Demo Data from VLDB 2026 / reviewers in the wild / expert
Jack Euesden
dblp:162/0534
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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 · 75% Medical and health informatics · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › genomics
genome-wide association study |
0.2 | 1 | 2015 | PRSice: Polygenic Risk Score software · Bioinform. 2015 |
Bioinformatics and computational biology › statistical genetics › genomic prediction
polygenic risk score |
0.2 | 1 | 2015 | PRSice: Polygenic Risk Score software · Bioinform. 2015 |
Medical and health informatics › clinical prediction
risk prediction |
0.2 | 1 | 2015 | PRSice: Polygenic Risk Score software · Bioinform. 2015 |
Bioinformatics and computational biology
statistical genetics |
0.2 | 1 | 2015 | PRSice: Polygenic Risk Score software · Bioinform. 2015 |
Methods — techniques the papers use, named apart from their topics
linkage disequilibrium pruning · 0.2genome-wide association study · 0.2ancestry-informative variables · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | PRSice: Polygenic Risk Score softwareabstractSUMMARY: A polygenic risk score (PRS) is a sum of trait-associated alleles across many genetic loci, typically weighted by effect sizes estimated from a genome-wide association study. The application of PRS has grown in recent years as their utility for detecting shared genetic aetiology among traits has become appreciated; PRS can also be used to establish the presence of a genetic signal in underpowered studies, to infer the genetic architecture of a trait, for screening in clinical trials, and can act as a biomarker for a phenotype. Here we present the first dedicated PRS software, PRSice ('precise'), for calculating, applying, evaluating and plotting the results of PRS. PRSice can calculate PRS at a large number of thresholds ("high resolution") to provide the best-fit PRS, as well as provide results calculated at broad P-value thresholds, can thin Single Nucleotide Polymorphisms (SNPs) according to linkage disequilibrium and P-value or use all SNPs, handles genotyped and imputed data, can calculate and incorporate ancestry-informative variables, and can apply PRS across multiple traits in a single run. We exemplify the use of PRSice via application to data on schizophrenia, major depressive disorder and smoking, illustrate the importance of identifying the best-fit PRS and estimate a P-value significance threshold for high-resolution PRS studies. AVAILABILITY AND IMPLEMENTATION: PRSice is written in R, including wrappers for bash data management scripts and PLINK-1.9 to minimize computational time. PRSice runs as a command-line program with a variety of user-options, and is freely available for download from http://PRSice.info CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Jack Euesden, Cathryn M. Lewis, Paul F. O'Reilly |
Bioinform. | 1 |