Elucidating Genetic Diversity in Apricot (Prunus armeniaca L.) Cultivated in the North-Western Himalayan Provinces of India Using SSR Markers

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Abstract

Apricot (Prunus armeniaca L.) is an important temperate fruit crop worldwide. The availability of wild apricot germplasm and its characterization through genomic studies can guide us towards its conservation, increasing productivity and nutritional composition. Therefore, in this study, we carried out the genomic characterization of 50 phenotypically variable accessions by using SSR markers in the erstwhile States of Jammu and Kashmir to reveal genetic variability among accessions and their genetic associations. The genetic parameter results revealed that the number of alleles per locus (Na) ranged from 1 to 6 with a mean Na value of 3.89 and the mean effective number of alleles (Ne) per locus 1.882 with a range of 1.22 to 2. Similarly, the polymorphic information content (PIC) values ranged from 0.464 to 0.104. The observed heterozygosity (Ho) (0.547) was found to have higher than expected heterozygosity (He) (0.453) with average heterozygosity of 0.4483. The dendrogram clustered genotypes into three main clades based on their pedigree. The population structure revealed IV sub-populations with all admixtures except the III sub-population, which was mainly formed of exotic cultivars. The average expected heterozygosity (He) and population differentiation within four sub-populations was 1.78 and 0.04, respectively, and explained 95.0% of the total genetic variance in the population. The results revealed that the SSR marker studies could easily decrypt the genetic variability present within the germplasm, which may form the base for the establishment of good gene banks by reducing redundancy of germplasm, selection of parents for any breeding program.

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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.