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024 7 $a10.1007/978-3-030-61121-7
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035   $a(DE-He213)978-3-030-61121-7
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100   $a20210604d2020      uy             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aHeterogeneity in statistical genetics $ehow to assess, address, and account for mixtures in association studies /$fDerek Gordon, Stephen J. Finch, Wonkuk Kim
205   $a1st ed. 2020.
210  1$aCham, Switzerland :$cSpringer,$d[2020]
210  4$d©2020
215   $a1 online resource (XX, 352 p. 41 illus., 26 illus. in color.) 
225 1 $aStatistics for Biology and Health,$x1431-8776
311   $a3-030-61120-5 
320   $aIncludes bibliographical references and index.
327   $a1. 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.
330   $aHeterogeneity, or mixtures, are ubiquitous in genetics. Even for data as simple as mono-genic diseases, populations are a mixture of affected and unaffected individuals. Still, most statistical genetic association analyses, designed to map genes for diseases and other genetic traits, ignore this phenomenon. In this book, we document methods that incorporate heterogeneity into the design and analysis of genetic and genomic association data. Among the key qualities of our developed statistics is that they include mixture parameters as part of the statistic, a unique component for tests of association. A critical feature of this work is the inclusion of at least one heterogeneity parameter when performing statistical power and sample size calculations for tests of genetic association. We anticipate that this book will be useful to researchers who want to estimate heterogeneity in their data, develop or apply genetic association statistics where heterogeneity exists, and accurately evaluate statistical power and sample size for genetic association through the application of robust experimental design.
410  0$aStatistics for Biology and Health,$x1431-8776
606   $aStatistics
615  0$aStatistics.
676   $a572.80727
700   $aGordon$b Derek$0947023
702   $aFinch$b Stephen J.
702   $aKim$b Wonkuk.
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bUtOrBLW
906   $aBOOK
912   $a9910484627203321
996   $aHeterogeneity in statistical genetics$92139692
997   $aUNINA