Evaluating the impact of scoring parameters on the structure of intra-specific genetic variation using RawGeno, an R package for automating AFLP scoring

Arrigo, Nils; Tuszynski, Jarek W; Ehrich, Dorothée; Gerdes, Tommy; Alvarez, Nadir

doi:10.1186/1471-2105-10-33

Cited by 140 publications

(129 citation statements)

References 19 publications

(36 reference statements)

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“…This investment also improves estimates of locusspecific mismatch error rates for accurate ranking of loci and high-error culling. A new tool for automated binset optimization was recently described along with novel descriptive statistics for evaluating the 'information content' of bins without the benefit of duplicate genotypes (Arrigo et al, 2009); comparing this approach to the optimization methods applied here will be a valuable next step for advancing the utility of automated binsetting.…”

Section: Discussionmentioning

confidence: 99%

“…Effects on estimation of genetic diversity and population structure have mostly been hypothesized (Bonin et al, 2004), demonstrated with simulations (Caballero et al, 2008), and only recently explored empirically (Arrigo et al, 2009;Herrmann et al, 2010;Crawford et al, 2011). Bonin et al (2004) hypothesized that 0.1% average genotyping error among loci may provide little improvement for population genetic inferences compared with 2%.…”

Section: Introductionmentioning

confidence: 99%

“…We are aware of only two programs that apply an optimization criterion to initial binset creation: Peakmatcher (DeHaan et al, 2002) minimizes differences between replicated samples while applying multiple criteria to choose among many overlapping bin candidates. RawGeno (Arrigo et al, 2009) evaluates fingerprints from all samples, starts with one massive bin and repeatedly applies rules for subdividing and retaining bins such that bin widths are minimized (not to exceed a defined value) and intervals between bins are maximized. Here, with the expectation that AFLP analysis steps two and three can 'clean up' a binset containing many error-prone bins (that is, we need not 'trust' the optimization, but simply use it as a starting point), one of the goals of this study was to apply automated binset optimization in anticipation of analyzing larger data sets in the future.…”

Section: Introductionmentioning

confidence: 99%

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Identifying and reducing AFLP genotyping error: an example of tradeoffs when comparing population structure in broadcast spawning versus brooding oysters

Zhang

Hare

2012

Heredity

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Phylogeographic inferences about gene flow are strengthened through comparison of co-distributed taxa, but also depend on adequate genomic sampling. Amplified fragment length polymorphisms (AFLPs) provide a rapid and inexpensive source of multilocus allele frequency data for making genomically robust inferences. Every AFLP study initially generates markers with a range of locus-specific genotyping error rates and applies criteria to select a subset for analysis. However, there has been very little empirical evaluation of the best tradeoff between culling all but the lowest-error loci to minimize overall genotyping error versus the potential for increasing population genetic signal by retaining more loci. Here, we used AFLPs to compare population structure in co-distributed broadcast spawning (Crassostrea virginica) and brooding (Ostrea equestris) oyster species. Using existing methods for almost entirely automated marker selection and scoring, genotyping error tradeoffs were evaluated by comparing results across a nested series of data sets with mean mismatch errors of 0, 1, 2, 3, 4 and 44%. Artifactual population structure was diagnosed in high-error data sets and we assessed the low-error point at which expected population substructure signal was lost. In both species, we identified substructure patterns deemed to be inaccurate at average mismatch error rates p2 and 44%. In the species comparison, the optimum data sets showed higher gene flow for the brooding oyster with more oceanic salinity tolerances. AFLP tradeoffs may differ among studies, but our results suggest that important signal may be lost in the pursuit of 'acceptable' error levels and our procedures provide a general method for empirically exploring these tradeoffs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying and reducing AFLP genotyping error: an example of tradeoffs when comparing population structure in broadcast spawning versus brooding oysters

Zhang

Hare

2012

Heredity

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“…PCR products were separated and quantified on an ABI 3730xl DNA analyzer (Applied Biosystems, Foster City, CA). AFLP fingerprints were generated from electrophoretogram alignments using the RawGeno R-package (Arrigo et al, 2009). Visualization of the number of fragments per sample in RawGeno was used to select herbarium specimens with the appropriate levels of DNA preservation (i.e.…”

