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PMID: 17411342 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Generalized analysis of molecular variance.

PLoS genetics ·Vol. 3 ·No. 4 ·2007-04-06 ·Pages e51

Nievergelt CM, Libiger O, Schork NJ

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.

MeSH Terms
Analysis of Variance Cell Line Cephalometry Chromosomes, Human/genetics Databases, Genetic Female Genetic Variation/genetics Haplotypes Humans Male Phylogeny
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Nievergelt Caroline M
Department of Psychiatry, University of California at San Diego, La Jolla, California, United States of America.
Libiger Ondrej
Schork Nicholas J
Conflict of Interest

Competing interests. The authors have declared that no competing interests exist.

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Article Info
Journal
PLoS genetics
Abbr.
PLoS Genet
ISSN
1553-7404
DOI
10.1371/journal.pgen.0030051
Published
2007-04-06
Epub
2007-00-22
Pages
e51
Language
English
Region
United States
NLM ID
101239074
PMCID
PMC1847693
Subset
IM
Grants
NHLBI NIH HHS · HL070137-01 · United States
NHLBI NIH HHS · U01 HL064777 · United States
NIA NIH HHS · U19 AG023122 · United States
NIA NIH HHS · U19 AG023122-01 · United States
NHLBI NIH HHS · R01 HL074730 · United States
NHLBI NIH HHS · HL074730-02 · United States
NHLBI NIH HHS · R01 HL070137 · United States
NHLBI NIH HHS · U01 HL064777-06 · United States
PHS HHS · 5 R01 HLMH065571-02 · United States
External Links
PubMed source DOI: 10.1371/journal.pgen.0030051 PMC Full Text View journal details
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