Evidence map›Paper›PMID 35324888›Full record

ArticlePLoS genetics2022

Significant sparse polygenic risk scores across 813 traits in UK Biobank.

Yosuke Tanigawa, Junyang Qian, Guhan Venkataraman, Johanne Marie Justesen, Ruilin Li, Robert Tibshirani, Trevor Hastie, Manuel A Rivas

Abstract read
In one paragraph

Article in PLoS genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 3 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

92 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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32 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

8 authors.

Yosuke TanigawaDepartment of Biomedical Data Science, Stanford University, Stanford, California, United States of America.ORCID 0000-0001-9759-157X
Junyang QianDepartment of Statistics, Stanford University, Stanford, California, United States of America.
Guhan VenkataramanDepartment of Biomedical Data Science, Stanford University, Stanford, California, United States of America.ORCID 0000-0003-0754-6631
Johanne Marie JustesenDepartment of Biomedical Data Science, Stanford University, Stanford, California, United States of America.ORCID 0000-0002-0484-8522
Ruilin LiInstitute for Computational and Mathematical Engineering, Stanford University, Stanford, California, United States of America.ORCID 0000-0002-5152-7086
Robert TibshiraniDepartment of Biomedical Data Science, Stanford University, Stanford, California, United States of America.
Trevor HastieDepartment of Biomedical Data Science, Stanford University, Stanford, California, United States of America.ORCID 0000-0002-0164-3142
Manuel A RivasDepartment of Biomedical Data Science, Stanford University, Stanford, California, United States of America.ORCID 0000-0003-1457-9925

Funding

New Statistical Methods for Medical Signals and ImagesR01EB001988 · NIBIB · STANFORD UNIVERSITY · PI JOHNSTONE, IAIN M · 2003 to 2018
$6.1M
Optimizing imputation for diverse populations in a distributed frameworkU01HG009080 · NHGRI · STANFORD UNIVERSITY · PI KENNY, EIMEAR ELIZABETH · 2016 to 2020
$5.2M
New Statistical Methods for Medical Signals and ImagesR01GM134483 · NIGMS · STANFORD UNIVERSITY · PI Iain M Johnstone · 2019 to 2026
$3.9M
Single-Cell Transcriptional and Epigenomic Dissection to Identify Therapeutic Targets for ALS and FTDR01AG067151 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BELZIL, VERONIQUE, KELLIS, MANOLIS · 2021 to 2025
$3.7M
Interpreting non-coding variants using epigenomics, regulatory models, & validation experimentsR01HG008155 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KELLIS, MANOLIS · 2015 to 2017
$2.3M
SOFTWARE FOR LARGE-SCALE INFERENCE OF THE GENETICS OF LIFESTYLE MEASURES, BIOMARKERS, AND COMMON AND RARE DISEASESR01HG010140 · NHGRI · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL, MONTGOMERY, STEPHEN · 2018 to 2022
$2.0M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS R01 HG008155NHGRI NIH HHS R01 HG010140NHGRI NIH HHS U01 HG009080NIA NIH HHS R01 AG067151NIBIB NIH HHS R01 EB001988NIGMS NIH HHS R01 GM134483
6 · The paper itself

Abstract

We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,500 traits using genetic and phenotype data in the UK Biobank. We report 813 sparse PRS models with significant (p < 2.5 x 10-5) incremental predictive performance when compared against the covariate-only model that considers age, sex, types of genotyping arrays, and the principal component loadings of genotypes. We report a significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance (Spearman's ⍴ = 0.61, p = 2.2 x 10-59 for quantitative traits, ⍴ = 0.21, p = 9.6 x 10-4 for binary traits). The sparse PRS model trained on European individuals showed limited transferability when evaluated on non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).

Indexed as

Genome-Wide Association StudyMultifactorial InheritanceBiological Specimen BanksGenetic Predisposition to DiseaseHumansPhenotypeRisk FactorsUnited Kingdom

Identifiers

PMID35324888
PMCPMC8946745

What OpenQuestion holds

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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.