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GWAS with longitudinal phenotypes: performance of approximate procedures.

Karolina Sikorska1,2, Nahid Mostafavi Montazeri1,3, André Uitterlinden2

  • 1Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands.

European Journal of Human Genetics : EJHG
|February 26, 2015
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Summary

The conditional two-step (CTS) approach significantly speeds up genome-wide association studies (GWAS) with longitudinal data. This method offers a fast approximation to P-values, reducing computation time from weeks to minutes without inflating errors.

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Standard linear mixed model (LMM) fitting for genome-wide association studies (GWAS) with longitudinal data is computationally intensive.
  • Accelerating these computations is crucial for efficient genetic analysis.

Purpose of the Study:

  • To further investigate the properties of the conditional two-step (CTS) approach for analyzing GWAS with longitudinal data.
  • To compare the performance of CTS with a related two-step procedure.

Main Methods:

  • The conditional two-step (CTS) approach involves fitting a reduced LMM without SNP terms, followed by regressing estimated random slopes on SNPs.
  • The study employed analytical derivations and simulations to evaluate CTS performance.
  • Comparison was made against a standard two-step procedure.

Main Results:

  • Analytically shown that for balanced data, CTS P-values are equivalent to LMM P-values under mild assumptions.
  • Simulations demonstrate that CTS does not inflate the type I error rate for unbalanced data.
  • CTS results in only a minimal loss of statistical power compared to LMM.

Conclusions:

  • The conditional two-step (CTS) approach provides a computationally efficient and accurate method for GWAS with longitudinal data.
  • CTS is a viable alternative to standard LMM fitting, especially for large datasets or limited computational resources.
  • The method maintains statistical rigor while drastically reducing analysis time.