Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family‐Based Association Testing
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| Format: | Artículo Open Access |
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Wiley
2025
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| _version_ | 1867019063446732801 |
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| author | Tyler R. C. Day Joshua C. Bis Nicola Chapman Alejandro Q. Nato Andrea R. V. R. Horimoto Harkirat Sohi Rafael Nafikov Elizabeth E. Blue Mohamad Saad Ellen M. Wijsman |
| author_facet | Tyler R. C. Day Joshua C. Bis Nicola Chapman Alejandro Q. Nato Andrea R. V. R. Horimoto Harkirat Sohi Rafael Nafikov Elizabeth E. Blue Mohamad Saad Ellen M. Wijsman Tyler R. C. Day Joshua C. Bis Nicola Chapman Alejandro Q. Nato Andrea R. V. R. Horimoto Harkirat Sohi Rafael Nafikov Elizabeth E. Blue Mohamad Saad Ellen M. Wijsman |
| collection | Wiley Open Access |
| contents | Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family‐Based Association Testing Tyler R. C. Day Joshua C. Bis Nicola Chapman Alejandro Q. Nato Andrea R. V. R. Horimoto Harkirat Sohi Rafael Nafikov Elizabeth E. Blue Mohamad Saad Ellen M. Wijsman Genetic Epidemiology ABSTRACT Genotype imputation is a widely‐used data augmentation approach that is applied to samples of related and/or unrelated individuals. Association testing may then be carried out on the complete data with commonly‐used methods. This approach has typically not accounted for the mix of observed and imputed data, although recent work has noted the potential for introduction of confounding in case‐control studies. In the Alzheimer's Disease Sequencing Project family sample we found severe inflation of the test statistics in logistic regression analysis following genotype imputation, even after standard covariate adjustments. Here we dissect sources of this inflation, which is driven by three factors: frequency‐dependent bias in imputation‐induced allele frequencies, differential measurement error, and differential genotyping rates in cases versus controls that introduces confounding. To address the problem, we propose a statistic, imputation deviance (), which can be easily computed from the observed and imputed genotype probabilities. We show that , as an additional fixed‐effect covariate, controls the genome‐wide inflation in analysis of this family‐based sample, and we speculate that use of imputation deviance may also provide a practical approach to correct for genotype imputation effects in other settings, particularly when a data set is unbalanced and includes related individuals. 10.1002/gepi.70021 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/gepi.70021 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_gepi_70021 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family‐Based Association Testing Tyler R. C. Day Joshua C. Bis Nicola Chapman Alejandro Q. Nato Andrea R. V. R. Horimoto Harkirat Sohi Rafael Nafikov Elizabeth E. Blue Mohamad Saad Ellen M. Wijsman Genetic Epidemiology Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family‐Based Association Testing Tyler R. C. Day Joshua C. Bis Nicola Chapman Alejandro Q. Nato Andrea R. V. R. Horimoto Harkirat Sohi Rafael Nafikov Elizabeth E. Blue Mohamad Saad Ellen M. Wijsman Genetic Epidemiology ABSTRACT Genotype imputation is a widely‐used data augmentation approach that is applied to samples of related and/or unrelated individuals. Association testing may then be carried out on the complete data with commonly‐used methods. This approach has typically not accounted for the mix of observed and imputed data, although recent work has noted the potential for introduction of confounding in case‐control studies. In the Alzheimer's Disease Sequencing Project family sample we found severe inflation of the test statistics in logistic regression analysis following genotype imputation, even after standard covariate adjustments. Here we dissect sources of this inflation, which is driven by three factors: frequency‐dependent bias in imputation‐induced allele frequencies, differential measurement error, and differential genotyping rates in cases versus controls that introduces confounding. To address the problem, we propose a statistic, imputation deviance (), which can be easily computed from the observed and imputed genotype probabilities. We show that , as an additional fixed‐effect covariate, controls the genome‐wide inflation in analysis of this family‐based sample, and we speculate that use of imputation deviance may also provide a practical approach to correct for genotype imputation effects in other settings, particularly when a data set is unbalanced and includes related individuals. 10.1002/gepi.70021 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family‐Based Association Testing |
| topic | Genetic Epidemiology |
| url | https://onlinelibrary.wiley.com/doi/10.1002/gepi.70021 |