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

Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing - 1 Jan 2018

Li Binglan, Verma Shefali S, Veturi Yogasudha C, Verma Anurag, Bradford Yuki, Haas David W, Ritchie Marylyn D

Abstract excerpt

Genome-wide association studies (GWAS) have been successful in facilitating the understanding of genetic architecture behind human diseases, but this approach faces many challenges. To identify disease-related loci with modest to weak effect size, GWAS requires very large sample sizes, which can be computational burdensome. In addition, the interpretation of discovered associations remains difficult. PrediXcan...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Databases, Genetic

Share to Gene Expression

Share to Genetic Association Studies

Share to Models, Statistical

Share to Quantitative Trait Loci

Share to Software

Source

Open in PubMed Open full text

Identifiers

PMID: 29218904
PMCID: PMC5749400

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression.PMID 29218904Read ↗

PrediXcan: Trait Mapping Using Human Transcriptome RegulationSemantic proximity Read ↗

A gene-based association method for mapping traits using reference transcriptome data.Semantic proximity Read ↗

Accuracy of gene expression prediction from genotype data with PrediXcan varies across diverse populationsSemantic proximity Read ↗

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.Semantic proximity Read ↗

Computational Assessment of the Expression-modulating Potential for Non-coding Variants.Semantic proximity Read ↗

Hi-GREx: A 3D Genome–Guided Framework for enhancing Gene Expression Prediction Using Hi-C–Selected Distal SNPsSemantic proximity Read ↗

PGA: post-GWAS analysis for disease gene identification.Semantic proximity Read ↗

Cross-population enhancement of PrediXcan predictions with a gnomAD-based east Asian reference framework.Semantic proximity Read ↗

Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models.Semantic proximity Read ↗

Simplifying causal gene identification in GWAS loci.Semantic proximity Read ↗

Predicting ExWAS findings from GWAS data: a shorter path to causal genes.Semantic proximity Read ↗

Accurate eQTL prioritization with an ensemble-based frameworkSemantic proximity Read ↗

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