This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.

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This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.

Background Circulating lipoprotein lipids cause coronary heart disease (CHD). However, the precise way in which one or more lipoprotein lipid-related entities account for this relationship remains unclear. Using genetic instruments for lipoprotein lipid traits implemented through multivariable Mendelian randomisation (MR), we sought to compare their causal roles in the aetiology of CHD. Methods and findings We conducted a genome-wide association study (GWAS) of circulating non-fasted lipoprotein lipid traits in the UK Biobank (UKBB) for low-density lipoprotein (LDL) cholesterol, triglycerides, and apolipoprotein B to identify lipid-associated single nucleotide polymorphisms (SNPs). Using data from CARDIoGRAMplusC4D for CHD (consisting of 60,801 cases and 123,504 controls), we performed univariable and multivariable MR analyses. Similar GWAS and MR analyses were conducted for high-density lipoprotein (HDL) cholesterol and apolipoprotein A-I. The GWAS of lipids and apolipoproteins in the UKBB included between 393,193 and 441,016 individuals in whom the mean age was 56.9 y (range 39-73

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms , Mika Ala-Korpela, PhD 1,5,6,7 AbstractObjective: Insulin resistance has deleterious effects on cardiometabolic disease. We used Mendelian randomization analyses to clarify the causal relationships of insulin resistance on circulating blood-based metabolites to shed light on potential mediators of the insulin resistance to cardiometabolic disease relationship. Research Design and Methods:We used 53 single nucleotide polymorphisms associated with insulin resistance from a recent genome-wide association study to explore their effects on circulating lipids and metabolites. We used published summary-level data from two genome-wide association studies (GWASs) of European individuals; data on the exposure (insulin resistance) were obtained from meta-GWASs of 188,577 individuals and data on the outcomes (58 metabolic measures assessed by NMR) were taken from a GWAS of 24,925 individuals. Results:One standard deviation (SD) genetically elevated insulin resistance (equivalent to 55% higher geometric mean of fasting insulin, 0.89 mmol/L higher triglycerides and 0.46 mmol/L lower HDL-C) was associated with higher concentrations of all branched-chain amino acids, isoleucine (0.56 SD; 95%CI: 0.43, 0.70), leucine (0.42 SD; 95%CI: 0.28, 0.55) and valine (0.26 SD; 95%CI: 0.12, 0.39) as well as with higher glycoprotein acetyls (an inflammation marker; 0.47 SD; 95%CI: 0.32, 0.62) (P<0.0003 for each). Results were broadly consistent when using multiple sensitivity analyses to account for potential genetic pleiotropy. Conclusions:We provide robust evidence that insulin resistance causally impacts on each individual branched-chain amino acid and inflammation. Taken together with existing studies, this implies that branched-chain amino acid metabolism lies on a causal pathway from adiposity and insulin resistance to type 2 diabetes. The obesity pandemic is a public health crisis leading to a dramatic surge in the incidence of type 2 diabetes mellitus (T2DM) and related diseases (e.g., cardiovascular diseases) (1). Adiposity, particularly visceral adiposity (2), is associated with insulin resistance (IR) and subsequent T2DM. Recent genetic studies employing the Mendelian randomization approach have shown adiposity traits (such as general adiposity, indexed by body mass index, and central adiposity, indexed by waist-to-hip ratio) to show causal relationships with blood pressure, lipids, coronary heart disease, stroke and diabetes (3-6). Furthermore, such studies have demonstrated that adiposity traits causally impact on insulin resistance (3,4,6). Insulin resistance is the clinical state of a reduced sensitivity to insulin, typically manifested as elevated levels of fasting insulin and often accompanied with higher levels of circulating triglycerides and lower levels of high-density lipoprotein cholesterol...

Obesity traits are causally implicated with risk of cardiometabolic diseases. It remains unclear whether there are similar causal effects of obesity traits on other non-communicable diseases. Also, it is largely unexplored whether there are any sex-specific differences in the causal effects of obesity traits on cardiometabolic diseases and other leading causes of death. We constructed sex-specific genetic risk scores (GRS) for three obesity traits; body mass index (BMI), waist-hip ratio (WHR), and WHR adjusted for BMI, including 565, 324, and 337 genetic variants, respectively. These GRSs were then used as instrumental variables to assess associations between the obesity traits and leading causes of mortality in the UK Biobank using Mendelian randomization. We also investigated associations with potential mediators, including smoking, glycemic and blood pressure traits. Sex-differences were subsequently assessed by Cochran’s Q-test (Phet). A Mendelian randomization analysis of 228,466 women and 195,041 men showed that obesity causes coronary artery disease, stroke (particularly ischemic), chronic obstructive pulmonary disease, lung cancer, type 2 and 1 diabetes mellitus, non-alcoholic fatty liver disease, chronic liver disease, and acute and chronic renal failure. Higher BMI led to higher risk of type 2 diabetes in women than in men (Phet = 1.4×10−5). Waist-hip-ratio led to a higher risk of chronic obstructive pulmonary disease (Phet = 3.7×10−6) and higher risk of chronic renal failure (Phet = 1.0×10−4) in men than women. Obesity traits have an etiological role in the majority of the leading global causes of death. Sex differences exist in the effects of obesity traits on risk of type 2 diabetes, chronic obstructive pulmonary disease, and renal failure, which may have downstream implications for public health.

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.