Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence

/pdf/reading-mendelian-randomisation-studies-a-guide-glossary-and-3nqnedeay2.pdf

Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians

Major depression is a debilitating psychiatric illness that is typically associated with low mood and anhedonia. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximize sample size, we meta-analyzed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 genesets associated with depression, including both genes and gene pathways associated with synaptic structure and neurotransmission. An enrichment analysis provided further evidence of the importance of prefrontal brain regions. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant after multiple testing correction. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding etiology and developing new treatment approaches.

https://www.pure.ed.ac.uk/ws/files/79772532/Meta_analysis_of_depression.pdf

Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

Pleiotropy, the phenomenon of a single genetic variant influencing multiple traits, is likely widespread in the human genome. If pleiotropy arises because the single nucleotide polymorphism (SNP) influences one trait, which in turn influences another ('vertical pleiotropy'), then Mendelian randomization (MR) can be used to estimate the causal influence between the traits. Of prime focus among the many limitations to MR is the unprovable assumption that apparent pleiotropic associations are mediated by the exposure (i.e. reflect vertical pleiotropy), and do not arise due to SNPs influencing the two traits through independent pathways ('horizontal pleiotropy'). The burgeoning treasure trove of genetic associations yielded through genome wide association studies makes for a tantalizing prospect of phenome-wide causal inference. Recent years have seen substantial attention devoted to the problem of horizontal pleiotropy, and in this review we outline how newly developed methods can be used together to improve the reliability of MR.

/pdf/evaluating-the-potential-role-of-pleiotropy-in-mendelian-lfrb3b0uhn.pdf

Evaluating the potential role of pleiotropy in Mendelian randomization studies.

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.

/pdf/guidelines-for-performing-mendelian-randomization-yxl6hefnd5.pdf

Guidelines for performing Mendelian randomization investigations.

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3-7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.

/pdf/mapping-the-human-genetic-architecture-of-covid-19-5905t99oq8.pdf

Mapping the human genetic architecture of COVID-19.

Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base ( http://www.mrbase.org ): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.

/pdf/the-mr-base-platform-supports-systematic-causal-inference-c759oagatn.pdf

The MR-Base platform supports systematic causal inference across the human phenome

Author response: The MR-Base platform supports systematic causal inference across the human phenome

Genomic imprinting is an epigenetic mechanism leading to parent-of-origin silencing of alleles. So far, the precise number of imprinted regions in humans is uncertain. In this study, we leveraged genome-wide DNA methylation in whole blood measured longitudinally at three time points (birth, childhood and adolescence) and genome-wide association studies (GWAS) data in 740 mother-child duos from the Avon Longitudinal Study of parents and children to identify candidate imprinted loci. We reasoned that cis-meQTLs at genomic regions that were imprinted would show strong evidence of parent-of-origin associations with DNA methylation, enabling the detection of imprinted regions. Using this approach, we identified genome-wide significant cis-meQTLs that exhibited parent-of-origin effects (POEs) at 82 loci, 34 novel and 48 regions previously implicated in imprinting (3.7-10<P < 10-300). Using an independent dataset from the Brisbane Systems Genetic Study, we replicated 76 out of the 82 identified loci. POEs were remarkably consistent across time points and were so strong at some loci that methylation levels enabled good discrimination of parental transmissions at these and surrounding genomic regions. The implication is that parental allelic transmissions could be modelled at many imprinted (and linked) loci in GWAS of unrelated individuals given a combination of genetic and methylation data. Novel regions showing parent of origin effects on methylation will require replication using a different technology and further functional experiments to confirm that such effects arise through a genomic imprinting mechanism.

/pdf/genome-wide-survey-of-parent-of-origin-effects-on-dna-1w5mycu9sh.pdf

Genome-wide survey of parent-of-origin effects on DNA methylation identifies candidate imprinted loci in humans.

Typically, genome-wide association studies consist of regressing the phenotype on each SNP separately using an additive genetic model. Although statistical models for recessive, dominant, SNP-SNP, or SNP-environment interactions exist, the testing burden makes an evaluation of all possible effects impractical for genome-wide data. We advocate a two-step approach where the first step consists of a filter that is sensitive to different types of SNP main and interactions effects. The aim is to substantially reduce the number of SNPs such that more specific modeling becomes feasible in a second step. We provide an evaluation of a statistical learning method called “gradient boosting machine” (GBM) that can be used as a filter. GBM does not require an a priori specification of a genetic model, and permits inclusion of large numbers of covariates. GBM can therefore be used to explore multiple GxE interactions, which would not be feasible within the parametric framework used in GWAS. We show in a simulation that GBM performs well even under conditions favorable to the standard additive regression model commonly used in GWAS, and is sensitive to the detection of interaction effects even if one of the interacting variables has a zero main effect. The latter would not be detected in GWAS. Our evaluation is accompanied by an analysis of empirical data concerning hair morphology. We estimate the phenotypic variance explained by increasing numbers of highest ranked SNPs, and show that it is sufficient to select 10K-20K SNPs in the first step of a two-step approach.

/pdf/gradient-boosting-as-a-snp-filter-an-evaluation-using-42vndtrjno.pdf

Charles Laurin

Papers

The MR-Base platform supports systematic causal inference across the human phenome

Author response: The MR-Base platform supports systematic causal inference across the human phenome

Genome-wide survey of parent-of-origin effects on DNA methylation identifies candidate imprinted loci in humans.

Gradient Boosting as a SNP Filter: an Evaluation Using Simulated and Hair