ISBN-10:
0262538385
ISBN-13:
9780262538381
Pub. Date:
02/18/2020
Publisher:
MIT Press
An Introduction to Statistical Genetic Data Analysis

An Introduction to Statistical Genetic Data Analysis

Paperback

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Overview

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.

Product Details

ISBN-13: 9780262538381
Publisher: MIT Press
Publication date: 02/18/2020
Series: The MIT Press
Pages: 432
Sales rank: 1,038,764
Product dimensions: 7.13(w) x 9.06(h) x 1.04(d)
Age Range: 18 Years

About the Author

Melinda C. Mills is Professor at the University of Oxford and Nuffield College, where she is also Director of the Leverhulme Centre for Demographic Science.

Nicola Barban is Associate Professor at the Institute for Social and Economic Research at the University of Essex.

Felix Tropf is Assistant Professor at École Nationale de la Statistique et de L'administration Économique (ENSAE) and Center for Research in Economics and Statistics (CREST), Paris.

What People are Saying About This

From the Publisher

I am regularly asked to recommend a book that provides a comprehensive overview of statistical genetics methods using accessible language with clear applications to important research questions. Look no further. Mills, Barban, and Tropf provide a superb example of such a book with An Introduction to Statistical Genetic Data Analysis.

Jason D. Boardman, Professor of Sociology and Health & Society Program Director at the Institute of Behavioral Science, University of Colorado at Boulder

Want to run some statistical analysis of the torrent of genetic data that is pouring into science these days?  An Introduction to Statistical Genetic Data Analysis is required reading for you. Mills, Barban, and Tropf walk the reader through the basics of what a gene is and march onto advanced data analysis techniques, providing plenty of compelling examples along the way.

Dalton Conley, Henry Putnam University Professor in Sociology, Princeton University and author of The Genome Factor

It is increasingly clear that genetics is not just important for diseases. It contributes to many aspects of human behavior and characteristics. This book is most valuable for those whose basic training was not in statistical genetics, but are starting to incorporate genetic data into their investigations.

Augustine Kong, Professor of Statistical Genetics, University of Oxford

Contemporary genetic data offers many opportunities, and this book is easily the best available introduction.  What is marvelous about the book is how comprehensive and sophisticated it is while remaining clear throughout.  The way the book weaves together its explanations with software examples makes it a perfect companion for anyone wanting to better understand what these methods have to offer and how a researcher can actually use them.

Jeremy Freese, Stanford University

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