File Download
  Links for fulltext
     (May Require Subscription)

Article: Reciprocal causation mixture model for robust Mendelian randomization analysis using genome-scale summary data.

TitleReciprocal causation mixture model for robust Mendelian randomization analysis using genome-scale summary data.
Authors
Issue Date28-Feb-2023
PublisherNature Research
Citation
Nature Communications, 2023, v. 14, n. 1 How to Cite?
AbstractMendelian randomization using GWAS summary statistics has become a popular method to infer causal relationships across complex diseases. However, the widespread pleiotropy observed in GWAS has made the selection of valid instrumental variables problematic, leading to possible violations of Mendelian randomization assumptions and thus potentially invalid inferences concerning causation. Furthermore, current MR methods can examine causation in only one direction, so that two separate analyses are required for bi-directional analysis. In this study, we propose a ststistical framework, MRCI (Mixture model Reciprocal Causation Inference), to estimate reciprocal causation between two phenotypes simultaneously using the genome-scale summary statistics of the two phenotypes and reference linkage disequilibrium information. Simulation studies, including strong correlated pleiotropy, showed that MRCI obtained nearly unbiased estimates of causation in both directions, and correct Type I error rates under the null hypothesis. In applications to real GWAS data, MRCI detected significant bi-directional and uni-directional causal influences between common diseases and putative risk factors.
Persistent Identifierhttp://hdl.handle.net/10722/332230
ISSN
2023 Impact Factor: 14.7
2023 SCImago Journal Rankings: 4.887
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Z-
dc.contributor.authorQin, Y-
dc.contributor.authorWu, T-
dc.contributor.authorTubbs, JD-
dc.contributor.authorBaum, L-
dc.contributor.authorMak, TSH-
dc.contributor.authorLi, M-
dc.contributor.authorZhang, YD-
dc.contributor.authorSham, PC-
dc.date.accessioned2023-10-04T07:21:05Z-
dc.date.available2023-10-04T07:21:05Z-
dc.date.issued2023-02-28-
dc.identifier.citationNature Communications, 2023, v. 14, n. 1-
dc.identifier.issn2041-1723-
dc.identifier.urihttp://hdl.handle.net/10722/332230-
dc.description.abstractMendelian randomization using GWAS summary statistics has become a popular method to infer causal relationships across complex diseases. However, the widespread pleiotropy observed in GWAS has made the selection of valid instrumental variables problematic, leading to possible violations of Mendelian randomization assumptions and thus potentially invalid inferences concerning causation. Furthermore, current MR methods can examine causation in only one direction, so that two separate analyses are required for bi-directional analysis. In this study, we propose a ststistical framework, MRCI (Mixture model Reciprocal Causation Inference), to estimate reciprocal causation between two phenotypes simultaneously using the genome-scale summary statistics of the two phenotypes and reference linkage disequilibrium information. Simulation studies, including strong correlated pleiotropy, showed that MRCI obtained nearly unbiased estimates of causation in both directions, and correct Type I error rates under the null hypothesis. In applications to real GWAS data, MRCI detected significant bi-directional and uni-directional causal influences between common diseases and putative risk factors.-
dc.languageeng-
dc.publisherNature Research-
dc.relation.ispartofNature Communications-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleReciprocal causation mixture model for robust Mendelian randomization analysis using genome-scale summary data.-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1038/s41467-023-36490-4-
dc.identifier.pmid36854672-
dc.identifier.scopuseid_2-s2.0-85149153193-
dc.identifier.volume14-
dc.identifier.issue1-
dc.identifier.eissn2041-1723-
dc.identifier.isiWOS:000955827800013-
dc.identifier.issnl2041-1723-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats