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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family-Based Association

Tyler R C Day1, Joshua C Bis2, Nicola Chapman1

  • 1Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, Washington, USA.

Genetic Epidemiology
|October 30, 2025
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Summary
This summary is machine-generated.

Genotype imputation can inflate association test statistics due to data mix. A new statistic, imputation deviance, corrects this inflation in family-based studies with related individuals.

Keywords:
GWASPedigreeWGSdata augmentationgenomic controlmissing datamixed model

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genotype imputation is a common data augmentation technique in genetic studies.
  • Standard association testing methods may not adequately account for the mix of observed and imputed genotypes.
  • This can lead to inflated test statistics and confounding, particularly in case-control studies and family-based samples.

Purpose of the Study:

  • To investigate the sources of severe inflation in test statistics observed after genotype imputation in the Alzheimer's Disease Sequencing Project family sample.
  • To propose a novel statistic to correct for genotype imputation effects.
  • To evaluate the effectiveness of the proposed statistic in controlling for inflation in family-based association analyses.

Main Methods:

  • Analysis of the Alzheimer's Disease Sequencing Project family sample using logistic regression.
  • Dissection of inflation sources, including frequency-dependent bias, differential measurement error, and differential genotyping rates.
  • Development and implementation of the imputation deviance (D) statistic.
  • Inclusion of imputation deviance as a fixed-effect covariate in association testing.

Main Results:

  • Severe inflation of test statistics was observed in logistic regression following genotype imputation, even after standard covariate adjustments.
  • Three key factors driving this inflation were identified: imputation-induced allele frequency bias, differential measurement error, and differential genotyping rates.
  • The proposed imputation deviance (D) statistic, when used as an additional fixed-effect covariate, effectively controlled genome-wide inflation in the analyzed family-based sample.

Conclusions:

  • Genotype imputation requires careful handling to avoid confounding and inflated test statistics, especially in unbalanced datasets with related individuals.
  • The imputation deviance (D) statistic provides a practical method to correct for genotype imputation effects.
  • This approach shows promise for improving the reliability of association testing in various genetic study settings.