Joint genotype- and ancestry-based genome-wide association studies in admixed populations
(2017) In Genetic Epidemiology 41(6). p.555-566- Abstract
In genome-wide association studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand, admixture mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific ancestry). Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests. Here, we extend this approach for population-based GWAS in the direction of multimarker models. A modified version of the Bayesian information criterion is developed for building a multilocus model that... (More)
In genome-wide association studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand, admixture mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific ancestry). Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests. Here, we extend this approach for population-based GWAS in the direction of multimarker models. A modified version of the Bayesian information criterion is developed for building a multilocus model that accounts for the differential correlation structure due to linkage disequilibrium (LD) and admixture LD. Simulation studies and a real data example illustrate the advantages of this new approach compared to single-marker analysis or modern model selection strategies based on separately analyzing genotype and ancestry data, as well as to single-marker analysis combining genotypic and ancestry information. Depending on the signal strength, our procedure automatically chooses whether genotypic or locus-specific ancestry markers are added to the model. This results in a good compromise between the power to detect causal mutations and the precision of their localization. The proposed method has been implemented in R and is available at http://www.math.uni.wroc.pl/~mbogdan/admixtures/.
(Less)
- author
- Szulc, Piotr ; Bogdan, Malgorzata LU ; Frommlet, Florian and Tang, Hua
- publishing date
- 2017-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- admixture mapping, model selection, multiple regression, quantitative trait
- in
- Genetic Epidemiology
- volume
- 41
- issue
- 6
- pages
- 12 pages
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:28657151
- scopus:85021416932
- ISSN
- 0741-0395
- DOI
- 10.1002/gepi.22056
- language
- English
- LU publication?
- no
- additional info
- Funding Information: The authors thank two anonymous referees, whose comments helped to improve the presentation. They also thank David O. Siegmund for discussions and helpful suggestions. PS and MB were supported by the European Union’s 7th Framework Programme for research, technological development, and demonstration under grant agreement no. 602552, cofinanced by the Polish Ministry of Science and Higher Education under grant agreement 2932/7.PR/2013/2. HT was supported by the US National Institutes of Health grants GM073059. Large part of this research was performed during MB visit at Stanford University, financed by the Advanced Researchers Fellowship from the Fulbright Association. Funding Information: The authors thank two anonymous referees, whose comments helped to improve the presentation. They also thank David O. Siegmund for discussions and helpful suggestions. PS and MB were supported by the European Union's 7th Framework Programme for research, technological development, and demonstration under grant agreement no. 602552, cofinanced by the Polish Ministry of Science and Higher Education under grant agreement 2932/7.PR/2013/2. HT was supported by the US National Institutes of Health grants GM073059. Large part of this research was performed during MB visit at Stanford University, financed by the Advanced Researchers Fellowship from the Fulbright Association. Publisher Copyright: © 2017 WILEY PERIODICALS, INC.
- id
- 7de1cba7-0db0-46f3-a886-95c6a6b27398
- date added to LUP
- 2023-12-08 09:22:08
- date last changed
- 2024-03-09 12:43:59
@article{7de1cba7-0db0-46f3-a886-95c6a6b27398,
abstract = {{<p>In genome-wide association studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand, admixture mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific ancestry). Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests. Here, we extend this approach for population-based GWAS in the direction of multimarker models. A modified version of the Bayesian information criterion is developed for building a multilocus model that accounts for the differential correlation structure due to linkage disequilibrium (LD) and admixture LD. Simulation studies and a real data example illustrate the advantages of this new approach compared to single-marker analysis or modern model selection strategies based on separately analyzing genotype and ancestry data, as well as to single-marker analysis combining genotypic and ancestry information. Depending on the signal strength, our procedure automatically chooses whether genotypic or locus-specific ancestry markers are added to the model. This results in a good compromise between the power to detect causal mutations and the precision of their localization. The proposed method has been implemented in R and is available at http://www.math.uni.wroc.pl/~mbogdan/admixtures/.</p>}},
author = {{Szulc, Piotr and Bogdan, Malgorzata and Frommlet, Florian and Tang, Hua}},
issn = {{0741-0395}},
keywords = {{admixture mapping; model selection; multiple regression; quantitative trait}},
language = {{eng}},
number = {{6}},
pages = {{555--566}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Genetic Epidemiology}},
title = {{Joint genotype- and ancestry-based genome-wide association studies in admixed populations}},
url = {{http://dx.doi.org/10.1002/gepi.22056}},
doi = {{10.1002/gepi.22056}},
volume = {{41}},
year = {{2017}},
}