Prioritization of SNP markers for genomic prediction in closed beef cattle populations

TL;DR

This study shows that selecting the most informative DNA markers can improve genetic predictions in beef cattle, but the effectiveness depends on the population and traits.

Contribution

The study introduces population-specific SNP prioritization methods to enhance genomic prediction accuracy in beef cattle.

Findings

Prioritized SNP markers improved prediction accuracy for growth traits in Line 1 Hereford cattle.

Prediction accuracy was higher in inbred populations compared to composite breeds.

SNP prioritization effectiveness depends on population structure, traits, and models.

Abstract

With the advances in high-throughput technologies, genomic information is becoming readily available. This has led to whole genome sequences and denser single nucleotide polymorphism (SNP) panels being generated for more individuals. However, the increase in genomic information has shown little benefit in improving the prediction accuracy of genomic estimated breeding values (GEBV). One method to best utilize the increased amount of SNP information is to optimize the selection of informative SNP markers. In this study, genomic prediction of growth traits in two closed beef cattle populations using various prioritization techniques was evaluated. The first population used is Line 1 Hereford. The data consisted of 1192 animals with genotypes and phenotypes. The second population is a composite breed (50% Red Angus, 25% Charolais, 25% Tarentaise) and included of 2776 genotypes and phenotypes. The SNP prioritization methods adopted in this study were based on fixation index (Fst) and GWAS based SNP marker effects. Using a subset of prioritized SNP markers increased the accuracy for all three traits for the Line 1 Hereford population. On the other hand, using a weighted G matrix based on Fst and SNP effects did not increase the accuracy and in some instances decreased. Furthermore, the predication accuracy was higher in Line 1 Hereford which is an inbred population compared to the composite population. The study showed that prediction accuracy of GEBV can be improved with SNP prioritization, however it is population specific, trait specific and model specific. Moreover, this study highlights the importance of population structure in the prediction accuracy of GEBV.

This study explores how selecting the most informative DNA markers can boost the accuracy of predicting breeding traits in closed beef cattle populations, and why population structure plays a crucial role in genetic predictions.

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