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

Functional regression method for whole genome eQTL epistasis analysis with sequencing data.

BMC genomics - 18 May 2017

Xu Kelin, Jin Li, Xiong Momiao

Abstract excerpt

BACKGROUND: Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional...

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 Gene Ontology
Share to Gene Regulatory Networks

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to MAP Kinase Signaling System

Share to Nonlinear Dynamics

Share to Quantitative Trait Loci

Share to Regression Analysis

Share to Whole Genome Sequencing

Source

Open in PubMed Open full text

Identifiers

PMID: 28521784
DOI: 10.1186/s12864-017-3777-4
PMCID: PMC5436462

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Functional regression method for whole genome eQTL epistasis analysis with sequencing data.PMID 28521784Read ↗

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.Semantic proximity Read ↗

Epistasis analysis for quantitative traits by functional regression model.Semantic proximity Read ↗

Integration of Multi-omics Data for Expression Quantitative Trait Loci (eQTL) Analysis and eQTL Epistasis.Semantic proximity Read ↗

Joint eQTL mapping and inference of gene regulatory network improves power of detecting both cis- and trans-eQTLs.Semantic proximity Read ↗

Detecting Epistasis with the Marginal Epistasis Test in Genetic Mapping Studies of Quantitative TraitsSemantic proximity Read ↗

Joint modeling of eQTLs and parent-of-origin effects using an orthogonal framework with RNA-seq data.Semantic proximity Read ↗

Modularity and interactions in the genetics of gene expression.Semantic proximity Read ↗

W-Test for Genetic Epistasis Testing.Semantic proximity Read ↗

Joint eQTL mapping and Inference of Gene Regulatory Network Improves Power of Detecting both <i>cis</i> - and <i>trans</i> -eQTLsSemantic proximity Read ↗

eQTL.Semantic proximity Read ↗

Quantitative trait locus analysis for next-generation sequencing with the functional linear models.Semantic proximity Read ↗

Interpretable network-guided epistasis detection.Semantic proximity Read ↗

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