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

Abstract

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.

Identification of Candidate Variants Associated With Bone Weight Using Whole Genome Sequence in Beef Cattle

“…Therefore, many approaches were proposed to identify the truly candidate variant from hundreds of significant variants with the high levels of LD, including condition and joint analysis ( Yang et al, 2012 ) and Bayesian fine-mapping ( Jiang et al, 2019 ). These strategies have been applied to precisely identify the causal variants and capture the secondary association signals for economically important traits in cattle ( Fang et al, 2019 ; Freebern et al, 2020 ; Sanchez et al, 2020 ; Warburton et al, 2020 ).…”

Imputation strategies for near real-time genomic prediction using nanopore sequencing

“…Such methods include using Bayesian derived SNP effects to better estimate the distribution of effects, and incorporating more SNP into the prediction, rather than imputing to whole genome sequence and then sub sampling back to 641,000 SNPs. Both these methods have been shown to slightly increase prediction accuracies [40,41] and would add little computational time once the SNP effects are calculated.…”

Sustainable Intensification of Beef Production in the Tropics: The Role of Genetically Improving Sexual Precocity of Heifers

“…In Nellore cattle, accuracies ranging from 0.23 to 0.63 were reported for age first calving [ 83 , 84 ] and from 0.25 to 0.67 for heifer early pregnancy, respectively [ 84 , 85 ]. Evaluations of age at puberty in Brahman and Tropical Composite indicated prediction accuracies varying between 0.20 and 0.50 [ 8 , 17 , 86 ]. Differences in terms of reference population size, study design, population structure, among others, explain this high predictive ability variation among studies.…”

“…Recently, Warburton et al [ 86 ] showed that genomic predictions for age at puberty in tropically-adapted beef heifers could benefit from the use of WGS data. The general idea is to add preselected WGS variants using genome-wide association study to a regular SNP array [ 90 ].…”

“…The general idea is to add preselected WGS variants using genome-wide association study to a regular SNP array [ 90 ]. Reasonable improvements in accuracies were achieved by adding a relatively small number of highly relevant WGS variants to low-density panels when using Genomic Best Linear Unbiased Prediction (GBLUP) as the prediction model [ 86 ]. Although, in general, high-density panels may be sufficient for accurate genomic predictions, studies have indicated that adding preselected sequenced variants to these arrays may also increase predictions for several traits, including fertility [ 90 ].…”