Applied Statistical Genetics with R For Population-based Association
The vast array of molecular level information now available presents exciting opportunities to characterize the genetic underpinnings of complex diseases while discovering novel biological pathways to disease progression. In this introductory graduate lev
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Andrea S. Foulkes
Applied Statistical Genetics with R For Population-based Association Studies
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Andrea S. Foulkes University of Massachusetts School of Public Health & Health Sciences 404 Arnold House 715 N. Pleasant Street Amherst, MA 01003 USA [email protected]
ISBN 978-0-387-89553-6 e-ISBN 978-0-387-89554-3 DOI 10.1007/978-0-387-89554-3 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: PCN applied for c Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
To Rich, Sophie and Ella
Preface
This book is intended to provide fundamental statistical concepts and tools relevant to the analysis of genetic data arising from population-based association studies. Elementary knowledge of statistical methods at the level of a first course in biostatistics is assumed. Chapters 1–3 provide a general overview of the genetic and epidemiological considerations relevant to this setting. Topics covered include: (1) types of investigations, typical data components and features in genetic association studies, and basic genetic vocabulary (Chapter 1); (2) epidemiological principles relevant to population-based studies, including confounding and effect modification (Chapter 2); (3) elementary statistical methods for estimating and testing association (Chapter 2); (4) the overarching analytical challenges inherent in these investigations (Chapter 2); (5) basic genetic concepts, including linkage disequilibrium, Hardy-Weinberg equilibrium, and haplotypic phase (Chapter 3); and (6) quality control methods for assessing genotyping errors and population substructure (Chapter 3). The remaining chapters are organized as follows. Chapters 4 and 5 deal primarily with methods that aim to identify single genetic polymorphisms or single genes that contribute individually to measures of disease progression or disease status. This includes testing concepts and methods for appropriately adjusting for multiple comparisons (Chapter 4) and approaches to the analysis of unobservable haplotypic phase (Chapter 5). Chapters 6 and 7 focus on methods for variable subset selection and particularly methods that simultane
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