An Introduction To Statistical Genetic Data Analysis PDF
It is an amazing Science book written by Melinda C. Mills and published by MIT Press. This book was released on 18 February 2020 with total pages 433. Read book in PDF, EPUB and Kindle directly from your devices anywhere anytime. Click Download button to get An Introduction to Statistical Genetic Data Analysis book now. This site is like a library, Use search box to get ebook that you want.
- Author : Melinda C. Mills
- Release Date : 18 February 2020
- Publisher : MIT Press
- Genre : Science
- Pages : 433
- ISBN 13 : 9780262538381
- Total Download : 688
- File Size : 42,7 Mb
An Introduction to Statistical Genetic Data Analysis PDF Summary
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.