ArticleGenetics2022
Disentangling genetic feature selection and aggregation in transcriptome-wide association studies.
Article in Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.PLoS computational biology · 2026Article
- Tissue-specific transfer learning improves functional variant and therapeutic target discoveries in breast and prostate cancer.PLoS genetics · 2026Article
- Population genetic variation characterised through serial independent pool-seq: the Cyprus Genome Project.Scientific reports · 2026Article
- MOKA: a pipeline for multiomics bridged SNP-set kernel association test.G3 (Bethesda, Md.) · 2026Article
- Learning image derived PDE-phenotypes from fMRI data.Brain informatics · 2025Article
- Multiome-wide Association Studies: Novel Approaches for Understanding Diseases.Genomics, proteomics & bioinformatics · 2024Review
- Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases.Computational and structural biotechnology journal · 2024Article
- TIPS: a novel pathway-guided joint model for transcriptome-wide association studies.Briefings in bioinformatics · 2024Article
- COFFEE: consensus single cell-type specific inference for gene regulatory networks.Briefings in bioinformatics · 2024Article
- Leveraging Random Effects in Cistrome-Wide Association Studies for Decoding the Genetic Determinants of Prostate Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Article
- FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization.Briefings in bioinformatics · 2024Article
- Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery.Briefings in bioinformatics · 2024Review
- A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders.American journal of human genetics · 2024Article
- Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels.Briefings in bioinformatics · 2023Article
- Review of T cell proliferation regulatory factors in treatment and prognostic prediction for solid tumors.Heliyon · 2023Review
- Editorial: Statistical methods for genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) and their applications.Frontiers in genetics · 2023Article
- Autoencoder-Transformed Transcriptome Improves Genotype-Phenotype Association Studies.IEEE transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The success of transcriptome-wide association studies (TWAS) has led to substantial research toward improving the predictive accuracy of its core component of genetically regulated expression (GReX). GReX links expression information with genotype and phenotype by playing two roles simultaneously: it acts as both the outcome of the genotype-based predictive models (for predicting expressions) and the linear combination of genotypes (as the predicted expressions) for association tests. From the perspective of machine learning (considering SNPs as features), these are actually two separable steps-feature selection and feature aggregation-which can be independently conducted. In this study, we show that the single approach of GReX limits the adaptability of TWAS methodology and practice. By conducting simulations and real data analysis, we demonstrate that disentangled protocols adapting straightforward approaches for feature selection (e.g., simple marker test) and aggregation (e.g., kernel machines) outperform the standard TWAS protocols that rely on GReX. Our development provides more powerful novel tools for conducting TWAS. More importantly, our characterization of the exact nature of TWAS suggests that, instead of questionably binding two distinct steps into the same statistical form (GReX), methodological research focusing on optimal combinations of feature selection and aggregation approaches will bring higher power to TWAS protocols.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.