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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
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A likelihood-based approach to transcriptome association analysis.

Jing Qian1, Evan Ray2, Regina L Brecha2

  • 1Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, Massachusetts.

Statistics in Medicine
|December 6, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method to analyze genetic associations with complex traits when gene expression data is missing. The approach improves power and controls errors compared to existing methods, aiding cardiovascular disease research.

Keywords:
EM algorithmRNAseqprotein coding genesregulatory elementstranscriptome association analysis

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding genetic associations with complex traits requires analyzing cell and tissue-specific gene expression.
  • Large public omics data resources offer new opportunities for such investigations.
  • Gene expression profiles are often partially or fully unobserved in genetic association studies.

Purpose of the Study:

  • To present a likelihood-based strategy for leveraging external resources when gene expression data is missing in genetic association studies.
  • To provide a general framework accommodating multiple data types.
  • To apply the method to study the genetics of evoked inflammatory response in cardiovascular disease.

Main Methods:

  • Developed a fully likelihood-based strategy to integrate external omics data.
  • Presented a general framework for handling partially or fully unobserved expression profiles.
  • Described implementation strategies using existing software packages.

Main Results:

  • Simulation studies demonstrated appropriate type-1 error control.
  • The proposed method showed power gains compared to single regression imputation.
  • Successfully applied the method to investigate genetic factors in cardiovascular disease-related inflammatory responses.

Conclusions:

  • The developed likelihood-based strategy effectively leverages external resources for genetic association studies with missing expression data.
  • This approach offers improved statistical power and error control over conventional methods.
  • The findings contribute to a better understanding of the genetic mechanisms underlying complex traits, particularly in cardiovascular disease.