Evidence map›Paper›PMID 38689000›Full record

ArticleNature genetics2024

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries.

Zhili Zheng, Shouye Liu, Julia Sidorenko, Ying Wang, Tian Lin, Loic Yengo, Patrick Turley, Alireza Ani, Rujia Wang, Ilja M Nolte and 7 more

Abstract read
In one paragraph

Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
125citing papers in PubMed, 9 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

125 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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65 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

17 authors.

Zhili ZhengInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia. zhili.zheng@broadinstitute.org.ORCID http://orcid.org/0000-0003-2102-221X
Shouye LiuInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
Julia SidorenkoInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.ORCID http://orcid.org/0000-0003-1494-6772
Ying WangInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.ORCID http://orcid.org/0000-0001-7808-6279
Tian LinInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.ORCID http://orcid.org/0000-0002-5981-1911
Loic YengoInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.ORCID http://orcid.org/0000-0002-4272-9305
Patrick TurleyCenter for Economic and Social Research, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-3643-8605
Alireza AniDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.ORCID http://orcid.org/0000-0002-7868-3118
Rujia WangDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.ORCID http://orcid.org/0000-0003-2574-990X
Ilja M NolteDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.ORCID http://orcid.org/0000-0001-5047-4077
Harold SniederDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.ORCID http://orcid.org/0000-0003-1949-2298
LifeLines Cohort Study
Jian YangSchool of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0000-0003-2001-2474
Naomi R WrayInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.ORCID http://orcid.org/0000-0001-7421-3357
Michael E GoddardFaculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Victoria, Australia.
Peter M VisscherInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.ORCID http://orcid.org/0000-0002-2143-8760
Jian ZengInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia. j.zeng@uq.edu.au.ORCID http://orcid.org/0000-0001-8801-5220

Funding

2/2 Genetics at an extreme: an efficient genomic study of individuals with clinically severe major depression receiving ECTR01MH121545 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SULLIVAN, PATRICK F · 2019 to 2023
$2.9M
Genome-wide analysis of late-onset Alzheimer's disease using intergenerational, multi-trait, and cross-ancestry dataR00AG062787 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TURLEY, PATRICK ANSEL · 2021 to 2023
$747k
Department of Health | National Health and Medical Research Council (NHMRC) 1177268NIA NIH HHS R00 AG062787NIMH NIH HHS R01 MH121545
6 · The paper itself

Abstract

We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.

Indexed as

Genome-Wide Association StudyMolecular Sequence AnnotationMultifactorial InheritancePolymorphism, Single NucleotideGenome, HumanGenomicsHumansModels, Genetic

Identifiers

PMID38689000
PMCPMC11096109

What OpenQuestion holds

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Registered trials

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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.