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A metabolite-GWAS (mGWAS) approach to unveil chronic kidney disease progression
In this issue, McMahon et al. report that, by combining phenotypic, metabolomic, and genetic data, they could better detect chronic kidney disease at the early stages and provide insight into its pathobiology. The most significant findings of the study are that several urinary metabolites (e.g., gly...
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| Publicado no: | Kidney Int |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5989707/ https://ncbi.nlm.nih.gov/pubmed/28501300 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.kint.2017.03.022 |
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