Authors:
- Provides an overview of past developments as well as a comprehensive look at new methodological techniques of heterogeneity as used in statistical genetic analyses
- Highlights the importance that mixture models has had on the development of statistical methods for gene localization
- Authors have an established expertise in the field and made specific contributions in researching this area
Part of the book series: Statistics for Biology and Health (SBH)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
About this book
Heterogeneity, or mixtures, are ubiquitous in genetics. Even for data as simple as mono-genic diseases, populations are a mixture of affected and unaffected individuals. Still, most statistical genetic association analyses, designed to map genes for diseases and other genetic traits, ignore this phenomenon.
In this book, we document methods that incorporate heterogeneity into the design and analysis of genetic and genomic association data. Among the key qualities of our developed statistics is that they include mixture parameters as part of the statistic, a unique component for tests of association. A critical feature of this work is the inclusion of at least one heterogeneity parameter when performing statistical power and sample size calculations for tests of genetic association.
We anticipate that this book will be useful to researchers who want to estimate heterogeneity in their data, develop or apply genetic association statistics where heterogeneity exists, and accurately evaluate statistical power and sample size for genetic association through the application of robust experimental design.Reviews
Authors and Affiliations
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Department of Genetics, Rutgers University, Piscataway, USA
Derek Gordon
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Department of AMS, Stony Brook University, Stony Brook, USA
Stephen J. Finch
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Department of Applied Statistics, Chung-Ang University, Seoul, Korea (Republic of)
Wonkuk Kim
About the authors
Derek Gordon, PhD, is Associate Professor in the Department of Genetics at Rutgers, The State University of New Jersey, and is Full Academic Member of the Human Genetics Institute of New Jersey. For more than a decade, Dr. Gordon has served on the Editorial Board of the journal Human Heredity. From 2004 to 2013, Dr. Gordon was the Managing Editor for this journal. Currently, Dr. Gordon serves on the Editorial Board of the online journal BMC Bioinformatics. He has maintained a role as statistical genetics consultant to researchers in industry and academia for several decades.
Stephen J. Finch, PhD, is Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. Professor Finch is co-author of the book, Data Collection in Adoption and Foster Care: The State of the Art in Obtaining Organized Information for Policy Analysis, Program Planning, and Practice (1991, with Fanshel and Grundy), and for several decades has served as statistical consultant to research teams performing longitudinal studies of adolescent social behavior.
Wonkuk Kim is Assistant Professor of Applied Statistics at Chung-Ang University in Korea. His research concerns mixture model-based genetic association and latent trajectory analysis of longitudinal data.
Bibliographic Information
Book Title: Heterogeneity in Statistical Genetics
Book Subtitle: How to Assess, Address, and Account for Mixtures in Association Studies
Authors: Derek Gordon, Stephen J. Finch, Wonkuk Kim
Series Title: Statistics for Biology and Health
DOI: https://doi.org/10.1007/978-3-030-61121-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-61120-0Published: 17 December 2020
Softcover ISBN: 978-3-030-61123-1Published: 18 December 2021
eBook ISBN: 978-3-030-61121-7Published: 16 December 2020
Series ISSN: 1431-8776
Series E-ISSN: 2197-5671
Edition Number: 1
Number of Pages: XX, 352
Number of Illustrations: 15 b/w illustrations, 26 illustrations in colour
Topics: Statistics for Life Sciences, Medicine, Health Sciences, Human Genetics, Genetics and Genomics