Mendelian Randomization versus Path Models: Making Causal Inferences in Genetic Epidemiology

2015 ◽  
Vol 79 (3-4) ◽  
pp. 194-204 ◽  
Author(s):  
Andreas Ziegler ◽  
Henry Mwambi ◽  
Inke R. König
Keyword(s):  
Genetic Epidemiology ◽  
Mendelian Randomization ◽  
Causal Inferences ◽  
Path Models

scholarly journals Genetic epidemiology and Mendelian randomization for informing disease therapeutics: conceptual and methodological challenges

2017 ◽  
Author(s):  
Lavinia Paternoster ◽  
Kate Tilling ◽  
George Davey Smith
Keyword(s):  
Genetic Epidemiology ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Disease Treatment ◽  
The Past ◽  
Genome Wide ◽  
Perceived Benefit ◽  
Progression Of Disease ◽  
High Yields

The past decade has been proclaimed as a hugely successful era of gene discovery through the high yields of many genome-wide association studies (GWAS). However, much of the perceived benefit of such discoveries lies in the promise that the identification of genes that influence disease would directly translate into the identification of potential therapeutic targets (1-4), but this has yet to be realised at a level reflecting expectation. One reason for this, we suggest, is that GWAS to date have generally not focused on phenotypes that directly relate to the progression of disease, and thus speak to disease treatment.

scholarly journals Within family Mendelian randomization studies

2019 ◽  
Vol 28 (R2) ◽  
pp. R170-R179 ◽  
Author(s):  
Neil M Davies ◽  
Laurence J Howe ◽  
Ben Brumpton ◽  
Alexandra Havdahl ◽  
David M Evans ◽  
...  
Keyword(s):  
Population Stratification ◽  
Mendelian Randomization ◽  
Transmission Ratio Distortion ◽  
Random Allocation ◽  
Causal Inferences ◽  
Sibling Pairs ◽  
Causal Processes ◽  
Wide Range ◽  
Ratio Distortion ◽  
Related Individuals

Abstract Mendelian randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP–phenotype associations. We explain how studies of related individuals such as sibling pairs or parent–offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.

scholarly journals Inferring Causal Relationships Between Risk Factors and Outcomes from Genome-Wide Association Study Data

2018 ◽  
Vol 19 (1) ◽  
pp. 303-327 ◽  
Author(s):  
Stephen Burgess ◽  
Christopher N. Foley ◽  
Verena Zuber
Keyword(s):  
Risk Factor ◽  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Single Gene ◽  
Study Data ◽  
Causal Inferences ◽  
Genome Wide ◽  
Multiple Regions ◽  
Polygenic Analysis

An observational correlation between a suspected risk factor and an outcome does not necessarily imply that interventions on levels of the risk factor will have a causal impact on the outcome (correlation is not causation). If genetic variants associated with the risk factor are also associated with the outcome, then this increases the plausibility that the risk factor is a causal determinant of the outcome. However, if the genetic variants in the analysis do not have a specific biological link to the risk factor, then causal claims can be spurious. We review the Mendelian randomization paradigm for making causal inferences using genetic variants. We consider monogenic analysis, in which genetic variants are taken from a single gene region, and polygenic analysis, which includes variants from multiple regions. We focus on answering two questions: When can Mendelian randomization be used to make reliable causal inferences, and when can it be used to make relevant causal inferences?

scholarly journals Cardiometabolic risks of SARS-CoV-2 hospitalization using Mendelian Randomization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Noah Lorincz-Comi ◽  
Xiaofeng Zhu
Keyword(s):  
Pulse Pressure ◽  
Type Ii Diabetes ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Risk Estimation ◽  
Estimation Method ◽  
Large Sample Size ◽  
Sample Type ◽  
Type Ii ◽  
Causal Inferences

