFisher identified more novel and literature-reported genetic factors than existing p-value combination methods. Relevant computation has been implemented into the R package TFisher to support similar data analysis. |
format | Online Article Text |
id | pubmed-9713700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97137002022-12-02 Simultaneous detection of novel genes and SNPs by adaptive p-value combination Chen, Xiaohui Zhang, Hong Liu, Ming Deng, Hong-Wen Wu, Zheyang Front Genet Genetics Combining SNP p-values from GWAS summary data is a promising strategy for detecting novel genetic factors. Existing statistical methods for the p-value-based SNP-set testing confront two challenges. First, the statistical power of different methods depends on unknown patterns of genetic effects that could drastically vary over different SNP sets. Second, they do not identify which SNPs primarily contribute to the global association of the whole set. We propose a new signal-adaptive analysis pipeline to address these challenges using the omnibus thresholding Fisher’s method (oTFisher). The oTFisher remains robustly powerful over various patterns of genetic effects. Its adaptive thresholding can be applied to estimate important SNPs contributing to the overall significance of the given SNP set. We develop efficient calculation algorithms to control the type I error rate, which accounts for the linkage disequilibrium among SNPs. Extensive simulations show that the oTFisher has robustly high power and provides a higher balanced accuracy in screening SNPs than the traditional Bonferroni and FDR procedures. We applied the oTFisher to study the genetic association of genes and haplotype blocks of the bone density-related traits using the summary data of the Genetic Factors for Osteoporosis Consortium. The oTFisher identified more novel and literature-reported genetic factors than existing p-value combination methods. Relevant computation has been implemented into the R package TFisher to support similar data analysis. Frontiers Media S.A. 2022-11-17 /pmc/articles/PMC9713700/ /pubmed/36468009 http://dx.doi.org/10.3389/fgene.2022.1009428 Text en Copyright © 2022 Chen, Zhang, Liu, Deng and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Chen, Xiaohui Zhang, Hong Liu, Ming Deng, Hong-Wen Wu, Zheyang Simultaneous detection of novel genes and SNPs by adaptive p-value combination |
title | Simultaneous detection of novel genes and SNPs by adaptive p-value combination |
title_full | Simultaneous detection of novel genes and SNPs by adaptive p-value combination |
title_fullStr | Simultaneous detection of novel genes and SNPs by adaptive p-value combination |
title_full_unstemmed | Simultaneous detection of novel genes and SNPs by adaptive p-value combination |
title_short | Simultaneous detection of novel genes and SNPs by adaptive p-value combination |
title_sort | simultaneous detection of novel genes and snps by adaptive p-value combination |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9713700/ https://www.ncbi.nlm.nih.gov/pubmed/36468009 http://dx.doi.org/10.3389/fgene.2022.1009428 |
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