Assets of imputation to ultra-high density for productive and functional traits

The aim of this study was to evaluate different-density genotyping panels for genotype imputation and genomic prediction. Genotypes from customized Golden Gate Bovine3K BeadChip [LD3K; low-density (LD) 3,000-marker (3K); Illumina Inc., San Diego, CA] and BovineLD BeadChip [LD6K; 6,000-marker (6K); Illumina Inc.] panels were imputed to the BovineSNP50v2 BeadChip [50K; 50,000-marker; Illumina Inc.]. In addition, LD3K, LD6K, and 50K genotypes were imputed to a BovineHD BeadChip [HD; high-density 800,000-marker (800K) panel], and with predictive ability evaluated and compared subsequently. Comparisons of prediction accuracy were carried out using Random boosting and genomic BLUP. Four traits under selection in the Spanish Holstein population were used: milk yield, fat percentage (FP), somatic cell count, and days open (DO). Training sets at 50K density for imputation and prediction included 1,632 genotypes. Testing sets for imputation from LD to 50K contained 834 genotypes and testing sets for genomic evaluation included 383 bulls. The reference population genotyped at HD included 192 bulls. Imputation using BEAGLE software (http://faculty.washington.edu/browning/beagle/beagle.html) was effective for reconstruction of dense 50K and HD genotypes, even when a small reference population was used, with 98.3% of SNP correctly imputed. Random boosting outperformed genomic BLUP in terms of prediction reliability, mean squared error, and selection effectiveness of top animals in the case of FP. For other traits, however, no clear differences existed between methods. No differences were found between imputed LD and 50K genotypes, whereas evaluation of genotypes imputed to HD was on average across data set, method, and trait, 4% more accurate than 50K prediction, and showed smaller (2%) mean squared error of predictions. Similar bias in regression coefficients was found across data sets but regressions were 0.32 units closer to unity for DO when genotypes were imputed to HD density. Imputation to HD genotypes might produce higher stability in the genomic proofs of young candidates. Regarding selection effectiveness of top animals, more (2%) top bulls were classified correctly with imputed LD6K genotypes than with LD3K. When the original 50K genotypes were used, correct classification of top bulls increased by 1%, and when those genotypes were imputed to HD, 3% more top bulls were detected. Selection effectiveness could be slightly enhanced for certain traits such as FP, somatic cell count, or DO when genotypes are imputed to HD. Genetic evaluation units may consider a trait-dependent strategy in terms of method and genotype density for use in the genome-enhanced evaluations.     

Selection and management of DNA markers for use in genomic evaluation

To facilitate routine genomic evaluation, a database was constructed to store genotypes for 50,972 single nucleotide polymorphisms (SNP) from the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). Multiple samples per animal are allowed. All SNP genotypes for a sample are stored in a single row. An indicator specifies whether the genotype for a sample was selected for use in genomic evaluation. Samples with low call rates or pedigree conflicts are designated as unusable. Among multiple samples that qualify for use in genomic evaluation, the one with the highest call rate is designated as usable. When multiple samples are stored for an animal, a composite is formed during extraction by using SNP genotypes from other samples to replace missing genotypes. To increase the number of SNP available, scanner output for approximately 19,000 samples was reprocessed. Any SNP with a minor allele frequency of ≥1% for Holsteins, Jerseys, or Brown Swiss was selected, which was the primary reason that the number of SNP used for USDA genomic evaluations increased. Few parent-progeny conflicts (≤1%) and a high call rate (≥90%) were additional requirements that eliminated 2,378 SNP. Because monomorphic SNP did not degrade convergence during estimation of SNP effects, a single set of 43,385 SNP was adopted for all breeds. The use of a database for genotypes, detection of conflicts as genotypes are stored, online access for problem resolution, and use of a single set of SNP for genomic evaluations have simplified tracking of genotypes and genomic evaluation as a routine and official process.     

Haplotypes responsible for early embryonic lethality detected in Nordic Holsteins

Widespread use of a limited number of elite sires in dairy cattle breeding increases the risk of some deleterious allelic variants spreading in the population. Genomic data are being used to detect relatively common (frequency >1%) haplotypes that never occur in the homozygous state in live animals. Such haplotypes likely include recessive lethal or semilethal alleles. The aim of this study was to detect such haplotypes in the Nordic Holstein population and to identify causal genetic factors underlying these haplotypes. Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA) genotypes for 26,312 Nordic Holstein animals were phased to construct haplotypes. Haplotypes that are common in the population but never observed as homozygous were identified. Two such haplotypes overlapped with previously identified recessive lethal mutations in Holsteins—namely, structural maintenance of chromosomes 2 (HH3) and brachyspina. In addition, we identified 9 novel putative recessive lethal-carrying haplotypes, with 26 to 36 homozygous individuals expected among the genotyped animals but only 0 to 3 homozygotes observed. For 2 out of 9 homozygous-deficient haplotypes, insemination records of at-risk mating (carrier bull with daughter of carrier sire) showed reduced insemination success compared with not-at-risk mating (noncarrier bull with daughter of noncarrier sire), supporting early embryonic mortality. To detect the causative variant underlying each homozygous-deficient haplotype, data from the 1000 Bull Genome Project were used. However, no variants or deletions identified in the chromosome regions covered by the haplotypes showed concordance with haplotype carrier status. The carrier status of detected haplotypes could be used to select bulls to reduce the frequency of the latent lethal mutations in the population. If desired, at-risk matings could be avoided.     

