Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies.

Methods in molecular biology (Clifton, N.J.) - 1 Jan 2021

Shi Xingjie, Yang Can, Liu Jin

Abstract excerpt

Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. TWASs have become increasingly popular. They have been used to analyze many complex traits with expression profiles from different tissues, successfully enhancing the discovery of genetic risk loci for...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Epistasis, Genetic
Share to Genetic Testing
Share to Genome, Human

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Multifactorial Inheritance

Share to Quantitative Trait Loci

Share to Software

Share to Transcriptome

Share to Uncertainty

Source

Identifiers

PMID: 33733352
DOI: 10.1007/978-1-0716-0947-7_7

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies.PMID 33733352Read ↗

CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies.Semantic proximity Read ↗

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits.Semantic proximity Read ↗

A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies.Semantic proximity Read ↗

Modeling prediction error improves power of transcriptome-wide association studiesSemantic proximity Read ↗

Integrative approaches for large-scale transcriptome-wide association studies.Semantic proximity Read ↗

Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability.Semantic proximity Read ↗

Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretabilitySemantic proximity Read ↗

Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits.Semantic proximity Read ↗

Accounting for nonlinear effects of gene expression identifies additional associated genes in transcriptome-wide association studies.Semantic proximity Read ↗

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations.Semantic proximity Read ↗

SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.Semantic proximity Read ↗

Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer's dementia.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.