Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables

This study compared genomic predictions using conventional estimated breeding values (EBV) and daughter yield deviations (DYD) as response variables based on simulated data. Eight scenarios were simulated in regard to heritability (0.05 and 0.30), number of daughters per sire (30, 100, and unequal numbers with an average of 100 per sire) and numbers of genotyped sires (all or half of sires were genotyped). The simulated genome had a length of 1200 cM with 15,000 equally spaced Single-nucleotide polymorphism (SNP) markers and 500 randomly distributed Quantitative trait locus (QTL). In the simulated scenarios, the EBV approach was as effective as or slightly better than the DYD approach at predicting breeding value, dependent on simulated scenarios and statistical models. Applying a Bayesian common prior model (the same prior distribution of marker effect variance) and a linear mixed model (GBLUP), the EBV and DYD approaches provided similar genomic estimated breeding value (GEBV) reliabilities, except for scenarios with unequal numbers of daughters and half of sires without genotype, for which the EBV approach was superior to the DYD approach (by 1.2 and 2.4%). Using a Bayesian mixture prior model (mixture prior distribution of marker effect variance), the EBV approach resulted in slightly higher reliabilities of GEBV than the DYD approach (by 0.3-3.6% with an average of 1.9%), and more obvious in scenarios with low heritability, small or unequal numbers of daughters, and half of sires without genotype. Moreover, the results showed that the correlation between GEBV and conventional parent average (PA) was lower (corresponding to a relatively larger gain by including PA) when using the DYD approach than when using the EBV approach. Consequently, the two approaches led to similar reliability of an index combining GEBV and PA in most scenarios. These results indicate that EBV can be used as an alternative response variable for genomic prediction.     

A single nucleotide polymorphism set for paternal identification to reduce the costs of trait recording in commercial pig breeding

In animal breeding, recording of correct pedigrees is essential to achieve genetic progress. Markers on DNA are useful to verify the on-farm pedigree records (parental verification) but can also be used to assign parents retrospectively (parental identification). This approach could reduce the costs of recording for traits with low incidence, such as those related to diseases or mortality. In this study, SNP were used to assign the true sires of 368 purebred animals from a Duroc-based sire line and 140 crossbred offspring from a commercial pig population. Some of the sires were closely related. There were 3 full sibs and 17 half sibs among the true fathers and 4 full sibs and 35 half sibs among all putative fathers. To define the number of SNP necessary, 5 SNP panels (40, 60, 80, 100, and 120 SNP) were assembled from the Illumina PorcineSNP60 Beadchip (Illumina, San Diego, CA) based on minor allele frequency (>0.3), high genotyping call rate (≥90%), and equal spacing across the genome. For paternal identification considering only the 66 true sires in the data set, 60 SNP resulted in 100% correct assignment of the sire. By including additional putative sires (n = 304), 80 SNP were sufficient for 100% correct assignment of the sire. The following criteria were derived to identify the correct sire for the current data set: the logarithm of odds (LOD) score for assigning the correct sire was ≥5, the number of mismatches was ≤1, and the difference in the LOD score between the first and the second most likely sire was >5. If the correct sire was not present among all putative sires, the mean LOD for the most likely sire was close to zero or negative when using 100 SNP. More SNP would be needed for paternal identification if the number of putative sires increased and the degree of relatedness was greater than in the data set used here. The threshold for the number of mismatches can be adjusted according to the practical situation to account for the trade-off between false negatives and false positives. The latter can be avoided efficiently, ensuring that the correct father is being sampled. Nevertheless, a restriction on the number of putative sires is advisable to reduce the risk of assigning close relatives.     

