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Family-Based Association Tests with longitudinal measurements: handling missing data.

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Family-based association tests (FBAT) can be extended for longitudinal genetic studies with missing data. Imputing phenotypes using novel methods enhances statistical power while maintaining unbiased results for genetic association testing.

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Area of Science:

  • Genetics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Family-based association tests (FBAT) are used for genetic association studies with longitudinal data.
  • Handling incomplete data in such studies is crucial for maintaining statistical power and test validity.

Purpose of the Study:

  • To extend FBAT approaches for longitudinal genetic association studies with incomplete data.
  • To introduce and evaluate imputation techniques for missing phenotypes to enhance power.

Main Methods:

  • Extension of existing FBAT methods to accommodate missing data.
  • Development of two imputation techniques: Expectation-Maximization (EM) algorithm and conditional mean model.
  • Simulation studies to assess performance and comparison with complete case analysis and mean imputation.

Main Results:

  • The extended FBAT approaches remain unbiased with incomplete data.
  • Proposed imputation techniques provide correct false positive rates.
  • Imputation methods generally achieve higher statistical power compared to complete case or mean imputation.

Conclusions:

  • FBAT approaches can be effectively adapted for longitudinal genetic studies with missing data.
  • Imputation of missing phenotypes using the proposed methods is a valid strategy to increase power.
  • The methods were successfully applied to analyze Body Mass Index association with a candidate SNP.