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 genomics complements quantitative genetics in identifying disease-gene associations.

PLoS computational biology - 11 Nov 2010

Guan Yuanfang, Ackert-Bicknell Cheryl L, Kell Braden, Troyanskaya Olga G, Hibbs Matthew A

Abstract excerpt

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these...

Read the complete abstract on PubMed

Topics

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

Share to Artificial Intelligence
Share to ATP Binding Cassette Transporter, Subfamily G, Member 8

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to ATP-Binding Cassette Transporters

Share to Bayes Theorem

Share to Bone Density

Share to Lipoproteins

Share to Osteoporosis

Share to Tissue Inhibitor of Metalloproteinase-2

Source

Open in PubMed Open full text

Identifiers

PMID: 21085640
DOI: 10.1371/journal.pcbi.1000991
PMCID: PMC2978695

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

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

Functional genomics complements quantitative genetics in identifying disease-gene associations.PMID 21085640Read ↗

A Framework to Analyse and Interpret Mouse Functional Genome by Prioritizing High Impact SNPsSemantic proximity Read ↗

An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function.Semantic proximity Read ↗

An integrative genetics approach to identify candidate genes regulating BMD: combining linkage, gene expression, and association.Semantic proximity Read ↗

Large-scale in silico mapping of complex quantitative traits in inbred mice.Semantic proximity Read ↗

Prioritization of Genes Relevant to Bone Fragility Through the Unbiased Integration of Aging Mouse Bone Transcriptomics and Human GWAS Analyses.Semantic proximity Read ↗

Joint mouse-human phenome-wide association to test gene function and disease risk.Semantic proximity Read ↗

Collaborative cross mice in a genetic association study reveal new candidate genes for bone microarchitecture.Semantic proximity Read ↗

High Throughput Computational Mouse Genetic AnalysisSemantic proximity Read ↗

Network-Based Functional Prediction Augments Genetic Association To Predict Candidate Genes for Histamine Hypersensitivity in Mice.Semantic proximity Read ↗

Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits.Semantic proximity Read ↗

A genetical genomics approach to genome scans increases power for QTL mapping.Semantic proximity Read ↗

An automated multi-modal graph-based pipeline for mouse genetic discovery.Semantic proximity Read ↗

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