Linear Regression in Genetic Association Studies
In genomic research phenotype transformations are commonly used as a straightforward way to reach normality of the model outcome. Many researchers still believe it to be necessary for proper inference. Using regression simulations, we show that phenotype transformations are typically not needed and,...
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| Format: | Online |
| Language: | English |
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Public Library of Science
2013
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3578817/ |
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pubmed-35788172013-02-22 Linear Regression in Genetic Association Studies Bůžková, Petra Research Article In genomic research phenotype transformations are commonly used as a straightforward way to reach normality of the model outcome. Many researchers still believe it to be necessary for proper inference. Using regression simulations, we show that phenotype transformations are typically not needed and, when used in phenotype with heteroscedasticity, result in inflated Type I error rates. We further explain that important is to address a combination of rare variant genotypes and heteroscedasticity. Incorrectly estimated parameter variability or incorrect choice of the distribution of the underlying test statistic provide spurious detection of associations. We conclude that it is a combination of heteroscedasticity, minor allele frequency, sample size, and to a much lesser extent the error distribution, that matter for proper statistical inference. Public Library of Science 2013-02-21 /pmc/articles/PMC3578817/ /pubmed/23437286 http://dx.doi.org/10.1371/journal.pone.0056976 Text en © 2013 Petra Buzkova http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Bůžková, Petra |
| spellingShingle |
Bůžková, Petra Linear Regression in Genetic Association Studies |
| author_facet |
Bůžková, Petra |
| author_sort |
Bůžková, Petra |
| title |
Linear Regression in Genetic Association Studies |
| title_short |
Linear Regression in Genetic Association Studies |
| title_full |
Linear Regression in Genetic Association Studies |
| title_fullStr |
Linear Regression in Genetic Association Studies |
| title_full_unstemmed |
Linear Regression in Genetic Association Studies |
| title_sort |
linear regression in genetic association studies |
| description |
In genomic research phenotype transformations are commonly used as a straightforward way to reach normality of the model outcome. Many researchers still believe it to be necessary for proper inference. Using regression simulations, we show that phenotype transformations are typically not needed and, when used in phenotype with heteroscedasticity, result in inflated Type I error rates. We further explain that important is to address a combination of rare variant genotypes and heteroscedasticity. Incorrectly estimated parameter variability or incorrect choice of the distribution of the underlying test statistic provide spurious detection of associations. We conclude that it is a combination of heteroscedasticity, minor allele frequency, sample size, and to a much lesser extent the error distribution, that matter for proper statistical inference. |
| publisher |
Public Library of Science |
| publishDate |
2013 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3578817/ |
| _version_ |
1611956555756863488 |