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Combining Human Genetics and Causal Inference to Understand Human Disease and Development

Book Series:  A Cold Spring Harbor Perspectives in Medicine Collection
Subject Area(s):  Human Biology and DiseaseGeneticsDiseases

Edited by George Davey Smith, MRC Integrative Epidemiology Unit, University of Bristol; Rebecca Richmond, MRC Integrative Epidemiology Unit, University of Bristol; John-Baptiste Pingault, University College London

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Causal Inference with Genetic Data: Past, Present, and Future

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Due January 2022 • 254 pages, illustrated (27 color and 23 B&W), index
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ISBN  978-1-621823-81-0
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  •     Description    
  •     Contents    


In human genetics, causal inference methods leverage large omics data sets and phenotypic information to decipher various cause-and-effect relationships in human health and disease (e.g., alcohol intake and hyptertension). The focus of such work is typically on modifiable variables (e.g., behavior or environmental exposure) that impact disease onset, progression, and outcome. A better understanding of these variables can lead to interventions and therapeutics that have a desirable impact on public health.

Written and edited by experts in the field, this collection from Cold Spring Harbor Perspectives in Medicine examines advances in causal inference approaches in human genetics and how they are being used to enhance our understanding of human development and disease. The contributors discuss family-based study designs for causal inference, including twin designs, adoption designs, and in vitro fertilization designs, that separate inherited factors from perinatal environmental exposures. They also review various forms of Mendelian randomization—a population-based approach that is growing in utility and popularity—as well as their integration with family-based designs.

The use of these approaches to investigate causal mechanisms in specific scenarios (e.g., maternal smoking during pregnancy and ADHD in offspring) is also covered. This volume is therefore an essential read for geneticists, epidemiologists, and all biomedical scientists and public health professionals dedicated to using genetic information to improve human health.


Ewan Birney
Causal Inference with Genetic Data: Past, Present, and Future
Jean-Baptiste Pingault, Rebecca Richmond, and George Davey Smith
The Meaning of “Cause” in Genetics
Kate E. Lynch
Twins and Causal Inference: Leveraging Nature’s Experiment
Tom A. McAdams, Fruhling V. Rijsdijk, Helena M.S. Zavos, and Jean-Baptiste Pingault
Family-Based Designs that Disentangle Inherited Factors from Pre- and Postnatal 
Environmental Exposures: In Vitro Fertilization, Discordant Sibling Pairs, 
Maternal versus Paternal Comparisons, and Adoption Designs
Anita Thapar and Frances Rice
Mendelian Randomization: Concepts and Scope
Rebecca Richmond and George Davey Smith
Polygenic Mendelian Randomization
Frank Dudbridge
Multivariable Mendelian Randomization and Mediation
Eleanor Sanderson
Integrating Family-Based and Mendelian Randomization Designs
Liang-Dar Hwang, Neil M. Davies, Nicole M. Warrington, and David M. Evans
Causal Inference Methods to Integrate Omics and Complex Traits
Eleonora Porcu, Jennifer Sjaarda, Kaido Lepik, Cristian Carmeli, Liza Darrous, 
Jonathan Sulc, Ninon Mounier, and Zoltán Kutalik
Computational Tools for Causal Inference in Genetics
Tom G. Richardson, Jie Zheng, and Tom R. Gaunt
Using Mendelian Randomization to Improve the Design of Randomized Trials
Brian A. Ference, Michael V. Holmes, and George Davey Smith
Human Genomics and Drug Development
Amand F. Schmidt, Aroon D. Hingorani, and Chris Finan
Triangulating Evidence through the Inclusion of Genetically Informed Designs
Marcus R. Munafò, Julian P.T. Higgins, and George Davey Smith