Extending Causality Tests with Genetic Instruments: An Integration of Mendelian Randomization with the Classical Twin Design

ABSTRACTMendelian Randomization (MR) is an important approach to modelling causality in non-experimental settings. MR uses genetic instruments to test causal relationships between exposures and outcomes of interest. Individual genetic variants have small effects, and so, when used as instruments, render MR liable to weak instrument bias. Polygenic scores have the advantage of larger effects, but may be characterized by direct pleiotropy, which violates a central assumption of MR.We developed the MR-DoC twin model by integrating MR with the Direction of Causation twin model. This model allows us to test pleiotropy directly. We considered the issue of parameter identification, and given identification, we conducted extensive power calculations. MR-DoC allows one to test causal hypotheses and to obtain unbiased estimates of the causal effect given pleiotropic instruments (polygenic scores), while controlling for genetic and environmental influences common to the outcome and exposure. Furthermore, MR-DoC in twins has appreciably greater statistical power than a standard MR analysis applied to singletons, if the unshared environmental effects on the exposure and the outcome are uncorrelated. Generally, power increases with: 1) decreasing residual exposure-outcome correlation, and 2) decreasing heritability of the exposure variable.MR-DoC allows one to employ strong instrumental variables (polygenic scores, possibly pleiotropic), guarding against weak instrument bias and increasing the power to detect causal effects. Our approach will enhance and extend MR’s range of applications, and increase the value of the large cohorts collected at twin registries as they correctly detect causation and estimate effect sizes even in the presence of pleiotropy.

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Genomics of ageing in twins

Proceedings of The Nutrition Society ◽

10.1017/s0029665114000640 ◽

2014 ◽

Vol 73 (4) ◽

pp. 526-531 ◽

Cited By ~ 3

Author(s):

Massimo Mangino

Keyword(s):

Molecular Mechanisms ◽

Twin Studies ◽

Later Life ◽

Twin Design ◽

The Past ◽

Human Ageing ◽

Classical Twin Design ◽

Environmental Component ◽

Early Life Events ◽

Generation Sequencing

Ageing is a complex multifactorial process, reflecting the progression of all degenerative pathways within an organism. Due to the increase of life expectancy, in recent years, there is a pressing need to identify early-life events and risk factors that determine health outcomes in later life. So far, genetic variation only explains ~20–25 % of the variability of human survival to age 80+. This clearly implies that other factors (environmental, epigenetic and lifestyle) contribute to lifespan and the rate of healthy ageing within an individual. Twin studies in the past two decades proved to be a very powerful tool to discriminate the genetic from the environmental component. The aim of this review is to describe the basic concepts of the twin study design and to report some of the latest studies in which high-throughput technologies (e.g. genome/epigenome-wide assay, next generation sequencing, MS metabolic profiling) combined with the classical twin design have been applied to the analysis of novel ‘omics’ to further understand the molecular mechanisms of human ageing.

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Classical twin design in modern pharmacogenomics studies

Pharmacogenomics ◽

10.2217/pgs.09.171 ◽

2010 ◽

Vol 11 (2) ◽

pp. 215-226 ◽

Cited By ~ 9

Author(s):

Nilüfer Rahmioğlu ◽

Kourosh R Ahmadi

Keyword(s):

Twin Design ◽

Classical Twin Design

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Genetic instruments with too many strings: acknowledging pleiotropy and population structure in Mendelian randomization studies

European Heart Journal ◽

10.1093/eurheartj/ehz943 ◽

2020 ◽

Vol 41 (7) ◽

pp. 892-893 ◽

Cited By ~ 2

Author(s):

Jeremy A Labrecque ◽

Jay S Kaufman ◽

Jonathan Y Huang

Keyword(s):

Population Structure ◽

Mendelian Randomization ◽

Genetic Instruments

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Application of Mendelian Randomization to Investigate the Association of Body Mass Index with Health Care Costs

Medical Decision Making ◽

10.1177/0272989x20905809 ◽

2020 ◽

Vol 40 (2) ◽

pp. 156-169 ◽

Cited By ~ 1

Author(s):

Christoph F. Kurz ◽

Michael Laxy

Keyword(s):

