--- _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: - access_level: open_access checksum: b5d2024e19fbad6f85a5e384e44d0f3b content_type: application/pdf creator: dernst date_created: 2022-08-08T07:31:19Z date_updated: 2022-08-08T07:31:19Z file_id: '11745' file_name: 2022_PNAS_Orliac.pdf file_size: 1001164 relation: main_file success: 1 file_date_updated: 2022-08-08T07:31:19Z has_accepted_license: '1' intvolume: ' 119' isi: 1 issue: '31' language: - iso: eng month: '07' oa: 1 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 quality_controlled: '1' related_material: record: - id: '13064' relation: research_data status: public scopus_import: '1' status: public 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 (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 119 year: '2022' ...