Association analysis for udder index and milking speed with imputed whole-genome sequence variants in Nordic Holstein cattle

Genome-wide association testing facilitates the identification of genetic variants associated with complex traits. Mapping genes that promote genetic resistance to mastitis could reduce the cost of antibiotic use and enhance animal welfare and milk production by improving outcomes of breeding for udder health. Using imputed whole-genome sequence variants, we carried out association studies for 2 traits related to udder health, udder index, and milking speed in Nordic Holstein cattle. A total of 4,921 bulls genotyped with the BovineSNP50 BeadChip array were imputed to high-density genotypes (Illumina BovineHD BeadChip, Illumina, San Diego, CA) and, subsequently, to whole-genome sequence variants. An association analysis was carried out using a linear mixed model. Phenotypes used in the association analyses were deregressed breeding values. Multitrait meta-analysis was carried out for these 2 traits. We identified 10 and 8 chromosomes harboring markers that were significantly associated with udder index and milking speed, respectively. Strongest association signals were observed on chromosome 20 for udder index and chromosome 19 for milking speed. Multitrait meta-analysis identified 13 chromosomes harboring associated markers for the combination of udder index and milking speed. The associated region on chromosome 20 overlapped with earlier reported quantitative trait loci for similar traits in other cattle populations. Moreover, this region was located close to the FYB gene, which is involved in platelet activation and controls IL-2 expression; FYB is a strong candidate gene for udder health and worthy of further investigation.     

Harmful recessive effects on fertility detected by absence of homozygous haplotypes

Five new recessive defects were discovered in Holsteins, Jerseys, and Brown Swiss by examining haplotypes that had a high population frequency but were never homozygous. The method required genotypes only from apparently normal individuals and not from affected embryos. Genotypes from the BovineSNP50 BeadChip (Illumina, San Diego, CA) were examined for 58,453 Holsteins, 5,288 Jerseys, and 1,991 Brown Swiss with genotypes in the North American database. Haplotypes with a length of ≤75 markers were obtained. Eleven candidate haplotypes were identified, with the earliest carrier born before 1980; 7 to 90 homozygous haplotypes were expected, but none were observed in the genomic data. Expected numbers were calculated using either the actual mating pattern or assuming random mating. Probability of observing no homozygotes ranged from 0.0002 for 7 to 10⁻⁴⁵ for 90 expected homozygotes. Phenotypic effects were confirmed for 5 of the 11 candidate haplotypes using 14,911,387 Holstein, 830,391 Jersey, and 68,443 Brown Swiss records for conception rate. Estimated effect for interaction of carrier service sire with carrier maternal grandsire ranged from −3.0 to −3.7 percentage points, which was slightly smaller than the −3.9 to −4.6 percentage points expected for lethal recessives but slightly larger than estimated effects for previously known lethal alleles of −2.5 percentage points for brachyspina and −2.9 percentage points for complex vertebral malformation. Conception rate was coded as a success only if the gestation went to term or the cow was confirmed to be pregnant. Estimated effect of carrier interaction for stillbirth rate based on 10,876,597 Holstein and 25,456 Jersey records was small. Thus, lethal effects may include conception, gestation, and stillbirth losses. Carrier frequency has been >20% for many years for the confirmed defect in Jerseys and is currently 16% for the defect in Brown Swiss. The 3 defects discovered in Holsteins have carrier frequencies of 2.7 to 6.4% in the current population. For previously known defects, map locations and lack of homozygotes were consistent with the literature and lethal recessive inheritance, but numbers of expected homozygotes for some were small because of low frequency. Very large genotypic and phenotypic data sets allow efficient detection of smaller and less frequent effects. Haplotype tests can help breeders avoid carrier matings for such defects and reduce future frequencies.     

