An approach to identify gene-environment interactions and reveal new biological insight in complex traits.

There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.

MRBEE: A bias-corrected multivariable Mendelian randomization method.

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies. However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using bias-corrected estimating equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.

simmrd: An open-source tool to perform simulations in Mendelian randomization.

Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods.

Patient Decisions Regarding Rescheduling Colonoscopies Postponed Due to the COVID-19 Pandemic.

BACKGROUND: Due to the COVID-19 pandemic, elective colonoscopies were postponed in Ohio from 3/17/2020 to 5/1/2020. When the ban was lifted, canceled patients determined whether to reschedule their colonoscopy in the midst of the ongoing pandemic. AIMS: We aim to determine whether demographic, colorectal cancer (CRC) risk, and COVID-19 morbidity and mortality risk factors are associated with rescheduling of colonoscopies canceled by the COVID-19 pandemic. METHODS: A medical record review of 420 participants ages 40-74 at a midwestern academic health system with elective colonoscopies canceled from 3/17/2020 to 5/1/2020 due to the COVID-19 pandemic was performed. RESULTS: More than half of participants (71.0%) rescheduled their colonoscopy within the next 8 months. Indication for colonoscopy being 'surveillance following adenoma', colonoscopy ordered by primary care provider rather than gastroenterologist, and dyslipidemia were independently associated with rescheduling colonoscopy. Higher body mass index, indication for colonoscopy being simply 'screening for CRC,' and stool testing were associated with not rescheduling. Diagnoses associated with colorectal cancer risk such as adenomas, personal or family history of colorectal cancer, and inflammatory bowel disease were not associated with rescheduling, nor were other comorbidities associated with increased COVID-19 severity. 4.5% (19/420) opted for stool fecal immunochemical test or Cologuard testing. CONCLUSIONS: Most patients rescheduled their colonoscopy despite the risk of virus exposure, suggesting that concern of missed colorectal cancer diagnosis outweighed coronavirus concerns. Patient trust in referring providers may be important for rescheduling, and colonoscopy indications were independently associated with rescheduling status.

A new Approach to Identify Gene-Environment Interactions and Reveal New Biological Insight in Complex traits.

There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the CHARGE Gene-Lifestyle Interactions Working Group has been spearheading efforts to investigate G×E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identified and confirmed 5 loci (6 independent signals) interacting with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrated that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated contribution ranges from 1.76% to 14.05% of SNP heritability of serum lipids in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.

MRBEE: A novel bias-corrected multivariable Mendelian Randomization method.

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes and can apply to summary data from genome-wide association studies (GWAS). Since GWAS summary statistics are subject to estimation errors, most existing MR approaches suffer from measurement error bias, whose scale and direction are influenced by weak instrumental variables and GWAS sample overlap, respectively. We introduce MRBEE (MR using Bias-corrected Estimating Equation), a novel multivariable MR method capable of simultaneously removing measurement error bias and identifying horizontal pleiotropy. In simulations, we showed that MRBEE is capable of effectively removing measurement error bias in the presence of weak instrumental variables and sample overlap. In two independent real data analyses, we discovered that the causal effect of BMI on coronary artery disease risk is entirely mediated by blood pressure, and that existing MR methods may underestimate the causal effect of cannabis use disorder on schizophrenia risk compared to MRBEE. MRBEE possesses significant potential for advancing genetic research by providing a valuable tool to study causality between multiple risk factors and disease outcomes, particularly as a large number of GWAS summary statistics become publicly available.

MRBEE: A novel bias-corrected multivariable Mendelian Randomization method.

Mendelian Randomization (MR) has been widely applied to infer causality of exposures on outcomes in the genome wide association (GWAS) era. Existing approaches are often subject to biases from multiple sources including weak instruments, sample overlap, and measurement error. We introduce MRBEE, a computationally efficient multivariable MR method that can correct for all known biases simultaneously, which is demonstrated in theory, simulations, and real data analysis. In comparison, all existing MR methods are biased. In two independent real data analyses, we observed that the causal effect of BMI on coronary artery disease risk is completely mediated by blood pressure, and that existing MR methods drastically underestimate the causal effect of cannabis use disorder on schizophrenia risk compared to MRBEE. We demonstrate that MRBEE can be a useful tool in studying causality between multiple risk factors and a disease outcome, especially as more GWAS summary statistics are being made publicly available.

Cardiometabolic risks of SARS-CoV-2 hospitalization using Mendelian Randomization.

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 = 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.

Prenatal Substance Exposure and Developmental Trajectories of Internalizing Symptoms: Toddlerhood to Preadolescence.

BACKGROUND: Little is known about how prenatal exposure to substances (alcohol, tobacco, marijuana, and cocaine) may contribute to heterogeneous childhood trajectories of internalizing symptoms (i.e., depression, withdrawal, anxiety). The present study aimed to identify developmental trajectories of internalizing symptoms in children using gender-separate analyses and to examine whether trajectories differ by prenatal substance exposure (PSE) and other environmental and biological correlates. METHODS: Data from two large community-based birth cohorts with PSE were integrated (N = 1,651, 848 boys, 803 girls): the Cleveland cohort and the Maternal Lifestyle Study (MLS). Internalizing symptoms were assessed with the Child Behavior Checklist at ages 2, 4, 6, 9, 10, 11, and 12 in the Cleveland study and at ages 3, 5, 7, 9, 11, and 13 in the MLS. RESULTS: Gender-separate group-based trajectory modeling yielded five distinctive developmental trajectories of internalizing symptoms from ages 2 to 13 in both boys and girls: low-risk group (14.4% girls, 28.8% boys); normative-decreasing group (35.3% girls, 33.1% boys); increasing risk group (14.4% girls, 13.0% boys); early-high group (22.3% girls, 17.9% boys); and chronic group (13.8% girls, 7.2% boys). Prenatal tobacco exposure, maternal psychological distress, and postnatal maternal alcohol use differentiated the longitudinal courses of internalizing symptoms. Boys were more likely to follow the low-risk trajectory, whereas girls were more likely to follow the chronic trajectory. CONCLUSIONS: Prenatal tobacco exposure was associated with suboptimal developmental trajectories of internalizing symptoms in the context of prenatal poly-drug exposure, highlighting a need for continued and increased effort toward prevention of prenatal tobacco use.