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Adjusting for Familial Relatedness in the Analysis of GWAS Data
Citation
Thomson, R and McWhirter, R, Adjusting for Familial Relatedness in the Analysis of GWAS Data, Methods in Molecular Biology: Bioinformatics - Volume II: Structure, Function, and Applications., Humana Press, JM Keith (ed), New York, United States, pp. 175-190. ISBN 978-1-4939-6613-4 (2017) [Research Book Chapter]
Copyright Statement
Copyright 2017 Springer Science+Business Media New York
DOI: doi:10.1007/978-1-4939-6613-4_10
Abstract
Relatedness within a sample can be of ancient (population stratification) or recent (familial structure) origin, and can either be known (pedigree data) or unknown (cryptic relatedness). All of these forms of familial relatedness have the potential to confound the results of genome-wide association studies. This chapter reviews the major methods available to researchers to adjust for the biases introduced by relatedness and maximize power to detect associations. The advantages and disadvantages of different methods are presented with reference to elements of study design, population characteristics, and computational requirements.
Item Details
| Item Type: | Research Book Chapter |
|---|---|
| Keywords: | genomewide association studies, GWAS, relatedness, confounding, population stratification, cryptic relatedness, familial structure |
| Research Division: | Biological Sciences |
| Research Group: | Genetics |
| Research Field: | Quantitative Genetics (incl. Disease and Trait Mapping Genetics) |
| Objective Division: | Expanding Knowledge |
| Objective Group: | Expanding Knowledge |
| Objective Field: | Expanding Knowledge in the Medical and Health Sciences |
| UTAS Author: | McWhirter, R (Dr Rebekah McWhirter) |
| ID Code: | 116357 |
| Year Published: | 2017 |
| Deposited By: | Menzies Institute for Medical Research |
| Deposited On: | 2017-05-08 |
| Last Modified: | 2018-06-01 |
| Downloads: | 0 |
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