The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Population Structure and Cryptic Relatedness in Genetic Association Studies
Open accessAbstract
We review the problem of confounding in genetic association studies, which arises principally because of population structure and cryptic relatedness. Many treatments of the problem consider only a simple “island” model of population structure. We take a broader approach, which views population structure and cryptic relatedness as different aspects of a single confounder: the unobserved pedigree defining the (often distant) relationships among the study subjects. Kinship is therefore a central concept, and we review methods of defining and estimating kinship coefficients, both pedigree-based and marker-based. In this unified framework we review solutions to the problem of population structure, including family-based study designs, genomic control, structured association, regression control, principal components adjustment and linear mixed models. The last solution makes the most explicit use of the kinships among the study subjects, and has an established role in the analysis of animal and plant breeding studies. Recent computational developments mean that analyses of human genetic association data are beginning to benefit from its powerful tests for association, which protect against population structure and cryptic kinship, as well as intermediate levels of confounding by the pedigree.
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PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Population Structure and Eigenanalysis
Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280
XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.
Fisher, R. A. (1919) · Transactions of the Royal Society of Edinburgh · doi:10.1017/s0080456800012163 · Cited by 4,387
Genomic Control for Association Studies
Devlin, B., & Roeder, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00997.x · Cited by 3,162 · cited 8×
“which generalizes the result of Devlin and Roeder (1999). In practice, it is unclear how λ should scale with n in well designed GWAS studies of homogeneous populations.”
BackgroundMethodInfluentialGenome-wide association studies for complex traits: consensus, uncertainty and challenges
McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P. A., & Hirschhorn, J. N. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2344 · Cited by 2,891
Use of Unlinked Genetic Markers to Detect Population Stratification in Association Studies
Pritchard, J. K., & Rosenberg, N. A. (1999) · The American Journal of Human Genetics · doi:10.1086/302449 · Cited by 1,198
Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits
Hill, W. G., Goddard, M. E., & Visscher, P. M. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000008 · Cited by 1,039
Cited by
Efficient multivariate linear mixed model algorithms for genome-wide association studies
Zhou, X., & Stephens, M. (2014) · Nature Methods · doi:10.1038/nmeth.2848 · Cited by 841
Huang, W., Massouras, A., Inoue, Y., Peiffer, J., Ràmia, M., Tarone, A. M., Turlapati, L., Zichner, T., Zhu, D., Lyman, R. F., et al. (2014) · Genome Research · doi:10.1101/gr.171546.113 · Cited by 661
ProbABEL package for genome-wide association analysis of imputed data
Aulchenko, Y. S., Struchalin, M. V., & van Duijn, C. M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-134 · Cited by 396
Deconstructing the sources of genotype-phenotype associations in humans
Young, A. I., Benonisdottir, S., Przeworski, M., & Kong, A. (2019) · Science · doi:10.1126/science.aax3710 · Cited by 288
Relatedness in the post-genomic era: is it still useful?
Speed, D., & Balding, D. J. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3821 · Cited by 316
Self-citation
Metadata
- Authors
- Astle, W., & Balding, D. J.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2009-11-01
- Volume
- 24
- Issue
- 4
- Pages
- 451-471
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W1967529528
- Cited by
- 459
- References
- 17
- FWCI
- 8.55
- Type
- journal article
- Language
- en
- Open access
- bronze
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