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 cattle

Rafter, Pierce; Gormley, Isobel Claire; Parnell, Andrew; Kearney, John F.; Berry, D.P.

doi:10.1186/s12864-020-6627-8

Cited by 11 publications

(8 citation statements)

References 48 publications

(79 reference statements)

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“…Unlike some previous efforts at building low-density SNP panels (for recent examples in cattle see for instance (Judge et al, 2016;Alihoo et al, 2018;He et al, 2018;Rafter et al, 2020) we have drawn our SNP from a complete genomic resource rather than pre-filtered high density SNP chips from an existing commercial chip. This is an important distinction because some potentially very informative SNP will have been removed during high density SNP chip design thereby setting an upper limit on the accuracy of the final low-density panel.…”

Section: Resultsmentioning

confidence: 99%

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

Reverter

Hudson

McWilliam

et al. 2020

Journal of Animal Science

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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.

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Section: Resultsmentioning

confidence: 99%

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

Reverter

Hudson

McWilliam

et al. 2020

Journal of Animal Science

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“…Array-based approaches would suit scenarios in which the downstream CNV analyses include phenotype association studies, due to the high number of available samples (Spencer et al, 2009;Yang et al, 2018). Each CNV detection method handles the control of false discovery rates differently; therefore, common results between different identification methods and types of information may represent CNV with higher confidence (Zhan et al, 2011;Rafter et al, 2020). Another factor affecting the accurate identification of structural variants is the quality of the reference assembly used to map either WGS or SNP array information (Winchester et al, 2009;Baes et al, 2014;Pirooznia et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association study between copy number variants and hoof health traits in Holstein dairy cattle

Butty

Chud

Cardoso

et al. 2021

Journal of Dairy Science

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Genome-wide association studies based on SNP have been completed for multiple traits in dairy cattle; however, copy number variants (CNV) could add genomic information that has yet to be harnessed. The objectives of this study were to identify CNV in genotyped Holstein animals and assess their association with hoof health traits using deregressed estimated breeding values as pseudophenotypes. A total of 23,256 CNV comprising 1,645 genomic regions were identified in 5,845 animals. Fourteen genomic regions harboring structural variations, including 9 deletions and 5 duplications, were associated with at least 1 of the studied hoof health traits. This group of traits included digital dermatitis, interdigital dermatitis, heel horn erosion, sole ulcer, white line lesion, sole hemorrhage, and interdigital hyperplasia; no regions were associated with toe ulcer. Twenty candidate genes overlapped with the regions associated with these traits including SCART1, NRXN2, KIF26A, GPHN, and OR7A17. In this study, an effect on infectious hoof lesions could be attributed to the PRAME (Preferentially Expressed Antigen in Melanoma) gene. Almost all genes detected in association with noninfectious hoof lesions could be linked to known metabolic disorders. The knowledge obtained considering information of associated CNV to the traits of interest in this study could improve the accuracy of estimated breeding values. This may further increase the genetic gain for these traits in the Canadian Holstein population, thus reducing the involuntary animal losses due to lameness.

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“…Copy number variants are typically considered to have a minimum length between 50 bp [ 13 ] and 1 kb [ 12 ] depending on the method used to detect the CNVs. Previous imputation studies indicate that CNVs may not be in strong linkage disequilibrium with flanking SNP alleles [ 14 , 15 ]. Xu et al [ 16 ] performed association analyses between CNVs and milk traits in Holsteins; approximately 25 % of the associated CNVs they identified were not in linkage disequilibrium with flanking SNPs.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association analyses of carcass traits using copy number variants and raw intensity values of single nucleotide polymorphisms in cattle

et al. 2021

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Background 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.

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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 cattle

Cited by 11 publications

References 48 publications

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

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

Genome-wide association study between copy number variants and hoof health traits in Holstein dairy cattle

Genome-wide association analyses of carcass traits using copy number variants and raw intensity values of single nucleotide polymorphisms in cattle

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