ArticleStatistics in medicine2019
A likelihood-based approach to transcriptome association analysis.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Incorporating transcriptomic data into genomic prediction models to improve the prediction accuracy of phenotypes of efficiency traits.Genetics, selection, evolution : GSE · 2025Article
- Extend mixed models to multilayer neural networks for genomic prediction including intermediate omics data.Genetics · 2022Article
- Genetic evaluation including intermediate omics features.Genetics · 2021Article
- Non-random sampling leads to biased estimates of transcriptome association.Scientific reports · 2020Article
- Stochastic imputation for integrated transcriptome association analysis of a longitudinally measured trait.Statistical methods in medical research · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
Elucidating the mechanistic underpinnings of genetic associations with complex traits requires formally characterizing and testing associated cell and tissue-specific expression profiles. New opportunities exist to bolster this investigation with the growing numbers of large publicly available omics level data resources. Herein, we describe a fully likelihood-based strategy to leveraging external resources in the setting that expression profiles are partially or fully unobserved in a genetic association study. A general framework is presented to accommodate multiple data types, and strategies for implementation using existing software packages are described. The method is applied to an investigation of the genetics of evoked inflammatory response in cardiovascular disease research. Simulation studies suggest appropriate type-1 error control and power gains compared to single regression imputation, the most commonly applied practice in this setting.
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Registered trials
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