---
_id: '11733'
abstract:
- lang: eng
text: Genetically informed, deep-phenotyped biobanks are an important research resource
and it is imperative that the most powerful, versatile, and efficient analysis
approaches are used. Here, we apply our recently developed Bayesian grouped mixture
of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest
genomic prediction accuracy reported to date across 21 heritable traits. When
compared to other approaches, GMRM accuracy was greater than annotation prediction
models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%),
respectively, and was 18% (SE 3%) greater than a baseline BayesR model without
single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage
disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy
R2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h2SNP.
We then extend our GMRM prediction model to provide mixed-linear model association
(MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which
increased the independent loci detected to 16,162 in unrelated UK Biobank individuals,
compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase,
respectively. The average χ2 value of the leading markers increased by 15.24 (SE
0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR
model across the traits. Thus, we show that modeling genetic associations accounting
for MAF and LD differences among SNP markers, and incorporating prior knowledge
of genomic function, is important for both genomic prediction and discovery in
large-scale individual-level studies.
acknowledgement: This project was funded by Swiss National Science Foundation Eccellenza
Grant PCEGP3-181181(toM.R.R.) and by core funding from the Institute of Science
and Technology Austria. P.M.V. acknowledges funding from the Australian National
Health and Medical Research Council (1113400) and the Australian Research Council
(FL180100072). K.L. and R.M. were supported by the Estonian Research Council Grant
PRG687. Estonian Biobank computations were performed in the High-Performance Computing
Centre, University of Tartu.
article_number: e2121279119
article_processing_charge: No
article_type: original
author:
- first_name: Etienne J.
full_name: Orliac, Etienne J.
last_name: Orliac
- first_name: Daniel
full_name: Trejo Banos, Daniel
last_name: Trejo Banos
- first_name: Sven E.
full_name: Ojavee, Sven E.
last_name: Ojavee
- first_name: Kristi
full_name: Läll, Kristi
last_name: Läll
- first_name: Reedik
full_name: Mägi, Reedik
last_name: Mägi
- first_name: Peter M.
full_name: Visscher, Peter M.
last_name: Visscher
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Orliac EJ, Trejo Banos D, Ojavee SE, et al. Improving GWAS discovery and genomic
prediction accuracy in biobank data. Proceedings of the National Academy of
Sciences of the United States of America. 2022;119(31). doi:10.1073/pnas.2121279119
apa: Orliac, E. J., Trejo Banos, D., Ojavee, S. E., Läll, K., Mägi, R., Visscher,
P. M., & Robinson, M. R. (2022). Improving GWAS discovery and genomic prediction
accuracy in biobank data. Proceedings of the National Academy of Sciences of
the United States of America. Proceedings of the National Academy of Sciences.
https://doi.org/10.1073/pnas.2121279119
chicago: Orliac, Etienne J., Daniel Trejo Banos, Sven E. Ojavee, Kristi Läll, Reedik
Mägi, Peter M. Visscher, and Matthew Richard Robinson. “Improving GWAS Discovery
and Genomic Prediction Accuracy in Biobank Data.” Proceedings of the National
Academy of Sciences of the United States of America. Proceedings of the National
Academy of Sciences, 2022. https://doi.org/10.1073/pnas.2121279119.
ieee: E. J. Orliac et al., “Improving GWAS discovery and genomic prediction
accuracy in biobank data,” Proceedings of the National Academy of Sciences
of the United States of America, vol. 119, no. 31. Proceedings of the National
Academy of Sciences, 2022.
ista: Orliac EJ, Trejo Banos D, Ojavee SE, Läll K, Mägi R, Visscher PM, Robinson
MR. 2022. Improving GWAS discovery and genomic prediction accuracy in biobank
data. Proceedings of the National Academy of Sciences of the United States of
America. 119(31), e2121279119.
mla: Orliac, Etienne J., et al. “Improving GWAS Discovery and Genomic Prediction
Accuracy in Biobank Data.” Proceedings of the National Academy of Sciences
of the United States of America, vol. 119, no. 31, e2121279119, Proceedings
of the National Academy of Sciences, 2022, doi:10.1073/pnas.2121279119.
short: E.J. Orliac, D. Trejo Banos, S.E. Ojavee, K. Läll, R. Mägi, P.M. Visscher,
M.R. Robinson, Proceedings of the National Academy of Sciences of the United States
of America 119 (2022).
date_created: 2022-08-07T22:01:56Z
date_published: 2022-07-29T00:00:00Z
date_updated: 2023-08-03T12:40:38Z
day: '29'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1073/pnas.2121279119
external_id:
isi:
- '000881496900003'
file:
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creator: dernst
date_created: 2022-08-08T07:31:19Z
date_updated: 2022-08-08T07:31:19Z
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oa_version: Published Version
publication: Proceedings of the National Academy of Sciences of the United States
of America
publication_identifier:
eissn:
- 1091-6490
publication_status: published
publisher: Proceedings of the National Academy of Sciences
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scopus_import: '1'
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title: Improving GWAS discovery and genomic prediction accuracy in biobank data
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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short: CC BY-NC-ND (4.0)
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...