Characterization of copy number variants in a large multibreed population of beef and dairy cattle using high-density single nucleotide polymorphism genotype data

Phenomics in Livestock Research: Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on a Global Scale

Bottlenecks in moving genomics to real-life applications also include phenomics. This is true not only for genomics medicine and public health genomics but also in ecology and livestock phenomics. This expert narrative review explores the intricate relationship between genetic makeup and observable phenotypic traits across various biological levels in the context of livestock research. We unpack and emphasize the significance of precise phenotypic data in selective breeding outcomes and examine the multifaceted applications of phenomics, ranging from improvement to assessing welfare, reproductive traits, and environmental adaptation in livestock. As phenotypic traits exhibit strong correlations, their measurement alongside specific biological outcomes provides insights into performance, overall health, and clinical endpoints like morbidity and disease. In addition, automated assessment of livestock holds potential for monitoring the dynamic phenotypic traits across various species, facilitating a deeper comprehension of how they adapt to their environment and attendant stressors. A key challenge in genetic improvement in livestock is predicting individuals with optimal fitness without direct measurement. Temporal predictions from unmanned aerial systems can surpass genomic predictions, offering in-depth data on livestock. In the near future, digital phenotyping and digital biomarkers may further unravel the genetic intricacies of stress tolerance, adaptation and welfare aspects of animals enabling the selection of climate-resilient and productive livestock. This expert review thus delves into challenges associated with phenotyping and discusses technological advancements shaping the future of biological research concerning livestock.

Animal board invited review: Practical applications of genomic information in livestock

Access to high-dimensional genomic information in many livestock species is accelerating. This has been greatly aided not only by continual reductions in genotyping costs but also an expansion in the services available that leverage genomic information to create a greater return-on-investment. Genomic information on individual animals has many uses including (1) parentage verification and discovery, (2) traceability, (3) karyotyping, (4) sex determination, (5) reporting and monitoring of mutations conferring major effects or congenital defects, (6) better estimating inbreeding of individuals and coancestry among individuals, (7) mating advice, (8) determining breed composition, (9) enabling precision management, and (10) genomic evaluations; genomic evaluations exploit genome-wide genotype information to improve the accuracy of predicting an animal's (and by extension its progeny's) genetic merit. Genomic data also provide a huge resource for research, albeit the outcome from this research, if successful, should eventually be realised through one of the ten applications already mentioned. The process for generating a genotype all the way from sample procurement to identifying erroneous genotypes is described, as are the steps that should be considered when developing a bespoke genotyping panel for practical application.

Segregation patterns and inheritance rate of copy number variations regions assessed in a Gochu Asturcelta pig pedigree

Copy Number Variation Regions (CNVR) were subjected to pedigree analysis to contribute to the understanding of their segregation patterns. Up to 492 Gochu Asturcelta pig individuals forming 478 different parents-offspring trios (61 different families) were genotyped using the Axiom_PigHDv1 Array (658,692 SNPs). CNVR calling, performed using two different platforms (PennCNV and QuantiSNP), allowed to identify a total of 344 candidate CNVR on the 18 porcine autosomes covering about 106.8 Mb of the pig genome. Sixty-nine CNVR were identified, to some extent, in both the parents and the offspring and were classified as segregating CNVR. The other candidate CNVR were called in one or more progeny but in neither parent and classified either as singleton or recurrent de novo CNVR. Segregating CNVR were, on average, larger and more frequent than the recurrent de novo CNVR (444.8 kb vs 287.9 kb long and 34 vs 5 individuals, respectively). In any case, segregating CNVR did not conform to strict Mendelian inheritance patterns: estimates of average paternal and maternal transmission rates ranged from 11.0 % to 13.4 % and mean inheritance rate was below 21 %. This issue should be carefully considered when interpreting the results of CNV studies. Segregating CNVR, present across generations, are unlikely to be artifacts or false positives and can be hypothesized to be important at the population level.

