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Heterogeneity in statistical genetics : how to assess, address, and account for mixtures in association studies / / Derek Gordon, Stephen J. Finch, Wonkuk Kim



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Autore: Gordon Derek Visualizza persona
Titolo: Heterogeneity in statistical genetics : how to assess, address, and account for mixtures in association studies / / Derek Gordon, Stephen J. Finch, Wonkuk Kim Visualizza cluster
Pubblicazione: Cham, Switzerland : , : Springer, , [2020]
©2020
Edizione: 1st ed. 2020.
Descrizione fisica: 1 online resource (XX, 352 p. 41 illus., 26 illus. in color.)
Disciplina: 572.80727
Soggetto topico: Statistics
Persona (resp. second.): FinchStephen J.
KimWonkuk
Nota di bibliografia: Includes bibliographical references and index.
Nota di contenuto: 1. Introduction to heterogeneity in statistical genetics -- 2. Overview of genomic heterogeneity in statistical genetics -- 3. Phenotypic heterogeneity -- 4. Association tests allowing for heterogeneity -- 5. Designing genetic linkage and association studies that maintain desired statistical power in the presence of mixtures -- 6. Threshold-selected quantitative trait loci and pleiotropy -- Index.
Sommario/riassunto: 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.
Titolo autorizzato: Heterogeneity in statistical genetics  Visualizza cluster
ISBN: 3-030-61121-3
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910484627203321
Lo trovi qui: Univ. Federico II
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Serie: Statistics for Biology and Health, . 1431-8776