Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.

doi:10.1371/journal.pgen.1006493

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

Community

Back to search

Article

Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.

PLoS genetics - 1 Dec 2016

Shi Jianxin, Park Ju-Hyun, Duan Jubao, Berndt Sonja T, Moy Winton, Yu Kai, Song Lei, Wheeler William, Hua Xing, Silverman Debra, Garcia-Closas Montserrat, Hsiung Chao Agnes, Figueroa Jonine D, Cortessis Victoria K, Malats Núria, Karagas Margaret R, Vineis Paolo, Chang I-Shou, Lin Dongxin, Zhou Baosen, Seow Adeline, Matsuo Keitaro, Hong Yun-Chul, Caporaso Neil E, Wolpin Brian, Jacobs Eric, Petersen Gloria M, Klein Alison P, Li Donghui, Risch Harvey, Sanders Alan R, Hsu Li, Schoen Robert E, Brenner Hermann, Stolzenberg-Solomon Rachael, Gejman Pablo, Lan Qing, Rothman Nathaniel, Amundadottir Laufey T, Landi Maria Teresa, Levinson Douglas F, Chanock Stephen J, Chatterjee Nilanjan

Abstract excerpt

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent...

Read the complete abstract on PubMed

Topics

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

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Algorithms

Share to Computer Simulation

Share to Genome-Wide Association Study

Share to Linkage Disequilibrium

Share to Models, Genetic

Share to Multifactorial Inheritance

Share to Polymorphism, Single Nucleotide

Share to Risk Factors

Source

Open in PubMed Open full text

Identifiers

PMID: 28036406
DOI: 10.1371/journal.pgen.1006493
PMCID: PMC5201242

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026. Manifest publication-graph-cohort-2026-09-05.1.

Source receipt: pubmed, retrieved 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.PMID 28036406Read ↗

Winner’s curse correction and variable thresholding improve performance of polygenic risk modeling based on genome-wide association study summary-level dataSemantic proximity Read ↗

Polygenic risk score prediction accuracy convergenceSemantic proximity Read ↗

SNP-based heritability and selection analyses: Improved models and new results.Semantic proximity Read ↗

Optimizing and benchmarking polygenic risk scores with GWAS summary statisticsSemantic proximity Read ↗

PGS-Depot: a comprehensive resource for polygenic scores constructed by summary statistics based methods.Semantic proximity Read ↗

On prs for complex polygenic trait predictionSemantic proximity Read ↗

Prediction of Polygenic Risks by Screening Thousands of Polygenic ScoresSemantic proximity Read ↗

Optimizing and benchmarking polygenic risk scores with GWAS summary statistics.Semantic proximity Read ↗

PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics.Semantic proximity Read ↗

Improving polygenic risk prediction from summary statistics by an empirical Bayes approach.Semantic proximity Read ↗

Fine-tuning Polygenic Risk Scores with GWAS Summary StatisticsSemantic proximity Read ↗

Partitioning heritability by functional category using GWAS summary statisticsSemantic proximity Read ↗

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