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Self-reported ethnicity, genetic structure and the impact of population stratification in a multiethnic study

Overview of attention for article published in Human Genetics, May 2010
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Title
Self-reported ethnicity, genetic structure and the impact of population stratification in a multiethnic study
Published in
Human Genetics, May 2010
DOI 10.1007/s00439-010-0841-4
Pubmed ID
Authors

Hansong Wang, Christopher A. Haiman, Laurence N. Kolonel, Brian E. Henderson, Lynne R. Wilkens, Loïc Le Marchand, Daniel O. Stram

Abstract

It is well-known that population substructure may lead to confounding in case-control association studies. Here, we examined genetic structure in a large racially and ethnically diverse sample consisting of five ethnic groups of the Multiethnic Cohort study (African Americans, Japanese Americans, Latinos, European Americans and Native Hawaiians) using 2,509 SNPs distributed across the genome. Principal component analysis on 6,213 study participants, 18 Native Americans and 11 HapMap III populations revealed four important principal components (PCs): the first two separated Asians, Europeans and Africans, and the third and fourth corresponded to Native American and Native Hawaiian (Polynesian) ancestry, respectively. Individual ethnic composition derived from self-reported parental information matched well to genetic ancestry for Japanese and European Americans. STRUCTURE-estimated individual ancestral proportions for African Americans and Latinos are consistent with previous reports. We quantified the East Asian (mean 27%), European (mean 27%) and Polynesian (mean 46%) ancestral proportions for the first time, to our knowledge, for Native Hawaiians. Simulations based on realistic settings of case-control studies nested in the Multiethnic Cohort found that the effect of population stratification was modest and readily corrected by adjusting for race/ethnicity or by adjusting for top PCs derived from all SNPs or from ancestry informative markers; the power of these approaches was similar when averaged across causal variants simulated based on allele frequencies of the 2,509 genotyped markers. The bias may be large in case-only analysis of gene by gene interactions but it can be corrected by top PCs derived from all SNPs.

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Geographical breakdown

Country Count As %
United States 2 3%
Canada 2 3%
South Africa 1 2%
Brazil 1 2%
United Kingdom 1 2%
Unknown 51 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 24%
Student > Ph. D. Student 8 14%
Student > Bachelor 7 12%
Professor 4 7%
Other 4 7%
Other 14 24%
Unknown 7 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 33%
Medicine and Dentistry 9 16%
Biochemistry, Genetics and Molecular Biology 5 9%
Social Sciences 5 9%
Economics, Econometrics and Finance 3 5%
Other 8 14%
Unknown 9 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 May 2018.
All research outputs
#15,998,900
of 23,749,054 outputs
Outputs from Human Genetics
#2,577
of 3,002 outputs
Outputs of similar age
#79,675
of 97,920 outputs
Outputs of similar age from Human Genetics
#13
of 14 outputs
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