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

Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores.

Bioinformatics (Oxford, England) - 27 Jan 2022

Warmerdam Robert, Lanting Pauline, Deelen Patrick, Franke Lude

Abstract excerpt

MOTIVATION: Identifying sample mix-ups in biobanks is essential to allow the repurposing of genetic data for clinical pharmacogenetics. Pharmacogenetic advice based on the genetic information of another individual is potentially harmful. Existing methods for identifying mix-ups are limited to datasets in which additional omics data (e.g. gene expression) is available. Cohorts lacking such data can only use sex,...

Read the complete abstract on PubMed

Topics

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

Share to Biological Specimen Banks
Share to Computer Simulation
Share to Genome-Wide Association Study

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Multifactorial Inheritance

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 34792549
DOI: 10.1093/bioinformatics/btab783
PMCID: PMC8796367

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores.PMID 34792549Read ↗

Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scoresSemantic proximity Read ↗

MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects.Semantic proximity Read ↗

Profiling and Leveraging Relatedness in a Precision Medicine Cohort of 92,455 Exomes.Semantic proximity Read ↗

Profiling and leveraging relatedness in a precision medicine cohort of 92,455 exomesSemantic proximity Read ↗

EraSOR: Erase Sample Overlap in polygenic score analysesSemantic proximity Read ↗

Publicly Available Privacy-preserving Benchmarks for Polygenic PredictionSemantic proximity Read ↗

Mitigating false-positive associations in rare disease gene discovery.Semantic proximity Read ↗

Empirical evaluation of analytic validity of polygenic scores.Semantic proximity Read ↗

SMaSH: Sample matching using SNPs in humans.Semantic proximity Read ↗

Analyzing the potential for incorrect haplotype calls with different pharmacogenomic assays in different populations: a simulation based on 1000 Genomes data.Semantic proximity Read ↗

Umpire 2.0: Simulating realistic, mixed-type, clinical data for machine learningSemantic proximity Read ↗

omicsPrint: detection of data linkage errors in multiple omics studies.Semantic proximity Read ↗

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