Invited Review: Reliability of genomic predictions for North American Holstein bulls

Genetic progress will increase when breeders examine genotypes in addition to pedigrees and phenotypes. Genotypes for 38,416 markers and August 2003 genetic evaluations for 3,576 Holstein bulls born before 1999 were used to predict January 2008 daughter deviations for 1,759 bulls born from 1999 through 2002. Genotypes were generated using the Illumina BovineSNP50 BeadChip and DNA from semen contributed by US and Canadian artificial-insemination organizations to the Cooperative Dairy DNA Repository. Genomic predictions for 5 yield traits, 5 fitness traits, 16 conformation traits, and net merit were computed using a linear model with an assumed normal distribution for marker effects and also using a nonlinear model with a heavier tailed prior distribution to account for major genes. The official parent average from 2003 and a 2003 parent average computed from only the subset of genotyped ancestors were combined with genomic predictions using a selection index. Combined predictions were more accurate than official parent averages for all 27 traits. The coefficients of determination (R²) were 0.05 to 0.38 greater with nonlinear genomic predictions included compared with those from parent average alone. Linear genomic predictions had R² values similar to those from nonlinear predictions but averaged just 0.01 lower. The greatest benefits of genomic prediction were for fat percentage because of a known gene with a large effect. The R² values were converted to realized reliabilities by dividing by mean reliability of 2008 daughter deviations and then adding the difference between published and observed reliabilities of 2003 parent averages. When averaged across all traits, combined genomic predictions had realized reliabilities that were 23% greater than reliabilities of parent averages (50 vs. 27%), and gains in information were equivalent to 11 additional daughter records. Reliability increased more by doubling the number of bulls genotyped than the number of markers genotyped. Genomic prediction improves reliability by tracing the inheritance of genes even with small effects.     

Reliability of genomic prediction for German Holsteins using imputed genotypes from low-density chips

With the availability of single nucleotide polymorphism (SNP) marker chips, such as the Illumina BovineSNP50 BeadChip (50K), genomic evaluation has been routinely implemented in dairy cattle breeding. However, for an average dairy producer, total costs associated with the 50K chip are still too high to have all the cows genotyped and genomically evaluated. To study the accuracy of cheaper low-density chips, genotypes were simulated for 2 low-density chips, the Illumina Bovine3K BeadChip (3K) and BovineLD BeadChip (6K), according to their original marker maps. Simulated missing genotypes of the 50K chip were imputed using the programs Beagle and Findhap. Three genotype data sets were used to study imputation accuracy: the EuroGenomics data set, with 14,405 reference bulls (data set I); the smaller EuroGenomics data set, with 11,670 older reference bulls (data set II); and the data set of all genotyped German Holsteins, with 31,597 reference animals (data set III). Imputed genotypes were compared with their original ones to calculate allele error rate for validation animals in the 3 data sets. To evaluate the loss in accuracy of genomic prediction when using imputed genotypes, a genomic evaluation was conducted only for EuroGenomics data set II. Furthermore, combined genome-enhanced breeding values calculated from the original and imputed genotypes were compared. Allele error rate for EuroGenomics data set II was highest for the Findhap program on the 3K chip (3.3%) and lowest for the Beagle program on the 6K chip (0.6%). Across the data sets, Beagle was shown to be about 2 times as accurate as Findhap. Compared with the real 50K genotypes, the reduction in reliability of the genomic prediction when using the imputed genotypes was highest for Findhap on the 3K chip (5.3%) and lowest for Beagle on the 6K chip (1%) when averaged over the 12 evaluated traits. Differences in genome-enhanced breeding values of the original and imputed genotypes were largest for Findhap on the 3K chip, whereas Beagle on the 6K chip had the smallest difference. The low-density chip, 6K, gave markedly higher imputation accuracy and more accurate genomic prediction than the 3K chip. On the basis of the relatively small reduction in accuracy of genomic prediction, we would recommend the BovineLD 6K chip for large-scale genotyping as long as its costs are acceptable to breeders.     

Use of the Illumina Bovine3K BeadChip in dairy genomic evaluation

Genomic evaluations using genotypes from the Illumina Bovine3K BeadChip (3K) became available in September 2010 and were made official in December 2010. The majority of 3K-genotyped animals have been Holstein females. Approximately 5% of male 3K genotypes and between 3.7 and 13.9%, depending on registry status, of female genotypes had sire conflicts. The chemistry used for the 3K is different from that of the Illumina BovineSNP50 BeadChip (50K) and causes greater variability in the accuracy of the genotypes. Approximately 2% of genotypes were rejected due to this inaccuracy. A single nucleotide polymorphism (SNP) was determined to be not usable for genomic evaluation based on percentage missing, percentage of parent-progeny conflicts, and Hardy-Weinberg equilibrium discrepancies. Those edits left 2,683 of the 2,900 3K SNP for use in genomic evaluations. The mean minor allele frequencies (MAF) for Holstein, Jersey, and Brown Swiss were 0.32, 0.28, and 0.29, respectively. Eighty-one SNP had both a large number of missing genotypes and a large number of parent-progeny conflicts, suggesting a correlation between call rate and accuracy. To calculate a genomic predicted transmitting ability (GPTA) the genotype of an animal tested on a 3K is imputed to the 45,187 SNP included in the current genomic evaluation based on the 50K. The accuracy of imputation increases as the number of genotyped parents increases from none to 1 to both. The average percentage of imputed genotypes that matched the corresponding actual 50K genotypes was 96.3%. The correlation of a GPTA calculated from a 3K genotype that had been imputed to 50K and GPTA from its actual 50K genotype averaged 0.959 across traits for Holsteins and was slightly higher for Jerseys at 0.963. The average difference in GPTA from the 50K- and 3K-based genotypes across trait was close to 0. The evaluation system has been modified to accommodate the characteristics of the 3K. The low cost of the 3K has greatly increased genotyping of females. Prior to the availability of the 3K (August 2010), female genotyping accounted for 38.7% of the genotyped animals. In the past year, the portion of total genotypes from females across all chip types rose to 59.0%.     

