Browsing Other Teagasc Research by Subject "CNV"
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Concordance rate between copy number variants detected using either high- or medium-density single nucleotide polymorphism genotype panels and the potential of imputing copy number variants from flanking high density single nucleotide polymorphism haplotypes in cattleBackground The trading of individual animal genotype information often involves only the exchange of the called genotypes and not necessarily the additional information required to effectively call structural variants. The main aim here was to determine if it is possible to impute copy number variants (CNVs) using the flanking single nucleotide polymorphism (SNP) haplotype structure in cattle. While this objective was achieved using high-density genotype panels (i.e., 713,162 SNPs), a secondary objective investigated the concordance of CNVs called with this high-density genotype panel compared to CNVs called from a medium-density panel (i.e., 45,677 SNPs in the present study). This is the first study to compare CNVs called from high-density and medium-density SNP genotypes from the same animals. High (and medium-density) genotypes were available on 991 Holstein-Friesian, 1015 Charolais, and 1394 Limousin bulls. The concordance between CNVs called from the medium-density and high-density genotypes were calculated separately for each animal. A subset of CNVs which were called from the high-density genotypes was selected for imputation. Imputation was carried out separately for each breed using a set of high-density SNPs flanking the midpoint of each CNV. A CNV was deemed to be imputed correctly when the called copy number matched the imputed copy number. Results For 97.0% of CNVs called from the high-density genotypes, the corresponding genomic position on the medium-density of the animal did not contain a called CNV. The average accuracy of imputation for CNV deletions was 0.281, with a standard deviation of 0.286. The average accuracy of imputation of the CNV normal state, i.e. the absence of a CNV, was 0.982 with a standard deviation of 0.022. Two CNV duplications were imputed in the Charolais, a single CNV duplication in the Limousins, and a single CNV duplication in the Holstein-Friesians; in all cases the CNV duplications were incorrectly imputed. Conclusion The vast majority of CNVs called from the high-density genotypes were not detected using the medium-density genotypes. Furthermore, CNVs cannot be accurately predicted from flanking SNP haplotypes, at least based on the imputation algorithms routinely used in cattle, and using the SNPs currently available on the high-density genotype panel.
Genome-wide association analyses of carcass traits using copy number variants and raw intensity values of single nucleotide polymorphisms in cattleBackground The carcass value of cattle is a function of carcass weight and quality. Given the economic importance of carcass merit to producers, it is routinely included in beef breeding objectives. A detailed understanding of the genetic variants that contribute to carcass merit is useful to maximize the efficiency of breeding for improved carcass merit. The objectives of the present study were two-fold: firstly, to perform genome-wide association analyses of carcass weight, carcass conformation, and carcass fat using copy number variant (CNV) data in a population of 923 Holstein-Friesian, 945 Charolais, and 974 Limousin bulls; and secondly to perform separate association analyses of carcass traits on the same population of cattle using the Log R ratio (LRR) values of 712,555 single nucleotide polymorphisms (SNPs). The LRR value of a SNP is a measure of the signal intensity of the SNP generated during the genotyping process. Results A total of 13,969, 3,954, and 2,805 detected CNVs were tested for association with the three carcass traits for the Holstein-Friesian, Charolais, and Limousin, respectively. The copy number of 16 CNVs and the LRR of 34 SNPs were associated with at least one of the three carcass traits in at least one of the three cattle breeds. With the exception of three SNPs, none of the quantitative trait loci detected in the CNV association analyses or the SNP LRR association analyses were also detected using traditional association analyses based on SNP allele counts. Many of the CNVs and SNPs associated with the carcass traits were located near genes related to the structure and function of the spliceosome and the ribosome; in particular, U6 which encodes a spliceosomal subunit and 5S rRNA which encodes a ribosomal subunit. Conclusions The present study demonstrates that CNV data and SNP LRR data can be used to detect genomic regions associated with carcass traits in cattle providing information on quantitative trait loci over and above those detected using just SNP allele counts, as is the approach typically employed in genome-wide association analyses.