Section: Aflp Genomic Fingerprints and Analysesmentioning

confidence: 99%

Does infraspecific taxonomy match species evolutionary history? A phylogeographic study of Arundo formosana (Poaceae)

Hardion

Verlaque

Vorontsova

et al. 2017

Botanical Journal of the Linnean Society

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Taxonomic resolution below the species rank often appears challenging, partly due to the recent decline in the popularity of infraspecific groupings in the evolutionary and conservation sciences. We test an integrative approach for reconstructing evolutionary history by reconciling past infraspecific taxonomy with molecular methods, using the Taiwanese endemic Arundo formosana (Poaceae). Based on 12 morphometric variables, we provide stronger support for the existence of three morphotypes previously described as varieties and then overlooked, with a clear geographical distribution between western, eastern and northern Taiwan. The phylogeographic analysis of five intergenic regions of plastid DNA supports only the eastern and western lineages and their divergence time according to molecular dating coincides with the orogenesis of the latitudinal central mountain range of Taiwan. AFLP (amplified fragment length polymorphism) nuclear fingerprints also support the east-west divergence in A. formosana followed by secondary contacts in the centre of the island, in addition to the monophyly of the northern morphotype nested in the eastern lineage. We suggest an integrative consensus for the taxonomy of A. formosana that demonstrates the pertinence of infraspecific taxa in integrative taxonomy and phylogeography below the species level.

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“…We used RawGeno package (Arrigo, Tuszynski, Ehrich, Gerdes, & Alvarez, 2009) in R 3.1.2 open‐source environment (R Development Core Team, 2011) to create groups of homologous amplicons based on their electrophoretic mobility (converted to units of base pairs). Maximum bin width was set at 1.2 base pairs and unconstrained for narrower bin sizes.…”

Section: Methodsmentioning

confidence: 99%

Genetic structure in Red Junglefowl (Gallus gallus) populations: Strong spatial patterns in the wild ancestors of domestic chickens in a core distribution range

Nguyen-Phuc

Berres

2018

Ecology and Evolution

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Red Junglefowl (Gallus gallus) are among the few remaining ancestors of an extant domesticated livestock species, the domestic chicken, that still occur in the wild. Little is known about genetic diversity, population structure, and demography of wild Red Junglefowl in their natural habitats. Extinction threats from habitat loss or genetic alteration from domestic introgression exacerbate further the conservation status of this progenitor species. In a previous study, we reported extraordinary adaptive genetic variation in the MHC B‐locus in wild Red Junglefowl and no evidence of allelic introgression between wild and domestic chickens was observed. In this study, we characterized spatial genetic variation and population structure in naturally occurring populations of Red Junglefowl in their core distribution range in South Central Vietnam. A sample of 212 Red Junglefowl was obtained from geographically and ecologically diverse habitats across an area of 250 × 350 km. We used amplified fragment‐length polymorphism markers obtained from 431 loci to determine whether genetic diversity and population structure varies. We found that Red Junglefowl are widely distributed but form small and isolated populations. Strong spatial genetic patterns occur at both local and regional scales. At local scale, population stratification can be identified to approximately 5 km. At regional scale, we identified distinct populations of Red Junglefowl in the southern lowlands, northern highlands, and eastern coastal portions of the study area. Both local and long‐distance genetic patterns observed in wild Red Junglefowl may reflect the species’ ground‐dwelling and territorial characteristics, including dispersal barriers imposed by the Annamite Mountain Range. Spatially explicit analyses with neutral genetic markers can be highly informative and here elevates the conservation profile of the wild ancestors of domesticated chickens.

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Evaluating the impact of scoring parameters on the structure of intra-specific genetic variation using RawGeno, an R package for automating AFLP scoring

Cited by 140 publications

References 19 publications

Identifying and reducing AFLP genotyping error: an example of tradeoffs when comparing population structure in broadcast spawning versus brooding oysters

Identifying and reducing AFLP genotyping error: an example of tradeoffs when comparing population structure in broadcast spawning versus brooding oysters

Does infraspecific taxonomy match species evolutionary history? A phylogeographic study of Arundo formosana (Poaceae)

Genetic structure in Red Junglefowl (Gallus gallus) populations: Strong spatial patterns in the wild ancestors of domestic chickens in a core distribution range

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