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A robust cis-Mendelian randomization method with application to drug target discovery.

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This study introduces cisMR-cML, a novel method for Mendelian randomization (MR) that robustly identifies causal relationships using genetic data. It improves drug target discovery for diseases like coronary artery disease (CAD).

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Area of Science:

  • Genetics
  • Epidemiology
  • Pharmacology

Background:

  • Mendelian randomization (MR) utilizes genetic variants as instrumental variables (IVs) to infer causal links between exposures and outcomes.
  • Conventional MR often faces challenges with pleiotropy and linkage disequilibrium (LD).
  • Cis-MR, focusing on a single genomic region with cis-single nucleotide polymorphisms (cis-SNPs), offers a cost-effective approach for drug target discovery, particularly using cis-protein quantitative trait loci (cis-pQTLs).

Purpose of the Study:

  • To address limitations in existing cis-MR methods regarding pleiotropy and LD.
  • To introduce a novel method, cisMR-cML, based on constrained maximum likelihood for robust causal inference.
  • To highlight the impact of using marginal versus conditional genetic effects and the selection of exposure-associated SNPs in cis-MR.

Main Methods:

  • Development of cisMR-cML, a constrained maximum likelihood method designed for cis-MR analysis.
  • Theoretical framework to address violations of instrumental variable assumptions.
  • Evaluation through numerical simulations comparing cisMR-cML against existing methods.
  • Application to a proteome-wide analysis for coronary artery disease (CAD) drug target identification.

Main Results:

  • cisMR-cML demonstrates superior performance compared to other existing cis-MR methods in simulations.
  • The study clarifies the consequences of current practices in modeling genetic effects and SNP selection.
  • Identification of three potential novel drug targets for CAD: PCSK9, COLEC11, and FGFR1.

Conclusions:

  • cisMR-cML provides a theoretically sound and robust method for cis-MR analysis.
  • The findings offer a more effective strategy for cost-effective drug target discovery.
  • The identified targets (PCSK9, COLEC11, FGFR1) warrant further investigation for CAD therapeutic development.