Health Care ◽

Body Mass Index ◽

Body Mass ◽

Health Care Costs ◽

Instrumental Variables ◽

Mendelian Randomization ◽

Causal Effect ◽

Omitted Variables ◽

Care Costs ◽

Genetic Instruments

Causal effect estimates for the association of obesity with health care costs can be biased by reversed causation and omitted variables. In this study, we use genetic variants as instrumental variables to overcome these limitations, a method that is often called Mendelian randomization (MR). We describe the assumptions, available methods, and potential pitfalls of using genetic information and how to address them. We estimate the effect of body mass index (BMI) on total health care costs using data from a German observational study and from published large-scale data. In a meta-analysis of several MR approaches, we find that models using genetic instruments identify additional annual costs of €280 for a 1-unit increase in BMI. This is more than 3 times higher than estimates from linear regression without instrumental variables (€75). We found little evidence of a nonlinear relationship between BMI and health care costs. Our results suggest that the use of genetic instruments can be a powerful tool for estimating causal effects in health economic evaluation that might be superior to other types of instruments where there is a strong association with a modifiable risk factor.

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Gene-Environment Interplay in Twin Models

Political Analysis ◽

10.1093/pan/mpt005 ◽

2013 ◽

Vol 21 (3) ◽

pp. 368-389 ◽

Cited By ~ 20

Author(s):

Brad Verhulst ◽

Peter K. Hatemi

Keyword(s):

Genetic Similarity ◽

Behavior Genetics ◽

Empirical Studies ◽

Environment Interaction ◽

Twin Design ◽

Gene Environment Interaction ◽

Twin Model ◽

Gene Environment ◽

Twin Models ◽

Classical Twin Design

In this article, we respond to Shultziner's critique that argues that identical twins are more alike not because of genetic similarity, but because they select into more similar environments and respond to stimuli in comparable ways, and that these effects bias twin model estimates to such an extent that they are invalid. The essay further argues that the theory and methods that undergird twin models, as well as the empirical studies which rely upon them, are unaware of these potential biases. We correct this and other misunderstandings in the essay and find that gene-environment (GE) interplay is a well-articulated concept in behavior genetics and political science, operationalized as gene-environment correlation and gene-environment interaction. Both are incorporated into interpretations of the classical twin design (CTD) and estimated in numerous empirical studies through extensions of the CTD. We then conduct simulations to quantify the influence of GE interplay on estimates from the CTD. Due to the criticism's mischaracterization of the CTD and GE interplay, combined with the absence of any empirical evidence to counter what is presented in the extant literature and this article, we conclude that the critique does not enhance our understanding of the processes that drive political traits, genetic or otherwise.

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Educational attainment impacts drinking behaviors and risk for alcohol dependence: results from a two-sample Mendelian randomization study with ~780,000 participants

Molecular Psychiatry ◽

10.1038/s41380-019-0535-9 ◽

2019 ◽

Cited By ~ 4

Author(s):

Daniel B. Rosoff ◽

Toni-Kim Clarke ◽

Mark J. Adams ◽

Andrew M. McIntosh ◽

George Davey Smith ◽

...

Keyword(s):

Alcohol Use ◽

Educational Attainment ◽

Alcohol Dependence ◽

Mendelian Randomization ◽

Association Studies ◽

Significant Snps ◽

Genome Wide Association Studies ◽

Reverse Causality ◽

Genome Wide ◽

Genetic Instruments

Abstract Observational studies suggest that lower educational attainment (EA) may be associated with risky alcohol use behaviors; however, these findings may be biased by confounding and reverse causality. We performed two-sample Mendelian randomization (MR) using summary statistics from recent genome-wide association studies (GWAS) with >780,000 participants to assess the causal effects of EA on alcohol use behaviors and alcohol dependence (AD). Fifty-three independent genome-wide significant SNPs previously associated with EA were tested for association with alcohol use behaviors. We show that while genetic instruments associated with increased EA are not associated with total amount of weekly drinks, they are associated with reduced frequency of binge drinking ≥6 drinks (ßIVW = −0.198, 95% CI, −0.297 to –0.099, PIVW = 9.14 × 10−5), reduced total drinks consumed per drinking day (ßIVW = −0.207, 95% CI, −0.293 to –0.120, PIVW = 2.87 × 10−6), as well as lower weekly distilled spirits intake (ßIVW = −0.148, 95% CI, −0.188 to –0.107, PIVW = 6.24 × 10−13). Conversely, genetic instruments for increased EA were associated with increased alcohol intake frequency (ßIVW = 0.331, 95% CI, 0.267–0.396, PIVW = 4.62 × 10−24), and increased weekly white wine (ßIVW = 0.199, 95% CI, 0.159–0.238, PIVW = 7.96 × 10−23) and red wine intake (ßIVW = 0.204, 95% CI, 0.161–0.248, PIVW = 6.67 × 10−20). Genetic instruments associated with increased EA reduced AD risk: an additional 3.61 years schooling reduced the risk by ~50% (ORIVW = 0.508, 95% CI, 0.315–0.819, PIVW = 5.52 × 10−3). Consistency of results across complementary MR methods accommodating different assumptions about genetic pleiotropy strengthened causal inference. Our findings suggest EA may have important effects on alcohol consumption patterns and may provide potential mechanisms explaining reported associations between EA and adverse health outcomes.

