Mendelian randomization (MR) in epidemiology is the use of genetic variants as instrumental variables (IVs) in non-experimental design to make causality of a modifiable exposure on an outcome or disease. It assesses the causal effect between risk factor and a clinical outcome. The main reason to approach MR is to avoid the problem of residual confounding. There is no association between the genotype of early pregnancy and the disease, and the genotype of an individual cannot be changed. For this reason, it results with randomly assigned case-control studies can be set by regressing the measurements. IVs in MR are used genetic variants for estimating the causality. Usually an outcome is a disease and an exposure is risk factor, intermediate phenotype which may be a biomarker. The choice of the genetic variable as IV (Z) is essential to a successful in MR analysis. MR is named ‘Mendelian deconfounding’ as it gives to estimate of the causality free from biases due to confounding (C). To estimate unbiased estimation of the causality of the exposure (X) on the clinically relevant outcome (Y), Z has the 3 core assumptions (A1-A3). A1) Z is independent of C; A2) Z is associated with X; and A3) Z is independent of Y given X and C; The purpose of this review provides an overview of the MR analysis and is to explain that using an IV is proposed as an alternative statistical method to estimate causal effect of exposure and outcome under controlling for a confounder.
Bias (Epidemiology) ; Case-Control Studies ; Epidemiology ; Genotype ; Mendelian Randomization Analysis ; Methods ; Molecular Epidemiology ; Phenotype ; Pregnancy ; Random Allocation ; Risk Factors

Genome-Wide Association Study ; Genotype ; Humans ; Molecular Epidemiology ; methods

Mendelian Randomization Analysis ; Bone Density ; Genome-Wide Association Study ; Minerals ; Life Expectancy ; Polymorphism, Single Nucleotide

OBJECTIVE: To investigate the causal relationship between neutrophil extracellular trap (NET) and sepsis based on Mendelian randomization analysis. METHODS: The genome wide association study (GWAS) dataset for the NET biomarker myeloperoxidase (MPO)-DNA complex based on Donkel et al. 's Rotterdam study (RS) and GWAS dataset for identifying sepsis from the UK biobank were selected to screen single nucleotide polymorphisms (SNPS) associated with MPO-DNA complex as instrumental variable (IV) for genetic variation, using MPO-DNA complex as exposure factor. Potential causal associations between MPO-DNA complex and the risk of occurrence of sepsis, 28-day death from sepsis, need for intensive care due to sepsis, and 28-day death from sepsis requiring intensive care were analyzed using a two-sample, one-way Mendelian randomization analysis primary analysis method of inverse analysis of variance (IVW). Potential pleiotropy was assessed using the MR Egger regression intercept test. Sensitivity analysis was performed using the "leave one out" test. RESULTS: The GWAS data were obtained from a European population of both sexes, and the screening criteria was based on the three main assumptions of Mendelian randomization, resulting in 22 SNP entering the Mendelian randomization analysis. The results of the Mendelian randomization causal association effect analysis using the IVW method showed that for every standard deviation increase in the level of the MPO-DNA complex, the risk of sepsis increased by approximately 18% [odds ratio (OR) = 1.18, 95% confidence interval (95%CI) was 1.07-1.29, P < 0.001], the risk of 28-day death from sepsis increased by approximately 51% (OR = 1.51, 95%CI was 1.27-1.81, P < 0.001), an increase of approximately 38% in the risk of occurrence of needing intensive care due to sepsis (OR = 1.38, 95%CI was 1.12-1.70, P = 0.002), and an increase of approximately 125% in the risk of 28-day death from sepsis requiring intensive care (OR = 2.25, 95%CI was 1.21-4.18, P = 0.01). MR Egger regression intercept test suggested that there was no horizontal pleiotropy in the included SNP, and the MR-PRESSO test did not find outliers. Sensitivity analysis suggested that the results of Mendelian randomization were robust. CONCLUSIONS Rising NET can increase the risk of sepsis onset, progression and death as derived from Mendelian randomization analysis.
Female ; Male ; Humans ; Extracellular Traps ; Genome-Wide Association Study ; Mendelian Randomization Analysis ; Sepsis/genetics* ; Nonoxynol ; DNA

Humans ; Stroke, Lacunar ; Mendelian Randomization Analysis ; Leukocytes ; Telomere/genetics* ; Genome-Wide Association Study ; Polymorphism, Single Nucleotide

OBJECTIVE: This study aimed to examine whether rheumatoid arthritis (RA) is causally associated with type 2 diabetes (T2D). METHODS: We performed a two-sample Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW), weighted median, and MR-Egger regression methods. We used the publicly available summary statistics datasets from a genome-wide association studies (GWAS) meta-analysis of 5,539 autoantibody-positive individuals with RA and 20,169 controls of European descent, and a GWAS dataset of 10,247 individuals with T2D and 53,924 controls, overwhelmingly of European descent as outcomes. RESULTS: We selected 10 single-nucleotide polymorphisms from GWAS data on RA as instrumental variables to improve the inference. The IVW method supported a causal association between RA and T2D (β=0.044, standard error [SE]=0.022, p=0.047). The MR-Egger analysis showed a causal association between RA and T2D (β=0.093, SE=0.033, p=0.023). In addition, the weighted median approach supported a causal association between RA and T2D (β=0.056, SE=0.025, p=0.028). The association between RA and T2D was consistently observed using IVW, MR Egger, and weighted median methods. Cochran's Q test indicated no evidence of heterogeneity between instrumental variable estimates based on individual variants and MR-Egger regression revealed that directional pleiotropy was unlikely to have biased the results (intercept=−0.030; p=0.101). CONCLUSION: MR analysis supports that RA may be causally associated with an increased risk of T2D.
Arthritis, Rheumatoid ; Bias (Epidemiology) ; Dataset ; Genome-Wide Association Study ; Mendelian Randomization Analysis ; Methods ; Population Characteristics ; Random Allocation