Identification and Characterization of Copy Number Variations Regions in West African Taurine Cattle

A total of 106 West African taurine cattle belonging to the Lagunaire breed of Benin (33), the N’Dama population of Burkina Faso (48), and N’Dama cattle sampled in Congo (25) were analyzed for Copy Number Variations (CNVs) using the BovineHDBeadChip of Illumina and two different CNV calling programs: PennCNV and QuantiSNP. Furthermore, 89 West African zebu samples (Bororo cattle of Mali and Zebu Peul sampled in Benin and Burkina Faso) were used as an outgroup to ensure that analyses reflect the taurine cattle genomic background. Analyses identified 307 taurine-specific CNV regions (CNVRs), covering about 56 Mb on all bovine autosomes. Gene annotation enrichment analysis identified a total of 840 candidate genes on 168 taurine-specific CNVRs. Three different statistically significant functional term annotation clusters (from ACt1 to ACt3) involved in the immune function were identified: ACt1 includes genes encoding lipocalins, proteins involved in the modulation of immune response and allergy; ACt2 includes genes encoding coding B-box-type zinc finger proteins and butyrophilins, involved in innate immune processes; and Act3 includes genes encoding lectin receptors, involved in the inflammatory responses to pathogens and B- and T-cell differentiation. The overlap between taurine-specific CNVRs and QTL regions associated with trypanotolerant response and tick-resistance was relatively low, suggesting that the mechanisms underlying such traits may not be determined by CNV alterations. However, four taurine-specific CNVRs overlapped with QTL regions associated with both traits on BTA23, therefore suggesting that CNV alterations in major histocompatibility complex (MHC) genes can partially explain the existence of genetic mechanisms shared between trypanotolerance and tick resistance in cattle. This research contributes to the understanding of the genomic features of West African taurine cattle.

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

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.

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

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.

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

Background

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.

Population Structure, and Selection Signatures Underlying High-Altitude Adaptation Inferred From Genome-Wide Copy Number Variations in Chinese Indigenous Cattle

Copy number variations (CNVs) have been demonstrated as crucial substrates for evolution, adaptation and breed formation. Chinese indigenous cattle breeds exhibit a broad geographical distribution and diverse environmental adaptability. Here, we analyzed the population structure and adaptation to high altitude of Chinese indigenous cattle based on genome-wide CNVs derived from the high-density BovineHD SNP array. We successfully detected the genome-wide CNVs of 318 individuals from 24 Chinese indigenous cattle breeds and 37 yaks as outgroups. A total of 5,818 autosomal CNV regions (683 bp-4,477,860 bp in size), covering ~14.34% of the bovine genome (UMD3.1), were identified, showing abundant CNV resources. Neighbor-joining clustering, principal component analysis (PCA), and population admixture analysis based on these CNVs support that most Chinese cattle breeds are hybrids of Bos taurus taurus (hereinafter to be referred as Bos taurus) and Bos taurus indicus (Bos indicus). The distribution patterns of the CNVs could to some extent be related to the geographical backgrounds of the habitat of the breeds, and admixture among cattle breeds from different districts. We analyzed the selective signatures of CNVs positively involved in high-altitude adaptation using pairwise Fst analysis within breeds with a strong Bos taurus background (taurine-type breeds) and within Bos taurus×Bos indicus hybrids, respectively. CNV-overlapping genes with strong selection signatures (at top 0.5% of Fst value), including LETM1 (Fst = 0.490), TXNRD2 (Fst = 0.440), and STUB1 (Fst = 0.420) within taurine-type breeds, and NOXA1 (Fst = 0.233), RUVBL1 (Fst = 0.222), and SLC4A3 (Fst=0.154) within hybrids, were potentially involved in the adaptation to hypoxia. Thus, we provide a new profile of population structure from the CNV aspects of Chinese indigenous cattle and new insights into high-altitude adaptation in cattle.