Imputation of genotypes from different single nucleotide polymorphism panels in dairy cattle

Imputation of missing genotypes is important to join data from animals genotyped on different single nucleotide polymorphism (SNP) panels. Because of the evolution of available technologies, economical reasons, or coexistence of several products from competing organizations, animals might be genotyped for different SNP chips. Combined analysis of all the data increases accuracy of genomic selection or fine-mapping precision. In the present study, real data from 4,738 Dutch Holstein animals genotyped with custom-made 60K Illumina panels (Illumina, San Diego, CA) were used to mimic imputation of genotypes between 2 SNP panels of approximately 27,500 markers each and with 9,265 SNP markers in common. Imputation efficiency increased with number of reference animals (genotyped for both chips), when animals genotyped on a single chip were included in the training data, with regional higher marker densities, with greater distance to chromosome ends, and with a closer relationship between imputed and reference animals. With 0 to 2,000 animals genotyped for both chips, the mean imputation error rate ranged from 2.774 to 0.415% and accuracy ranged from 0.81 to 0.96. Then, imputation was applied in the Dutch Holstein population to predict alleles from markers of the Illumina Bovine SNP50 chip with markers from a custom-made 60K Illumina panel. A cross-validation study performed on 102 bulls indicated that the mean error rate per bull was approximately equal to 1.0%. This study showed the feasibility to impute markers in dairy cattle with the current marker panels and with error rates below 1%.     

Reliability of genomic evaluations in Holstein-Friesians using haplotypes based on the BovineHD BeadChip

The objectives of this study were to make subsets of high-density (HD) loci based on localized haplotype clusters, without loss of genomic information, to reduce computing time compared with the use of all HD loci and to investigate the effect on the reliability of the direct genomic value (DGV) when using this HD subset based on localized haplotype clusters in the genomic evaluation for Holstein-Friesians. The DNA was isolated from semen samples of 548 bulls (key ancestors) of the EuroGenomics Consortium, a collaboration between 4 European dairy cattle breeding organizations and scientific partners. These bulls were genotyped with the BovineHD BeadChip [~777,000 (777K) single nucleotide polymorphisms (SNP); Illumina Inc., San Diego, CA] and used to impute all 30,483 Holstein-Friesians from the BovineSNP50 BeadChip [~50,000 (50K) SNP; Illumina Inc.] to HD, using the BEAGLE software package. The final data set consisted of 30,483 animals and 603,145 SNP. For each locus, localized haplotype clusters (i.e., edges of the fitted graph model) identifications were obtained from BEAGLE. Three subsets [38,000 (38K), 116,000 (116K), and 322,000 (322K) loci] were made based on deleting obsolete loci (i.e., loci that do not give extra information compared with the neighboring loci). A fourth data set was based on 38K SNP, which is currently used for routine genomic evaluation at the Cattle Improvement Cooperative (CRV, Arnhem, the Netherlands). A validation study using the HD loci subsets based on localized haplotype clusters was performed for 9 traits (production, conformation, and functional traits). Error of imputation from 50K to HD averaged 0.78%. Three thresholds (0.17, 0.05, and 0.008%) were used for the identification of obsolete HD loci based on localized haplotype clusters to obtain a desired number of HD loci (38K, 116K, and 322K). On average, 46% (using threshold 0.008%) to 93% (using threshold 0.17%) of HD loci were eliminated. The computing time was about 9 d for 38K loci, 15.5 d for 116K loci, 21 d for 322K loci, and 7.5 d for 38K SNP. The increase in reliability of DGV compared with pedigree-based estimated breeding values for kilograms of protein was similar for 322K and 116K loci (30.7%), but was 1.5 to 2% higher compared with 38K loci and 38K SNP. Averaged over 9 traits, subset 116K loci resulted in a higher increase in reliability compared with 38K loci and 38K SNP. Eliminating obsolete loci enormously decreased the amount of data to be analyzed for genomic evaluations. The more HD loci used in a genomic evaluation, the higher the increase in reliability of DGV. It is possible to increase the reliability of DGV by 1 to 2% compared with the SNP currently used for routine genomic evaluation.     

