Cited by 227Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

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A Powerful and Flexible Multilocus Association Test for Quantitative Traits

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Kwee, Lydia Coulter, Liu, Dawei, Lin, Xihong, Ghosh, Debashis, Epstein, Michael P.·2008·The American Journal of Human Genetics

DOI 10.1016/j.ajhg.2007.10.010PMC PMC2664991

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Abstract

Association mapping of complex traits typically employs tagSNP genotype data to identify a trait locus within a region of interest. However, considerable debate exists regarding the most powerful strategy for utilizing such tagSNP data for inference. A popular approach tests each tagSNP within the region individually, but such tests could lose power as a result of incomplete linkage disequilibrium between the genotyped tagSNP and the trait locus. Alternatively, one can jointly test all tagSNPs simultaneously within the region (by using genotypes or haplotypes), but such multivariate tests have large degrees of freedom that can also compromise power. Here, we consider a semiparametric model for quantitative-trait mapping that uses genetic information from multiple tagSNPs simultaneously in analysis but produces a test statistic with reduced degrees of freedom compared to existing multivariate approaches. We fit this model by using a dimension-reducing technique called least-squares kernel machines, which we show is identical to analysis using a specific linear mixed model (which we can fit by using standard software packages like SAS and R). Using simulated SNP data based on real data from the International HapMap Project, we demonstrate that our approach often has superior performance for association mapping of quantitative traits compared to the popular approach of single-tagSNP testing. Our approach is also flexible, because it allows easy modeling of covariates and, if interest exists, high-dimensional interactions among tagSNPs and environmental predictors.

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Almasy, L. A., & Blangero, J. (1998) · The American Journal of Human Genetics · doi:10.1086/301844 · Cited by 3,021
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Authors: Kwee, L. C., Liu, D., Lin, X., Ghosh, D., & Epstein, M. P.
Affiliations: Emory University (US)
Brown University (US)
Harvard University (US)
Pennsylvania State University (US)
Corresponding author: Epstein, Michael P.
ORCID: Kwee, Lydia Coulter
Liu, Dawei
Lin, Xihong
Ghosh, Debashis
Epstein, Michael P.
Venue: The American Journal of Human Genetics
Publisher: Elsevier BV
Published: 2008-02
Volume: 82
Issue: 2
Pages: 386-397
ISSN: 0002-9297 (ISSN-L)
DOI: 10.1016/j.ajhg.2007.10.010
PMC: PMC2664991
OpenAlex: W2164143816
Cited by: 227
References: 49
Citations by year: 2026: 4 · 2025: 6 · 2024: 6 · 2023: 4 · 2022: 8 · 2021: 8 · 2020: 8 · 2019: 12 · 2018: 14 · 2017: 12 · 2016: 17 · 2015: 19 · 2014: 24 · 2013: 22 · 2012: 21
FWCI: 7.88
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 1995 d)
Locations: The American Journal of Human Genetics (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Collection of Biostatistics Research Archive (submittedVersion)
Funders: National Institutes of Health (5t32gm074909-07)
National Human Genome Research Institute (HG003618, R01HG003618)
National Cancer Institute (R01CA076404, R37CA076404, CA76404, R29CA076404)
National Institute of General Medical Sciences (T32-GM074909, GM074909)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords: quantitative trait mapping, tag SNPs, linkage disequilibrium, linear mixed models, semiparametric models, dimensionality reduction, statistical power, haplotypes, environmental variables
MeSH terms: Chromosome Mapping, Computer Simulation, Models, Genetic, Quantitative Trait, Heritable, Polymorphism, Single Nucleotide, Quantitative Trait Loci

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A Powerful and Flexible Multilocus Association Test for Quantitative Traits

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