Dataset Information

Genomic selection to enhance tilapia breeding

ABSTRACT: This project aims to develop and apply genomic tools to enhance selective breeding of Nile tilapia, with a focus on disease resistance.

INSTRUMENT(S): Illumina HiSeq X Ten

ORGANISM(S): Oreochromis Niloticus

SUBMITTER: The Roslin Institute, University of Edinburgh

PROVIDER: PRJEB38548 | EVA | 2020-06-01

REPOSITORIES: EVA

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Dataset's files

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			Action	DRS
	tilapia_snp_array.accessioned.vcf.gz	Vcf
	tilapia_snp_array.vcf.gz	Vcf
	tilapia_snp_array.vcf.gz.tbi	Vcf

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Project description:Key messageWe evaluated genomic selection across five breeding cycles of bread wheat breeding. Bias of within-cycle cross-validation and methods for improving the prediction accuracy were assessed. The prospect of genomic selection has been frequently shown by cross-validation studies using the same genetic material across multiple environments, but studies investigating genomic selection across multiple breeding cycles in applied bread wheat breeding are lacking. We estimated the prediction accuracy of grain yield, protein content and protein yield of 659 inbred lines across five independent breeding cycles and assessed the bias of within-cycle cross-validation. We investigated the influence of outliers on the prediction accuracy and predicted protein yield by its components traits. A high average heritability was estimated for protein content, followed by grain yield and protein yield. The bias of the prediction accuracy using populations from individual cycles using fivefold cross-validation was accordingly substantial for protein yield (17-712 %) and less pronounced for protein content (8-86 %). Cross-validation using the cycles as folds aimed to avoid this bias and reached a maximum prediction accuracy of [Formula: see text] = 0.51 for protein content, [Formula: see text] = 0.38 for grain yield and [Formula: see text] = 0.16 for protein yield. Dropping outlier cycles increased the prediction accuracy of grain yield to [Formula: see text] = 0.41 as estimated by cross-validation, while dropping outlier environments did not have a significant effect on the prediction accuracy. Independent validation suggests, on the other hand, that careful consideration is necessary before an outlier correction is undertaken, which removes lines from the training population. Predicting protein yield by multiplying genomic estimated breeding values of grain yield and protein content raised the prediction accuracy to [Formula: see text] = 0.19 for this derived trait.

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Genomic selection to enhance tilapia breeding

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