“‘Mendelian Randomization’ Tag”,2020-01-28 (causality, meta-analysis)⁠:

Bibliography for tag genetics/heritable/correlation/mendelian-randomization, most recent first: 63 annotations & 4 links (parent).

• See Also
• Links
  • “Predicting Political Beliefs With Polygenic Scores for Cognitive Performance and Educational Attainment”, Edwards et al 2024
  • “Causal Interpretations of Family GWAS in the Presence of Heterogeneous Effects”, Veller et al 2023
  • “Exploring Pleiotropy in Mendelian Randomization Analyses: What Are Genetic Variants Associated With ‘Cigarette Smoking Initiation’ Really Capturing?”, Reed et al 2023
  • “Mendelian Randomization Supports Causality between Overweight Status and Accelerated Aging”, Chen et al 2023
  • “Causal Relevance of Different Blood Pressure Traits on Risk of Cardiovascular Diseases: GWAS and Mendelian Randomization in 100,000 Chinese Adults”, Pozarickij et al 2023
  • “Causal Association of Genetically Determined Circulating Vitamin D Metabolites and Calcium With Multiple Sclerosis in Participants of European Descent”, Zhang et al 2023
  • “Leveraging Family Data to Design Mendelian Randomization That Is Provably Robust to Population Stratification”, LaPierre et al 2023
  • “Effect of Basal Metabolic Rate on Lifespan: a Sex-Specific Mendelian Randomization Study”, Ng & Schooling2023
  • “Korea4K: Whole Genome Sequences of 4,157 Koreans With 107 Phenotypes Derived from Extensive Health Check-Ups”, Jeon et al 2022
  • “Quantifying the Causal Impact of Biological Risk Factors on Healthcare Costs”, Lee et al 2022
  • “Correction for Participation Bias in the UK Biobank Reveals Non-Negligible Impact on Genetic Associations and Downstream Analyses”, Schoeler et al 2022
  • “Genome-Wide Association Study Meta-Analysis of Suicide Attempt in 43,871 Cases Identifies Twelve Genome-Wide Statistically-Significant Loci”, Docherty et al 2022
  • “Multi-Ancestry GWAS of Major Depression Aids Locus Discovery, Fine-Mapping, Gene Prioritization, and Causal Inference”, Meng et al 2022
  • “MRSL: A Phenome-Wide Causal Discovery Algorithm Based on GWAS Summary Data”, Hou et al 2022
  • “The Contribution of Mate-Choice, Couple Convergence and Confounding to Assortative Mating”, Sjaarda & Kutalik2022
  • “Genome-Wide Association Study of Occupational Attainment As a Proxy for Cognitive Reserve”, Ko et al 2022
  • “Sleep Duration and Brain Structure—Phenotypic Associations and Genotypic Covariance”, Fjell et al 2022
  • “Mendelian Randomization of Genetically Independent Aging Phenotypes Identifies LPA and VCAM1 As Biological Targets for Human Aging”, Timmers et al 2022
  • “Educational Attainment, Health Outcomes and Mortality: a Within-Sibship Mendelian Randomization Study”, Howe et al 2022
  • “Polynomial Mendelian Randomization Reveals Widespread Non-Linear Causal Effects in the UK Biobank”, Sulc et al 2021
  • “A Selection Pressure Landscape for 870 Human Polygenic Traits”, Song et al 2021
  • “Genetically Proxied Diurnal Preference, Sleep Timing, and Risk of Major Depressive Disorder”, Daghlas et al 2021
  • “The Relationship between Cannabis and Schizophrenia: a Genetically Informed Perspective”, Johnson et al 2021
  • “Causal Relationships between Genetically Determined Metabolites and Human Intelligence: a Mendelian Randomization Study”, Yang et al 2021
  • “Disentangling Sex Differences in the Shared Genetic Architecture of Post-Traumatic Stress Disorder, Traumatic Experiences, and Social Support With Body Size and Composition”, Carvalho et al 2021
  • “Evidence for Shared Genetics between Physical Activity, Sedentary Behavior and Adiposity-Related Traits”, Schnurr et al 2020
  • “A Genetic Perspective on the Association between Exercise and Mental Health in the Era of Genome-Wide Association Studies”, Geus2020
  • “Discovery of Rare Variants Associated With Blood Pressure Regulation through Meta-Analysis of 1.3 Million Individuals”, Surendran et al 2020
  • “Genetic Predictors of Educational Attainment and Intelligence Test Performance Predict Voter Turnout”, Aarøe et al 2020
  • “Use of Genetically Informed Methods to Clarify the Nature of the Association Between Cannabis Use and Risk for Schizophrenia”, Gillespie & Kendler2020
  • “Amyloid, Tau and Risk of Alzheimer’s Disease: a Mendelian Randomization Study”, Yeung et al 2020