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The Potential Effect of Aberrant Testosterone Levels on Common Diseases: A Mendelian Randomization Study

Genes ◽

10.3390/genes11070721 ◽

2020 ◽

Vol 11 (7) ◽

pp. 721

Author(s):

Ali Alamdar Shah Syed ◽

Lin He ◽

Yongyong Shi

Keyword(s):

Type Ii Diabetes ◽

Mendelian Randomization ◽

Serum Testosterone ◽

Potential Effect ◽

Type Ii ◽

Degenerative Diseases ◽

Testosterone Levels ◽

Unit Increase ◽

Low Levels ◽

Genetic Instruments

Testosterone has historically been linked to sexual dysfunction; however, it has recently been shown to affect other physical and mental attributes. We attempted to determine whether changes in serum testosterone could play a role in chronic or degenerative diseases. We used two separate genetic instruments comprising of variants from JMJD1C and SHBG regions and conducted a two-sample Mendelian randomization for type II diabetes (T2D), gout, rheumatoid arthritis (RA), schizophrenia, bipolar disorder, Alzheimer’s disease and depression. For the JMJD1C locus, one unit increase in log transformed testosterone was significantly associated with RA (OR = 1.69, p = 0.02), gout (OR = 0.469, p = 0.001) and T2D (OR = 0.769, p = 0.048). Similarly, one unit increase in log transformed testosterone using variants from the SHBG locus was associated with depression (OR = 1.02, p = 0.001), RA (OR = 1.32, p < 0.001) and T2D (OR = 0.88, p = 0.003). Our results show that low levels of serum testosterone levels may cause gout and T2D, while higher than normal levels of testosterone may result in RA and depression. Our findings suggest that fluctuations in testosterone levels may have severe consequences that warrant further investigation.

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Choosing sides: The genetics of why we go with the loudest

Journal of Theoretical Politics ◽

10.1177/0951629812437750 ◽

2012 ◽

Vol 24 (3) ◽

pp. 389-408 ◽

Cited By ~ 9

Author(s):

Zoltán Fazekas ◽

Levente Littvay

Keyword(s):

Individual Differences ◽

Party Identification ◽

Decision Strategy ◽

Twin Design ◽

Spatial Voting ◽

Recent Developments ◽

Classical Twin Design ◽

Voter Models ◽

Rational Voter ◽

The Individual

Recent developments in spatial voting have moved beyond finding the most appropriate utility function and started to assess individual differences in decision strategy. The question is not if a proximity or directional worldview performs better in general, rather under what conditions do people pick one strategy over the other? We draw on psychological theories to develop a survey-based measure of individual decision strategy and take a behavior genetic route to explaining the individual differences. We argue that dispositional traits shape whether an individual develops a directional or proximity worldview of the political arena. Utilizing a classical twin design, we capitalize on the documented relationship between partisanship and a directionalist worldview. We find that, in the Minnesota Twin Political Survey, both the strength of party identification and directional voting are moderately (~20 percent) but significantly ( p < 0.05) heritable with no socialized component contributing to the variance. The covariation between the two traits is predominantly driven by common underlying genetic effects ( p < 0.01). Implications for the rational voter models are discussed in light of the findings.

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Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

10.1101/2020.05.06.077982 ◽

2020 ◽

Author(s):

Jingshu Wang ◽

Qingyuan Zhao ◽

Jack Bowden ◽

Gilbran Hemani ◽

George Davey Smith ◽

...

Keyword(s):

Risk Factors ◽

Complex Traits ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Genome Wide ◽

Genetic Instruments

Over a decade of genome-wide association studies have led to the finding that significant genetic associations tend to spread across the genome for complex traits. The extreme polygenicity where "all genes affect every complex trait" complicates Mendelian Randomization studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing Mendelian Randomization methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using summary statistics from genome-wide association studies, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, adjust for confounding risk factors, and determine the causal direction. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.

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