Dataset Information

Local ancestry inference provides insight into Tilapia breeding programmes.

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.

SUBMITTER: Avallone A

PROVIDER: S-EPMC7596482 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Local ancestry inference provides insight into Tilapia breeding programmes.

Avallone Alex A Bartie Kerry L KL Selly Sarah-Louise C SC Taslima Khanam K Campos Mendoza Antonio A Bekaert Michaël M

Scientific reports 20201029 1

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 Inte ...[more]

PMID: 33122794

Similar Datasets

Project description:MotivationLocal ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications.ResultsWe present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models' ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.Availability and implementationWe provide an open source implementation and links to publicly available data at github.com/AI-sandbox/SALAI-Net. Data is publicly available as follows: https://www.internationalgenome.org (1000 Genomes), https://www.simonsfoundation.org/simons-genome-diversity-project (Simons Genome Diversity Project), https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html (HapMap), ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516 (Human Genome Diversity Project) and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448733 (Canid genomes).Supplementary informationSupplementary data are available from Bioinformatics online.

Dataset Information

Local ancestry inference provides insight into Tilapia breeding programmes.

Publications

Local ancestry inference provides insight into Tilapia breeding programmes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets