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A likelihood‐based approach to transcriptome association analysis
Author(s) -
Qian Jing,
Ray Evan,
Brecha Regina L.,
Reilly Muredach P.,
Foulkes Andrea S.
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8040
Subject(s) - imputation (statistics) , computer science , genetic association , data mining , association (psychology) , software , type i and type ii errors , genome wide association study , regression , machine learning , statistics , missing data , biology , genetics , single nucleotide polymorphism , mathematics , psychology , genotype , psychotherapist , gene , programming language
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.