GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update

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  • Rod Peakall
    1 Evolution, Ecology and Genetics, Research School of Biology, The Australian National University, Canberra ACT 0200, Australia and 2Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ 08901-8551, USA
  • Peter E. Smouse
    1 Evolution, Ecology and Genetics, Research School of Biology, The Australian National University, Canberra ACT 0200, Australia and 2Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ 08901-8551, USA

Description

<jats:title>Abstract</jats:title> <jats:p>Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G′ST, G′′ST, Jost’s Dest and F′ST through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised.</jats:p> <jats:p>Availability and implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx.</jats:p> <jats:p>Contact:  rod.peakall@anu.edu.au</jats:p>

Journal

  • Bioinformatics

    Bioinformatics 28 (19), 2537-2539, 2012-07-20

    Oxford University Press (OUP)

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