OBJECTIVE: This study explored the potentially modifiable factors for depression and major depressive disorder (MDD) from the MR-Base database and further evaluated the associations between drug targets with MDD. METHODS: We analyzed two-sample of Mendelian randomization (2SMR) using genetic variant depression ( n = 113,154) and MDD ( n = 208,811) from Genome-Wide Association Studies (GWAS). Separate calculations were performed with modifiable risk factors from MR-Base for 1,001 genomes. The MR analysis was performed by screening drug targets with MDD in the DrugBank database to explore the therapeutic targets for MDD. Inverse variance weighted (IVW), fixed-effect inverse variance weighted (FE-IVW), MR-Egger, weighted median, and weighted mode were used for complementary calculation. RESULTS: The potential causal relationship between modifiable risk factors and depression contained 459 results for depression and 424 for MDD. Also, the associations between drug targets and MDD showed that SLC6A4, GRIN2A, GRIN2C, SCN10A, and IL1B expression are associated with an increased risk of depression. In contrast, ADRB1, CHRNA3, HTR3A, GSTP1, and GABRG2 genes are candidate protective factors against depression. CONCLUSION This study identified the risk factors causally associated with depression and MDD, and estimated 10 drug targets with significant impact on MDD, providing essential information for formulating strategies to prevent and treat depression.
Humans ; Depressive Disorder, Major/genetics* ; Depression ; Genome-Wide Association Study ; Mendelian Randomization Analysis ; Risk Factors ; Serotonin Plasma Membrane Transport Proteins

Objective: To explore the causal effects of the serum Vitamin D levels on the risk of systemic lupus erythematosus (SLE). Methods: A two-sample Mendelian randomization (MR) study was performed to infer the causality. Three Genome-wide association studies (GWAS) for circulating Vitamin D levels, including 25-hydroxyvitamin D [25(OH)D], 25-hydroxyvitamin D₃ [25(OH)D₃] and C3-epimer of 25-hydroxyvitamin D₃ [C3-epi-25(OH)D₃] published in 2020, and one GWAS for SLE published in 2015 were utilized to analyze the causal effects of the serum Vitamin D levels on the risk of SLE. MR analyses were conducted using the inverse-variance weighted method (IVW), weighted median, MR-Egger methods, MR-pleiotropy residual sum and outlier (MR-PRESSO) method. Results: 34, 29 and 6 SNPs were respectively selected as instrumental variables to analyze the causal association of total 25 (OH) D level, 25 (OH) D₃ level and C3-epi-25 (OH) D₃ level with the risk of SLE. The MR results showed that each standard deviation decrease in the level of 25(OH)D₃ would result in 14.2% higher risk of SLE (OR, 0.858; 95%CI, 0.753-0.978; P=0.022). The levels of 25(OH)D and C3-epi-25(OH)D₃ had null associations with risk of SLE (OR, 0.849; 95%CI, 0.653-1.104; P=0.222; OR, 0.904; 95%CI, 0.695-1.176; P=0.452). Conclusion: This study have identified a causal effect of 25(OH)D₃ on increased risk of SLE. These findings highlighted the significance of active monitoring and prevention of SLE in population of low Vitamin D levels.
Humans ; Genome-Wide Association Study ; Vitamin D ; Lupus Erythematosus, Systemic/complications* ; Vitamins ; Causality ; Mendelian Randomization Analysis/methods* ; Polymorphism, Single Nucleotide

Gene set analysis is a powerful tool for interpreting a genome-wide association study result and is gaining popularity these days. Comparison of the gene sets obtained for a variety of traits measured from a single genetic epidemiology dataset may give insights into the biological mechanisms underlying these traits. Based on the previously published single nucleotide polymorphism (SNP) genotype data on 8,842 individuals enrolled in the Korea Association Resource project, we performed a series of systematic genome-wide association analyses for 49 quantitative traits of basic epidemiological, anthropometric, or blood chemistry parameters. Each analysis result was subjected to subsequent gene set analyses based on Gene Ontology (GO) terms using gene set analysis software, GSA-SNP, identifying a set of GO terms significantly associated to each trait (pcorr < 0.05). Pairwise comparison of the traits in terms of the semantic similarity in their GO sets revealed surprising cases where phenotypically uncorrelated traits showed high similarity in terms of biological pathways. For example, the pH level was related to 7 other traits that showed low phenotypic correlations with it. A literature survey implies that these traits may be regulated partly by common pathways that involve neuronal or nerve systems.
Genome-Wide Association Study ; Genotype ; Hydrogen-Ion Concentration ; Korea ; Molecular Epidemiology ; Neurons ; Polymorphism, Single Nucleotide ; Semantics

No abstract available.
Molecular Epidemiology*