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Testing and estimation of X‐chromosome SNP effects: Impact of model assumptions
Interest in analyzing X chromosome single nucleotide polymorphisms (SNPs) is growing and several approaches have been proposed. Prior studies have compared power of different approaches, but bias and interpretation of coefficients have received less attention. We performed simulations to demonstrate...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8453908/ https://www.ncbi.nlm.nih.gov/pubmed/34082482 http://dx.doi.org/10.1002/gepi.22393 |
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author | Song, Yilin Biernacka, Joanna M. Winham, Stacey J. |
author_facet | Song, Yilin Biernacka, Joanna M. Winham, Stacey J. |
author_sort | Song, Yilin |
collection | PubMed |
description | Interest in analyzing X chromosome single nucleotide polymorphisms (SNPs) is growing and several approaches have been proposed. Prior studies have compared power of different approaches, but bias and interpretation of coefficients have received less attention. We performed simulations to demonstrate the impact of X chromosome model assumptions on effect estimates. We investigated the coefficient biases of SNP and sex effects with commonly used models for X chromosome SNPs, including models with and without assumptions of X chromosome inactivation (XCI), and with and without SNP–sex interaction terms. Sex and SNP coefficient biases were observed when assumptions made about XCI and sex differences in SNP effect in the analysis model were inconsistent with the data‐generating model. However, including a SNP–sex interaction term often eliminated these biases. To illustrate these findings, estimates under different genetic model assumptions are compared and interpreted in a real data example. Models to analyze X chromosome SNPs make assumptions beyond those made in autosomal variant analysis. Assumptions made about X chromosome SNP effects should be stated clearly when reporting and interpreting X chromosome associations. Fitting models with SNP × Sex interaction terms can avoid reliance on assumptions, eliminating coefficient bias even in the absence of sex differences in SNP effect. |
format | Online Article Text |
id | pubmed-8453908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84539082021-09-27 Testing and estimation of X‐chromosome SNP effects: Impact of model assumptions Song, Yilin Biernacka, Joanna M. Winham, Stacey J. Genet Epidemiol Research Articles Interest in analyzing X chromosome single nucleotide polymorphisms (SNPs) is growing and several approaches have been proposed. Prior studies have compared power of different approaches, but bias and interpretation of coefficients have received less attention. We performed simulations to demonstrate the impact of X chromosome model assumptions on effect estimates. We investigated the coefficient biases of SNP and sex effects with commonly used models for X chromosome SNPs, including models with and without assumptions of X chromosome inactivation (XCI), and with and without SNP–sex interaction terms. Sex and SNP coefficient biases were observed when assumptions made about XCI and sex differences in SNP effect in the analysis model were inconsistent with the data‐generating model. However, including a SNP–sex interaction term often eliminated these biases. To illustrate these findings, estimates under different genetic model assumptions are compared and interpreted in a real data example. Models to analyze X chromosome SNPs make assumptions beyond those made in autosomal variant analysis. Assumptions made about X chromosome SNP effects should be stated clearly when reporting and interpreting X chromosome associations. Fitting models with SNP × Sex interaction terms can avoid reliance on assumptions, eliminating coefficient bias even in the absence of sex differences in SNP effect. John Wiley and Sons Inc. 2021-06-03 2021-09 /pmc/articles/PMC8453908/ /pubmed/34082482 http://dx.doi.org/10.1002/gepi.22393 Text en © 2021 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Articles Song, Yilin Biernacka, Joanna M. Winham, Stacey J. Testing and estimation of X‐chromosome SNP effects: Impact of model assumptions |
title | Testing and estimation of X‐chromosome SNP effects: Impact of model assumptions |
title_full | Testing and estimation of X‐chromosome SNP effects: Impact of model assumptions |
title_fullStr | Testing and estimation of X‐chromosome SNP effects: Impact of model assumptions |
title_full_unstemmed | Testing and estimation of X‐chromosome SNP effects: Impact of model assumptions |
title_short | Testing and estimation of X‐chromosome SNP effects: Impact of model assumptions |
title_sort | testing and estimation of x‐chromosome snp effects: impact of model assumptions |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8453908/ https://www.ncbi.nlm.nih.gov/pubmed/34082482 http://dx.doi.org/10.1002/gepi.22393 |
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