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Why we wrote this book

There are many excellent introductory books on human genetics or statistical population genetics, yet most of them are written for advanced graduate and PhD students in biology or genetics. With advances in computing power, availability of data, and new techniques, this area of research has disrupted many conventions of how we think about disease and behavior. Genetics has now stretched beyond multiple disciplines and for the first time in history is it now possible to integrate large-scale molecular genetic information into research across a broad range of topics. We realized that there was an enormous chasm between advanced articles and books and hands-on applied instructional material focussed on computer-based data manipulation and analysis.


The aim of this book is to show applied researchers from various disciplinary backgrounds how to understand, apply, and work with genetic data for your own research topics. The knowledge of this book will allow you to properly and responsibly understand and interpret the data and use it as a blueprint to apply to your own data and research. We also hope that by making this type of data analysis more accessible, we work toward the loftier goal of diversification of both the people that are studied as subjects in human genetics but also the researchers themselves and topics that are covered.

Who should read this book

This book is for current and aspiring students and researchers from any empirically oriented medical, biological, behavioral, or social science discipline who would like to understand the main concepts of human statistical genetic data analysis but also practitioners looking for solutions to enter and undertake this research. Readers are given a blueprint of applied molecular genetic data analysis with hands-on computer exercises and a focus on substantive interpretation. The book operates as a launching pad to enter this seemingly daunting field. It is an introductory book, written for those who do not have a strong background in molecular biology, human genetics, or statistical genetics but would like to integrate genetic data into their research. Elementary knowledge of statistical methods at the first level of a statistics or biostatistics course is optimal. You will get the most out of this book if you first engage in some background tutorial work in R and RStudio and, for some more advanced applications, a basic familiarity with Python. We also made a concerted effort to focus on the basic terminology and practical aspects of statistical genetic data analysis rather than the math, statistics, and biology behind it. We refer readers to an ample further reading section and references to explore more advanced material in this area.

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