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A practical problem with Egger regression in Mendelian randomization
Mendelian randomization (MR) is an instrumental variable (IV) method using genetic variants such as single nucleotide polymorphisms (SNPs) as IVs to disentangle the causal relationship between an exposure and an outcome. Since any causal conclusion critically depends on the three valid IV assumption...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109933/ https://www.ncbi.nlm.nih.gov/pubmed/35507585 http://dx.doi.org/10.1371/journal.pgen.1010166 |
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author | Lin, Zhaotong Pan, Isaac Pan, Wei |
author_facet | Lin, Zhaotong Pan, Isaac Pan, Wei |
author_sort | Lin, Zhaotong |
collection | PubMed |
description | Mendelian randomization (MR) is an instrumental variable (IV) method using genetic variants such as single nucleotide polymorphisms (SNPs) as IVs to disentangle the causal relationship between an exposure and an outcome. Since any causal conclusion critically depends on the three valid IV assumptions, which will likely be violated in practice, MR methods robust to the IV assumptions are greatly needed. As such a method, Egger regression stands out as one of the most widely used due to its easy use and perceived robustness. Although Egger regression is claimed to be robust to directional pleiotropy under the instrument strength independent of direct effect (InSIDE) assumption, it is known to be dependent on the orientations/coding schemes of SNPs (i.e. which allele of an SNP is selected as the reference group). The current practice, as recommended as the default setting in some popular MR software packages, is to orientate the SNPs to be all positively associated with the exposure, which however, to our knowledge, has not been fully studied to assess its robustness and potential impact. We use both numerical examples (with both real data and simulated data) and analytical results to demonstrate the practical problem of Egger regression with respect to its heavy dependence on the SNP orientations. Under the assumption that InSIDE holds for some specific (and unknown) coding scheme of the SNPs, we analytically show that other coding schemes would in general lead to the violation of InSIDE. Other related MR and IV regression methods may suffer from the same problem. Cautions should be taken when applying Egger regression (and related MR and IV regression methods) in practice. |
format | Online Article Text |
id | pubmed-9109933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91099332022-05-17 A practical problem with Egger regression in Mendelian randomization Lin, Zhaotong Pan, Isaac Pan, Wei PLoS Genet Research Article Mendelian randomization (MR) is an instrumental variable (IV) method using genetic variants such as single nucleotide polymorphisms (SNPs) as IVs to disentangle the causal relationship between an exposure and an outcome. Since any causal conclusion critically depends on the three valid IV assumptions, which will likely be violated in practice, MR methods robust to the IV assumptions are greatly needed. As such a method, Egger regression stands out as one of the most widely used due to its easy use and perceived robustness. Although Egger regression is claimed to be robust to directional pleiotropy under the instrument strength independent of direct effect (InSIDE) assumption, it is known to be dependent on the orientations/coding schemes of SNPs (i.e. which allele of an SNP is selected as the reference group). The current practice, as recommended as the default setting in some popular MR software packages, is to orientate the SNPs to be all positively associated with the exposure, which however, to our knowledge, has not been fully studied to assess its robustness and potential impact. We use both numerical examples (with both real data and simulated data) and analytical results to demonstrate the practical problem of Egger regression with respect to its heavy dependence on the SNP orientations. Under the assumption that InSIDE holds for some specific (and unknown) coding scheme of the SNPs, we analytically show that other coding schemes would in general lead to the violation of InSIDE. Other related MR and IV regression methods may suffer from the same problem. Cautions should be taken when applying Egger regression (and related MR and IV regression methods) in practice. Public Library of Science 2022-05-04 /pmc/articles/PMC9109933/ /pubmed/35507585 http://dx.doi.org/10.1371/journal.pgen.1010166 Text en © 2022 Lin et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Lin, Zhaotong Pan, Isaac Pan, Wei A practical problem with Egger regression in Mendelian randomization |
title | A practical problem with Egger regression in Mendelian randomization |
title_full | A practical problem with Egger regression in Mendelian randomization |
title_fullStr | A practical problem with Egger regression in Mendelian randomization |
title_full_unstemmed | A practical problem with Egger regression in Mendelian randomization |
title_short | A practical problem with Egger regression in Mendelian randomization |
title_sort | practical problem with egger regression in mendelian randomization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109933/ https://www.ncbi.nlm.nih.gov/pubmed/35507585 http://dx.doi.org/10.1371/journal.pgen.1010166 |
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