Abstract
In this study, we aimed to provide a wet laboratory validation for a set of single nucleotide polymorphisms (SNP), which had been identified as candidate functional variants in silico. Genotyping for candidate SNP was performed in Brahman and Tropical Composite cattle. After quality control, 29 SNP were first investigated individually for their association with female reproductive traits and then used as a panel for genomic predictions. The reproductive traits studied were age at first corpus luteum (AGECL; days), post-partum anoestrus interval (PPAI; days), and a binary trait that described if the cow had ovulated before weaning the first calf or not (PW, 0-1). Single nucleotide polymorphisms in six genes (FOXA2, TRAF4, IRF2, IRF1, BPTF, and CPEB1) were found to be significantly associated with reproduction traits . The genomic prediction method used was BayesR, to accommodate the 29 new SNP and compare their performance with predictions based on 50K genotypes (Illumina SNP chip). When new SNP and PLAG1 mutation rs109231213 were included in the genomic predictions for female reproductive traits their accuracies improved. The best predictions were obtained by combining the new SNP and the 50K SNP using BayesR analysis, with a 4% improvement in accuracy. The proportion of the genetic variance explained by the new SNP together was 0.07 for AGECL, 0.03 for PPAI, and 0.02 for PW. It would be favourable to include these new SNP in future versions of bovine SNP chips to target selection for female reproductive traits. These new SNP are likely to improve genomic predictions for female reproductive traits in tropical beef cattle breeds, with varying degrees of Bos indicus content.
Highlights
Single nucleotide polymorphisms (SNP) are used to predict genomic estimated breeding values (GEBV)
In this study, we aimed to provide a wet laboratory validation for a set of single nucleotide polymorphisms (SNP), which had been identified as candidate functional variants in silico
The lowest minor allele frequency (MAF) had a value of 0.43%, and only eight Brahman animals were heterozygous for this SNP
Summary
Single nucleotide polymorphisms (SNP) are used to predict genomic estimated breeding values (GEBV). Causative mutations could improve GEBV across breeds (Saatchi et al, 2014). Gene networks and pathways have been used to select functional mutations that yield more accurate GEBV than high-density SNP chip (Snelling et al, 2013). Selected SNP panels formed by functional mutations may improve the portability of GEBV across breeds and into crossbreds (Snelling et al, 2012; 2013). The use of causative mutations yielded an increase in accuracy of 2.5-3.7%, when using sequenced genomes (Meuwissen and Goddard, 2010). Single nucleotide polymorphisms panels composed of functional markers could aid across breed predictions and aid adoption in beef industries
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