Knockoff procedure improves causal gene identifications in conditional transcriptome-wide association studies DOI Creative Commons

Xiangyu Zhang,

Lijun Wang, Jia Zhao

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 8, 2025

Abstract Transcriptome-wide association studies (TWASs) have been developed to nominate candidate genes associated with complex traits by integrating genome-wide (GWASs) expression quantitative trait loci (eQTL) data. However, most existing TWAS methods evaluate the marginal between a single gene and of interest without accounting for other within same genomic region or from different tissues. Additionally, false-positive gene-trait pairs can arise due correlations direct effects genetic variants. In this study, we introduce TWASKnockoff, new knockoff-based framework detecting causal gene-tissue using GWAS summary statistics eQTL Unlike testing in traditional methods, TWASKnockoff examines conditional independence each pair, considering both cis-predicted across levels estimates theoretical correlation matrix all elements (cis-predicted genotypes variants) averaging estimations parametric boot-strap samples then performs inference detect while controlling false discovery rate (FDR). Through empirical simulations an application type 2 diabetes (T2D) data, demonstrate that achieves superior FDR control improves average power at fixed level.

Язык: Английский

Knockoff procedure improves causal gene identifications in conditional transcriptome-wide association studies DOI Creative Commons

Xiangyu Zhang,

Lijun Wang, Jia Zhao

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 8, 2025

Abstract Transcriptome-wide association studies (TWASs) have been developed to nominate candidate genes associated with complex traits by integrating genome-wide (GWASs) expression quantitative trait loci (eQTL) data. However, most existing TWAS methods evaluate the marginal between a single gene and of interest without accounting for other within same genomic region or from different tissues. Additionally, false-positive gene-trait pairs can arise due correlations direct effects genetic variants. In this study, we introduce TWASKnockoff, new knockoff-based framework detecting causal gene-tissue using GWAS summary statistics eQTL Unlike testing in traditional methods, TWASKnockoff examines conditional independence each pair, considering both cis-predicted across levels estimates theoretical correlation matrix all elements (cis-predicted genotypes variants) averaging estimations parametric boot-strap samples then performs inference detect while controlling false discovery rate (FDR). Through empirical simulations an application type 2 diabetes (T2D) data, demonstrate that achieves superior FDR control improves average power at fixed level.

Язык: Английский

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