Genomic prediction for Nordic Red Cattle using one-step and selection index blending

This study investigated the accuracy of direct genomic breeding values (DGV) using a genomic BLUP model, genomic enhanced breeding values (GEBV) using a one-step blending approach, and GEBV using a selection index blending approach for 15 traits of Nordic Red Cattle. The data comprised 6,631 bulls of which 4,408 bulls were genotyped using Illumina Bovine SNP50 BeadChip (Illumina, San Diego, CA). To validate reliability of genomic predictions, about 20% of the youngest genotyped bulls were taken as test data set. Deregressed proofs (DRP) were used as response variables for genomic predictions. Reliabilities of genomic predictions in the validation analyses were measured as squared correlations between DRP and genomic predictions corrected for reliability of DRP, based on the bulls in the test data sets. A set of weighting (scaling) factors was used to construct the combined relationship matrix among genotyped and nongenotyped bulls for one-step blending, and to scale DGV and its expected reliability in the selection index blending. Weighting (scaling) factors had a small influence on reliabilities of GEBV, but a large influence on the variation of GEBV. Based on the validation analyses, averaged over the 15 traits, the reliability of DGV for bulls without daughter records was 11.0 percentage points higher than the reliability of conventional pedigree index. Further gain of 0.9 percentage points was achieved by combining information from conventional pedigree index using the selection index blending, and gain of 1.3 percentage points was achieved by combining information of genotyped and nongenotyped bulls simultaneously applying the one-step blending. These results indicate that genomic selection can greatly improve the accuracy of preselection for young bulls in Nordic Red population, and the one-step blending approach is a good alternative to predict GEBV in practical genetic evaluation program.     

Application of a posteriori granddaughter and modified granddaughter designs to determine Holstein haplotype effects

A posteriori and modified granddaughter designs were applied to determine haplotype effects for Holstein bulls and cows with BovineSNP50 [~50,000 single nucleotide polymorphisms (SNP); Illumina Inc., San Diego, CA] genotypes. The a posteriori granddaughter design was applied to 52 sire families, each with ≥100 genotyped sons with genetic evaluations based on progeny tests. For 33 traits (milk, fat, and protein yields; fat and protein percentages; somatic cell score; productive life; daughter pregnancy rate; heifer and cow conception rates; service-sire and daughter calving ease; service-sire and daughter stillbirth; 18 conformation traits; and net merit), the analysis was applied to the autosomal segment with the SNP with the greatest effect in the genomic evaluation of each trait. All traits except 2 had a within-family haplotype effect. The same design was applied with the genetic evaluations of sons corrected for SNP effects associated with chromosomes besides the one under analysis. The number of within-family contrasts was 166 without adjustment and 211 with adjustment. Of the 52 bulls analyzed, 36 had BovineHD (high density; Illumina Inc.) genotypes that were used to test for concordance between sire quantitative trait loci and SNP genotypes; complete concordance was not obtained for any effects. Of the 31 traits with effects from the a posteriori granddaughter design, 21 were analyzed with the modified granddaughter design. Only sires with a contrast for the a posteriori granddaughter design and ≥200 granddaughters with a record usable for genetic evaluation were included. Calving traits could not be analyzed because individual cow evaluations were not computed. Eight traits had within-family haplotype effects. With respect to milk and fat yields and fat percentage, the results on Bos taurus autosome (BTA) 14 corresponded to the hypothesis that a missense mutation in the diacylglycerol O-acyltransferase 1 (DGAT1) gene is the main causative mutation, although other polymorphisms in that gene also modify fat yield and percentage. The positive allele for protein concentration was less frequent, which indicated that selection on that locus could be effective. Although the results can be used to determine causative polymorphisms for most of the analyzed traits, complete DNA sequencing of most of the analyzed sires probably will be required.     

Genomic prediction of milk-production traits and somatic cell score using single-step genomic best linear unbiased predictor with random regression test-day model in Thai dairy cattle

