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Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

Crossref DOI link: https://doi.org/10.1186/s40246-022-00406-y

Published Online: 2022-09-08

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Lopez-Pineda, Arturo

Vernekar, Manvi

Moreno-Grau, Sonia

Rojas-Muñoz, Agustin

Moatamed, Babak

Lee, Ming Ta Michael

Nava-Aguilar, Marco A.

Gonzalez-Arroyo, Gilberto

Numakura, Kensuke

Matsuda, Yuta

Ioannidis, Alexander

Katsanis, Nicholas

Takano, Tomohiro

Bustamante, Carlos D.
Funding

Funding for this research was provided by:

New Energy and Industrial Technology Development Organization (JPNP19001, JPNP19001, JPNP19001, JPNP19001)
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Text and Data Mining valid from 2022-09-08

Version of Record valid from 2022-09-08
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Article History

Received: 8 March 2022

Accepted: 6 August 2022

First Online: 8 September 2022

Declarations

:

: This study was approved by the institutional review board (IRB) at WCG IRB () under IRB tracking number protocol number 20201332.

: ARM, SMG, MTML, ALP, CDB, AI, MNA, and NK are employees of or consultants to Galatea Bio. MV, KN, YM, and TT are employees of Genomelink. CDB, IA, and NK are shareholders of Galatea Bio stock. CDB, KN, YM, and TT are shareholders of Genomelink stock. The remaining authors declare that there is no conflict of interest regarding the publication of this article.

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