Assessing the impact of population stratification on genetic association studies

Matthew L. Freedman; David Reich; Kathryn L. Penney; Gavin J. McDonald; Andre A. Mignault; Nick Patterson; Stacey B. Gabriel; Eric J. Topol; Jordan W. Smoller; Carlos N. Pato; Michele T. Pato; Tracey L. Petryshen; Laurence N. Kolonel; Eric S. Lander; Pamela Sklar; Brian Henderson; Joel N. Hirschhorn; David Altshuler

doi:10.1038/ng1333

Assessing the impact of population stratification on genetic association studies

Matthew L. Freedman, David Reich, Kathryn L. Penney, Gavin J. McDonald, Andre A. Mignault, Nick Patterson, Stacey B. Gabriel, Eric J. Topol, Jordan W. Smoller, Carlos N. Pato, Michele T. Pato, Tracey L. Petryshen, Laurence N. Kolonel, Eric S. Lander, Pamela Sklar, Brian Henderson, Joel N. Hirschhorn, David Altshuler

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668 Scopus citations

Abstract

Population stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease. It has been proposed that false positive associations due to stratification can be controlled by genotyping a few dozen unlinked genetic markers. To assess stratification empirically, we analyzed data from 11 case-control and case-cohort association studies. We did not detect statistically significant evidence for stratification but did observe that assessments based on a few dozen markers lack power to rule out moderate levels of stratification that could cause false positive associations in studies designed to detect modest genetic risk factors. After increasing the number of markers and samples in a case-cohort study (the design most immune to stratification), we found that stratification was in fact present. Our results suggest that modest amounts of stratification can exist even in well designed studies.

Original language	English (US)
Pages (from-to)	388-393
Number of pages	6
Journal	Nature genetics
Volume	36
Issue number	4
DOIs	https://doi.org/10.1038/ng1333
State	Published - Apr 2004
Externally published	Yes

All Science Journal Classification (ASJC) codes

Genetics

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10.1038/ng1333

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Freedman, M. L., Reich, D., Penney, K. L., McDonald, G. J., Mignault, A. A., Patterson, N., Gabriel, S. B., Topol, E. J., Smoller, J. W., Pato, C. N., Pato, M. T., Petryshen, T. L., Kolonel, L. N., Lander, E. S., Sklar, P., Henderson, B., Hirschhorn, J. N., & Altshuler, D. (2004). Assessing the impact of population stratification on genetic association studies. Nature genetics, 36(4), 388-393. https://doi.org/10.1038/ng1333

@article{0220a6f16a1b4c93b34b1658bac20c00,

title = "Assessing the impact of population stratification on genetic association studies",

abstract = "Population stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease. It has been proposed that false positive associations due to stratification can be controlled by genotyping a few dozen unlinked genetic markers. To assess stratification empirically, we analyzed data from 11 case-control and case-cohort association studies. We did not detect statistically significant evidence for stratification but did observe that assessments based on a few dozen markers lack power to rule out moderate levels of stratification that could cause false positive associations in studies designed to detect modest genetic risk factors. After increasing the number of markers and samples in a case-cohort study (the design most immune to stratification), we found that stratification was in fact present. Our results suggest that modest amounts of stratification can exist even in well designed studies.",

author = "Freedman, \{Matthew L.\} and David Reich and Penney, \{Kathryn L.\} and McDonald, \{Gavin J.\} and Mignault, \{Andre A.\} and Nick Patterson and Gabriel, \{Stacey B.\} and Topol, \{Eric J.\} and Smoller, \{Jordan W.\} and Pato, \{Carlos N.\} and Pato, \{Michele T.\} and Petryshen, \{Tracey L.\} and Kolonel, \{Laurence N.\} and Lander, \{Eric S.\} and Pamela Sklar and Brian Henderson and Hirschhorn, \{Joel N.\} and David Altshuler",