Cow genotypes are expected to improve the accuracy of genomic estimated breeding values (GEBV) for young bulls in relatively small populations such as Thai Holstein-Friesian crossbred dairy cattle in Thailand. The objective of this study was to investigate the effect of cow genotypes on the predictive ability and individual accuracies of GEBV for young dairy bulls in Thailand. Test-day data included milk yield (n = 170,666), milk component traits (fat yield, protein yield, total solids yield, fat percentage, protein percentage, and total solids percentage; n = 160,526), and somatic cell score (n = 82,378) from 23,201, 82,378, and 13,737 (for milk yield, milk component traits, and SCS, respectively) cows calving between 1993 and 2017, respectively. Pedigree information included 51,128; 48,834; and 32,743 animals for milk yield, milk component traits, and somatic cell score, respectively. Additionally, 876, 868, and 632 pedigreed animals (for milk yield, milk component traits, and SCS, respectively) were genotyped (152 bulls and 724 cows), respectively, using Illumina Bovine SNP50 BeadChip. We cut off the data in the last 6 yr, and the validation animals were defined as genotyped bulls with no daughters in the truncated set. We calculated GEBV using a single-step random regression test-day model (SS-RR-TDM), in comparison with estimated breed value (EBV) based on the pedigree-based model used as the official method in Thailand (RR-TDM). Individual accuracies of GEBV were obtained by inverting the coefficient matrix of the mixed model equations, whereas validation accuracies were measured by the Pearson correlation between deregressed EBV from the full data set and (G)EBV predicted with the reduced data set. When only bull genotypes were used, on average, SS-RR-TDM increased individual accuracies by 0.22 and validation accuracies by 0.07, compared with RR-TDM. With cow genotypes, the additional increase was 0.02 for individual accuracies and 0.06 for validation accuracies. The inflation of GEBV tended to be reduced using cow genotypes. Genomic evaluation by SS-RR-TDM is feasible to select young bulls for the longitudinal traits in Thai dairy cattle, and the accuracy of selection is expected to be increased with more genotypes. Genomic selection using the SS-RR-TDM should be implemented in the routine genetic evaluation of the Thai dairy cattle population. The genetic evaluation should consider including genotypes of both sires and cows.     

Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe,North America,and Australasia using 2 reference populations

Combining data from research herds may be advantageous, especially for difficult or expensive-to-measure traits (such as dry matter intake). Cows in research herds are often genotyped using low-density single nucleotide polymorphism (SNP) panels. However, the precision of quantitative trait loci detection in genome-wide association studies and the accuracy of genomic selection may increase when the low-density genotypes are imputed to higher density. Genotype data were available from 10 research herds: 5 from Europe [Denmark, Germany, Ireland, the Netherlands, and the United Kingdom (UK)], 2 from Australasia (Australia and New Zealand), and 3 from North America (Canada and the United States). Heifers from the Australian and New Zealand research herds were already genotyped at high density (approximately 700,000 SNP). The remaining genotypes were imputed from around 50,000 SNP to 700,000 using 2 reference populations. Although it was not possible to use a combined reference population, which would probably result in the highest accuracies of imputation, differences arising from using 2 high-density reference populations on imputing 50,000-marker genotypes of 583 animals (from the UK) were quantified. The European genotypes (n = 4,097) were imputed as 1 data set, using a reference population of 3,150 that included genotypes from 835 Australian and 1,053 New Zealand females, with the remainder being males. Imputation was undertaken using population-wide linkage disequilibrium with no family information exploited. The UK animals were also included in the North American data set (n = 1,579) that was imputed to high density using a reference population of 2,018 bulls. After editing, 591,213 genotypes on 5,999 animals from 10 research herds remained. The correlation between imputed allele frequencies of the 2 imputed data sets was high (>0.98) and even stronger (>0.99) for the UK animals that were part of each imputation data set. For the UK genotypes, 2.2% were imputed differently in the 2 high-density reference data sets used. Only 0.025% of these were homozygous switches. The number of discordant SNP was lower for animals that had sires that were genotyped. Discordant imputed SNP genotypes were most common when a large difference existed in allele frequency between the 2 imputed genotype data sets. For SNP that had ≥20% discordant genotypes, the difference between imputed data sets of allele frequencies of the UK (imputed) genotypes was 0.07, whereas the difference in allele frequencies of the (reference) high-density genotypes was 0.30. In fact, regions existed across the genome where the frequency of discordant SNP was higher. For example, on chromosome 10 (centered on 520,948 bp), 52 SNP (out of a total of 103 SNP) had ≥20% discordant SNP. Four hundred and eight SNP had more than 20% discordant genotypes and were removed from the final set of imputed genotypes. We concluded that both discordance of imputed SNP genotypes and differences in allele frequencies, after imputation using different reference data sets, may be used to identify and remove poorly imputed SNP.     

Genome-wide association study of conformation and milk yield in mixed-breed dairy goats

Identification of genetic markers that affect economically important traits is of high value from a biological point of view, enabling the targeting of candidate genes and providing practical benefits for the industry such as wide-scale genomic selection. This study is one of the first to investigate the genetic background of economically important traits in dairy goats using the caprine 50K single nucleotide polymorphism (SNP) chip. The aim of the project was to perform a genome-wide association study for milk yield and conformation of udder, teat, and feet and legs. A total of 137,235 milk yield records on 4,563 goats each scored for 10 conformation traits were available. Out of these, 2,381 goats were genotyped with the Illumina Caprine 50K BeadChip (Illumina Inc., San Diego, CA). A range of pseudo-phenotypes were used including deregressed breeding values and pseudo-estimated breeding values. Genome-wide association studies were performed using the multi-locus mixed model (MLMM) algorithm implemented in SNP & Variation Suite v7.7.8 (Golden Helix Inc., Bozeman, MT). A genome-wise significant [−log₁₀(P-value) > 5.95] SNP for milk yield was identified on chromosome 19, with additional chromosome-wise significant (−log₁₀(P-value) > 4.46] SNP on chromosomes 4, 8, 14, and 29. Three genome-wise significant SNP for conformation of udder attachment, udder depth, and front legs were identified on chromosome 19, and chromosome-wise SNP were found on chromosomes 4, 5, 6, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 23, and 27. The proportion of variance explained by the significant SNP was between 0.4 and 7.0% for milk yield and between 0.1 and 13.8% for conformation traits. This study is the first attempt to identify SNP associated with milk yield and conformation in dairy goats. Two genome-wise significant SNP for milk yield and 3 SNP for conformation of udder attachment, udder depth, and front legs were found. Our results suggest that conformation traits have a polygenic background because, for most of them, we did not identify any quantitative trait loci with major effect.     

Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18

Over the last decades, a dramatic decrease in reproductive performance has been observed in Holstein cattle and fertility problems have become the most common reason for a cow to leave the herd. The premature removal of animals with high breeding values results in both economic and breeding losses. For efficient future Holstein breeding, the identification of loci associated with low fertility is of major interest and thus constitutes the aim of this study. To reach this aim, a genome-wide combined linkage disequilibrium and linkage analysis (cLDLA) was conducted using data on the following 10 calving and fertility traits in the form of estimated breeding values: days from first service to conception of heifers and cows, nonreturn rate on d 56 of heifers and cows, days from calving to first insemination, days open, paternal and maternal calving ease, paternal and maternal stillbirth. The animal data set contained 2,527 daughter-proven Holstein bulls from Germany that were genotyped with Illumina's BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For the cLDLA, 41,635 sliding windows of 40 adjacent single nucleotide polymorphisms (SNP) were used. At each window midpoint, a variance component analysis was executed using ASReml. The underlying mixed linear model included random quantitative trait locus (QTL) and polygenic effects. We identified 50 genome-wide significant QTL. The most significant peak was detected for direct calving ease at 59,179,424 bp on chromosome 18 (BTA18). Next, a mixed-linear model association (MLMA) analysis was conducted. A comparison of the cLDLA and MLMA results with special regard to BTA18 showed that the genome-wide most significant SNP from the MLMA was associated with the same trait and located on the same chromosome at 57,589,121 bp (i.e., about 1.5 Mb apart from the cLDLA peak). The results of 5 different cLDLA and 2 MLMA models, which included the fixed effects of either SNP or haplotypes, suggested that the cLDLA method outperformed the MLMA in accuracy and precision. The haplotype-based cLDLA method allowed for a more precise mapping and the definition of ancestral and derived QTL alleles, both of which are essential for the detection of underlying quantitative trait nucleotides.     

Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population

Single-breed genomic selection (GS) based on medium single nucleotide polymorphism (SNP) density (~50,000; 50K) is now routinely implemented in several large cattle breeds. However, building large enough reference populations remains a challenge for many medium or small breeds. The high-density BovineHD BeadChip (HD chip; Illumina Inc., San Diego, CA) containing 777,609 SNP developed in 2010 is characterized by short-distance linkage disequilibrium expected to be maintained across breeds. Therefore, combining reference populations can be envisioned. A population of 1,869 influential ancestors from 3 dairy breeds (Holstein, Montbéliarde, and Normande) was genotyped with the HD chip. Using this sample, 50K genotypes were imputed within breed to high-density genotypes, leading to a large HD reference population. This population was used to develop a multi-breed genomic evaluation. The goal of this paper was to investigate the gain of multi-breed genomic evaluation for a small breed. The advantage of using a large breed (Normande in the present study) to mimic a small breed is the large potential validation population to compare alternative genomic selection approaches more reliably. In the Normande breed, 3 training sets were defined with 1,597, 404, and 198 bulls, and a unique validation set included the 394 youngest bulls. For each training set, estimated breeding values (EBV) were computed using pedigree-based BLUP, single-breed BayesC, or multi-breed BayesC for which the reference population was formed by any of the Normande training data sets and 4,989 Holstein and 1,788 Montbéliarde bulls. Phenotypes were standardized by within-breed genetic standard deviation, the proportion of polygenic variance was set to 30%, and the estimated number of SNP with a nonzero effect was about 7,000. The 2 genomic selection (GS) approaches were performed using either the 50K or HD genotypes. The correlations between EBV and observed daughter yield deviations (DYD) were computed for 6 traits and using the different prediction approaches. Compared with pedigree-based BLUP, the average gain in accuracy with GS in small populations was 0.057 for the single-breed and 0.086 for multi-breed approach. This gain was up to 0.193 and 0.209, respectively, with the large reference population. Improvement of EBV prediction due to the multi-breed evaluation was higher for animals not closely related to the reference population. In the case of a breed with a small reference population size, the increase in correlation due to multi-breed GS was 0.141 for bulls without their sire in reference population compared with 0.016 for bulls with their sire in reference population. These results demonstrate that multi-breed GS can contribute to increase genomic evaluation accuracy in small breeds.     

