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Structural Model Analysis of Multiple Quantitative Traits

We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate...

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Detalles Bibliográficos
Autores principales: Li, Renhua, Tsaih, Shirng-Wern, Shockley, Keith, Stylianou, Ioannis M, Wergedal, Jon, Paigen, Beverly, Churchill, Gary A
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1513264/
https://www.ncbi.nlm.nih.gov/pubmed/16848643
http://dx.doi.org/10.1371/journal.pgen.0020114
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author Li, Renhua
Tsaih, Shirng-Wern
Shockley, Keith
Stylianou, Ioannis M
Wergedal, Jon
Paigen, Beverly
Churchill, Gary A
author_facet Li, Renhua
Tsaih, Shirng-Wern
Shockley, Keith
Stylianou, Ioannis M
Wergedal, Jon
Paigen, Beverly
Churchill, Gary A
author_sort Li, Renhua
collection PubMed
description We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems.
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spelling pubmed-15132642006-07-21 Structural Model Analysis of Multiple Quantitative Traits Li, Renhua Tsaih, Shirng-Wern Shockley, Keith Stylianou, Ioannis M Wergedal, Jon Paigen, Beverly Churchill, Gary A PLoS Genet Research Article We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems. Public Library of Science 2006-07 2006-07-21 /pmc/articles/PMC1513264/ /pubmed/16848643 http://dx.doi.org/10.1371/journal.pgen.0020114 Text en © 2006 Li et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Li, Renhua
Tsaih, Shirng-Wern
Shockley, Keith
Stylianou, Ioannis M
Wergedal, Jon
Paigen, Beverly
Churchill, Gary A
Structural Model Analysis of Multiple Quantitative Traits
title Structural Model Analysis of Multiple Quantitative Traits
title_full Structural Model Analysis of Multiple Quantitative Traits
title_fullStr Structural Model Analysis of Multiple Quantitative Traits
title_full_unstemmed Structural Model Analysis of Multiple Quantitative Traits
title_short Structural Model Analysis of Multiple Quantitative Traits
title_sort structural model analysis of multiple quantitative traits
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1513264/
https://www.ncbi.nlm.nih.gov/pubmed/16848643
http://dx.doi.org/10.1371/journal.pgen.0020114
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