| Title: | TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference |
| Journal: | Bioinformatics |
| Published: | 2 Aug 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39189955/ |
| DOI: | https://doi.org/10.1093/bioinformatics/btae502 |
Publication 12944
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Abstract
MOTIVATION: Transcriptome-wide association study (TWAS) aims to identify trait-associated genes regulated by significant variants to explore the underlying biological mechanisms at a tissue-specific level. Despite the advancement of current TWAS methods to cover diverse traits, traditional approaches still face two main challenges: (i) the lack of methods that can guarantee finite-sample false discovery rate (FDR) control in identifying trait-associated genes; and (ii) the requirement for individual-level data, which is often inaccessible.</p>
RESULTS: To address this challenge, we propose a powerful knockoff inference method termed TWAS-GKF to identify candidate trait-associated genes with a guaranteed finite-sample FDR control. TWAS-GKF introduces the main idea of Ghostknockoff inference to generate knockoff variables using only summary statistics instead of individual-level data. In extensive studies, we demonstrate that TWAS-GKF successfully controls the finite-sample FDR under a pre-specified FDR level across all settings. We further apply TWAS-GKF to identify genes in brain cerebellum tissue from the Genotype-Tissue Expression (GTEx) v8 project associated with schizophrenia (SCZ) from the Psychiatric Genomics Consortium (PGC), and genes in liver tissue related to low-density lipoprotein cholesterol (LDL-C) from the UK Biobank, respectively. The results reveal that the majority of the identified genes are validated by Open Targets Validation Platform.</p>
AVAILABILITY AND IMPLEMENTATION: The R package TWAS.GKF is publicly available at https://github.com/AnqiWang2021/TWAS.GKF.</p>
8 Keywords
- Algorithms
- Gene Expression Profiling
- Genome-Wide Association Study
- Humans
- Polymorphism, Single Nucleotide
- Quantitative Trait Loci
- Schizophrenia
- Transcriptome
3 Authors
- Anqi Wang
- Peixin Tian
- Yan Dora Zhang
1 Application
| Application ID | Title |
|---|---|
| 140822 | Developing statistical methods to improve fine-mapping |
Enabling scientific discoveries that improve human health