Evaluation of developed low-density genotype panels for imputation to higher density in independent dairy and beef cattle populations

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.

Assessing single-nucleotide polymorphism selection methods for the development of a low-density panel optimized for imputation in South African Drakensberger beef cattle

A major obstacle in applying genomic selection (GS) to uniquely adapted local breeds in less-developed countries has been the cost of genotyping at high densities of single-nucleotide polymorphisms (SNP). Cost reduction can be achieved by imputing genotypes from lower to higher densities. Locally adapted breeds tend to be admixed and exhibit a high degree of genomic heterogeneity thus necessitating the optimization of SNP selection for downstream imputation. The aim of this study was to quantify the achievable imputation accuracy for a sample of 1,135 South African (SA) Drakensberger cattle using several custom-derived lower-density panels varying in both SNP density and how the SNP were selected. From a pool of 120,608 genotyped SNP, subsets of SNP were chosen (1) at random, (2) with even genomic dispersion, (3) by maximizing the mean minor allele frequency (MAF), (4) using a combined score of MAF and linkage disequilibrium (LD), (5) using a partitioning-around-medoids (PAM) algorithm, and finally (6) using a hierarchical LD-based clustering algorithm. Imputation accuracy to higher density improved as SNP density increased; animal-wise imputation accuracy defined as the within-animal correlation between the imputed and actual alleles ranged from 0.625 to 0.990 when 2,500 randomly selected SNP were chosen vs. a range of 0.918 to 0.999 when 50,000 randomly selected SNP were used. At a panel density of 10,000 SNP, the mean (standard deviation) animal-wise allele concordance rate was 0.976 (0.018) vs. 0.982 (0.014) when the worst (i.e., random) as opposed to the best (i.e., combination of MAF and LD) SNP selection strategy was employed. A difference of 0.071 units was observed between the mean correlation-based accuracy of imputed SNP categorized as low (0.01 < MAF ≤ 0.1) vs. high MAF (0.4 < MAF ≤ 0.5). Greater mean imputation accuracy was achieved for SNP located on autosomal extremes when these regions were populated with more SNP. The presented results suggested that genotype imputation can be a practical cost-saving strategy for indigenous breeds such as the SA Drakensberger. Based on the results, a genotyping panel consisting of ~10,000 SNP selected based on a combination of MAF and LD would suffice in achieving a <3% imputation error rate for a breed characterized by genomic admixture on the condition that these SNP are selected based on breed-specific selection criteria.

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

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 economized 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 subspecies. Fifteen successively smaller panels (from 500,000 to 50 SNP) were built from those SNP in the calibration data that increasingly satisfied 2 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 3 benchmarking approaches: (1) % error (expressed relative to the estimated GBC made from over 1 million SNP), (2) % breed misassignment (expressed relative to each individual's breed recorded), and (3) Shannon's entropy of estimated GBC across the 17 target breeds. Our analyses suggest that 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.

Concordance rate in cattle and sheep between genotypes differing in Illumina GenCall quality score

Proper quality control of data prior to downstream analyses is fundamental to ensure integrity of results; quality control of genomic data is no exception. While many metrics of quality control of genomic data exist, the objective of the present study was to quantify the genotype and allele concordance rate between called single nucleotide polymorphism (SNP) genotypes differing in GenCall (GC) score; the GC score is a confidence measure assigned to each Illumina genotype call. This objective was achieved using Illumina beadchip genotype data from 771 cattle (12 428 767 genotypes in total post-editing) and 80 sheep (1 557 360 SNPs genotypes in total post-editing) each genotyped in duplicate. The called genotype with the lowest associated GC score was compared to the genotype called for the same SNP in the same duplicated animal sample but with a GC score of >0.90 (assumed to represent the true genotype). The mean genotype concordance rate for a GC score of <0.300, 0.300-0.549, and ≥0.550 in the cattle (sheep in parenthesis) was 0.9467 (0.9864), 0.9707 (0.9953), and 0.9994 (0.99997) respectively; the respective allele concordance rate was 0.9730 (0.9930), 0.9849 (0.9976), and 0.9997 (0.99998). Hence, concordance eroded as the GC score of the called genotype reduced, albeit the impact was not dramatic and was not very noticeable until a GC score of <0.55. Moreover, the impact was greater and more consistent in the cattle population than in the sheep population. Furthermore, an impact of GC score on genotype concordance rate existed even for the same SNP GenTrain value; the GenTrain value is a statistical score that depicts the shape of the genotype clusters and the relative distance between the called genotype clusters.