Hot topic: performance of bovine high-density genotyping platforms in Holsteins and Jerseys

Two high-density single nucleotide polymorphism (SNP) genotyping arrays have recently become available for bovine genomic analyses, the Illumina High-Density Bovine BeadChip Array (777,962 SNP) and the Affymetrix Axiom Genome-Wide BOS 1 Array (648,874 SNP). These products each have unique design and chemistry attributes, and the extent of marker overlap and their potential utility for quantitative trait loci fine mapping, detection of copy number variation, and multibreed genomic selection are of significant interest to the cattle community. This is the first study to compare the performance of these 2 arrays. Deoxyribonucleic acid samples from 16 dairy cattle (10 Holstein, 6 Jersey) were used for the comparison. An independent set of DNA samples taken from 46 Jersey cattle and 18 Holstein cattle were used to ascertain the amount of SNP variation accounted by the 16 experimental samples. Data were analyzed with SVS7 software (Golden Helix Inc., Bozeman, MT) to remove SNP having a call rate less than 90%, and linkage disequilibrium pruning was used to remove linked SNP (r² ≥ 0.9). Maximum, average, and median gaps were calculated for each analysis based on genomic position of SNP on the bovine UMD3.1 genome assembly. All samples were successfully genotyped (≥98% SNP genotyped) with both platforms. The average number of genotyped SNP in the Illumina platform was 775,681 and 637,249 for the Affymetrix platform. Based on genomic position, a total of 107,896 SNP were shared between the 2 platforms; however, based on genotype concordance, only 96,031 SNP had complete concordance at these loci. Both Affymetrix BOS 1 and Illumina BovineHD genotyping platforms are well designed and provide high-quality genotypes and similar coverage of informative SNP. Despite fewer total SNP on BOS 1, 19% more SNP remained after linkage disequilibrium pruning, resulting in a smaller gap size (5.2 vs. 6.9 kb) in Holstein and Jersey samples relative to BovineHD. However, only 224,115 Illumina and 241,038 Affymetrix SNP remained following removal of SNP with a minor allele frequency of zero in Holstein and Jersey samples, resulting in an average gap size of 11,887 bp and 11,018 bp, respectively. Combining the 354,348 informative (r² ≥ 0.9), polymorphic (minor allele frequency ≥ 0), unique SNP data from both platforms decreased the average gap size to 7,560 bp. Genome-wide copy number variant analyses were performed using intensity files from both platforms. The BovineHD platform provided an advantage to the copy number variant data compared with the BOS 1 because of the larger number of SNP, higher intensity signals, and lower background effects. The combined use of both platforms significantly improved coverage over either platform alone and decreased the gap size between SNP, providing a valuable tool for fine mapping quantitative trait loci and multibreed animal evaluation.     

A missense mutation (p.Tyr452Cys) in the CAD gene compromises reproductive success in French Normande cattle

We scanned the genome of 77,815 Normande cattle with different Illumina SNP chips (Illumina Inc., San Diego, CA) to map recessive embryonic lethal mutations using homozygous haplotype deficiency. We detected 2 novel haplotypes on chromosomes 11 and 24 but did not confirm 6 previously reported haplotypes. The one on chromosome 11 showed a marked reduction in conception rates and moderate decrease in nonreturn rate in at-risk versus control mating, supporting late embryonic mortality. After fine mapping and analyzing whole-genome sequences, we prioritized a missense mutation in CAD (g.72399397T>C; p.Tyr452Cys)—a gene encoding a protein (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase) essential for de novo pyrimidine biosynthesis—as a candidate causal variant. This transition mutation replaces a tyrosine residue, which is perfectly conserved among living organisms, with a cysteine residue in the carbamoyl-phosphate synthetase 2 domain of the protein. A single animal was confirmed to be homozygous for the mutation based on Sanger sequencing. However, large-scale genotyping of the candidate variant with the Illumina EuroG10k BeadChip revealed an absence of live homozygotes in a panel of 33,323 Normande animals and an absence of carriers in 348,593 animals from 19 other cattle breeds. These results support recessive embryonic lethality with nearly complete penetrance, as was previously reported in CAD mutants in several eukaryote species. The only homozygous cow had extremely poor udder conformation, suggesting a potential role of CAD in udder development, but no effect was detected when comparing daughter yield deviations of 250 heterozygous bulls with that of 2,912 homozygotes for the ancestral allele. Together, our results showed the importance of large-scale screening for homozygous haplotype deficiency with hundreds of thousands of animals, validating results with an independent data set, and considering unexpected live homozygotes, to avoid both false-positive and false-negative discoveries. These discoveries will be used primarily in mating decisions to avoid at-risk mating. In addition, we recommend including CAD in the breeding objectives of Normande cattle.