Discover Community Create You

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

Article

Improved polygenic prediction by Bayesian multiple regression on summary statistics.

Nature communications - 8 Nov 2019

Lloyd-Jones Luke R, Zeng Jian, Sidorenko Julia, Yengo Loïc, Moser Gerhard, Kemper Kathryn E, Wang Huanwei, Zheng Zhili, Magi Reedik, Esko Tõnu, Metspalu Andres, Wray Naomi R, Goddard Michael E, Yang Jian, Visscher Peter M

Abstract excerpt

Accurate prediction of an individual's phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000...

Read the complete abstract on PubMed

Topics

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

Share to Alopecia
Share to Basal Metabolism

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Biological Specimen Banks

Share to Birth Weight

Share to Body Height

Share to Forced Expiratory Volume

Share to Vital Capacity

Share to Waist-Hip Ratio

Source

Open in PubMed Open full text

Identifiers

PMID: 31704910
DOI: 10.1038/s41467-019-12653-0
PMCID: PMC6841727

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Improved polygenic prediction by Bayesian multiple regression on summary statistics.PMID 31704910Read ↗

Improved polygenic prediction by Bayesian multiple regression on summary statisticsSemantic proximity Read ↗

Bayesian large-scale multiple regression with summary statistics from genome-wide association studiesSemantic proximity Read ↗

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

Optimizing and benchmarking 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 ↗

Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.Semantic proximity Read ↗

Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets.Semantic proximity Read ↗

Expanded utility of the R package qgg with applications within genomic medicineSemantic proximity Read ↗

Improved genetic prediction of complex traits from individual-level data or summary statistics.Semantic proximity Read ↗

Fast and scalable ensemble learning method for versatile polygenic risk prediction.Semantic proximity Read ↗

Polygenic Prediction via Bayesian Regression and Continuous Shrinkage PriorsSemantic proximity Read ↗

Evaluating and improving heritability models using summary statistics.Semantic proximity Read ↗

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