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Cathryn M. Lewis

dblp:19/273 · DBLP profile ↗
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3ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0002-8249-8476ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 3 · 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
3 papers
Bioinformatics and computational biology · 87% Medical and health informatics · 13%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics › genomic prediction
polygenic risk score
1.022024
The GenoPred pipeline: a comprehensive and scalable pipeline for polygenic scoring · Bioinform. 2024
PRSice: Polygenic Risk Score software · Bioinform. 2015
Bioinformatics and computational biology › genomics
genome-wide association study
0.212015
PRSice: Polygenic Risk Score software · Bioinform. 2015
Medical and health informatics › clinical prediction
risk prediction
0.212015
PRSice: Polygenic Risk Score software · Bioinform. 2015
Bioinformatics and computational biology
statistical genetics
0.212015
PRSice: Polygenic Risk Score software · Bioinform. 2015
Bioinformatics and computational biology › statistical genetics
genetic association study
0.112005
GSMA: software implementation of the genome search meta-analysis method · Bioinform. 2005

Methods — techniques the papers use, named apart from their topics

snakemake · 0.8quality control · 0.8ancestry inference · 0.8linkage disequilibrium pruning · 0.2genome-wide association study · 0.2ancestry-informative variables · 0.2genome search meta-analysis · 0.1
YearPublicationVenuePosition
2024 The GenoPred pipeline: a comprehensive and scalable pipeline for polygenic scoring
abstract
MOTIVATION: Polygenic scoring is an approach for estimating an individual's likelihood of a given outcome. Polygenic scores are typically calculated from genome-wide association study (GWAS) summary statistics and individual-level genotype data for the target sample. Going from genotype to interpretable polygenic scores involves many steps and there are many methods available, limiting the accessibility of polygenic scores for research and clinical application. Additional challenges exist for studies in ancestrally diverse populations. We have implemented the leading polygenic scoring methodologies within an easy-to-use pipeline called GenoPred. RESULTS: Here, we present the GenoPred pipeline, an easy-to-use, high-performance, reference-standardized, and reproducible workflow for polygenic scoring. It requires minimal inputs and offers various configuration options to cater to a range of use cases. GenoPred implements a comprehensive set of analyses, including genotype and GWAS quality control, target sample ancestry inference, polygenic score file generation using a range of leading methods, and target sample scoring. GenoPred standardizes the polygenic scoring process using reference genetic data, providing interpretable polygenic scores. The pipeline is applicable to GWAS and targets data from any population within the reference, facilitating studies of diverse ancestry. GenoPred is a Snakemake pipeline with associated Conda software environments, ensuring reproducibility. We apply the pipeline to UK Biobank data demonstrating the pipeline's simplicity, efficiency, and performance. The GenoPred pipeline provides a novel resource for polygenic scoring, integrating a range of complex processes within an easy-to-use framework. GenoPred widens access to the leading polygenic scoring methodology and their application to studies of diverse ancestry. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at https://github.com/opain/GenoPred.
Oliver Pain, Ammar Al-Chalabi, Cathryn M. Lewis
Bioinform.3
2015 PRSice: Polygenic Risk Score software
abstract
SUMMARY: 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.2
2005 GSMA: software implementation of the genome search meta-analysis method
abstract
Abstract Meta-analysis can be used to pool results of genome-wide linkage scans. This is of great value in complex diseases, where replication of linked regions occurs infrequently. The genome search meta-analysis (GSMA) method is widely used for this analysis, and a computer program is now available to implement the GSMA. Availability: Contact: [email protected]
Fabio Pardi, Douglas F. Levinson, Cathryn M. Lewis
Bioinform.3