Strategy for applying genome-wide selection in dairy cattle

Animals can be genotyped for thousands of single nucleotide polymorphisms (SNPs) at one time, where the SNPs are located at roughly 1‐cM intervals throughout the genome. For each contiguous pair of SNPs there are four possible haplotypes that could be inherited from the sire. The effects of each interval on a trait can be estimated for all intervals simultaneously in a model where interval effects are random factors. Given the estimated effects of each haplotype for every interval in the genome, and given an animal's genotype, a ‘genomic’ estimated breeding value is obtained by summing the estimated effects for that genotype. The accuracy of that estimator of breeding values is around 80%. Because the genomic estimated breeding values can be calculated at birth, and because it has a high accuracy, a strategy that utilizes these advantages was compared with a traditional progeny testing strategy under a typical Canadian‐like dairy cattle situation. Costs of proving bulls were reduced by 92% and genetic change was increased by a factor of 2. Genome‐wide selection may become a popular tool for genetic improvement in livestock.     

Need for sharp phenotypes in QTL detection for calving traits in dairy cattle

The aim of the study was to investigate whether parity‐specific phenotypes provide a clearer picture of quantitative trait loci (QTL) affecting calving traits in German Holsteins than breeding values estimated across parities. In experiment I, approximate daughter yield deviations were calculated by applying a univariate sire model assuming unrelated sires used as phenotypes in a QTL mapping study. These results were compared with those obtained using deregressed estimated breeding values obtained from the routine German sire evaluation (experiment II). In experiment I, 17 chromosome‐wise significant QTL were found for the first parity, but only 12 for the second parity. Only three QTL for maternal stillbirth, located on BTA7, 15 and 23, showed an experiment‐wise significance. Experiment II revealed 15 chromosome‐wise significant QTL. The results differed markedly between first and second parity within experiment I, as well as between experiment I and II. The present study showed that parity‐specific daughter yield deviations are beneficial for mapping QTL for calving traits. Furthermore, it is expected that the use of sharper phenotypes will also be advantageous for QTL fine mapping and the identification of candidate genes.     

Meat quality traits were unaffected by a quantitative trait locus affecting leg composition traits in Texel sheep

A QTL affecting leg muscle and fat traits has been identified within the New Zealand Texel population. The QTL maps to a region on OAR 2 with a two-marker haplotype test established at markers BULGE20 and BM81124. These markers encompass the likely position of Growth Differentiation Factor 8 (GDF8). The pleiotropic effects of this QTL on meat quality traits are tested. Objective measures of meat quality including pH, color (L*, a*, and b*), and tenderness (as assessed by Warner-Bratzler shear force measurements) were assessed on longissimus and semi-membranosus muscles of 540 progeny from six Texel sires. Four of these sires were subsequently identified as segregating for leg muscle and fat traits. For these segregating sires, comparison of progeny that had inherited the favorable haplotype from their sire with those that had received the alternate haplotype revealed no significant differences in the meat quality traits assessed. This finding suggests that the muscling QTL does not have pleiotropic effects on meat quality. A general scan for meat quality QTL was carried out using genotype data for eight markers from FCB128 to RM356 flanking 122cM of OAR 2 using Haley-Knott regression. This analysis revealed two QTL for a single sire. A QTL detected in the region of Marker INRA40 for color L* mapped to a site close to the muscling QTL, but there was evidence to suggest it is at a distinct locus. The QTL in the region of Marker RM356 might map distal to Marker RM356, as no peak was observed. This QTL, which seems to affect pH, color a*, color b*, and Warner-Bratzler shear measurements, requires further characterization.     

Interval mapping of growth quantitative trait loci in Japanese Black beef cattle using microsatellite DNA markers and half-sib regression analysis

A confirmatory scan for the regions of bovine chromosome 1 segregating the quantitative trait loci (QTL) influencing birthweight, weaning weight, yearling weight, and preweaning and postweaning average daily gains was performed by genotyping half‐sib progeny of four Japanese Black sires using microsatellite DNA markers. Data were analyzed by generating an F‐statistic every 1 cM on a linkage map by the regression of phenotype on the probabilities of inheriting an allele from the sire after adjusting for the fixed effects of sire, sex, parity and season of birth as well as age as a covariate. Permutation tests at chromosome‐wide significance thresholds were carried out over 10 000 iterations. A significant QTL for birthweight at 114 cM was detected in the sire 2 family. This identification of a birthweight QTL in Japanese Black cattle may be useful for the implementation of marker‐assisted selection.     

