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Heterogeneity in statistical genetic...

Finch, Stephen J.

Heterogeneity in statistical geneticshow to assess, address, and account for mixtures in association studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Heterogeneity in statistical geneticsby Derek Gordon, Stephen J. Finch, Wonkuk Kim.
Reminder of title:	how to assess, address, and account for mixtures in association studies /
Author:	Gordon, Derek.
other author:	Finch, Stephen J.
Published:	Cham :Springer International Publishing :2020.
Description:	xx, 352 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	GeneticsStatistical methods.
Online resource:	https://doi.org/10.1007/978-3-030-61121-7
ISBN:	9783030611217$q(electronic bk.)

Heterogeneity in statistical geneticshow to assess, address, and account for mixtures in association studies /
Gordon, Derek.

Heterogeneity in statistical geneticshow to assess, address, and account for mixtures in association studies /[electronic resource] :by Derek Gordon, Stephen J. Finch, Wonkuk Kim. - Cham :Springer International Publishing :2020. - xx, 352 p. :ill., digital ;24 cm. - Statistics for biology and health,1431-8776. - Statistics for biology and health..

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.

ISBN: 9783030611217$q(electronic bk.)

Standard No.: 10.1007/978-3-030-61121-7doiSubjects--Topical Terms:

186254
Genetics
--Statistical methods.

LC Class. No.: QH438.4.S73

Dewey Class. No.: 572.80727

Heterogeneity in statistical geneticshow to assess, address, and account for mixtures in association studies /
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520 $a 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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