Heterogeneity in Statistical Genetics: How to Assess, Address, and Account for Mixtures in Association Studies (Statistics for Biology and Health) - Softcover

Über die Autorin bzw. den Autor

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.

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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.