Identification of genetic elements in metabolism by high-throughput mouse phenotyping

Identification of genetic elements in metabolism by high-throughput mouse phenotyping

The genetic basis of metabolic diseases is incompletely understood. Here, by high-throughput phenotyping of 2,016 knockout mouse strains, Rozman and colleagues identify candidate metabolic genes, many of which are associated with unexplored regulatory gene networks and metabolic traits in human GWAS...

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Main Authors: Jan Rozman, Birgit Rathkolb, Manuela A. Oestereicher, Christine Schütt, Aakash Chavan Ravindranath, Stefanie Leuchtenberger, Sapna Sharma, Martin Kistler, Monja Willershäuser, Robert Brommage, Terrence F. Meehan, Jeremy Mason, Hamed Haselimashhadi, IMPC Consortium, Tertius Hough, Ann-Marie Mallon, Sara Wells, Luis Santos, Christopher J. Lelliott, Jacqueline K. White, Tania Sorg, Marie-France Champy, Lynette R. Bower, Corey L. Reynolds, Ann M. Flenniken, Stephen A. Murray, Lauryl M. J. Nutter, Karen L. Svenson, David West, Glauco P. Tocchini-Valentini, Arthur L. Beaudet, Fatima Bosch, Robert B. Braun, Michael S. Dobbie, Xiang Gao, Yann Herault, Ala Moshiri, Bret A. Moore, K. C. Kent Lloyd, Colin McKerlie, Hiroshi Masuya, Nobuhiko Tanaka, Paul Flicek, Helen E. Parkinson, Radislav Sedlacek, Je Kyung Seong, Chi-Kuang Leo Wang, Mark Moore, Steve D. Brown, Matthias H. Tschöp, Wolfgang Wurst, Martin Klingenspor, Eckhard Wolf, Johannes Beckers, Fausto Machicao, Andreas Peter, Harald Staiger, Hans-Ulrich Häring, Harald Grallert, Monica Campillos, Holger Maier, Helmut Fuchs, Valerie Gailus-Durner, Thomas Werner, Martin Hrabe de Angelis
Format: article
Language:EN
Published: Nature Portfolio 2018
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Science
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Online Access:https://doaj.org/article/7b0bd2741d2c4a34a03d78a177e5cce3
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https://doaj.org/article/7b0bd2741d2c4a34a03d78a177e5cce3

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