SwePub - search: WFRF:(Rogel Noga)

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1.	Chen, Jenny, et al. (author) A quantitative framework for characterizing the evolutionary history of mammalian gene expression 2019 In: Genome Research. - : Cold Spring Harbor Laboratory. - 1088-9051 .- 1549-5469. ; 29:1, s. 53-63 Journal article (peer-reviewed)abstract The evolutionary history of a gene helps predict its function and relationship to phenotypic traits. Although sequence conservation is commonly used to decipher gene function and assess medical relevance, methods for functional inference from comparative expression data are lacking. Here, we use RNA-seq across seven tissues from 17 mammalian species to show that expression evolution across mammals is accurately modeled by the Ornstein-Uhlenbeck process, a commonly proposed model of continuous trait evolution. We apply this model to identify expression pathways under neutral, stabilizing, and directional selection. We further demonstrate novel applications of this model to quantify the extent of stabilizing selection on a gene's expression, parameterize the distribution of each gene's optimal expression level, and detect deleterious expression levels in expression data from individual patients. Our work provides a statistical framework for interpreting expression data across species and in disease.

Chen, Jenny, et al. (author)
A quantitative framework for characterizing the evolutionary history of mammalian gene expression
2019
In: Genome Research. - : Cold Spring Harbor Laboratory. - 1088-9051 .- 1549-5469. ; 29:1, s. 53-63
Journal article (peer-reviewed)abstract
- The evolutionary history of a gene helps predict its function and relationship to phenotypic traits. Although sequence conservation is commonly used to decipher gene function and assess medical relevance, methods for functional inference from comparative expression data are lacking. Here, we use RNA-seq across seven tissues from 17 mammalian species to show that expression evolution across mammals is accurately modeled by the Ornstein-Uhlenbeck process, a commonly proposed model of continuous trait evolution. We apply this model to identify expression pathways under neutral, stabilizing, and directional selection. We further demonstrate novel applications of this model to quantify the extent of stabilizing selection on a gene's expression, parameterize the distribution of each gene's optimal expression level, and detect deleterious expression levels in expression data from individual patients. Our work provides a statistical framework for interpreting expression data across species and in disease.

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