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Structural model analysis of multiple quantitative traits.

Renhua Li·Shirng-Wern Tsaih·Keith Shockley·Ioannis M Stylianou·Jon Wergedal·Beverly Paigen·Gary A Churchill
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Abstract

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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Adipose TissueAnimalsBody CompositionBone DensityCrosses, GeneticGenetic VariationGenomicsMiceModels, GeneticModels, StatisticalObesityPhenotypeQuantitative Trait LociSpecies SpecificityJournal ArticleResearch Support, N.I.H., Extramural

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Renhua Li, Shirng-Wern Tsaih, Keith Shockley, Ioannis M Stylianou, Jon Wergedal, Beverly Paigen, Gary A Churchill. 2006-06-07. Structural model analysis of multiple quantitative traits.. https://doi.org/10.1371/journal.pgen.0020114

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