Evidence map›Paper›PMID 30992449›Full record

ArticleNature communications2019

Polygenic prediction via Bayesian regression and continuous shrinkage priors.

Tian Ge, Chia-Yen Chen, Yang Ni, Yen-Chen Anne Feng, Jordan W Smoller

Abstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,300 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,300citing papers in PubMed, 11 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1,300 citing papers in PubMed, 11 syntheses or guidelines pooled it.

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1,240 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tian GePsychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA. tge1@mgh.harvard.edu.
Chia-Yen ChenPsychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.ORCID http://orcid.org/0000-0001-9548-5597
Yang NiDepartment of Statistics, Texas A&M University, College Station, TX, 77843, USA.ORCID http://orcid.org/0000-0003-0636-2363
Yen-Chen Anne FengPsychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
Jordan W SmollerPsychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.

Funding

Finding Genomic Profiles of COVID-19 Phenotypes from the EHRU01HG008685 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIZABETH W KARLSON, Matthew S Lebo · 2015 to 2026
$13.6M
Epidemiologic StudiesU19CA148065 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI AHSAN, HABIBUL, BRUGGE, JOAN SIEFERT · 2010 to 2014
$10.6M
Exploring the genetic basis of AD progressionR00AG054573 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI GE, TIAN · 2020 to 2022
$732k
Exploring the genetic basis of AD progressionK99AG054573 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI GE, TIAN · 2017 to 2018
$274k
NCI NIH HHS U19 CA148065NHGRI NIH HHS U01 HG008685NHGRI NIH HHS X01HG007492NIA NIH HHS K99 AG054573NIA NIH HHS R00 AG054573
6 · The paper itself

Abstract

Polygenic risk scores (PRS) have shown promise in predicting human complex traits and diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six common complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods.

Indexed as

Genetic Predisposition to DiseaseModels, GeneticQuantitative Trait, HeritableArthritis, RheumatoidBayes TheoremBreast NeoplasmsComputer SimulationCoronary Artery DiseaseDatabases, GeneticDepressionDiabetes Mellitus, Type 2FemaleGenome-Wide Association StudyHumansInflammatory Bowel DiseasesLinkage Disequilibrium

Identifiers

PMID30992449
PMCPMC6467998

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.