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Published Erratum
. 2022 Nov 16;14(1):128.
doi: 10.1186/s13073-022-01128-5.

Author Correction: Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative

Affiliations
Published Erratum

Author Correction: Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative

Ruth Johnson et al. Genome Med. .
No abstract available

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Figures

Fig. 2
Fig. 2
Global PCA reflects self-identified race/ethnicity and language of ATLAS participants. A Genetic PCs 1 and 2 of individuals in ATLAS (N=36,736) shaded by continental GIA as inferred from 1000 Genomes. B, C The first two genetic PCs of the ATLAS participants shaded by SIRE and preferred language, respectively. To improve visualization in C, only languages with >10 responses were assigned a color
Fig. 5
Fig. 5
Disease associations vary across continental genetically inferred ancestry groups in ATLAS. We show the odds ratio computed from associating each phenotype with individuals’ genetically inferred ancestry in ATLAS (N=36,736) under a logistic regression model. Error bars represent 95% confidence intervals

Erratum for

References

    1. Johnson R, et al. Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative. Genome Med. 2022;14:104. doi: 10.1186/s13073-022-01106-x. - DOI - PMC - PubMed

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