Assessing the genetic diversity of farmed and wild Rufiji tilapia (Oreochromis urolepis urolepis) populations using ddRAD sequencing

Abstract

Rufiji tilapia (Oreochromis urolepis urolepis) is an endemic cichlid in Tanzania. In addition to its importance for biodiversity conservation, Rufiji tilapia is also attractive for farming due to its high growth rate, salinity tolerance, and the production of all‐male hybrids when crossed with Nile tilapia (Oreochromis niloticus). The aim of the current study was to assess the genetic diversity and population structure of both wild and farmed Rufiji tilapia populations in order to inform conservation and aquaculture practices. Double‐digest restriction‐site‐associated DNA (ddRAD) libraries were constructed from 195 animals originating from eight wild (Nyamisati, Utete, Mansi, Mindu, Wami, Ruaha, Kibasira, and Kilola) and two farmed (Bwawani and Chemchem) populations. The identified single nucleotide polymorphisms (SNPs; n = 2,182) were used to investigate the genetic variation within and among the studied populations. Genetic distance estimates (Fst) were low among populations from neighboring locations, with the exception of Utete and Chemchem populations (Fst = 0.34). Isolation‐by‐distance (IBD) analysis among the wild populations did not detect any significant correlation signal (r = .05; p‐value = .4) between the genetic distance and the sampling (Euclidean distance) locations. Population structure and putative ancestry were further investigated using both Bayesian (Structure) and multivariate approaches (discriminant analysis of principal components). Both analysis indicated the existence of three distinct genetic clusters. Two cross‐validation scenarios were conducted in order to test the efficiency of the SNP dataset for discriminating between farmed and wild animals or predicting the population of origin. Approximately 95% of the test dataset was correctly classified in the first scenario, while in the case of predicting for the population of origin 68% of the test dataset was correctly classified. Overall, our results provide novel insights regarding the population structure of Rufiji tilapia and a new database of informative SNP markers for both conservation management and aquaculture activities.

A genomic‐based vision on the genetic diversity and key performance traits in selectively bred Arctic charr (Salvelinus alpinus)

“…ddRAD‐seq is one of the most popular reduced representation sequencing platforms combining a relatively easy library construction workflow with cost‐efficiency (Peterson et al, 2012 ). Up to now, ddRAD‐seq has proven to be particularly useful in studying the genetic diversity (Hosoya et al, 2018 ; Nyinondi et al, 2020 ; Sherman et al, 2020 ; Torati et al, 2019 ) and mapping quantitative trait loci in various fish species (Barría et al, 2018 ; Brown et al, 2016 ; Gong et al, 2021 ; Jiang et al, 2019 ; Kyriakis et al, 2019 ; Palaiokostas et al, 2015 ; Taslima et al, 2020 ).…”

Genome-Wide SNP Discovery and Population Genetic Analysis of Mesocentrotus nudus in China Seas

“…Besides, compared with previous studies on the population genetics of aquatic animals by using genome-wide SNPs, a moderate level of heterozygosity was observed for M. nudus in the current study. For example, the average Ho in M. nudus was lower than that in Oncorhynchus keta ( Waples et al, 2017 ) and Paracentrotus lividus ( Carreras et al, 2020 ), but higher than in several species such as Oreochromis urolepis ( Nyinondi et al, 2020 ), O. lurida ( Silliman, 2019 ), and Isocladus armatus ( Wells and Dale, 2018 ). However, positive F is values have been observed in M. nudus .…”

Genotyping Strategies Using ddRAD Sequencing in Farmed Arctic Charr (Salvelinus alpinus)

“…It should be highlighted that in our study we tried to decipher the population structures and genetic clustering of full-sibling families in a closed breeding nucleus, a task that is more challenging and demanding than studying genetic diversity and differentiation among distant populations [ 44 ]. Even though differing MAF thresholds could potentially affect population structure inference [ 45 , 46 ], setting stricter MAF filtering thresholds for the PstI-NlaIII dataset in our study did not affect the clustering performance.…”