Generalized Analysis of Molecular Variance

Nievergelt, Caroline M; Libiger, Ondrej; Schork, Nicholas J

doi:10.1371/journal.pgen.0030051

Cited by 80Genetic diversity and population structureForensic and Genetic ResearchGenetic Associations and Epidemiology

Generalized Analysis of Molecular Variance

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Nievergelt, Caroline M, Libiger, Ondrej, Schork, Nicholas J·2007·PLoS Genetics

DOI 10.1371/journal.pgen.0030051PMC PMC1847693

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Abstract

Many studies in the fields of genetic epidemiology and applied population genetics are predicated on, or require, an assessment of the genetic background diversity of the individuals chosen for study. A number of strategies have been developed for assessing genetic background diversity. These strategies typically focus on genotype data collected on the individuals in the study, based on a panel of DNA markers. However, many of these strategies are either rooted in cluster analysis techniques, and hence suffer from problems inherent to the assignment of the biological and statistical meaning to resulting clusters, or have formulations that do not permit easy and intuitive extensions. We describe a very general approach to the problem of assessing genetic background diversity that extends the analysis of molecular variance (AMOVA) strategy introduced by Excoffier and colleagues some time ago. As in the original AMOVA strategy, the proposed approach, termed generalized AMOVA (GAMOVA), requires a genetic similarity matrix constructed from the allelic profiles of individuals under study and/or allele frequency summaries of the populations from which the individuals have been sampled. The proposed strategy can be used to either estimate the fraction of genetic variation explained by grouping factors such as country of origin, race, or ethnicity, or to quantify the strength of the relationship of the observed genetic background variation to quantitative measures collected on the subjects, such as blood pressure levels or anthropometric measures. Since the formulation of our test statistic is rooted in multivariate linear models, sets of variables can be related to genetic background in multiple regression-like contexts. GAMOVA can also be used to complement graphical representations of genetic diversity such as tree diagrams (dendrograms) or heatmaps. We examine features, advantages, and power of the proposed procedure and showcase its flexibility by using it to analyze a wide variety of published data sets, including data from the Human Genome Diversity Project, classical anthropometry data collected by Howells, and the International HapMap Project.

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Approaches that capture subtle synergistic effects among many loci in a region, or among different variant types affecting gene function, such as sequence- or functional-similarity-based methods, are probably also suitable.
“However, methods which can account for subtle synergistic effects of many loci within a defined region and/or different forms of variation that might contribute to gene function, such as those rooted in sequence or functional similarity56, 57, 87, 88 are also likely to be appropriate.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [88]

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Authors: Nievergelt, C. M., Libiger, O., & Schork, N. J.
Affiliations: University of California San Diego (US)
Scripps Research Institute (US)
Corresponding author: Schork, Nicholas J
ORCID: Nievergelt, Caroline M
Schork, Nicholas J
Venue: PLoS Genetics
Publisher: Public Library of Science (PLoS)
Published: 2007-04-06
Volume: 3
Issue: 4
Pages: e51
ISSN: 1553-7390 (ISSN-L)
1553-7404
DOI: 10.1371/journal.pgen.0030051
PMC: PMC1847693
OpenAlex: W2080850930
Cited by: 80
References: 82
Citations by year: 2026: 2 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 3 · 2020: 2 · 2019: 3 · 2018: 2 · 2017: 2 · 2016: 2 · 2015: 4 · 2014: 3 · 2013: 4 · 2012: 2
FWCI: 6.63
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Genetics (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: U.S. Public Health Service (5 R01 HLMH065571-02)
National Institute on Aging (U19AG023122)
National Heart, Lung, and Blood Institute (HL074730-02, U01HL064777, R01HL070137, HL070137-01, R01HL074730)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords: multivariate linear models, genetic epidemiology, population genetics, Human Genome Diversity Project, HapMap, anthropometry
MeSH terms: Analysis of Variance, Cell Line, Cephalometry, Chromosomes, Human, Female, Haplotypes, Humans, Male, Phylogeny, Genetic Variation, Databases, Genetic

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