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Summary
This summary is machine-generated.

We developed a new method for joint regression analysis using reference panels, crucial for genetic fine-mapping. Ignoring reference panel uncertainty causes false discoveries; our method ensures valid inference for reproducible genetic association studies.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genomewide association studies (GWAS) often report marginal association coefficients.
  • Genetic fine-mapping commonly uses these marginal estimates with a reference panel for joint regression analysis.
  • The uncertainty introduced by using a reference panel instead of the original data is often overlooked.

Purpose of the Study:

  • To present a statistically sound method for inferring joint regression coefficients from marginal regressions using a reference panel.
  • To address the issue of inflated false discoveries and lack of replicability caused by ignoring reference panel uncertainty.
  • To provide a framework for valid statistical inference in genetic fine-mapping.

Main Methods:

  • Derivation of the asymptotic distribution for estimated coefficients in a joint regression model with a reference panel.
  • Development of a methodology to account for the uncertainty associated with reference panel data.
  • Application of the method to both pre-selected regions and data-driven selected regions.

Main Results:

  • Ignoring reference panel uncertainty leads to inflated false discovery rates and reduced replicability in genetic fine-mapping.
  • The derived asymptotic distribution enables valid statistical inference for joint regression coefficients.
  • Simulations and real data examples demonstrate the effectiveness and utility of the proposed methodology.

Conclusions:

  • Accurate inference in joint regression models using reference panels is essential for reliable genetic fine-mapping.
  • The proposed method corrects for reference panel uncertainty, improving the validity of association studies.
  • This approach enhances the reproducibility and accuracy of findings in genetic association analyses.