Plant parasitic nematodes occurrence and genetic diversity of banana cultivars grown in Tanzania

Abstract

The genetic diversity of bananas (Musa spp.), one of the most economically important crops in Tanzania, is underestimated with scarce reports available. In addition, cultivation of banana is severely constrained by plant parasitic nematodes (PPN). We assessed genetic diversity and population structure of 159 banana varieties from four agro-ecological zones (Northern, Southern highlands, Lake and Zanzibar islands) of Tanzania using 20 SSR markers analysed with UPGMA method. We also, assessed the PPN occurrence through isolation and counting of PPN from each banana genotype where leaf samples were collected. SSR primer pairs were polymorphic; and generated 63 distinct reproducible bands. The polymorphic information content values of each SSR marker ranged from 0.50 to 0.75 indicating high level of genetic diversity among banana varieties. The 159 banana varieties were grouped into two clusters: A and B, based on UPGMA cluster and population structure analysis. A total of 128 independent and 31 overlapping genotypes were identified. Higher kinship among genotypes was observed in cluster A compared with cluster B. Some of the clusters in A and B genotypes showed high genetic distance. The most prevalent and abundant nematode species was Pratylenchus goodeyi with a mean count of 63%, followed by Radopholus similis (31%) and P. coffeae (6%). The results from this study provides a foundation for understanding genetic variability of bananas existing in Tanzania and PPN occurrence that will be valuable information for breeding disease and pest-resistant bananas with high yield traits.

Diversity of nematodes on banana (Musa spp.) in Kenya linked to altitude and with a focus on the pathogenicity of Pratylenchus goodeyi

“…Further, the results from the current study indicate that P. goodeyi is adapted to a wide range of temperatures despite showing thermal sensitivity in aggressiveness. The current findings indicate that P. goodeyi is spreading geographically, regardless of climatic conditions or altitudinal elevation Mgonja et al, 2020).…”

Comparative assessment of microsatellite and retrotransposon‐based markers for genetic characterization of commercial banana cultivars (Musa spp.)

“…In this sense, different works have been carried out for characterization of SSR markers from M. balbisiana (Buhariwalla et al, 2005;Ravishankar et al, 2013;Wang et al, 2011) and M. acuminata (Amorim et al, 2012;Creste et al, 2006;Crouch et al, 1997Crouch et al, , 1998Miller et al, 2010;Wang et al, 2010), whereas other studies have been focused on the development of SSR markers suitable for analysis of both species and/or hybrid cultivars (Brisibe & Ubi, 2020;Kaemmer et al, 1997;Lagoda et al, 1998;Ravishankar et al, 2012;Silva et al, 2015). In other publications, sets of primers previously developed have been used for molecular characterization of Musa varieties (Bawin et al, 2019;Durai et al, 2018;Grapin et al, 1998), including cultivars from certain regions as Brazil (Creste et al, 2003(Creste et al, , 2004, Malawi (Changadeya et al, 2012), East Africa (Nzawele et al, 2018;Onyango et al, 2008;Perrier et al, 2019), Indonesia (Retnoningsih et al, 2009), the Philippines (dela Cruz et al, 2020) or Tanzania (Mgonja et al, 2020). Moreover, SSRs have been used to obtain a saturated genome map of M. acuminata (Hippolyte et al, 2010), to investigate genetic variability between M. acuminata mutants resistant to salinity (Miri et al, 2014) or to assess the identification of Musa accessions in reference DNA collections (Christelová et al, 2011).…”

Development of an early detection tool for banana diseases: A case of Mbeya and Arusha region

“…Studies in Tanzania indicate that the infestation of banana fungal diseases is still high (Ramadhani et al, 2017;Shimwela et al, 2016). In Tanzania production of banana is about 2.5 million tons of fruit for each year and ~290000 ha (Mgonja et al, 2020) is the total area under banana production. Also in Bukoba district, about 11 876 farms were affected by these diseases (Nkuba et al, 2015).…”