Mendelian Randomization for Causal Inference - Online Course
A 3-Day Livestream Seminar Taught by
Renato PolimantiWednesday, April 9 –
Friday, April 11, 2025
10:00am-12:30pm ET (convert to your local time)
1:30pm-3:30pm ET
Genetically Informed Causal Inference Through Mendelian Randomization and Other Approaches
Genome-wide association studies (GWAS) have revolutionized the investigation of human traits and diseases, generating an unprecedented amount of information. In addition to identifying genes and pathways involved in the pathogenesis of many medical conditions, GWAS data can be used to gain insights into the epidemiology of health outcomes and other complex phenotypes. In particular, genetic variation can be used as an anchor for causal inference. Indeed, the fact that genetic variation cannot be reversed by environmental factors permits genetically informed causal inference analysis to avoid some of the biases that affect observational studies (e.g., reverse causation).
This seminar provides a comprehensive theoretical background regarding the integration of causal inference and human genetics. It also offers detailed guidelines regarding how Mendelian randomization and other approaches can be used to make genetically informed causal inferences using large-scale datasets. To ensure the robustness of the results, a key portion of the seminar will be devoted to issues of data quality control, sensitivity analysis, and triangulation.
Starting April 9, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about course content
Mendelian randomization and other genetically informed causal inference analyses are being widely used by scientists to understand the dynamics linking human traits and diseases. Indeed, the availability of large-scale genome-wide datasets permits investigators to conduct these analyses quickly and inexpensively. However, there are several challenges in ensuring that the findings generated by genetically informed causal inference analyses are reliable and robust. Understanding the assumptions and implications of different Mendelian randomization methods and the meaning of sensitivity analyses can be challenging. Additionally, there are many different methods for performing genetically informed causal inference, and new ones are being developed every year. It can be difficult to figure out which approaches are best to test specific hypotheses.
This seminar will introduce causal inference and human genetics, reviewing the theoretical framework supporting the use of genetic variation as an anchor to infer causal relationships. Subsequently, it will focus on differences across Mendelian randomization approaches, reviewing assumptions and sensitivity analyses. We will also compare Mendelian randomization with other designs to perform genetically informed causal inference analyses. To more accurately model real-world scenarios, we will also discuss multivariable analyses to test mediation and moderation hypotheses. With respect to genetically informed analyses, we will focus on multivariable Mendelian randomization and genomic structural equation modeling. Additional multivariable methods will also be introduced.
Mendelian randomization and other genetically informed causal inference analyses are being widely used by scientists to understand the dynamics linking human traits and diseases. Indeed, the availability of large-scale genome-wide datasets permits investigators to conduct these analyses quickly and inexpensively. However, there are several challenges in ensuring that the findings generated by genetically informed causal inference analyses are reliable and robust. Understanding the assumptions and implications of different Mendelian randomization methods and the meaning of sensitivity analyses can be challenging. Additionally, there are many different methods for performing genetically informed causal inference, and new ones are being developed every year. It can be difficult to figure out which approaches are best to test specific hypotheses.
This seminar will introduce causal inference and human genetics, reviewing the theoretical framework supporting the use of genetic variation as an anchor to infer causal relationships. Subsequently, it will focus on differences across Mendelian randomization approaches, reviewing assumptions and sensitivity analyses. We will also compare Mendelian randomization with other designs to perform genetically informed causal inference analyses. To more accurately model real-world scenarios, we will also discuss multivariable analyses to test mediation and moderation hypotheses. With respect to genetically informed analyses, we will focus on multivariable Mendelian randomization and genomic structural equation modeling. Additional multivariable methods will also be introduced.
Computing
You will be provided all relevant materials (i.e., scripts and data) before the beginning of the course. To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R installed. You are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms. Installation instructions for R and RStudio will also be provided before the beginning of the course.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
You will be provided all relevant materials (i.e., scripts and data) before the beginning of the course. To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R installed. You are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms. Installation instructions for R and RStudio will also be provided before the beginning of the course.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Who should register?
If you want to learn the fundamental principles of genetically informed causal inference analyses to enrich your scientific background and/or apply them to your biomedical research, this course is for you. It will impart the skills to leverage information generated by large-scale genome-wide association studies to conduct genetically informed causal inference analyses. You should have basic knowledge of statistics including the application of regression models.
If you want to learn the fundamental principles of genetically informed causal inference analyses to enrich your scientific background and/or apply them to your biomedical research, this course is for you. It will impart the skills to leverage information generated by large-scale genome-wide association studies to conduct genetically informed causal inference analyses. You should have basic knowledge of statistics including the application of regression models.
Seminar outline
Day 1: Introduction to causal inference and human genetics
- Introduction to causal inference
- From candidate genes to genome-wide association studies
- Polygenicity and pleiotropy
- Genetically informed analyses
Day 2: Mendelian randomization and other approaches
- Genetic anchors for causal inference
- One-sample vs. two-sample Mendelian randomization
- Sensitivity analyses
- Other genetically informed causal inference methods
Day 3: Multivariable analyses
- Mediation vs. moderation
- Multivariable Mendelian randomization
- Mediation analysis using genomic structural equation modeling
- Other approaches for multivariable analyses
Day 1: Introduction to causal inference and human genetics
- Introduction to causal inference
- From candidate genes to genome-wide association studies
- Polygenicity and pleiotropy
- Genetically informed analyses
Day 2: Mendelian randomization and other approaches
- Genetic anchors for causal inference
- One-sample vs. two-sample Mendelian randomization
- Sensitivity analyses
- Other genetically informed causal inference methods
Day 3: Multivariable analyses
- Mediation vs. moderation
- Multivariable Mendelian randomization
- Mediation analysis using genomic structural equation modeling
- Other approaches for multivariable analyses
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.