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

Using GWAS summary data to impute traits for genotyped individuals.

HGG advances - 13 Jul 2023

Ren Jingchen, Lin Zhaotong, He Ruoyu, Shen Xiaotong, Pan Wei

Abstract excerpt

Genome-wide association study (GWAS) summary data have become extremely useful in daily routine data analysis, largely facilitating new methods development and new applications. However, a severe limitation with the current use of GWAS summary data is its exclusive restriction to only linear single nucleotide polymorphism (SNP)-trait association analyses. To further expand the use of GWAS summary data, along with...

Read the complete abstract on PubMed

Topics

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

Share to Genome-Wide Association Study
Share to Genotype
Share to Phenotype

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Polymorphism, Single Nucleotide

Source

Open in PubMed Open full text

Identifiers

PMID: 37181332
DOI: 10.1016/j.xhgg.2023.100197
PMCID: PMC10173780

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

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

Using GWAS summary data to impute traits for genotyped individuals.PMID 37181332Read ↗

Trait imputation enhances nonlinear genetic prediction for some traits.Semantic proximity Read ↗

Integrating GWAS summary statistics, individual-level genotypic and omic data to enhance the performance for large-scale trait imputation.Semantic proximity Read ↗

Local True Discovery Rate Weighted Polygenic Scores Using GWAS Summary Data.Semantic proximity Read ↗

Non-parametric polygenic risk prediction using partitioned GWAS summary statisticsSemantic proximity Read ↗

Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.Semantic proximity Read ↗

Statistical inference with large-scale trait imputation.Semantic proximity Read ↗

Reconstructing SNP Allele and Genotype Frequencies from GWAS Summary StatisticsSemantic proximity Read ↗

Dissecting the genetics of complex traits using summary association statistics.Semantic proximity Read ↗

Dissecting the genetics of complex traits using summary association statisticsSemantic proximity Read ↗

Reconstructing SNP allele and genotype frequencies from GWAS summary statistics.Semantic proximity Read ↗

Nonlinear post-selection inference for genome-wide association studies.Semantic proximity Read ↗

Accurate and efficient estimation of local heritability using summary statistics and the linkage disequilibrium matrix.Semantic proximity Read ↗

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