Genomic selection

Genomic selection is a form of marker-assisted selection in which genetic markers covering the whole genome are used so that all quantitative trait loci (QTL) are in linkage disequilibrium with at least one marker. This approach has become feasible thanks to the large number of single nucleotide polymorphisms (SNP) discovered by genome sequencing and new methods to efficiently genotype large number of SNP. Simulation results and limited experimental results suggest that breeding values can be predicted with high accuracy using genetic markers alone but more validation is required especially in samples of the population different from that in which the effect of the markers was estimated. The ideal method to estimate the breeding value from genomic data is to calculate the conditional mean of the breeding value given the genotype of the animal at each QTL. This conditional mean can only be calculated by using a prior distribution of QTL effects so this should be part of the research carried out to implement genomic selection. In practice, this method of estimating breeding values is approximated by using the marker genotypes instead of the QTL genotypes but the ideal method is likely to be approached more closely as more sequence and SNP data is obtained. Implementation of genomic selection is likely to have major implications for genetic evaluation systems and for genetic improvement programmes generally and these are discussed.     

Genome-wide association studies for osteochondrosis in French Trotter horses

A genome-wide association study for osteochondrosis (OC) in French Trotter horses was carried out to detect QTL using genotype data from the Illumina EquineSNP50 BeadChip assay. Analysis data came from 161 sire families of French Trotter horses with 525 progeny and family sizes ranging from 1 to 20. Genotypes were available for progeny (n = 525) and sires with at least 2 progeny (n = 98). Radiographic data were obtained from progeny using at least 10 views to reveal OC. All radiographic findings were described by at least 2 veterinary experts in equine orthopedics, and severity indices (scores) were assigned based on the size and location of the lesion. Traits used were a global score, the sum of all severity scores lesions (GM, quantitative measurement), and the presence or absence of OC on the fetlock (FM), hock (HM), and other sites (other). Data were analyzed using 2 mixed models including fixed effects, polygenic effects, and SNP or haplotype cluster effects. By combining results with both methods at moderate evidence of association threshold P < 5 × 10(-5), this genome-wide association study displayed 1 region for GM on the Equus caballus chromosome (ECA) 13, 2 for HM on ECA 3 and 14, and 1 for other on ECA 15. One region on ECA 3 for HM represented the most significant hit (P = 3 × 10(-6)). By comparing QTL between traits at a decreased threshold (P < 5 × 10(-4)), the 4 QTL detected for GM were associated to a QTL detected for FM or HM but never both. Another interesting result was that no QTL were found in common between HM and FM.     

Estimation of genetic parameters based on individual and group mean records in laying hens

(1) The study was conducted to estimate the heritability, genetic correlations and breeding values of laying hens based on individual records and group mean records. (2) Records of two pure lines from a commercial breeding programme of White Leghorns from three generations housed in single cages and in group cages were used. A total of 8483 and 8817 individual records of lines A and D, respectively, and a total of 1358 (line A) and 1161 (line D) group mean records were analysed. (3) An animal model using Restricted Maximum Likelihood (REML) was used to estimate variance components of individual records. Group mean records were analysed using the sire model, taking heterogeneity of error variance and correlated residual effects into account. Breeding values of sires were estimated based on the BLUP method using a multivariate sire model. Spearman Rank correlations were used to compare sire breeding values estimated from individual records and from group mean records. The traits studied were monthly egg production, cumulative production and egg weight. (4) Heritability estimates based on individual records were higher than from group mean records. Heritabilities for cumulative production records were higher than for monthly production, based on individual as well as group mean records. The estimates of genetic correlations between monthly egg production and cumulative production were moderate to high. Egg production and egg weight recorded individually were highly genetically correlated with those recorded on group means. Sire breeding values estimated from individual records showed high correlations with those from group mean records. (5) Differences in the ranking of sire breeding values estimated from individual vs group mean records were negligible, indicating that no genotype x environment interaction exists. Selection based on individual performance records of laying hens housed in single cages could give a good response on performance of laying hens housed in group cages. Cumulative egg production over periods 1 to 6 is the best trait for the selection programme.     