Evaluation of the International Society for Animal Genetics bovine single nucleotide polymorphism parentage panel in South African Bonsmara and Drakensberger cattle

A panel of 200 single nucleotide polymorphisms (SNPs) have been recommended by the International Society for Animal Genetics (ISAG) for use in parentage verification of cattle. While the SNPs included on the ISAG panel are segregating in European Bos taurus and Bos indicus breeds, their applicability in South African (SA) Sanga cattle has never been evaluated. This study, therefore, assessed the usefulness of the ISAG panel in SA Bonsmara (BON) and Drakensberger (DRB) cattle. Genotypes of 185 ISAG SNPs from 64 BON and 97 DRB sire-offspring pairs were available, all of which were validated with 119,375 SNPs. Of the 185 ISAG SNPs, 14 and 18 in the BON and DRB, respectively (9 in common to both breeds), were either monomorphic, exhibited at least one discordance between validated sire-offspring pairs, or had poor call rate or clustering issue. The mean minor allele frequency of the 185 ISAG SNPs was 0.331 in the BON and 0.359 in the DRB. The combined probability of parentage exclusion (P_E) was the same (99.46%) for both breeds, while the probability of identity varied from 1.61 × 10^-48 (BON) to 1.11 × 10^-54 (DRB). Fifteen (23.4%) and 32 (33%) of the already validated sire-offspring pairs for the BON and DRB, respectively, were determined by the ISAG panel to be false-negatives based on a threshold of having at least two discordant SNPs. In comparison to sire discovery using the 119,375 SNPs, sire discovery using only the ISAG panel identified correctly 44 (out of 64 identified using the 119,375 SNPs) unique sire-offspring BON pairs and 62 (out of 97 identified using the 119,375 SNPs) unique sire-offspring DRB when all sires were masked. Five BON and three DRB offspring had > 1 sire nominated. This study demonstrated that the use of the ISAG panel may result in incorrect exclusions and multiple candidate sires for a given animal. Selection of more informative SNPs is, therefore, necessary in the pursuit of a low-cost and effective SNP panel for indigenous cattle breeds in SA.

Using the difference in actual and expected calf liveweight relative to its dam liveweight as a statistic for interherd and intraherd benchmarking and genetic evaluations1

The importance of improving the efficiency of beef production systems using both genetic and management strategies has long been discussed. Despite the contribution of the mature beef herd to the overall cost of production in the sector as a whole, most strategies for improving (feed) efficiency have focused on the growing animal. The objective of the present study was to quantify the phenotypic and genetic variability in several novel measures that relate the weight of a calf to that of its dam and vice versa. Two novel residual traits, representing the deviation in calf weight relative to its expectation from the population based on its dam's weight (DIFFcalf) or the deviation in the weight of the dam relative to its expectation from the population based on its calf's weight (DIFFdam), were calculated while simultaneously accounting for some nuisance factors in a multiple regression model. Four supplementary traits were also calculated, namely, 1) the deviation in calf weight from its expectation expressed relative to the weight of the dam (DIFFcalf_ratio), 2) the deviation in dam weight from its expectation relative to the weight of the dam (DIFFdam_ratio), 3) DIFFcalf-DIFFdam, and 4) the simple ratio of calf weight to its dam's weight (RATIOcalfdam). Genetic and residual variance components for each of the 6 traits were estimated using animal-dam linear mixed models. The phenotypic SD for DIFFcalf was 42 kg and, when expressed relative to the weight of the dam (i.e., DIFFcalf_ratio), was 0.07. The genetic SD for DIFFcalf and DIFFcalf_ratio was 16.66 kg and 0.02, respectively. The direct and maternal heritability estimated for DIFFcalf was 0.28 (SE = 0.04) and 0.11 (SE = 0.02), respectively, and for DIFFcalf_ratio was 0.24 (SE = 0.04) and 0.17 (SE = 0.03), respectively. The genetic SD for DIFFdam was 47.09 kg; the direct heritability was 0.50 (SE = 0.03), and the dam repeatability was 0.75 (SE = 0.01). The genetic SD for RATIOcalfdam was 0.03; the direct and maternal heritability was 0.24 (SE = 0.04) and 0.24 (SE = 0.03), respectively. The suggested traits outlined in the present study provide useful metrics for benchmarking dam-calf efficiency; in addition, the genetic variability detected in these traits suggest genetic progress for more efficient dam-calf pairs is indeed possible.

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.

