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Structural variation of the coding and non-coding human pharmacogenome.

Roman Tremmel1,2, Yitian Zhou3, Matthias Schwab1,2,4

  • 1Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.

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|September 8, 2023
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Summary
This summary is machine-generated.

Structural variants (SVs) in pharmacogenes are poorly understood. This study maps SVs in 908 pharmacogenes, revealing their impact on drug response and personalized medicine.

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Area of Science:

  • Genomics
  • Pharmacogenomics
  • Human Genetics

Background:

  • Genetic variants in drug targets and ADME genes influence drug pharmacokinetics, response, and toxicity.
  • While single nucleotide variants (SNVs) are well-studied, structural variations (SVs) in pharmacogenes remain poorly understood.

Purpose of the Study:

  • To systematically analyze genetic structural variability across 908 pharmacogenes, including ADME genes and drug targets.
  • To identify and characterize the functional impact of structural variants on pharmacogene expression and regulation.
  • To provide a comprehensive map of structural variation in pharmacogenes for improved personalized medicine.

Main Methods:

  • Analysis of whole genome sequencing data from 10,847 individuals.
  • Extraction and characterization of over 14,984 distinct structural variants (SVs).
  • Integration of SV data with transcription factor binding data to identify regulatory variants.

Main Results:

  • Identification of 14,984 distinct structural variants (SVs) across 908 pharmacogenes.
  • Each individual carries an average of 10.3 coding SVs in ADME genes and 1.5 in drug targets.
  • 1276 non-coding SVs were identified in regulatory elements, accounting for 22% of pharmacogenomic variability.

Conclusions:

  • This study presents the first comprehensive map of structural variability across pharmacogenes.
  • Non-coding structural variants significantly contribute to pharmacogenomic variability and functional impact.
  • Incorporating structural genomic data is crucial for accurate personalized drug response predictions.