AbstractMany cardiometabolic conditions have demonstrated associative evidence with COVID-19 hospitalization risk. However, the observational designs of the studies in which these associations are observed preclude causal inferences of hospitalization risk. Mendelian Randomization (MR) is an alternative risk estimation method more robust to these limitations that allows for causal inferences. We applied four MR methods (MRMix, IMRP, IVW, MREgger) to publicly available GWAS summary statistics from European (COVID-19 GWAS n = 2956) and multi-ethnic populations (COVID-19 GWAS n = 10,908) to better understand extant causal associations between Type II Diabetes (GWAS n = 659,316), BMI (n = 681,275), diastolic and systolic blood pressure, and pulse pressure (n = 757,601 for each) and COVID-19 hospitalization risk across populations. Although no significant causal effect evidence was observed, our data suggested a trend of increasing hospitalization risk for Type II diabetes (IMRP OR, 95% CI 1.67, 0.96–2.92) and pulse pressure (OR, 95% CI 1.27, 0.97–1.66) in the multi-ethnic sample. Type II diabetes and Pulse pressure demonstrates a potential causal association with COVID-19 hospitalization risk, the proper treatment of which may work to reduce the risk of a severe COVID-19 illness requiring hospitalization. However, GWAS of COVID-19 with large sample size is warranted to confirm the causality.

scholarly journals Mendelian randomization: potential use of genetics to enable causal inferences regarding HIV-associated biomarkers and outcomes

2010 ◽  
Vol 5 (6) ◽  
pp. 545-559 ◽  
Author(s):  
Weijing He ◽  
John Castiblanco ◽  
Elizabeth A Walter ◽  
Jason F Okulicz ◽  
Sunil K Ahuja
Keyword(s):  
Mendelian Randomization ◽  
Causal Inferences ◽  
Potential Use

scholarly journals Cardiometabolic risks of SARS-CoV-2 hospitalization using Mendelian Randomization

2021 ◽  
Author(s):  
Noah J Lorincz-Comi ◽  
Xiaofeng Zhu
Keyword(s):  
Pulse Pressure ◽  
Type Ii Diabetes ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Risk Estimation ◽  
Estimation Method ◽  
Large Sample Size ◽  
Sample Type ◽  
Type Ii ◽  
Causal Inferences

Many cardiometabolic conditions have demonstrated associative evidence with COVID-19 hospitalization risk. However, the observational designs of the studies in which these associations are observed preclude causal inferences of hospitalization risk. Mendelian Randomization (MR) is an alternative risk estimation method more robust to these limitations that allows for causal inferences. We applied four MR methods (MRMix, IMRP, IVW, MREgger) to publicly available GWAS summary statistics from European (COVID-19 GWAS n=2,956) and multi-ethnic populations (COVID-19 GWAS n=10,808) to better understand extant causal associations between Type II Diabetes (GWAS n=659,316), BMI (n=681,275), diastolic and systolic blood pressure, and pulse pressure (n=757,601 for each) and COVID-19 hospitalization risk across populations. Although no significant causal effect evidence was observed, our data suggested a trend of increasing hospitalization risk for Type II diabetes (IMRP OR, 95% CI: 1.67, 0.96-2.92) and pulse pressure (OR, 95% CI: 1.27, 0.97-1.66) in the multi-ethnic sample. Type II diabetes and Pulse pressure demonstrates a potential causal association with COVID-19 hospitalization risk, the proper treatment of which may work to reduce the risk of a severe COVID-19 illness requiring hospitalization. However, GWAS of COVID-19 with large sample size is warranted to confirm the causality.

scholarly journals Mendelian randomization while jointly modeling cis genetics identifies causal relationships between gene expression and lipids

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Adriaan van der Graaf ◽  
◽  
Annique Claringbould ◽  
Antoine Rimbert ◽  
Harm-Jan Westra ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Summary Statistics ◽  
Low Density Lipoprotein Cholesterol ◽  
Causal Inferences ◽  
Individual Level ◽  
Level Data

Abstract Inference of causality between gene expression and complex traits using Mendelian randomization (MR) is confounded by pleiotropy and linkage disequilibrium (LD) of gene-expression quantitative trait loci (eQTL). Here, we propose an MR method, MR-link, that accounts for unobserved pleiotropy and LD by leveraging information from individual-level data, even when only one eQTL variant is present. In simulations, MR-link shows false-positive rates close to expectation (median 0.05) and high power (up to 0.89), outperforming all other tested MR methods and coloc. Application of MR-link to low-density lipoprotein cholesterol (LDL-C) measurements in 12,449 individuals with expression and protein QTL summary statistics from blood and liver identifies 25 genes causally linked to LDL-C. These include the known SORT1 and ApoE genes as well as PVRL2, located in the APOE locus, for which a causal role in liver was not known. Our results showcase the strength of MR-link for transcriptome-wide causal inferences.

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