Bayesian analysis of the genetic structure of a Brazilian popcorn germplasm using data from simple sequence repeats (SSR)

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Abstract

Several studies have confirmed that popcorn (Zea mays L. var. everta) has a narrow genetic basis, which affects the quality of breeding programs. In this study, we present a genetic characterization of 420 individuals representing 28 popcorn populations from Brazilian germplasm banks. All individuals were genotyped using 11 microsatellite markers from the Maize Genetics and Genomics Database. A Bayesian clustering approach via Monte Carlo Markov chains was performed to examine the genetic differentiation (Fst values) among different clusters. The results indicate the existence of three distinct and strongly differentiated genetic groups (K = 3). Moreover, the Fst values (calculated among clusters) were significantly different according to Bayesian credible intervals of the posterior Fst values. The estimates of posterior mean (and 95% credible interval) of the Fst values were 0.086 (0.04-0.14), 0.49 (0.376-0.624) and 0.243 (0.173-0.324) for clusters 1, 2, and 3, respectively. Clusters 1 and 3 showed a high level of genetic diversity in terms of expected heterozygosity and number of alleles, indicating their potential for broadening the genetic basis of popcorn in future breeding programs. Additionally, the 11 microsatellites were informative and presented a suitable number of alleles for determining parameters related to genetic diversity and genetic structure. This information is important for increasing our knowledge regarding genetic relationships, for the identification of heterotic groups, and for developing strategies of gene introgression in popcorn.

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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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Inference of Population Structure Using Multilocus Genotype Data

Jonathan Pritchard, Matthew Stephens, Peter Donnelly … (2001)

We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.