Demo Data from EDBT 2026 / reviewers in the wild / expert
Pauline Lanting
dblp:313/4863
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-7288-4445ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › statistical genetics › genomic prediction
polygenic risk score |
0.6 | 1 | 2022 | Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores · Bioinform. 2022 |
Bioinformatics and computational biology › genomics › pharmacogenomics
pharmacogenetics |
0.2 | 1 | 2022 | Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores · Bioinform. 2022 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.6polygenic score · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scoresabstractMOTIVATION: 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, which can reveal only half of the mix-ups. Here, we describe Idéfix, a method for the identification of accidental sample mix-ups in biobanks using polygenic scores. RESULTS: In the Lifelines population-based biobank, we calculated polygenic scores (PGSs) for 25 traits for 32 786 participants. We then applied Idéfix to compare the actual phenotypes to PGSs, and to use the relative discordance that is expected for mix-ups, compared to correct samples. In a simulation, using induced mix-ups, Idéfix reaches an AUC of 0.90 using 25 polygenic scores and sex. This is a substantial improvement over using only sex, which has an AUC of 0.75. Subsequent simulations present Idéfix's potential in varying datasets with more powerful PGSs. This suggests its performance will likely improve when more highly powered GWASs for commonly measured traits will become available. Idéfix can be used to identify a set of high-quality participants for whom it is very unlikely that they reflect sample mix-ups, and for these participants we can use genetic data for clinical purposes, such as pharmacogenetic profiles. For instance, in Lifelines, we can select 34.4% of participants, reducing the sample mix-up rate from 0.15% to 0.01%. AVAILABILITYAND IMPLEMENTATION: Idéfix is freely available at https://github.com/molgenis/systemsgenetics/wiki/Idefix. The individual-level data that support the findings were obtained from the Lifelines biobank under project application number ov16_0365. Data is made available upon reasonable request submitted to the LifeLines Research office ([email protected], https://www.lifelines.nl/researcher/how-to-apply/apply-here). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Robert Warmerdam, Pauline Lanting, Patrick Deelen, Lude Franke |
Bioinform. | 2 |