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Sandwich corrected standard errors in family-based genome-wide association studies.

Camelia C Minică1, Conor V Dolan1, Maarten M D Kampert2

  • 1Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

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|June 12, 2014
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Maximum Likelihood (ML) is superior to Unweighted Least Squares (ULS) for genome-wide scans with family data. ML offers greater power and robustness, even with model misspecification, making it ideal for complex genetic traits.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genome-wide scans utilize genotype and phenotype data from family members.
  • Choosing the optimal statistical estimator is crucial for maximizing data utility.

Purpose of the Study:

  • To compare the performance of Unweighted Least Squares (ULS) and Maximum Likelihood (ML) estimators in genome-wide scans using family data.
  • To evaluate estimator performance based on type I and type II error rates under various model specifications.

Main Methods:

  • Simulations were conducted to compare ULS (implemented in Plink with sandwich correction) and ML (implemented in fast linear mixed models).
  • Model specification for ML included both correct and incorrect scenarios, particularly concerning shared environmental effects.
  • Performance was assessed by type I and type II error rates.

Main Results:

  • ML procedures with correctly specified models are preferred for traits with moderate to large familial resemblance.
  • The sandwich-corrected ULS procedure showed a potential loss in power compared to ML.
  • ML demonstrated robustness under model misspecification and was more powerful than sandwich-corrected ULS.

Conclusions:

  • For traits with significant familial resemblance, ML with a correctly specified model is the recommended approach for genome-wide scans.
  • A proposed sandwich correction can be formulated for ML to address model misspecification in broader contexts.