High confidence copy number variants identified in Holstein dairy cattle from whole genome sequence and genotype array data

Abstract

Multiple methods to detect copy number variants (CNV) relying on different types of data have been developed and CNV have been shown to have an impact on phenotypes of numerous traits of economic importance in cattle, such as reproduction and immunity. Further improvements in CNV detection are still needed in regard to the trade-off between high-true and low-false positive variant identification rates. Instead of improving single CNV detection methods, variants can be identified in silico with high confidence when multiple methods and datasets are combined. Here, CNV were identified from whole-genome sequences (WGS) and genotype array (GEN) data on 96 Holstein animals. After CNV detection, two sets of high confidence CNV regions (CNVR) were created that contained variants found in both WGS and GEN data following an animal-based (n = 52) and a population-based (n = 36) pipeline. Furthermore, the change in false positive CNV identification rates using different GEN marker densities was evaluated. The population-based approach characterized CNVR, which were more often shared among animals (average 40% more samples per CNVR) and were more often linked to putative functions (48 vs 56% of CNVR) than CNV identified with the animal-based approach. Moreover, false positive identification rates up to 22% were estimated on GEN information. Further research using larger datasets should use a population-wide approach to identify high confidence CNVR. Dairy cattle genetics has made great advances since the effects of single nucleotide polymorphisms (SNP) have been recognized on a wide range of mono or polygenic traits economically important for the dairy industry 1-5. Genomic variation, however, is not only caused by SNP. Recent studies have shown that structural variants (SV) also have an important impact on phenotypes of a multitude of traits, such as milk production, reproduction, health, and feed efficiency 6-8. Types of SV include translocations, inversions and copy number variation 9. Copy number variants (CNV) form the most common class of SV in the human, plant and animal genome and can be identified as two types of event: copy number loss (CNL) or copy number gain (CNG). As the amount of DNA changes between samples with or without multiple copies of a segment, CNV are a type of the unbalanced structural variations 9. Although the number of bovine CNV described in the literature is lower than the number of SNP, the fact that they have multiple alleles makes them highly informative 10. The CNV can affect both monogenic traits, such as the coat color of cattle 11 , and polygenic traits such as feed efficiency, production traits, and reproduction traits of cattle 12,13. For instance, a study by Liu et al. 14 showed associations between CNV and production traits specifically in Holstein dairy cattle. Identifying CNV is challenging and no consensus on the best method of identification has been reached because multiple factors, starting with the source of information on which the CNV are identified, influence the results. Mo...

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

“…Association analyses between hoof health traits and the discovered CNV of 1,889 bulls led to the identification of 23 CNVR significantly associated with at least 1 of the evaluated traits (P < 0.0005; Supplemental Table S1, https: / / data .mendeley .com/ datasets/ xwmnrgn97f/ 1; Butty, 2021). Fourteen of these regions overlapped with CNVR previously identified with the WGS information of 80 Holstein bulls (Butty et al, 2020), corresponding to the most likely true-positive results. The 14 regions represented 9 deletions and 5 duplications, distributed on 13 chromosomes (Table 3, Figure 2), and had an average length of 104Kb (ranging from 9.8-343.3Kb).…”

“…To reinforce the control of false-positive results, only significantly (P < 0.0005) associated regions that had overlap with CNVR previously identified with WGS information of 80 Holstein bulls (Butty et al, 2020) were kept to functional annotation.…”

“…As the CNV and the resulting CNVR were based on genotype array information, the associated regions' breakpoints had to be at a SNP position. Use of the WGS information of 80 Holstein bulls (Butty et al, 2020) to filter the associated CNVR showed that the true breakpoint of the associated CNVR is probably a few bases next to the breakpoints given by the array information. Figure 3 depicts the read depth over CNVR7 on BTA16 in 3 sequenced samples; the 2 top samples represent a deletion that starts before and ends after the region as described with genotype array (red bar).…”

“…As a result, imputation methods for CNV are not well established (Butty et al, 2019). Most commonly used methods to detect CNV include comparative genome Genome-wide association study between copy number variants and hoof health traits in Holstein dairy cattle hybridization, obtaining CNV information from SNP arrays, and whole-genome sequence (WGS) approaches (Alkan et al, 2011;Yang et al, 2018;Butty et al, 2020). The latter might be more precise in detecting CNV boundaries than array methods (Alkan et al, 2011).…”

Investigating the Effect of Imputed Structural Variants from Whole-Genome Sequence on Genome-Wide Association and Genomic Prediction in Dairy Cattle

“…This result suggested that the sire–son validation is useful to remove false positive SVs as concluded in Chen et al [ 28 ]. A high false positive rate for CNVs has been reported [ 37 ]. In addition, low-MAF SVs are difficult to impute [ 18 ], while common SVs are imputed more accurately and require an SV to be observed in both sire and son, which increases the average MAF for the FAM sets.…”