Development of a low-density panel for genomic selection of pigs in Russia

Genomic selection is routinely used worldwide in agricultural breeding. However, in Russia, it is still not used to its full potential partially due to high genotyping costs. The use of genotypes imputed from the low-density chips (LD-chip) provides a valuable opportunity for reducing the genotyping costs. Pork production in Russia is based on the conventional 3-tier pyramid involving 3 breeds; therefore, the best option would be the development of a single LD-chip that could be used for all of them. Here, we for the first time have analyzed genomic variability in 3 breeds of Russian pigs, namely, Landrace, Duroc, and Large White and generated the LD-chip that can be used in pig breeding with the negligible loss in genotyping quality. We have demonstrated that out of the 3 methods commonly used for LD-chip construction, the block method shows the best results. The imputation quality depends strongly on the presence of close ancestors in the reference population. We have demonstrated that for the animals with both parents genotyped using high-density panels high-quality genotypes (allelic discordance rate < 0.05) could be obtained using a 300 single nucleotide polymorphism (SNP) chip, while in the absence of genotyped ancestors at least 2,000 SNP markers are required. We have shown that imputation quality varies between chromosomes, and it is lower near the chromosome ends and drops with the increase in minor allele frequency. Imputation quality of the individual SNPs correlated well across breeds. Using the same LD-chip, we were able to obtain comparable imputation quality in all 3 breeds, so it may be suggested that a single chip could be used for all of them. Our findings also suggest that the presence of markers with extremely low imputation quality is likely to be explained by wrong mapping of the markers to the chromosomal positions.

A breeding index to rank beef bulls for use on dairy females to maximize profit

The desire to increase profit on dairy farms necessitates consideration of the revenue attainable from the sale of surplus calves for meat production. However, the generation of calves that are expected to excel in efficiency of growth and carcass merit must not be achieved to the detriment of the dairy female and her ability to calve and re-establish pregnancy early postcalving without any compromise in milk production. Given the relatively high heritability of many traits associated with calving performance and carcass merit, and the tendency for many of these traits to be moderately to strongly antagonistic, a breeding index that encompasses both calving performance and meat production could be a useful tool to fill the void in supporting decisions on bull selection. The objective of the present study was to derive a dairy-beef index (DBI) framework to rank beef bulls for use on dairy females with the aim of striking a balance between the efficiency of valuable meat growth in the calf and the subsequent performance of the dam. Traits considered for inclusion in this DBI were (1) direct calving difficulty; (2) direct gestation length; (3) calf mortality; (4) feed intake; (5) carcass merit reflected by carcass weight, conformation, and fat and the ability to achieve minimum standards for each; (6) docility; and (7) whether the calf was polled. Each trait was weighted by its respective economic weight, most of which were derived from the analyses of available phenotypic data, supplemented with some assumptions on costs and prices. The genetic merit for a range of performance metrics of 3,835 artificial insemination beef bulls from 14 breeds ranked on this proposed DBI was compared with an index comprising only direct calving difficulty and gestation length (the 2 generally most important characteristics of dairy farmers when selecting beef bulls). Within the Angus breed (i.e., the beef breed most commonly used on dairy females), the correlation between the DBI and the index of genetic merit for direct calving difficulty plus gestation length was 0.74; the mean of the within-breed correlations across all other breeds was 0.87. The ranking of breeds changed considerably when ranked based on the top 20 artificial insemination bulls excelling in the DBI versus excelling in the index of calving difficulty and gestation length. Dairy breeds ranked highest on the index of calving difficulty and gestation length, whereas the Holstein and Friesian breeds were intermediate on the DBI; the Jersey breed was one of the poorest breeds on DBI, superior only to the Charolais breed. The results clearly demonstrate that superior carcass and growth performance can be achieved with the appropriate selection of beef bulls for use on dairy females with only a very modest increase in collateral effect on cow performance (i.e., 2-3% greater dystocia expected and a 6-d-longer gestation length).