note = "Funding Information: We thank A. Villapakkam for assistance in genotyping and data checking and K. Ardlie, D. Goldstein and C. Haiman for discussions. M.L.F. is supported by a Department of Defense Health Disparity training grant and a Postdoctoral Fellowship for Physicians from the Howard Hughes Medical Institute. D.A. is a Clinical Scholar in Translational Research from the Burroughs Wellcome Fund, as well as a Charles E. Culpeper Medical Scholar. J.N.H. and D.R. are recipients of Career Development Awards from the Burroughs Welcome Fund. T.L.P. is supported by a Canadian Institutes of Health Research Postdoctoral Fellowship and is a NARSAD Young Investigator. This work was supported in part by a grant from the Functional Genomics Program at the Whitehead Institute/MIT Center for Genome Research (supported by Affymetrix, Millennium Pharmaceuticals and Bristol Myers Squibb) and by a grant from the US National Institutes of Health to B.H. and L.K.",

year = "2004",

month = apr,

doi = "10.1038/ng1333",

language = "English (US)",

volume = "36",

pages = "388--393",

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issn = "1061-4036",

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Freedman, ML, Reich, D, Penney, KL, McDonald, GJ, Mignault, AA, Patterson, N, Gabriel, SB, Topol, EJ, Smoller, JW, Pato, CN , Pato, MT, Petryshen, TL, Kolonel, LN, Lander, ES, Sklar, P, Henderson, B, Hirschhorn, JN & Altshuler, D 2004, 'Assessing the impact of population stratification on genetic association studies', Nature genetics, vol. 36, no. 4, pp. 388-393. https://doi.org/10.1038/ng1333

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T1 - Assessing the impact of population stratification on genetic association studies

AU - Freedman, Matthew L.

AU - Reich, David

AU - Penney, Kathryn L.

AU - McDonald, Gavin J.

AU - Mignault, Andre A.

AU - Patterson, Nick

AU - Gabriel, Stacey B.

AU - Topol, Eric J.

AU - Smoller, Jordan W.

AU - Pato, Carlos N.

AU - Pato, Michele T.

AU - Petryshen, Tracey L.

AU - Kolonel, Laurence N.

AU - Lander, Eric S.

AU - Sklar, Pamela

AU - Henderson, Brian

AU - Hirschhorn, Joel N.

AU - Altshuler, David

N1 - Funding Information: We thank A. Villapakkam for assistance in genotyping and data checking and K. Ardlie, D. Goldstein and C. Haiman for discussions. M.L.F. is supported by a Department of Defense Health Disparity training grant and a Postdoctoral Fellowship for Physicians from the Howard Hughes Medical Institute. D.A. is a Clinical Scholar in Translational Research from the Burroughs Wellcome Fund, as well as a Charles E. Culpeper Medical Scholar. J.N.H. and D.R. are recipients of Career Development Awards from the Burroughs Welcome Fund. T.L.P. is supported by a Canadian Institutes of Health Research Postdoctoral Fellowship and is a NARSAD Young Investigator. This work was supported in part by a grant from the Functional Genomics Program at the Whitehead Institute/MIT Center for Genome Research (supported by Affymetrix, Millennium Pharmaceuticals and Bristol Myers Squibb) and by a grant from the US National Institutes of Health to B.H. and L.K.

PY - 2004/4

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N2 - Population stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease. It has been proposed that false positive associations due to stratification can be controlled by genotyping a few dozen unlinked genetic markers. To assess stratification empirically, we analyzed data from 11 case-control and case-cohort association studies. We did not detect statistically significant evidence for stratification but did observe that assessments based on a few dozen markers lack power to rule out moderate levels of stratification that could cause false positive associations in studies designed to detect modest genetic risk factors. After increasing the number of markers and samples in a case-cohort study (the design most immune to stratification), we found that stratification was in fact present. Our results suggest that modest amounts of stratification can exist even in well designed studies.

AB - Population stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease. It has been proposed that false positive associations due to stratification can be controlled by genotyping a few dozen unlinked genetic markers. To assess stratification empirically, we analyzed data from 11 case-control and case-cohort association studies. We did not detect statistically significant evidence for stratification but did observe that assessments based on a few dozen markers lack power to rule out moderate levels of stratification that could cause false positive associations in studies designed to detect modest genetic risk factors. After increasing the number of markers and samples in a case-cohort study (the design most immune to stratification), we found that stratification was in fact present. Our results suggest that modest amounts of stratification can exist even in well designed studies.

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DO - 10.1038/ng1333

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JO - Nature genetics

JF - Nature genetics

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ER -

Assessing the impact of population stratification on genetic association studies

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