Identification of recombination hotspots and quantitative trait loci for recombination rate in layer chickens

Background: The frequency of recombination events varies across the genome and between individuals, which may be related to some genomic features. The objective of this study was to assess the frequency of recombination events and to identify QTL(quantitative trait loci) for recombination rate in two purebred layer chicken lines.Methods: A total of 1200 white-egg layers(WL) were genotyped with 580 K SNPs and 5108 brown-egg layers(BL)were genotyped with 42 K SNPs(single nucleotide polymorphisms). Recombination events were identified within half-sib families and both the number of recombination events and the recombination rate was calculated within each0.5 Mb window of the genome. The 10% of windows with the highest recombination rate on each chromosome were considered to be recombination hotspots. A BayesB model was used separately for each line to identify genomic regions associated with the genome-wide number of recombination event per meiosis. Regions that explained more than 0.8% of genetic variance of recombination rate were considered to harbor QTL.Results: Heritability of recombination rate was estimated at 0.17 in WL and 0.16 in BL. On average, 11.3 and 23.2 recombination events were detected per individual across the genome in 1301 and 9292 meioses in the WL and BL,respectively. The estimated recombination rates differed significantly between the lines, which could be due to differences in inbreeding levels, and haplotype structures. Dams had about 5% to 20% higher recombination rates per meiosis than sires in both lines. Recombination rate per 0.5 Mb window had a strong negative correlation with chromosome size and a strong positive correlation with GC content and with CpG island density across the genome in both lines. Different QTL for recombination rate were identified in the two lines. There were 190 and 199 non-overlapping recombination hotspots detected in WL and BL respectively, 28 of which were common to both lines.Conclusions: Differences in the recombination rates, hotspot locations, and QTL regions associated with genomewide recombination were observed between lines, indicating the breed-specific feature of detected recombination events and the control of recombination events is a complex polygenic trait.     

Robust QTL fine mapping by applying a quantitative transmission disequilibrium test to the Mendelian sampling term

In many farm animal populations, high‐density single nucleotide polymorphism (SNP) genotypes are becoming available on a large scale, and routine estimation of breeding values is implemented for a multiplicity of traits. We propose to apply the basic principle of the quantitative transmission disequilibrium test (QTDT) to estimated Mendelian sampling terms. A two‐step procedure is suggested, where in the first step additive breeding values are estimated with a mixed linear model and the Mendelian sampling terms are calculated from the estimated breeding values. In the second step, the QTDT is applied to these estimated Mendelian sampling terms. The resulting test is expected to yield significant results if the SNP is in sufficient linkage disequilibrium and linkage with quantitative trait loci (QTL). This principle is illustrated with a simulated data set comprising 4665 individuals genotyped for 6000 SNP and 15 true QTL. Thirteen of the fifteen QTL were significant on a genome‐wide 0.1% error level. Results for the empirical power are derived from repeated samples of 1000 and 3000 genotyped individuals, respectively. General properties and potential extensions of the methodology are indicated. Owing to its computational simplicity and speed, the suggested procedure is well suited to scan whole genomes with high‐density SNP coverage in samples of substantial size and for a multiplicity of different traits.     

Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM

Genomic selection is based on breeding values that are estimated using genome-wide dense marker maps. The objective of this paper was to investigate the effect of including or ignoring the polygenic effect on the accuracy of total genomic breeding values, when there is coverage of the genome with approximately one SNP per cM. The importance of the polygenic effect might differ for high and low heritability traits, and might depend on the design of the reference dataset. Hence, different scenarios were evaluated using stochastic simulation. Accuracies of the total breeding value of juvenile selection candidates depended on the number of animals included in the reference data. When excluding polygenic effects, those accuracies ranged from 0.38 to 0.55 and from 0.73 to 0.79 for traits with heritabilities of 10 and 50%, respectively. Accuracies were improved by including a polygenic effect in the model for the low heritability trait, when the LD-measure r2 between adjacent markers became smaller than approximately 0.10, while for the high heritability trait there was already a small improvement at r2 between adjacent markers of 0.14. In all situations, the estimated total genetic variance was underestimated, particularly when the polygenic effect was excluded from the model. The haplotype variance was less underestimated when more animals were added in the reference dataset.     

Variance components and breeding values for growth traits from different statistical models.

Estimates of genetic parameters resulting from various analytical models for birth weight (BWT, n = 4,155), 205-d weight (WWT, n = 3,884), and 365-d weight (YWT, n = 3,476) were compared. Data consisted of records for Line 1 Hereford cattle selected for postweaning growth from 1934 to 1989 at ARS-USDA, Miles City, MT. Twelve models were compared. Model 1 included fixed effects of year, sex, age of dam; covariates for birth day and inbreeding coefficients of animal and of dam; and random animal genetic and residual effects. Model 2 was the same as Model 1 but ignored inbreeding coefficients. Model 3 was the same as Model 1 and included random maternal genetic effects with covariance between direct and maternal genetic effects, and maternal permanent environmental effects. Model 4 was the same as Model 3 but ignored inbreeding. Model 5 was the same as Model 1 but with a random sire effect instead of animal genetic effect. Model 6 was the same as Model 5 but ignored inbreeding. Model 7 was a sire model that considered relationships among males. Model 8 was a sire model, assuming sires to be unrelated, but with dam effects as uncorrelated random effects to account for maternal effects. Model 9 was a sire and dam model but with relationships to account for direct and maternal genetic effects; dams also were included as uncorrelated random effects to account for maternal permanent environmental effects. Model 10 was a sire model with maternal grandsire and dam effects all as uncorrelated random effects. Model 11 was a sire and maternal grandsire model, with dams as uncorrelated random effects but with sires and maternal grandsires assumed to be related using male relationships. Model 12 was the same as Model 11 but with all pedigree relationships from the full animal model for sires and maternal grandsires. Rankings on predictions of breeding values were the same regardless of whether inbreeding coefficients for animal and dam were included in the models. Heritability estimates were similar regardless of whether inbreeding effects were in the model. Models 3 and 9 best fit the data for estimation of variances and covariances for direct, maternal genetic, and permanent environmental effects. Other models resulted in changes in ranking for predicted breeding values and for estimates of direct and maternal heritability. Heritability estimates of direct effects were smallest with sire and sire-maternal grandsire models.     

The importance of haplotype length and heritability using genomic selection in dairy cattle

Reliabilities for genomic estimated breeding values (GEBV) were investigated by simulation for a typical dairy cattle breeding setting. Scenarios were simulated with different heritabilites ( h ²) and for different haplotype sizes, and seven generations with only genotypes were generated to investigate reliability of GEBV over time. A genome with 5000 single nucleotide polymorphisms (SNP) at distances of 0.1 cM and 50 quantitative trait loci (QTL) was simulated, and a Bayesian variable selection model was implemented to predict GEBV. Highest reliabilities were obtained for 10 SNP haplotypes. At optimal haplotype size, reliabilities in generation 1 without phenotypes ranged from 0.80 for h ² = 0.02 to 0.93 for h ² = 0.30, and in the seventh generation without phenotypes ranged from 0.69 for h ² = 0.02 to 0.86 for h ² = 0.30. Reliabilities of GEBV were found sufficiently high to implement dairy selection schemes without progeny testing in which case a data time-lag of two to three generations may be present. Reliabilities were also relatively high for low heritable traits, implying that genomic selection could be especially beneficial to improve the selection on, e.g. health and fertility.     

