A low-density SNP genotyping panel for the accurate prediction of cattle breeds

Abstract

Genomic tools to better define breed composition in agriculturally important species have sparked scientific and commercial industry interest. Knowledge of breed composition can inform multiple scientifically important decisions of industry application including DNA marker assisted selection, identification of signatures of selection and inference of product provenance to improve supply chain integrity. Genomic tools are expensive but can be economised by deploying a relatively small number of highly informative single nucleotide polymorphisms (SNP) scattered evenly across the genome. Using resources from the 1000 Bull Genomes Project we established calibration (more stringent quality criteria; N = 1,243 cattle) and validation (less stringent; N = 864) data sets representing 17 breeds derived from both taurine and indicine bovine sub-species. Fifteen successively smaller panels (from 500,000 to 50 SNP) were built from those SNP in the calibration data that increasingly satisfied two criteria, high differential allele frequencies across the breeds as measured by average Euclidean distance (AED) and high uniformity (even spacing) across the physical genome. Those SNP awarded the highest AED were in or near genes previously identified as important signatures of selection in cattle such as LCORL, NCAPG, KITLG and PLAG1. For each panel, the genomic breed composition (GBC) of each animal in the validation dataset was estimated using a linear regression model. A systematic exploration of the predictive accuracy of the various sized panels was then undertaken on the validation population using three benchmarking approaches: i) % error (expressed relative to the estimated GBC made from over 1 million SNP); ii) % breed mis-assignment (expressed relative to each individual’s breed recorded); and iii) Shannon’s entropy of estimated GBC across the 17 target breeds. Our analyses suggest a panel of just 250 SNP represents an adequate balance between accuracy and cost - only modest gains in accuracy are made as one increases panel density beyond this point.

Genomic breed composition of Ningxiang pig via different SNP panels

“…These results demonstrates that GBC could assist the conservation for thoroughbred identification and more accurate than pedigree estimation (Frkonja et al, 2012; Huang et al, 2014; Wu et al, 2000) and assist in correcting problems such as missing, incorrect or incomplete Ningxiang pig pedigree (Vanraden & Cooper, 2015). At the same time, these results confirmed the practicability and efficiency of the 10K SNP panel constructed by MED method in the GBC application of Ningxiang pig, further explained the importance of low‐density SNPs selection strategy on estimating the GBC of individual animals (Reverter et al, 2020).…”

Defining Candidate Imprinted loci in Bos taurus

“…SNPs are important to genetic studies. Examples include accurate prediction of cattle breeds [62], association of body stature with frame size at puberty in cattle [63], and comprehensive characterization of loss of imprinting in LOS induced by assisted reproduction technologies [16]. On the browser, uploading the datasets reveals three custom tracks (Figure 1).…”

Ultra-small SNP panels to uniquely identify individuals in thousands of samples

“…It provided a genetically heterogeneous dataset, with multiple breeds and cross-bred animals. The full dataset contained 3817 animals, but after selecting only breeds with at least 20 animals, and individuals with genome sequence coverage of at least 5• and heterozygosity of at least 2.5%, a total of 2107 cattle from 17 of the original 172 breeds remained (Reverter et al 2020). After removal of one repeat record, 2106 animals remained in the dataset for analysis.…”

“…For each animal, a total of 40 525 757 SNP genotypes was available. To reduce the linkage disequilibrium, only every 10th SNP was included and only if they had <5% missing genotypes and a minor allele frequency of >5% (Reverter et al 2020). This greatly reduced the number of SNPs, resulting in a final dataset of 1 001 234 informative SNPs for the purpose of the study.…”