Hidden Bibliographic Details
| Other authors / contributors: | Finch, Stephen J., author.
Kim, Wonkuk, author.
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| ISBN: | 9783030611217
3030611213
3030611205
9783030611200
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| Digital file characteristics: | text file
PDF
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| Notes: | Includes bibliographical references and index.
Online resource; title from PDF title page (SpringerLink, viewed February 18, 2021).
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| Summary: | 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.
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| Other form: | Print version: GORDON, DEREK. HETEROGENEITY IN STATISTICAL GENETICS. [S.l.] : SPRINGER NATURE, 2021 3030611205
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| Standard no.: | 10.1007/978-3-030-61121-7
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