#Cite genodive series
Genetics 150:921–930Įxcoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Ronfort J, Jenczewski E, Bataillon T, Rousset F (1998) Analysis of population structure in autotetraploid species. J Hered:1–36Īrnold BJ, Bomblies K, Wakeley J (2012) Extending coalescent theory to autotetraploids. Meirmans PG, Liu S, Van Tienderen PH (2018) The analysis of polyploid genetic data. Monnahan P, Kolář F, Baduel P et al (2019) Pervasive population genomic consequences of genome duplication in Arabidopsis arenosa. Oxford Surv Evol Biol 8:185–217ĭufresne F, Stift M, Vergilino R, Mable BK (2014) Recent progress and challenges in population genetics of polyploid organisms: an overview of current state-of-the-art molecular and statistical tools. Heredity 110:131–137īever JD, Felber F (1992) The theoretical population genetics of autopolyploidy. Meirmans PG, Van Tienderen PH (2013) The effects of inheritance in tetraploids on genetic diversity and population divergence. Genetics 179:2113–2123Ĭhester M, Gallagher JP, Symonds VV et al (2012) Extensive chromosomal variation in a recently formed natural allopolyploid species, Tragopogon miscellus (Asteraceae). Stift M, Berenos C, Kuperus P, Van Tienderen PH (2008) Segregation models for disomic, tetrasomic and intermediate inheritance in tetraploids: a general procedure applied to Rorippa (yellow cress) microsatellite data. G enoD ive can be downloaded freely from. Specifically, I focus on analyses of genetic diversity, Hardy-Weinberg equilibrium, quantifying population differentiation, clustering, and calculation of genetic distances. I then explain how G enoD ive approaches these analyses and whether and how it can overcome possible biases.
In this chapter, I outline several frequently used types of population genetic analyses and explain how these apply to polyploid data, including possible pitfalls and biases.
#Cite genodive software
The software G enoD ive is one of the most widely used programs for the analysis of polyploid genetic data, presenting a wide array of different methods. However, over the last years, the number of software programs that can deal with polyploid data is slowly increasing. Analyzing autopolyploid genetic data still presents numerous challenges due to, e.g., missing dosage information of genotypes and the presence of multiple ploidy levels within species or populations, but also because the choice of software is limited when compared to what is available for diploid data.
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