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

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data.

BMC medical informatics and decision making - 1 Jan 2013

Kang Chiyong, Yu Hyeji, Yi Gwan-Su

Abstract excerpt

BACKGROUND: Due to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity. METHODS: We aim to detect type 2 diabetes...

Read the complete abstract on PubMed

Topics

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

Share to Computer Communication Networks
Share to Databases as Topic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Diabetes Mellitus, Type 2

Share to False Positive Reactions

Share to Genome-Wide Association Study

Share to Genotyping Techniques

Share to Models, Genetic

Share to Polymorphism, Single Nucleotide

Source

Open in PubMed Open full text

Identifiers

PMID: 23566118
DOI: 10.1186/1472-6947-13-S1-S3
PMCID: PMC3618247

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

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data.PMID 23566118Read ↗

Evaluation of Bayesian Linear Regression Models for Gene Set Prioritization in Complex DiseasesSemantic proximity Read ↗

Prioritising Causal Genes at Type 2 Diabetes Risk Loci.Semantic proximity Read ↗

Integration of GWAS-Derived Polygenic Risk Scores with Single-Cell RNA Sequencing to Identify Cell-Type–Specific Genetic Risk in Type 2 Diabetes.Semantic proximity Read ↗

Identification of susceptible genes for complex chronic diseases based on disease risk functional SNPs and interaction networks.Semantic proximity Read ↗

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.Semantic proximity Read ↗

Identification of Gene Signature Associated with Type 2 Diabetes Mellitus by Integrating Mutation and Expression Data.Semantic proximity Read ↗

Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes.Semantic proximity Read ↗

Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity.Semantic proximity Read ↗

In silico functional and pathway analysis of risk genes and SNPs for type 2 diabetes in Asian population.Semantic proximity Read ↗

Comparing the efficacy of SNP filtering methods for identifying a single causal SNP in a known association region.Semantic proximity Read ↗

3DFAACTS-SNP: Using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of Type-1 Diabetes (T1D) riskSemantic proximity Read ↗

A machine-learning framework to characterize functional disease architectures and prioritize disease variantsSemantic proximity Read ↗

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