Seed Phenotyping and Genetic Diversity Assessment of Cowpea (V. unguiculata) Germplasm Collection

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Abstract

Cowpea is a nutrient-rich staple legume and climate-resilient crop for vulnerable agroecosystems. However, the crop still remains underutilized, mainly due to its narrow genetic base, and the production is often ravaged by aphid infestation outbreaks. Thus, genetic diversity assessment and the detection of defense-related alleles are fundamental to germplasm management and utilization in breeding strategies to support food safety in climate change times. A germplasm collection of 87 cowpea landraces sourced from Greece was subjected to seed phenotyping, SSR genotyping and to screening for the presence of aphid-resistance-conferring alleles. Significant diversity in the species’ local germplasm was revealed. The landraces were grouped in metapopulations based on their broader geographical origin. High amounts of variation and statistically significant differences were detected among the landraces regarding the seed morphological traits, the seed color and eye color according to MANOVA (Wilk’s λ = 0.2, p < 0.01) and significant correlations were revealed among these features according to Pearson’s test (p < 0.05). High levels of genetic polymorphism were detected for the metapopulations, ranging from 59% (VuPop3) to 82% (VuPop4). The AMOVA revealed that 93% of the molecular diversity was distributed among the landraces of each metapopulation. Further population structure analysis presumed the existence of two inferred populations, where in population A, 79% of the landraces have a cream/cream-brown seed coat, whereas in population B, 94% of the landraces are brown-ochre to black-seeded. Molecular screening for alleles conferring aphid resistance revealed the correspondence of 12 landraces to the resistant genotype of TVu-2876. The study highlights the importance of cowpea germplasm collection genetic diversity, as a source of important agronomic traits, to support breeding efforts and expand cowpea cultivation to foster food security and agriculture sustainability and diversification in climate change.

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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.