Relationship between litters per sow per year sire breeding values and sire progeny means for farrowing rate,removal parity and lifetime born alive

The objective of this study was to determine the relationship between individual sire estimated breeding values (EBV) for litters/sow/year (LSY) and sire progeny means for farrowing rate (FR), removal parity and lifetime born alive (LTBA). Genetic parameters and breeding values were estimated using ASREML. The heritability estimate for LSY was 0.11. When all sires with 10 or more daughters with records were included in the analysis, Spearman rank correlations between the sire's LSY EBV and the sires' daughter means for FR, removal parity and LTBA were 0.49, 0.23 and 0.25 (p < 0.01). The sire EBV for LSY was favourably correlated with sires' daughter means for all three traits. This provides evidence that selecting sires with high EBV for LSY could improve herd FR, removal parity and LTBA. By including LSY as part of the selection criterion, the LTBA may be indirectly improved. The positive genetic correlation between LTBA and LSY may be a result of the improved longevity of sows with greater LSY compared with sows with lower LSY. The relationships between LSY and FR, removal parity and LTBA are strongly supported by the correlations between the sire progeny means for each trait and the sire LSY EBV.     

Comparison of estimated breeding values, daughter yield deviations and de-regressed proofs within a whole genome scan for QTL

An important issue in quantitative trait loci (QTL) detection is the use of phenotypic measurement as a dependent variable. Daughter yield deviations (DYDs) as the unit of choice are not available for all traits of interest. The use of de-regressed proofs (DRPFs) of estimated breeding values (EBVs) is an alternative to using daughter yield deviations. The objective of this study was to examine possible differences between DYDs and DRPFs within the use of QTL detection. The pedigree used was part of the granddaughter design of the German QTL effort. Consisting marker maps for livestock species were derived from all available data of 16 German Holstein paternal half-sib families with a total of 872 sires. The number of progeny ranged from 19 to 127. A whole genome scan was performed using weighted and unweighted multimarker regression with DYDs, DRPFs and EBVs as dependent variables for the traits milk, fat and protein yields. Results were compared with respect to the number of QTL detected. A similar number of QTL was detected with DRPFs and DYDs. Also, when dependent variables were weighted according to the variance of the trait, a higher number of QTL was detected at the desired level of significance as compared to using unweighted variables.     

Sire x herd interactions for weaning weight in beef cattle.

Weaning weight records of 44,357 Australian Angus calves produced by 1,020 sires in 90 herds were used to evaluate the importance of sire x herd interactions. Models fitted fixed effects of contemporary group (herd-year-date of weighing subclass), sex, calf age, and dam age and random effects of sire or of sire and sire x herd interaction using REML. Effects of standardizing the data, including sire relationships and including dam maternal breeding values (MBV) as a covariate were also investigated. Sire x herd interactions were found (P less than .05) in all cases and, in the most complete model, accounted for 3.3% of phenotypic variance. Across-herd heritabilities ranged from .19 to .28. Differential nonrandom mating among herds seemed to occur in the data. Significant sire x herd effects were observed for dam MBV, and adjustment for dam MBV yielded the smallest estimates of interaction variance and across-herd heritability. If sire x herd interactions were due only to genotype x environment interaction, within-herd heritabilities would range from .33 to .49. These estimates are larger than previously reported estimates. Thus, unreported environmental effects common to progeny of individual sires may also be involved in the observed interaction but could not be disentangled from true genotype x environment interaction effects using these data. Results of these analyses suggest that some accommodation of sire x herd interaction effects on weaning weight may be needed in beef cattle genetic evaluations, but a compelling case for development of herd-specific breeding value prediction cannot be made.     

