Use of whole-genome sequence data and novel genomic selection strategies to improve selection for age at puberty in tropically-adapted beef heifers.

Publisher: BioMed Central Country of Publication: France NLM ID: 9114088 Publication Model: Electronic Cited Medium: Internet ISSN: 1297-9686 (Electronic) Linking ISSN: 0999193X NLM ISO Abbreviation: Genet Sel Evol Subsets: MEDLINE

Publication: London : BioMed Central
Original Publication: Paris : Elsevier, c1989-

Cattle/*genetics ; Genomics/*methods ; Sexual Maturation/*genetics; Animals ; Bayes Theorem ; Breeding ; Female ; Genome/genetics ; Genome-Wide Association Study ; Genotype ; Phenotype ; Polymorphism, Single Nucleotide/genetics ; Sexual Maturation/physiology ; Whole Genome Sequencing/methods

Background: In tropically-adapted beef heifers, application of genomic prediction for age at puberty has been limited due to low prediction accuracies. Our aim was to investigate novel methods of pre-selecting whole-genome sequence (WGS) variants and alternative analysis methodologies; including genomic best linear unbiased prediction (GBLUP) with multiple genomic relationship matrices (MGRM) and Bayesian (BayesR) analyses, to determine if prediction accuracy for age at puberty can be improved.
Methods: Genotypes and phenotypes were obtained from two research herds. In total, 868 Brahman and 960 Tropical Composite heifers were recorded in the first population and 3695 Brahman, Santa Gertrudis and Droughtmaster heifers were recorded in the second population. Genotypes were imputed to 23 million whole-genome sequence variants. Eight strategies were used to pre-select variants from genome-wide association study (GWAS) results using conditional or joint (COJO) analyses. Pre-selected variants were included in three models, GBLUP with a single genomic relationship matrix (SGRM), GBLUP MGRM and BayesR. Five-way cross-validation was used to test the effect of marker panel density (6 K, 50 K and 800 K), analysis model, and inclusion of pre-selected WGS variants on prediction accuracy.
Results: In all tested scenarios, prediction accuracies for age at puberty were highest in BayesR analyses. The addition of pre-selected WGS variants had little effect on the accuracy of prediction when BayesR was used. The inclusion of WGS variants that were pre-selected using a meta-analysis with COJO analyses by chromosome, fitted in a MGRM model, had the highest prediction accuracies in the GBLUP analyses, regardless of marker density. When the low-density (6 K) panel was used, the prediction accuracy of GBLUP was equal (0.42) to that with the high-density panel when only six additional sequence variants (identified using meta-analysis COJO by chromosome) were included.
Conclusions: While BayesR consistently outperforms other methods in terms of prediction accuracies, reasonable improvements in accuracy can be achieved when using GBLUP and low-density panels with the inclusion of a relatively small number of highly relevant WGS variants.

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LP160101626 ARC Linkage Grant

Date Created: 20200529 Date Completed: 20201006 Latest Revision: 20201006

20221213

PMC7251835

10.1186/s12711-020-00547-5

32460805