An Introduction to Statistical Genetic Data Analysis
An Introduction to Statistical Genetic Data Analysis by Melinda C. Mills, published by MIT Press on February 18, 2020, is an illustrated edition comprising 432 pages. This book serves as a comprehensive introduction to modern applied statistical genetic data analysis, designed for readers without a background in molecular biology or genetics. It highlights the relevance of human genetic research across various fields, including psychology, sociology, and economics, while integrating large-scale molecular genetic information into empirical research.
Readers will find foundational concepts in statistical genetic data analysis, along with practical guidance on data preparation and analysis. The book addresses analytical challenges and data management, and it includes hands-on computer exercises to enhance understanding. Topics covered range from polygenic scores to gene-environment interactions and ethical considerations in genetic research. The software and data referenced in the book are freely accessible, providing additional resources for students and researchers in medical, biological, or social science disciplines.
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A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics.
Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises. It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required.
The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management. Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues. The software and data used in the book are freely available and can be found on the book’s website.
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