Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.

It is of great interest and potential to discover causal relationships between pairs of exposures and outcomes using genetic variants as instrumental variables (IVs) to deal with hidden confounding in observational studies. Two most popular approaches are Mendelian randomization (MR), which usually...

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Autores principales:	Yangqing Deng, Wei Pan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/bcc004943ed44a448c2aa11b742247eb
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spelling	oai:doaj.org-article:bcc004943ed44a448c2aa11b742247eb2021-12-02T19:58:09ZModel checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.1553-734X1553-735810.1371/journal.pcbi.1009266https://doaj.org/article/bcc004943ed44a448c2aa11b742247eb2021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009266https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358It is of great interest and potential to discover causal relationships between pairs of exposures and outcomes using genetic variants as instrumental variables (IVs) to deal with hidden confounding in observational studies. Two most popular approaches are Mendelian randomization (MR), which usually use independent genetic variants/SNPs across the genome, and transcriptome-wide association studies (TWAS) (or their generalizations) using cis-SNPs local to a gene (or some genome-wide and likely dependent SNPs), as IVs. In spite of their many promising applications, both approaches face a major challenge: the validity of their causal conclusions depends on three critical assumptions on valid IVs, and more generally on other modeling assumptions, which however may not hold in practice. The most likely as well as challenging situation is due to the wide-spread horizontal pleiotropy, leading to two of the three IV assumptions being violated and thus to biased statistical inference. More generally, we'd like to conduct a goodness-of-fit (GOF) test to check the model being used. Although some methods have been proposed as being robust to various degrees to the violation of some modeling assumptions, they often give different and even conflicting results due to their own modeling assumptions and possibly lower statistical efficiency, imposing difficulties to the practitioner in choosing and interpreting varying results across different methods. Hence, it would help to directly test whether any assumption is violated or not. In particular, there is a lack of such tests for TWAS. We propose a new and general GOF test, called TEDE (TEsting Direct Effects), applicable to both correlated and independent SNPs/IVs (as commonly used in TWAS and MR respectively). Through simulation studies and real data examples, we demonstrate high statistical power and advantages of our new method, while confirming the frequent violation of modeling (including valid IV) assumptions in practice and thus the importance of model checking by applying such a test in MR/TWAS analysis.Yangqing DengWei PanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009266 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Yangqing Deng Wei Pan Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.
description	It is of great interest and potential to discover causal relationships between pairs of exposures and outcomes using genetic variants as instrumental variables (IVs) to deal with hidden confounding in observational studies. Two most popular approaches are Mendelian randomization (MR), which usually use independent genetic variants/SNPs across the genome, and transcriptome-wide association studies (TWAS) (or their generalizations) using cis-SNPs local to a gene (or some genome-wide and likely dependent SNPs), as IVs. In spite of their many promising applications, both approaches face a major challenge: the validity of their causal conclusions depends on three critical assumptions on valid IVs, and more generally on other modeling assumptions, which however may not hold in practice. The most likely as well as challenging situation is due to the wide-spread horizontal pleiotropy, leading to two of the three IV assumptions being violated and thus to biased statistical inference. More generally, we'd like to conduct a goodness-of-fit (GOF) test to check the model being used. Although some methods have been proposed as being robust to various degrees to the violation of some modeling assumptions, they often give different and even conflicting results due to their own modeling assumptions and possibly lower statistical efficiency, imposing difficulties to the practitioner in choosing and interpreting varying results across different methods. Hence, it would help to directly test whether any assumption is violated or not. In particular, there is a lack of such tests for TWAS. We propose a new and general GOF test, called TEDE (TEsting Direct Effects), applicable to both correlated and independent SNPs/IVs (as commonly used in TWAS and MR respectively). Through simulation studies and real data examples, we demonstrate high statistical power and advantages of our new method, while confirming the frequent violation of modeling (including valid IV) assumptions in practice and thus the importance of model checking by applying such a test in MR/TWAS analysis.
format	article
author	Yangqing Deng Wei Pan
author_facet	Yangqing Deng Wei Pan
author_sort	Yangqing Deng
title	Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.
title_short	Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.
title_full	Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.
title_fullStr	Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.
title_full_unstemmed	Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.
title_sort	model checking via testing for direct effects in mendelian randomization and transcriptome-wide association studies.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/bcc004943ed44a448c2aa11b742247eb
work_keys_str_mv	AT yangqingdeng modelcheckingviatestingfordirecteffectsinmendelianrandomizationandtranscriptomewideassociationstudies AT weipan modelcheckingviatestingfordirecteffectsinmendelianrandomizationandtranscriptomewideassociationstudies
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Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.

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