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Cited by 528Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockGenetics and Physical Performance

Improved polygenic prediction by Bayesian multiple regression on summary statistics

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Lloyd-Jones, Luke R., Zeng, Jian, Sidorenko, Julia, Yengo, Loïc, Moser, Gerhard, Kemper, Kathryn E., Wang, Huanwei, Zheng, Zhili +7 more·2019·Nature Communications
DOI 10.1038/s41467-019-12653-0PMC PMC6841727

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Abstract

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 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis ( n ≈ 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R 2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.

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  • A PGI generated from ~2.5 million pruned common SNPs using SBayesR is more predictive than the LDpred PGI, explaining 17.0% of EduYears variance in Add Health and 12.9% in HRS, with a sample-size-weighted mean of 14.3%.

    “In post hoc analyses, we found that a PGI generated from ~2.5 million pruned common SNPs using the software SBayesR22 is more predictive than our LDpred PGI. It explains 17.0% of the variance in EduYears in Add Health and 12.9% in HRS, with a sample-size-weighted mean of 14.3% (Supplementary Table 3).”

    From Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals (2022) · cites this paper as [22]

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Authors
Lloyd-Jones, L. R., Zeng, J., Sidorenko, J., Yengo, L., Moser, G., Kemper, K. E., Wang, H., Zheng, Z., Magi, R., Esko, T., Metspalu, A., Wray, N. R., Goddard, M. E., Yang, J., & Visscher, P. M.
Affiliations
  • The University of Queensland (AU)
  • Central Queensland University (AU)
  • University of Tartu (EE)
  • The University of Melbourne (AU)
Corresponding author
  • Zeng, Jian
  • Yang, Jian
  • Visscher, Peter M.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-11-08
Volume
10
Issue
1
Pages
5086-5086
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-12653-0
PMC
PMC6841727
OpenAlex
W2983607182
Cited by
528
References
9
Citations by year
2026: 65 · 2025: 95 · 2024: 99 · 2023: 87 · 2022: 81 · 2021: 63 · 2020: 29 · 2019: 8 · 2018: 1
FWCI
29.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Funders
  • University of Washington
  • Johns Hopkins University (RC2 AG036495, RC4 AG039029)
  • Wellcome Trust (UK10K, 201413, WT091310, HEALTH-F4-2007-201413)
  • National Institute for Health and Care Research (HHSN268200625226C)
  • British Heart Foundation (PG/12/38/29615)
  • Diabetes UK
  • King's College London
  • Eesti Teadusagentuur (IUT20-60, 2014-2020, 2014-2020.4.01.15-0012)
  • Northwest Regional Development Agency
  • Tartu Ülikool
  • National Institutes of Health (HHSN268201100012C, HHSN268201100008C, HHSN268201100006C, HHSN26820- 1100005C, HHSN268201100007C, HHSN268201100011C, HHSN268201100010C, HHSN268201100009C, HHSN268200625226C, contract HHSN268200625226C)
  • Medical Research Council (1113400, 1078037, 1078901, MC_PC_17228)
  • Australian Research Council (DP160102400, 1113400, FT180100186)
  • National Health and Medical Research Council (1080157, 1113400, 1078901, 1078037)
  • European Regional Development Fund (2014-2020’, 2014-2020.4.01.15-0012)
  • National Institute on Aging (NIAU01AG009740, RC2AG036495, RC4AG039029, R56AG042568, R01AG042568, U01AG009740)
  • National Heart, Lung, and Blood Institute (HHSN268201 100012C, HHSN268201100007C, R01HL087641, R01HL59367 and R01HL086694, HHSN2682 01100008C, HH-SN-268201100006C, HHSN268201100011C, R01 HL-59367, HHSN268201100005C, HHSN268201100010C, HHSN268200625226C, HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C, R01HL087641, R01HL059367, HHSN26820 1100009C, R01HL086694)
  • National Institute of Mental Health (R01MH100141)
  • National Human Genome Research Institute (U01HG004402)
  • National Institute of Environmental Health Sciences (R21ES025052)
  • National Center for Research Resources (UL1RR025005)
  • EIT Health
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
Keywords
polygenic risk prediction, summary statistics, BayesR, genome-wide association study, UK Biobank, height, body mass index, clumping, computational efficiency
MeSH terms
Adipose Tissue, Alopecia, Basal Metabolism, Bayes Theorem, Birth Weight, Body Composition, Body Height, Diabetes Mellitus, Type 2, Forced Expiratory Volume, Humans, Regression Analysis, Statistics as Topic, Vital Capacity, Bone Density, Body Mass Index, Biological Specimen Banks, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Waist-Hip Ratio, Genome-Wide Association Study, Genetic Association Studies

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