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Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?

ABSTRACT: Observational epidemiologic studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) uses genetic variants to proxy modifiable exposures to generate more reliable estimates of the causal effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within cardio-metabolic epidemiology, but also holds much promise for identifying possible interventions for cancer prevention and treatment. However, some methodologic challenges in the implementation of MR are particularly pertinent when applying this method to cancer etiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies. In this review, we provide an overview of the key principles and assumptions of MR, focusing on applications of this method to the study of cancer etiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared with conventional epidemiological studies. Finally, limitations of MR and recent methodologic developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer. Cancer Epidemiol Biomarkers Prev; 27(9); 995-1010. ©2018 AACR.

SUBMITTER: Yarmolinsky J

PROVIDER: S-EPMC6522350 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?

Yarmolinsky James J Wade Kaitlin H KH Richmond Rebecca C RC Langdon Ryan J RJ Bull Caroline J CJ Tilling Kate M KM Relton Caroline L CL Lewis Sarah J SJ Davey Smith George G Martin Richard M RM

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20180625 9

Observational epidemiologic studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) uses genetic variants to proxy modifiable exposures to generate more reliable estimates of the causal effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within cardio-metabolic epidemiology, but also holds much promise for identifying possible interventions for cancer prevention and treatm ...[more]

PMID: 29941659

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Project description:BackgroundCross-sectional observational studies have reported obesity and cardiometabolic co-morbidities as important predictors of coronavirus disease 2019 (COVID-19) hospitalization. The causal impact of these risk factors is unknown at present.MethodsWe conducted multivariable logistic regression to evaluate the observational associations between obesity traits (body mass index [BMI], waist circumference [WC]), quantitative cardiometabolic parameters (systolic blood pressure [SBP], serum glucose, serum glycated hemoglobin [HbA1c], low-density lipoprotein [LDL] cholesterol, high-density lipoprotein [HDL] cholesterol and triglycerides [TG]) and SARS-CoV-2 positivity in the UK Biobank cohort. One-sample MR was performed by using the genetic risk scores of obesity and cardiometabolic traits constructed from independent datasets and the genotype and phenotype data from the UK Biobank. Two-sample MR was performed using the summary statistics from COVID-19 host genetics initiative. Cox proportional hazard models were fitted to assess the risk conferred by different genetic quintiles of causative exposure traits.ResultsThe study comprised 1,211 European participants who were tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and 387,079 participants who were either untested or tested negative between 16 March 2020 to 31 May 2020. Observationally, higher BMI, WC, HbA1c and lower HDL-cholesterol were associated with higher odds of COVID-19 infection. One-sample MR analyses found causal associations between higher genetically determined BMI and LDL cholesterol and increased risk of COVID-19 (odds ratio [OR]: 1.15, confidence interval [CI]: 1.05-1.26 and OR: 1.58, CI: 1.21-2.06, per 1 standard deviation increment in BMI and LDL cholesterol respectively). Two-sample MR produced concordant results. Cox models indicated that individuals in the higher genetic risk score quintiles of BMI and LDL were more predisposed to COVID-19 (hazard ratio [HR]: 1.24, CI: 1.03-1.49 and HR: 1.37, CI: 1.14-1.65, for the top vs the bottom quintile for BMI and LDL cholesterol, respectively).ConclusionWe identified causal associations between BMI, LDL cholesterol and susceptibility to COVID-19. In particular, individuals in higher genetic risk categories were predisposed to SARS-CoV-2 infection. These findings support the integration of BMI into the risk assessment of COVID-19 and allude to a potential role of lipid modification in the prevention and treatment.

Project description:BackgroundComplex immune-brain interactions that affect neural development, survival and function might have causal and therapeutic implications for psychiatric illnesses. However, previous studies examining the association between immune inflammation and schizophrenia (SCZ) have yielded inconsistent findings.MethodsComprehensive two-sample Mendelian randomization (MR) analysis was performed to determine the causal association between immune cell signatures and SCZ in this study. Based on publicly available genetic data, we explored causal associations between 731 immune cell signatures and SCZ risk. A total of four types of immune signatures (median fluorescence intensities (MFI), relative cell (RC), absolute cell (AC), and morphological parameters (MP)) were included. Comprehensive sensitivity analyses were used to verify the robustness, heterogeneity, and horizontal pleiotropy of the results.ResultsAfter FDR correction, SCZ had no statistically significant effect on immunophenotypes. It was worth mentioning some phenotypes with unadjusted low P-values, including FSC-A on NKT (β = 0.119, 95% CI = 0.044 ~ 0.194, P = 0.002), DN (CD4-CD8-) NKT %T cell (β = 0.131, 95% CI = 0.054 ~ 0.208, P = 9.03 × 10- 4), and SSC-A on lymphocytes (β = 0.136, 95% CI = 0.059 ~ 0.213, P = 5.43 × 10- 4). The causal effect of SCZ IgD on transitional was estimated to 0.127 (95% CI = 0.051 ~ 0.203, P = 1.09 × 10- 3). SCZ also had a causal effect on IgD+ %B cell (β = 0.130, 95% CI = 0.054 ~ 0.207, P = 8.69 × 10- 4), and DP (CD4+CD8+) %T cell (β = 0.131, 95% CI = 0.054 ~ 0.207, P = 8.05 × 10- 4). Furthermore, four immunophenotypes were identified to be significantly associated with SCZ risk: naive CD4+ %T cell (OR = 0.986, 95% CI = 0.979 ~ 0.992, P = 1.37 × 10- 5), HLA DR on CD14- CD16- (OR = 0.738 (95% CI = 0.642 ~ 0.849, P = 2.00 × 10- 5), CD33dim HLA DR+ CD11b- AC (OR = 0.631, 95% CI = 0.529 ~ 0.753, P = 3.40 × 10- 7) and activated & resting Treg % CD4 Treg (OR = 0.937, 95% CI = 0.906 ~ 0.970, P = 1.96 × 10- 4).ConclusionsOur study has demonstrated the close connection between immune cells and SCZ by genetic means, thus providing guidance for future clinical research.

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Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?

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Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?

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