Evidence map›Paper›PMID 39294559›Full record

ArticleBMC genomics2024

Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2.

Jonathan P Tyrer, Pei-Chen Peng, Amber A DeVries, Simon A Gayther, Michelle R Jones, Paul D Pharoah

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Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jonathan P Tyrer *Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
Pei-Chen Peng *Department of Computational Biomedicine, Cedars-Sinai Medical Center, California, 90048, United States of America.
Amber A DeVriesCenter for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, California, 90048, United States of America.
Simon A GaytherCenter for Inherited Oncogenesis, Department of Medicine, UT Health San Antonio, Texas, 78229, United States of America.
Michelle R JonesCenter for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, California, 90048, United States of America. michelle.jones@csmc.edu.
Paul D PharoahDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, California, 90048, United States of America.

Funding

Identifying new drivers of ovarian cancer from the non-coding genome by converging germline risk variants and somatic mutationsR00CA256519 · NCI · CEDARS-SINAI MEDICAL CENTER · PI PENG, PEI-CHEN · 2023 to 2025
$745k
American Cancer Society RSG-23-1019823-01-CPSHCancer Research UK PRCPJT\100006NCI NIH HHS R00 CA256519NCI NIH HHS R00CA256519
6 · The paper itself

Abstract

backgroundAs precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method.

resultsThe S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics.

conclusionsTaken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

Indexed as

Genome-Wide Association StudyMultifactorial InheritanceFemaleHumansModels, GeneticPhenotypePolymorphism, Single NucleotideCross-ancestryGenome-wide association study (GWAS)Multiple phenotypesPolygenic scores

Identifiers

PMID39294559
PMCPMC11411995

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.