A likelihood-based approach to transcriptome association analysis.
Jing QianEvan RayRegina L BrechaMuredach P ReillyAndrea S FoulkesPublished in: Statistics in medicine (2018)
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.
Keyphrases
- genome wide
- single cell
- cardiovascular disease
- inflammatory response
- primary care
- healthcare
- electronic health record
- rna seq
- big data
- data analysis
- quality improvement
- dna methylation
- copy number
- type diabetes
- gene expression
- metabolic syndrome
- machine learning
- cell therapy
- mesenchymal stem cells
- immune response
- coronary artery disease
- cardiovascular events