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The phenotype-genotype reference map: Improving biobank data science through replication

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, w...

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Detalles Bibliográficos
Autores principales: Bastarache, Lisa, Delozier, Sarah, Pandit, Anita, He, Jing, Lewis, Adam, Annis, Aubrey C., LeFaive, Jonathon, Denny, Joshua C., Carroll, Robert J., Altman, Russ B., Hughey, Jacob J., Zawistowski, Matthew, Peterson, Josh F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502848/
https://www.ncbi.nlm.nih.gov/pubmed/37607538
http://dx.doi.org/10.1016/j.ajhg.2023.07.012
Descripción
Sumario:Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results.