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BIGKnock：通过对生物库规模数据的 knockoff 分析进行基于基因的关联精细映射。

BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data.

机构信息

Department of Biostatistics, Columbia University, New York, NY, USA.

Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Genome Biol. 2023 Feb 13;24(1):24. doi: 10.1186/s13059-023-02864-6.

DOI:10.1186/s13059-023-02864-6
PMID:36782330
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9926792/
Abstract

We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at [Formula: see text] of the associated loci.

摘要

我们提出了 BIGKnock（通过置换检验进行生物银行规模的基于基因的关联测试），这是一种针对生物银行规模数据的计算高效的基于基因的测试方法，利用长程染色质相互作用数据，并通过置换检验进行条件全基因组测试。BIGKnock 可以在基因座上优先考虑因果基因而不是代理关联。我们将 BIGKnock 应用于 UK Biobank 数据，该数据包含 405296 名参与者的多种二分类和定量性状，结果表明，与传统的基于基因的测试相比，BIGKnock 产生的显著基因集合更小，其中包含因果基因的概率很高。我们进一步说明了它在关联基因座的 [Formula: see text] 处确定潜在因果基因的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/05ab41468511/13059_2023_2864_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/3bdd629aeeb4/13059_2023_2864_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/4dc069a42000/13059_2023_2864_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/a19e4fc0ff75/13059_2023_2864_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/6d3e4298da3d/13059_2023_2864_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/4f217ce336ef/13059_2023_2864_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/9fb52ceb8b44/13059_2023_2864_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/8ac7ebefd8e9/13059_2023_2864_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/05ab41468511/13059_2023_2864_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/3bdd629aeeb4/13059_2023_2864_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/4dc069a42000/13059_2023_2864_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/a19e4fc0ff75/13059_2023_2864_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/6d3e4298da3d/13059_2023_2864_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/4f217ce336ef/13059_2023_2864_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/9fb52ceb8b44/13059_2023_2864_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/8ac7ebefd8e9/13059_2023_2864_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7daf/9926792/05ab41468511/13059_2023_2864_Fig8_HTML.jpg

相似文献

[1]
BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data.

Genome Biol. 2023-2-13

[2]
Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics.

Am J Hum Genet. 2021-12-2

[3]
Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes.

Proc Natl Acad Sci U S A. 2021-11-23

[4]
An atlas of genetic associations in UK Biobank.

Nat Genet. 2018-10-22

[5]
Fast kernel-based association testing of non-linear genetic effects for biobank-scale data.

Nat Commun. 2023-8-15

[6]
Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses.

Nat Genet. 2021-8

[7]
FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts.

PLoS Genet. 2023-12

[8]
Fine-scale population structure in the UK Biobank: implications for genome-wide association studies.

Hum Mol Genet. 2020-9-29

[9]
GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.

Nat Commun. 2022-11-23

[10]
A powerful subset-based method identifies gene set associations and improves interpretation in UK Biobank.

Am J Hum Genet. 2021-4-1

引用本文的文献

[1]
Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms.

Genome Biol. 2025-3-30

[2]
TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference.

Bioinformatics. 2024-8-2

[3]
Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring.

Diagnostics (Basel). 2023-2-16

本文引用的文献

[1]
SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests.

Nat Genet. 2022-10

[2]
Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity.

Nat Genet. 2022-6

[3]
Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases.

Nat Genet. 2022-3

[4]
Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank.

Nat Genet. 2022-3

[5]
An effector index to predict target genes at GWAS loci.

Hum Genet. 2022-8

[6]
Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes.

Proc Natl Acad Sci U S A. 2021-11-23

[7]
Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics.

Am J Hum Genet. 2021-12-2

[8]
Deficiency of ASGR1 in pigs recapitulates reduced risk factor for cardiovascular disease in humans.

PLoS Genet. 2021-11

[9]
An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci.

Nat Genet. 2021-11

[10]
Exome sequencing and analysis of 454,787 UK Biobank participants.

Nature. 2021-11

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