  • “An Exposure-Wide and Mendelian Randomization Approach to Identifying Modifiable Factors for the Prevention of Depression”, Choi et al 2020
  • “Genome-Wide Meta-Analysis of Problematic Alcohol Use in 435,563 Individuals Yields Insights into Biology and Relationships With Other Traits”, Zhou et al 2020
  • “Comprehensive Genomic Analysis of Dietary Habits in UK Biobank Identifies Hundreds of Genetic Associations”, Cole et al 2020
  • “Mendelian Randomization Accounting for Correlated and Uncorrelated Pleiotropic Effects Using Genome-Wide Summary Statistics”, Morrison et al 2020
  • “The Interplay between Host Genetics and the Gut Microbiome Reveals Common and Distinct Microbiome Features for Human Complex Diseases”, Xu et al 2019
  • “Genetic Analysis Identifies Molecular Systems and Biological Pathways Associated With Household Income”, Hill et al 2019
  • “Genome-Wide Association Study Identifies Genetic Loci for Self-Reported Habitual Sleep Duration Supported by Accelerometer-Derived Estimates”, Dashti et al 2019
  • “Cannabis Use, Depression and Self-Harm: Phenotypic and Genetic Relationships”, Hodgson et al 2019
  • “Schizophrenia Risk and Reproductive Success: a Mendelian Randomization Study”, Lawn et al 2019
  • “Distinguishing Genetic Correlation from Causation across 52 Diseases and Complex Traits”, O’Connor & Price2018
  • “Using Genetic Data to Strengthen Causal Inference in Observational Research”, Pingault et al 2018
  • “The Landscape of Pervasive Horizontal Pleiotropy in Human Genetic Variation Is Driven by Extreme Polygenicity of Human Traits and Diseases”, Jordan et al 2018
  • “GWAS in 446,118 European Adults Identifies 78 Genetic Loci for Self-Reported Habitual Sleep Duration Supported by Accelerometer-Derived Estimates”, Dashti et al 2018
  • “Meta-Analysis of Genome-Wide Association Studies for Height and Body Mass Index in ∼700,000 Individuals of European Ancestry”, Yengo et al 2018
  • “A Genetic Perspective on the Relationship between Eudaimonic and Hedonic Well-Being”, Baselmans & Bartels2018
  • “Genome-Wide Analysis of Insomnia (N = 1,331,010) Identifies Novel Loci and Functional Pathways”, Jansen et al 2018
  • “Detection of Widespread Horizontal Pleiotropy in Causal Relationships Inferred from Mendelian Randomization between Complex Traits and Diseases”, Verbanck et al 2018
  • “GWAS of Lifetime Cannabis Use Reveals New Risk Loci, Genetic Overlap With Psychiatric Traits, and a Causal Influence of Schizophrenia”, Pasman et al 2018
  • “Genome-Wide Association Meta-Analysis in 269,867 Individuals Identifies New Genetic and Functional Links to Intelligence”, Savage et al 2018
  • “Meta-Analysis of Genome-Wide Association Studies for Height and Body Mass Index in ∼700000 Individuals of European Ancestry”, Yengo et al 2018
  • “Fitness, Physical Activity, and Cardiovascular Disease: Longitudinal and Genetic Analyses in the UK Biobank Study”, Tikkanen et al 2017
  • “Extending the MR-Egger Method for Multivariable Mendelian Randomization to Correct for Both Measured and Unmeasured Pleiotropy”, Rees et al 2017
  • “Genomic Analyses for Age at Menarche Identify 389 Independent Signals and Indicate BMI-Independent Effects of Puberty Timing on Cancer Susceptibility”, Day et al 2016
  • “Physical and Neurobehavioral Determinants of Reproductive Onset and Success”, Day et al 2016
  • “Height, Body Mass Index, and Socioeconomic Status: Mendelian Randomization Study in UK Biobank”, Tyrrell2016
  • “Mendelian Randomization With Invalid Instruments: Effect Estimation and Bias Detection through Egger Regression (MR-Egger)”, Bowden et al 2015
  • “Mendelian Randomization: Can Genetic Epidemiology Help Redress the Failures of Observational Epidemiology?”, Ebrahim & Smith2007
  • “Clustered Environments and Randomized Genes: A Fundamental Distinction between Conventional and Genetic Epidemiology”, Smith et al 2007
  • “A Cautionary Note on the Use of Mendelian Randomization to Infer Causation in Observational Epidemiology”, Bochud et al 2007
  • “Genetic Epidemiology and Public Health: Hope, Hype, and Future Prospects”, Smith et al 2005
  • “What Can Mendelian Randomization Tell Us about Modifiable Behavioral and Environmental Exposures?”, Smith & Ebrahim2005
  • “A Primer on Why Microbiome Research Is Hard”
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