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A robust method for collider bias correction in conditional genome-wide association studies.

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Collider bias can distort genetic association estimates. Slope-Hunter, a novel method, uses clustering to identify phenotype-specific variants, effectively removing bias in genetic prognosis studies.

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Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Genetic association studies can be confounded by collider bias.
  • Conditioning on a phenotype can induce spurious associations between its causes and prognosis.

Purpose of the Study:

  • To introduce and validate 'Slope-Hunter', a method to correct for collider bias in genetic prognosis studies.
  • To accurately estimate genetic associations with prognosis by accounting for phenotype-specific variants.

Main Methods:

  • Model-based clustering to identify variant classes.
  • Utilizing phenotype-only variants for estimating the adjustment factor.
  • Simulation studies to assess performance against alternatives.
  • Application to fasting insulin (FI) and body mass index (BMI), and breast cancer mortality.

Main Results:

  • Slope-Hunter successfully eliminates collider bias in simulations.
  • The method outperforms existing approaches, even with genetic correlation.
  • Paradoxical associations between underweight loci and increased FI were resolved.
  • A novel association for rs1421085 (FTO) with FI was identified.
  • A single region showed pronounced associations in a breast cancer mortality case-only study.

Conclusions:

  • Slope-Hunter provides a robust solution for mitigating collider bias in genetic association studies.
  • The method enables more accurate estimation of genetic effects on prognosis.
  • The findings have implications for understanding genetic contributions to complex diseases and mortality.