Estimation of genetic diversity and relatedness in a mango germplasm collection using SNP markers and a simplified visual analysis method

Authors: KUHN, DAVID; WARSCHEFSKY, EMILY - FLORIDA INTERNATIONAL UNIVERSITY; GROH, AMY - FLORIDA INTERNATIONAL UNIVERSITY; RAHAMAN, JORDON - FLORIDA INTERNATIONAL UNIVERSITY; Freeman, Barbara - Barbie; BALLY, IAN - DEPARTMENT OF AGRICULTURE - AUSTRALIA; DILLON, NATALIE - DEPARTMENT OF AGRICULTURE - AUSTRALIA; INNES, DAVID - DEPARTMENT OF AGRICULTURE - AUSTRALIA; CHAMBERS, ALAN - UNIVERSITY OF FLORIDA

Submitted to: Scientia Horticulturae
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/20/2019
Publication Date: 6/27/2019
Citation: Kuhn, D.N., Warschefsky, E., Groh, A.M., Rahaman, J., Freeman, B.L., Bally, I., Dillon, N., Innes, D., Chambers, A. 2019. Estimation of genetic diversity and relatedness in a mango germplasm collection using SNP markers and a simplified visual analysis method. Scientia Horticulturae. 252:156-168. https://doi.org/10.1016/j.scienta.2019.03.037.
DOI: https://doi.org/10.1016/j.scienta.2019.03.037

Interpretive Summary: Tree breeding is a difficult and time consuming endeavour. To improve the efficiency of tree breeding, breeders search for some type of marker that will help them identify potentially improved trees at the seedling stage. We have developed many thousands of DNA markers for mango and begun the association of traits with those markers to aid both breeders and producers. This paper describes the genotyping of 1915 mango trees with 272 SNP markers and the association of the commercially important qualitative polyembryony trait with 10 markers. Because of the size of the dataset (~580,000 genotypes), a simple visual method has been developed to allow breeders and other interested researchers to analyze the data using only common spreadsheet functions rather than having to develop programming scripts in Perl, Python or R. By using this database along with genotype data of newly developed hybrids or farmers’ selection, breeders can easily determine if a tree in their program is the result of self-pollination, is misidentified/mislabeled, and even identify the paternal parent in some cases. The information presented in this paper is of importance to mango research scientists, breeders and producers.

Technical Abstract: Mango is a globally important tropical fruit but lacks genomic tools to support cultivar identification and to enable breeding efforts. In general, mango displays great phenotypic diversity for agronomic and fruit quality traits, but similarities between commercially-relevant cultivars at the juvenile stage can lead to costly misidentifications during propagation. Assessing the genetic diversity and relatedness of mango germplasm is also essential to identifying genetically distant parents with favorable agronomic traits to produce hybrid populations enabling selection of improved cultivars. To overcome these limitations and enhance future breeding efforts, 1915 mango accessions from the United States, Senegal, Thailand, and Australia were genotyped with 272 single nucleotide polymorphism (SNP) markers identifying over 520,000 genotypes. The SNP markers are unambiguous in the sense that they are solely based on sequence variation at a nucleotide position that can be determined by multiple methods. Thus, this database is useful for mango identification by interested researchers assaying a cultivar of interest with a subset of these 272 SNPs and comparing it to the database. These accessions represent the available diversity from both public and private germplasm collections in these countries, as well as accessions from smaller international collections. The study included Mangifera indica, other Mangifera species, and accessions from half sibling populations. Genotype data were analyzed using an affinity propagation method to define 258 groups. Using a simple visual method, no more than 30 SNPs are needed to distinguish a single cultivar of interest from all other cultivars in the dataset enabling the accurate identification of important commercial cultivars. As the SHRS germplasm collection is a part of the GRINGlobal database, we have used these genotypes to remove mislabeled accessions to prevent distribution of incorrectly identified accessions. As these SNP markers provided accurate genotype data for accessions from different genera as well as half siblings, the majority of the genetic diversity of the mango germplasm and related species that were genotyped has been captured. The dataset contains a large collection of open-pollinated half siblings from known maternal parents. A simple visual method can also be used to identify self-pollinated individuals among the half siblings of known maternal parents and, in some cases, to infer likely candidates for the paternal parent. Identification of self-pollinated individuals is particularly important in terms of selection of improved cultivars, as due to high levels of heterozygosity, self-pollinated progeny are likely to uncover deleterious recessive alleles. Genotyping of progeny at the seedling stage and removal of self-pollinated progeny can increase the efficiency and decrease the costs of selection of improved cultivars from open-pollinated populations.