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＜電子ブック＞
Heterogeneity in Statistical Genetics : How to Assess, Address, and Account for Mixtures in Association Studies / by Derek Gordon, Stephen J. Finch, Wonkuk Kim
(Statistics for Biology and Health. ISSN:21975671)

版	1st ed. 2020.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2020
本文言語	英語
大きさ	XX, 352 p. 41 illus., 26 illus. in color : online resource
著者標目	*Gordon, Derek author Finch, Stephen J author Kim, Wonkuk author SpringerLink (Online service)
件　名	LCSH:Biometry LCSH:Medical genetics LCSH:Genetics FREE:Biostatistics FREE:Medical Genetics FREE:Genetics and Genomics
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/978-3-030-61121-7

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