A Male Genetic Map Designed for Quantitative Trait Loci Mapping in Norwegian Cattle

A male bovine linkage map was constructed using six half-sib families of the Norwegian Cattle population. The family material consisted of six sires and 285 sons, with the number of sons per sire ranging from 32 to 71. Of the 288 markers analysed, 261 were anonymous microsatellites and 27 were coding genes. Eleven of the coding genes were mapped by a two-step procedure involving intron/exon sequencing in the six sires and development of polymerase chain reaction-based methods for efficient genotyping in heterozygous families. Linkage analysis including both informative and non-informative sons was performed in order to obtain unbiased estimates of recombination fractions. The autosomal genome length of the Norwegian Cattle Map (NCM) was estimated as 2682 cM, with an average interval of 12.5 cM. The map provides sufficient marker density for scanning the whole bovine genome to locate quantitative trait loci. The large number of common markers between NCM and previously published maps will contribute to the comparison and integration of bovine maps.     

DNA-based paternity analysis and genetic evaluation in a large, commercial cattle ranch setting

Deoxyribonucleic acid-based tests were used to assign paternity to 625 calves from a multiple-sire breeding pasture. There was a large variability in calf output and a large proportion of young bulls that did not sire any offspring. Five of 27 herd sires produced over 50% of the calves, whereas 10 sires produced no progeny and 9 of these were yearling bulls. A comparison was made between the paternity results obtained when using a DNA marker panel with a high (0.999), cumulative parentage exclusion probability (P(E)) and those obtained when using a marker panel with a lower P(E) (0.956). A large percentage (67%) of the calves had multiple qualifying sires when using the lower resolution panel. Assignment of the most probable sire using a likelihood-based method based on genotypic information resolved this problem in approximately 80% of the cases, resulting in 75% agreement between the 2 marker panels. The correlation between weaning weight, on-farm EPD based on pedigrees inferred from the 2 marker panels was 0.94 for the 24 bulls that sired progeny. Partial progeny assignments inferred from the lower resolution panel resulted in the generation of EPD for bulls that actually sired no progeny according to the high-P(E) panel, although the Beef Improvement Federation accuracies of EPD for these bulls were never greater than 0.14. Simulations were performed to model the effect of loci number, minor allele frequency, and the number of offspring per bull on the accuracy of genetic evaluations based on parentage determinations derived from SNP marker panels. The SNP marker panels of 36 and 40 loci produced EPD with accuracies nearly identical to those EPD resulting from use of the true pedigree. However, in field situations where factors including variable calf output per sire, large sire cohorts, relatedness among sires, low minor allele frequencies, and missing data can occur concurrently, the use of marker panels with a larger number of SNP loci will be required to obtain accurate on-farm EPD.     

Pregnancy rate and first-service conception rate in Angus heifers

The objective of this project was to determine the genetic control of conception rate, or pregnancy percentage in Angus beef heifers. Producers from 6 herds in 5 states provided 3,144 heifer records that included breeding dates, breeding contemporary groups, service sires, and pregnancy check information. Two hundred fourteen sires of the heifers were represented; with 104 sires having less than 5 progeny, and 14 sires having greater than 50 progeny. These data were combined with performance and pedigree information, including actual and adjusted birth weights, weaning weights, and yearling weights, from the American Angus Association database. Heifer pregnancy rate varied from 75 to 95% between herds, and from 65 to 100% between sires, with an overall pregnancy rate of 93%, measured as the percentage of heifers pregnant at pregnancy check after the breeding season. Pregnancy was analyzed as a threshold trait with an underlying continuous distribution. A generalized linear animal model, using a relationship matrix, was fitted. This model included the fixed effects of contemporary group, age of dam, and first AI service sire, and the covariates of heifer age at the beginning of breeding, adjusted birth weight, adjusted weaning weight, and adjusted yearling weight. The relationship matrix included 4 generations of pedigree. The heritability of pregnancy and first-service conception rates on the underlying scale was 0.13 +/- 0.07 and 0.03 +/- 0.03, respectively. Estimated breeding values for pregnancy rate on the observed scale ranged from -0.02 to 0.05 for sires of heifers. Including growth traits with pregnancy rate as 2-trait analyses did not change the heritability of pregnancy rate. As expected for a reproductive trait, the heritability of pregnancy rate was low. Because of its low heritability, genetic improvement in fertility by selection on heifer pregnancy rate would be expected to be slow.