High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is achievable in sheep1

The objective of the present study was to quantify the accuracy of imputing medium-density single nucleotide polymorphism (SNP) genotypes from lower-density panels (384 to 12,000 SNPs) derived using alternative selection methods to select the most informative SNPs. Four different selection methods were used to select SNPs based on genomic characteristics (i.e., minor allele frequency (MAF) and linkage disequilibrium (LD)) within five sheep breeds (642 Belclare, 645 Charollais, 715 Suffolk, 440 Texel, and 620 Vendeen) separately. Selection methods evaluated included (i) random, (ii) splitting the genome into blocks of equal length and selecting SNPs within block based on MAF and LD patterns, (iii) equidistant location while optimizing MAF, (iv) a combination of MAF, distance from already selected SNPs, and weak LD with the SNP(s) already selected. All animals were genotyped on the Illumina OvineSNP50 Beadchip containing 51,135 SNPs of which 44,040 remained after edits. Within each breed separately, the youngest 100 animals were assumed to represent the validation population; the remaining animals represented the reference population. Imputation was undertaken under three different conditions: (i) SNPs were selected within a given breed and imputed for all breeds individually, (ii) all breeds were collectively used to select SNPs and were included as the reference population, and (iii) the SNPs were selected for each breed separately and imputation was undertaken for all breeds but excluding from the reference population, the breed from which the SNPs were selected. Regardless of SNP selection method, mean animal allele concordance rate improved at a diminishing rate while the variability in mean animal allele concordance rate reduced as the panel density increased. The SNP selection method impacted the accuracy of imputation although the effect reduced as the density of the panel increased. Overall, the most accurate SNP selection method for panels with <9,000 SNPs was that based on MAF and LD pattern within genomic blocks. The mean animal allele concordance rate varied from 0.89 in Texel to 0.97 in Vendeen. Greater imputation accuracy was achieved when SNPs were selected and imputed within each breed individually compared with when SNPs were selected across all breeds and imputed using a multi-breed reference population. In all, results indicate that accurate genotype imputation to medium density is achievable with low-density genotype panels with at least 6,000 SNPs.

Genotype imputation in a tropical crossbred dairy cattle population

The objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr × Holstein) dairy cattle. The data set consisted of 478 Girolando, 583 Gyr, and 1,198 Holstein sires genotyped at high density with the Illumina BovineHD (Illumina, San Diego, CA) panel, which includes ∼777K markers. The accuracy of imputation from low (20K) and medium densities (50K and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios using different reference populations (RPop) considering Girolando, Gyr, and Holstein breeds separately or combinations of animals of these breeds were tested for imputing genotypes of 166 randomly chosen Girolando animals. The population genotype imputation were performed using FImpute. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper on imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low and medium densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The CORR_anim exceeded 0.96 (for 50K and 70K panels) when only Girolando animals were included in RPop (S1). We found smaller CORR_anim when Gyr (S2) was used instead of Holstein (S3) as RPop. The same behavior was observed between S4 (Gyr + Girolando) and S5 (Holstein + Girolando) because the target animals were more related to the Holstein population than to the Gyr population. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had a greater effect on accuracy than the purebred haplotypes. All chromosomes had similar imputation accuracies (CORR_snp) within each scenario. Crossbred animals (Girolando) must be included in the reference population to provide the best imputation accuracies.

The impact of multi-generational genotype imputation strategies on imputation accuracy and subsequent genomic predictions

The objective of the present study was to quantify, using simulations, the impact of successive generations of genotype imputation on genomic predictions. The impact of using a small reference population of true genotypes versus a larger reference population of imputed genotypes on the accuracy of genomic predictions was also investigated. After construction of a founder population, high-density (HD) genotypes ( = 43,500 single nucleotide polymorphisms, SNP) were simulated across 25 generations ( = 46,800 per generation); a low-density genotype panel ( = 3,000 SNP) was developed from these HD genotypes, which was then used to impute genotypes using 7 alternative imputation strategies. Both low (0.03) and moderately (0.35) heritable phenotypes were simulated. Direct genomic values (DGV) were estimated using imputed genotypes from the investigated scenarios and the accuracy of predicting the simulated true breeding values (TBV) were expressed relative to the accuracy when the true genotypes were used. Mean allele concordance rate and the rate of change in mean allele concordance per generation differed between the imputation strategies investigated. Imputation was most accurate when the true HD genotypes of sires and 50% of the dams of the generation being imputed were included in the reference population; the average allele concordance rate for this scenario across generations was 0.9707. The strongest correlation between the TBV and DGV of the last generation was when the reference population included sequentially imputed HD genotypes of all previous generations, plus the true HD genotypes of all sires of the previous generations (0.987 as efficient as when the true genotypes were used in the reference population). With a moderate heritability, the correlation between the TBV and the DGV using a small reference population of accurate genotypes were, on average, 0.07 units stronger compared to DGV generated using a larger population of imputed genotypes. When the heritability was low, the accuracy of genomic predictions benefited from a larger reference population, even if SNP were imputed. The impact on the accuracy of genomic predictions from the accumulation of imputation errors across generations indicates the need to routinely generate HD genotypes on influential animals to reduce the accumulation of imputation errors over generations.