Genome wide simple sequence repeats development and their application in genetic diversity analysis in wax gourd ( <i>Benincasa hispida</i> )

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Abstract

Wax gourd is one of the most important winter vegetables of the Cucurbitaceae family. There are only limited markers available for this crop and the draft genome of wax gourd provides a powerful tool for simple sequence repeats (SSR) marker development. In this study, we developed genome‐wide SSR markers from wax gourd genome and characterized their distribution and frequency of different motifs and repeats. A total of 52,431 microsatellites from wax gourd genome were identified, of which 39,319 SSR markers were developed. The 1152 non‐wax gourd SSR markers were selected from cucumber, melon, watermelon and pumpkin to test their transferability in wax gourd. The 580 SSR markers could be transferable in wax gourd, and 42 of them were detected with polymorphic in 11 tested accessions of wax gourd. In addition, 11 good polymorphic transferrable SSR markers and 21 SSR markers of wax gourd were selected to investigate the genetic diversity and population structure of 129 wax gourd accessions. One hundred twelve alleles were detected by these 32 SSR markers. The result of population structure showed that the 129 wax gourd accessions were divided into two main populations, and the genetic diversity analysis separated them into two clusters. The large number of wax gourd SSR markers developed in this study provides a valuable resource for genetic linkage map construction, molecular mapping, and marker‐assisted selection (MAS) in wax gourd.

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MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.

Koichiro Tamura, Glen Stecher, Daniel Walter Peterson … (2013)

We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.

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Detecting the number of clusters of individuals using the software structure: a simulation study

G Evanno, S. Regnaut, J Goudet (2005)

The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.

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

Rod Peakall, Peter E. Smouse (2012)

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 D est 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. 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. Contact: rod.peakall@anu.edu.au