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Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision
Statistical fine-mapping prioritizes putative causal variants from a large number of candidate variants, and is widely used in expression quantitative loci (eQTLs) studies. In eQTL fine-mapping, the existence of causal variants for gene expression is not guaranteed, since the genetic heritability of...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10616627/ https://www.ncbi.nlm.nih.gov/pubmed/37915762 http://dx.doi.org/10.1093/nargab/lqad090 |
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author | Wang, Qingbo S Edahiro, Ryuya Namkoong, Ho Hasegawa, Takanori Shirai, Yuya Sonehara, Kyuto Kumanogoh, Atsushi Ishii, Makoto Koike, Ryuji Kimura, Akinori Imoto, Seiya Miyano, Satoru Ogawa, Seishi Kanai, Takanori Fukunaga, Koichi Okada, Yukinori |
author_facet | Wang, Qingbo S Edahiro, Ryuya Namkoong, Ho Hasegawa, Takanori Shirai, Yuya Sonehara, Kyuto Kumanogoh, Atsushi Ishii, Makoto Koike, Ryuji Kimura, Akinori Imoto, Seiya Miyano, Satoru Ogawa, Seishi Kanai, Takanori Fukunaga, Koichi Okada, Yukinori |
author_sort | Wang, Qingbo S |
collection | PubMed |
description | Statistical fine-mapping prioritizes putative causal variants from a large number of candidate variants, and is widely used in expression quantitative loci (eQTLs) studies. In eQTL fine-mapping, the existence of causal variants for gene expression is not guaranteed, since the genetic heritability of gene expression explained by nearby (cis-) variants is limited. Here we introduce a refined fine-mapping algorithm, named Knockoff–Finemap combination (KFc). KFc estimates the probability that the causal variant(s) exist in the cis-window of a gene through construction of knockoff genotypes (i.e. a set of synthetic genotypes that resembles the original genotypes), and uses it to adjust the posterior inclusion probabilities (PIPs). Utilizing simulated gene expression data, we show that KFc results in calibrated PIP distribution with improved precision. When applied to gene expression data of 465 genotyped samples from the Japan COVID-19 Task Force (JCTF), KFc resulted in significant enrichment of a functional score as well as reporter assay hits in the top PIP bins. When combined with functional priors derived from an external fine-mapping study (GTEx), KFc resulted in a significantly higher proportion of hematopoietic trait putative causal variants in the top PIP bins. Our work presents improvements in the precision of a major fine-mapping algorithm. |
format | Online Article Text |
id | pubmed-10616627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-106166272023-11-01 Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision Wang, Qingbo S Edahiro, Ryuya Namkoong, Ho Hasegawa, Takanori Shirai, Yuya Sonehara, Kyuto Kumanogoh, Atsushi Ishii, Makoto Koike, Ryuji Kimura, Akinori Imoto, Seiya Miyano, Satoru Ogawa, Seishi Kanai, Takanori Fukunaga, Koichi Okada, Yukinori NAR Genom Bioinform Standard Article Statistical fine-mapping prioritizes putative causal variants from a large number of candidate variants, and is widely used in expression quantitative loci (eQTLs) studies. In eQTL fine-mapping, the existence of causal variants for gene expression is not guaranteed, since the genetic heritability of gene expression explained by nearby (cis-) variants is limited. Here we introduce a refined fine-mapping algorithm, named Knockoff–Finemap combination (KFc). KFc estimates the probability that the causal variant(s) exist in the cis-window of a gene through construction of knockoff genotypes (i.e. a set of synthetic genotypes that resembles the original genotypes), and uses it to adjust the posterior inclusion probabilities (PIPs). Utilizing simulated gene expression data, we show that KFc results in calibrated PIP distribution with improved precision. When applied to gene expression data of 465 genotyped samples from the Japan COVID-19 Task Force (JCTF), KFc resulted in significant enrichment of a functional score as well as reporter assay hits in the top PIP bins. When combined with functional priors derived from an external fine-mapping study (GTEx), KFc resulted in a significantly higher proportion of hematopoietic trait putative causal variants in the top PIP bins. Our work presents improvements in the precision of a major fine-mapping algorithm. Oxford University Press 2023-10-30 /pmc/articles/PMC10616627/ /pubmed/37915762 http://dx.doi.org/10.1093/nargab/lqad090 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Standard Article Wang, Qingbo S Edahiro, Ryuya Namkoong, Ho Hasegawa, Takanori Shirai, Yuya Sonehara, Kyuto Kumanogoh, Atsushi Ishii, Makoto Koike, Ryuji Kimura, Akinori Imoto, Seiya Miyano, Satoru Ogawa, Seishi Kanai, Takanori Fukunaga, Koichi Okada, Yukinori Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision |
title | Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision |
title_full | Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision |
title_fullStr | Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision |
title_full_unstemmed | Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision |
title_short | Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision |
title_sort | estimating gene-level false discovery probability improves eqtl statistical fine-mapping precision |
topic | Standard Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10616627/ https://www.ncbi.nlm.nih.gov/pubmed/37915762 http://dx.doi.org/10.1093/nargab/lqad090 |
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