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

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

Bioinformatics (Oxford, England) - 22 Dec 2021

Zhou Xin, Cai Xiaodong

Abstract excerpt

MOTIVATION: Genetic variations of expression quantitative trait loci (eQTLs) play a critical role in influencing complex traits and diseases development. Two main factors that affect the statistical power of detecting eQTLs are: (i) relatively small size of samples available, and (ii) heavy burden of multiple testing due to a very large number of variants to be tested. The later issue is particularly severe when...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computer Simulation
Share to Gene Expression Regulation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Gene Regulatory Networks

Share to Genome-Wide Association Study

Share to Polymorphism, Single Nucleotide

Share to Quantitative Trait Loci

Source

Open in PubMed Open full text

Identifiers

PMID: 34487140
DOI: 10.1093/bioinformatics/btab609
PMCID: PMC8696109

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Joint eQTL mapping and inference of gene regulatory network improves power of detecting both cis- and trans-eQTLs.PMID 34487140Read ↗

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

<i>Trans</i> -eQTLs reveal the architecture of human gene regulatory networksSemantic proximity Read ↗

Types of cis- and trans-gene regulation of expression quantitative trait loci across human tissuesSemantic proximity Read ↗

Sparse regression models for unraveling group and individual associations in eQTL mapping.Semantic proximity Read ↗

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.Semantic proximity Read ↗

Trans-eQTL mapping in gene sets identifies network effects of genetic variants.Semantic proximity Read ↗

Leveraging genome-wide effects on gene expression to identify disease-critical genes with <i>trans</i> -genetic componentsSemantic proximity Read ↗

Regulatory network topology and the genetic architecture of gene expressionSemantic proximity Read ↗

Reverse regression increases power for detecting trans-eQTLsSemantic proximity Read ↗

Tejaas: reverse regression increases power for detecting trans-eQTLs.Semantic proximity Read ↗

Joint variable selection and network modeling for detecting eQTLs.Semantic proximity Read ↗

Bayesian integration of genetics and epigenetics detects causal regulatory SNPs underlying expression variability.Semantic proximity Read ↗

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