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

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits.

Genes - 21 Sept 2022

Svishcheva Gulnara R, Tiys Evgeny S, Elgaeva Elizaveta E, Feoktistova Sofia G, Timmers Paul R H J, Sharapov Sodbo Zh, Axenovich Tatiana I, Tsepilov Yakov A

Abstract excerpt

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and...

Read the complete abstract on PubMed

Topics

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

Share to Genetic Background
Share to Genome-Wide Association Study

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Lipids

Share to Phenotype

Share to Polymorphism, Single Nucleotide

Source

Open in PubMed Open full text

Identifiers

PMID: 36292579
DOI: 10.3390/genes13101694
PMCID: PMC9602050

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits.PMID 36292579Read ↗

GWAShug: a comprehensive platform for decoding the shared genetic basis between complex traits based on summary statistics.Semantic proximity Read ↗

Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data.Semantic proximity Read ↗

Contrasting the genetic architecture of 30 complex traits from summary association dataSemantic proximity Read ↗

Integrating common and rare variants improves polygenic risk prediction across diverse populations.Semantic proximity Read ↗

Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits.Semantic proximity Read ↗

Integrating multiple traits for improving polygenic risk prediction in disease and pharmacogenomics GWAS.Semantic proximity Read ↗

Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary dataSemantic proximity Read ↗

Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies.Semantic proximity Read ↗

An enhanced framework for local genetic correlation analysisSemantic proximity Read ↗

Improving genetic prediction by leveraging genetic correlations among human diseases and traits.Semantic proximity Read ↗

Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data.Semantic proximity Read ↗

Estimating heritability and its enrichment in tissue-specific gene sets in admixed populationsSemantic proximity Read ↗

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