Methods For Causal Inference Using Genetic Variants

Establishing causality is critical in biomedical research, yet it poses significant challenges due to confounding factors and unobserved variables. Genetic variants offer a unique and powerful tool to overcome these hurdles, enabling researchers to unravel the true causal relationships between exposures and outcomes.

Mendelian Randomization: Methods for Causal Inference Using Genetic Variants (Chapman & Hall/CRC Interdisciplinary Statistics)

by Simon G. Thompson

5 out of 5

Language	:	English
File size	:	16954 KB
Screen Reader	:	Supported
Print length	:	240 pages

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This comprehensive guidebook, "Methods For Causal Inference Using Genetic Variants," provides an in-depth exploration of advanced statistical techniques and cutting-edge genetic data to empower researchers in their quest for causality.

Principles of Causal Inference

The book begins by laying a solid foundation in the principles of causal inference, introducing fundamental concepts such as confounding, bias, and identification strategies.

Readers will gain a thorough understanding of:

• The Bradford Hill criteria for causality
• Counterfactual and potential outcomes frameworks
• Assumptions and limitations of causal inference methods

Mendelian Randomization

Mendelian randomization (MR) is a cornerstone of causal inference using genetic variants. The book provides a comprehensive overview of MR, covering its principles, assumptions, and applications.

Readers will learn about:

• Instrumental variable analysis using genetic variants
• Two-sample and one-sample MR designs
• Methods for handling pleiotropy and heterogeneity

Instrumental Variables

Beyond MR, the book explores other instrumental variable (IV) methods for causal inference. Readers will discover:

• Regression discontinuity design
• Difference-in-differences analysis
• Natural experiments and quasi-experiments

These methods provide complementary approaches to establish causality in various research settings.

Family and Twin Studies

The book also examines the use of family and twin studies in causal inference. These designs exploit genetic relatedness to control for confounding factors and enhance causal identification.

Readers will learn about:

• Twin designs for estimating heritability and causal effects
• Family-based designs for controlling confounding
• Methods for analyzing gene-environment interactions

Longitudinal and Mediation Analysis

Causal inference often requires examining longitudinal data and mediating pathways. The book covers advanced techniques for:

• Causal inference in longitudinal studies
• Mediation analysis to identify intermediate mechanisms
• Path analysis and structural equation modeling for complex causal relationships

These methods enable researchers to unravel the temporal relationships and causal mechanisms underlying health outcomes.

Applications and Case Studies

To illustrate the practical applications of these methods, the book presents real-world case studies in various biomedical research areas, such as:

• Cardiovascular disease
• Cancer
• Neurodegenerative disFree Downloads
• Behavioral traits

These case studies demonstrate how genetic variants have revolutionized our understanding of disease etiology and provided valuable insights for developing effective interventions.

"Methods For Causal Inference Using Genetic Variants" is an indispensable resource for researchers seeking to advance their understanding and application of causal inference techniques. This comprehensive guide empowers readers with the knowledge and skills to unravel the complex web of causality in biomedical research, leading to groundbreaking discoveries and improved healthcare outcomes.

Free Download your copy today and embark on the journey of unlocking the secrets of causality!