{'fraction_non_alphanumeric': 0.015904572564612324, 'fraction_numerical': 0.007952286282306162, 'mean_word_length': 5.11854103343465, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The Impact of Rare Single Nucleotide Polymorphism Variants on the Genomic Evaluation of Dairy Cattle

The experiments described in this research article were designed to test the effect of rare variants into genomic prediction in dairy cattle. Common polymorphisms are able to explain only a small proportion of the underlying genetic variation of complex phenotypes. Variants representing functional mutations with large effects on complex phenotypes are expected to be rare due to natural (humans) or artificial (livestock) selection pressure. Therefore, it is important to check whether the use of rare variants could increase the accuracy of ranking of animals by providing the tool for more precise differentiation among the bulls with high additive genetic merit. The goal of our study was to verify whether including rare variants in a genomic selection model allows for a more accurate description of the additive genetic background of traits under selection in dairy cattle. We used the linear mixed model for comparison SNP estimates for Holstein-Friesian cattle of the two data sets – a set containing only single nucleotide polymorphisms defined by minor allele frequency ≥ 0.01, which is routinely used in the Polish genomic evaluation system (46,216 SNPs), and a set containing SNPs selected based only on the call rate (54,378 SNPs). Based on the SNP estimates we also calculated DGV and GEBV and compared them between both data sets. In all the analyses we used production, fertility, conformation and udder health traits. We also assessed the time required for the two most computationally demanding components of genomic selection: preparing genotype data, and estimation of SNP effects between those two data sets. The results of our study indicated that the analysis including rare variants resulted in changes in the individual ranking of the top 100 male and female candidates, but had no effect on the outcome of the quality of EBV prediction as expressed by the Interbull validation test.