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Your search keyword '"Estrada-Smith, Daria"' showing total 2 results
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2 results on '"Estrada-Smith, Daria"'

1. Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Author

Yang X, Deignan JL, Qi H, Zhu J, Qian S, Zhong J, Torosyan G, Majid S, Falkard B, Kleinhanz RR, Karlsson J, Castellani LW, Mumick S, Wang K, Xie T, Coon M, Zhang C, Estrada-Smith D, Farber CR, Wang SS, van Nas A, Ghazalpour A, Zhang B, Macneil DJ, Lamb JR, Dipple KM, Reitman ML, Mehrabian M, Lum PY, Schadt EE, Lusis AJ, and Drake TA

Subjects
Abdomen anatomy & histology, Adipose Tissue anatomy & histology, Animals, Disease Models, Animal, Female, Gene Expression Profiling, Genetic Variation, Humans, Liver physiology, Male, Mice, Mice, Knockout, Mice, Transgenic, Muscle, Skeletal anatomy & histology, Phenotype, Reproducibility of Results, Transcription, Genetic, Vesicular Transport Proteins, Carrier Proteins genetics, Glutathione Peroxidase genetics, Glycoproteins genetics, Nerve Tissue Proteins genetics, Obesity genetics
Abstract

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

Published
2009
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2. Validation of Candidate Causal Genes for Abdominal Obesity Which Affect Shared Metabolic Pathways and Networks

Author

Yang, Xia, Deignan, Joshua L., Qi, Hongxiu, Zhu, Jun, Qian, Su, Zhong, Judy, Torosyan, Gevork, Majid, Sana, Falkard, Brie, Kleinhanz, Robert R., Karlsson, Jenny, Castellani, Lawrence W., Mumick, Sheena, Wang, Kai, Xie, Tao, Coon, Michael, Zhang, Chunsheng, Estrada-Smith, Daria, Farber, Charles R., Wang, Susanna S., Van Nas, Atila, Ghazalpour, Anatole, Zhang, Bin, MacNeil, Douglas J., Lamb, John R., Dipple, Katrina M., Reitman, Marc L., Mehrabian, Margarete, Lum, Pek Y., Schadt, Eric E., Lusis, Aldons J., and Drake, Thomas A.

Subjects
Male, Mice, Knockout, Glutathione Peroxidase, Transcription, Genetic, Gene Expression Profiling, Vesicular Transport Proteins, Genetic Variation, Reproducibility of Results, Mice, Transgenic, Nerve Tissue Proteins, Article, Disease Models, Animal, Mice, Phenotype, Adipose Tissue, Liver, Abdomen, Animals, Humans, Female, Obesity, Carrier Proteins, Muscle, Skeletal, Glycoproteins
Abstract

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

Published
2009

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