scholarly journals Local ancestry inference provides insight into Tilapia breeding programmes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alex Avallone ◽  
Kerry L. Bartie ◽  
Sarah-Louise C. Selly ◽  
Khanam Taslima ◽  
Antonio Campos Mendoza ◽  
...  

Abstract Tilapia is one of the most commercially valuable species in aquaculture with over 5 million tonnes of Nile tilapia, Oreochromis niloticus, produced worldwide every year. It has become increasingly important to keep track of the inheritance of the selected traits under continuous improvement (e.g. growth rate, size at maturity or genetic gender), as selective breeding has also resulted in genes that can hitchhike as part of the process. The goal of this study was to generate a Local Ancestry Interence workflow that harnessed existing tilapia genotyping-by-sequencing studies, such as Double Digest RAD-seq derived Single-Nucleotide Polymorphism markers. We developed a workflow and implemented a suite of tools to resolve the local ancestry of each chromosomal locus based on reference panels of tilapia species of known origin. We used tilapia species, wild populations and breeding programmes to validate our methods. The precision of the pipeline was evaluated on the basis of its ability to identify the genetic makeup of samples of known ancestry. The easy and inexpensive application of local ancestry inference in breeding programmes will facilitate the monitoring of the genetic profile of individuals of interest, the tracking of the movement of genes from parents to offspring and the detection of hybrids and their origin.

2019 ◽  
Vol 10 (2) ◽  
pp. 569-579
Author(s):  
Aurélien Cottin ◽  
Benjamin Penaud ◽  
Jean-Christophe Glaszmann ◽  
Nabila Yahiaoui ◽  
Mathieu Gautier

Hybridizations between species and subspecies represented major steps in the history of many crop species. Such events generally lead to genomes with mosaic patterns of chromosomal segments of various origins that may be assessed by local ancestry inference methods. However, these methods have mainly been developed in the context of human population genetics with implicit assumptions that may not always fit plant models. The purpose of this study was to evaluate the suitability of three state-of-the-art inference methods (SABER, ELAI and WINPOP) for local ancestry inference under scenarios that can be encountered in plant species. For this, we developed an R package to simulate genotyping data under such scenarios. The tested inference methods performed similarly well as far as representatives of source populations were available. As expected, the higher the level of differentiation between ancestral source populations and the lower the number of generations since admixture, the more accurate were the results. Interestingly, the accuracy of the methods was only marginally affected by i) the number of ancestries (up to six tested); ii) the sample design (i.e., unbalanced representation of source populations); and iii) the reproduction mode (e.g., selfing, vegetative propagation). If a source population was not represented in the data set, no bias was observed in inference accuracy for regions originating from represented sources and regions from the missing source were assigned differently depending on the methods. Overall, the selected ancestry inference methods may be used for crop plant analysis if all ancestral sources are known.


2013 ◽  
Vol 93 (2) ◽  
pp. 278-288 ◽  
Author(s):  
Brian K. Maples ◽  
Simon Gravel ◽  
Eimear E. Kenny ◽  
Carlos D. Bustamante

BMC Genetics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Daniel Hui ◽  
Zhou Fang ◽  
Jerome Lin ◽  
Qing Duan ◽  
Yun Li ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Heming Wang ◽  
Tamar Sofer ◽  
Xiang Zhang ◽  
Robert C. Elston ◽  
Susan Redline ◽  
...  

2019 ◽  
Author(s):  
Caitlin Uren ◽  
Eileen G. Hoal ◽  
Marlo Möller

AbstractGlobal and local ancestry inference in admixed human populations can be performed using computational tools implementing distinct algorithms, such as RFMix and ADMIXTURE. The accuracy of these tools has been tested largely on populations with relatively straightforward admixture histories but little is known about how well they perform in more complex admixture scenarios. Using simulations, we show that RFMix outperforms ADMIXTURE in determining global ancestry proportions in a complex 5-way admixed population. In addition, RFMix correctly assigns local ancestry with an accuracy of 89%. The increase in reported local ancestry inference accuracy in this population (as compared to previous studies) can largely be attributed to the recent availability of large-scale genotyping data for more representative reference populations. The ability of RFMix to determine global and local ancestry to a high degree of accuracy, allows for more reliable population structure analysis, scans for natural selection, admixture mapping and case-control association studies. This study highlights the utility of the extension of computational tools to become more relevant to genetically structured populations, as seen with RFMix. This is particularly noteworthy as modern-day societies are becoming increasingly genetically complex and some genetic tools are therefore less appropriate. We therefore suggest that RFMix be used for both global and local ancestry estimation in complex admixture scenarios.


2013 ◽  
Vol 93 (5) ◽  
pp. 891-899 ◽  
Author(s):  
Youna Hu ◽  
Cristen Willer ◽  
Xiaowei Zhan ◽  
Hyun Min Kang ◽  
Gonçalo R. Abecasis

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