Genetic parameters for fertility of dairy bulls

Review: Genetic mutations affecting bull fertility

Cattle are a well-suited "model organism" to study the genetic underpinnings of variation in male reproductive performance. The adoption of artificial insemination and genomic prediction in many cattle breeds provide access to microarray-derived genotypes and repeated measurements for semen quality and insemination success in several thousand bulls. Similar-sized mapping cohorts with phenotypes for male fertility are not available for most other species precluding powerful association testing. The repeated measurements of the artificial insemination bulls' semen quality enable the differentiation between transient and biologically relevant trait fluctuations, and thus, are an ideal source of phenotypes for variance components estimation and genome-wide association testing. Genome-wide case-control association testing involving bulls with either aberrant sperm quality or low insemination success revealed several causal recessive loss-of-function alleles underpinning monogenic reproductive disorders. These variants are routinely monitored with customised genotyping arrays in the male selection candidates to avoid the use of subfertile or infertile bulls for artificial insemination and natural service. Genome-wide association studies with quantitative measurements of semen quality and insemination success revealed quantitative trait loci for male fertility, but the underlying causal variants remain largely unknown. Moreover, these loci explain only a small part of the heritability of male fertility. Integrating genome-wide association studies with gene expression and other omics data from male reproductive tissues is required for the fine-mapping of candidate causal variants underlying variation in male reproductive performance in cattle.

Activation of cryptic splicing in bovine WDR19 is associated with reduced semen quality and male fertility

Cattle are ideally suited to investigate the genetics of male reproduction, because semen quality and fertility are recorded for all ejaculates of artificial insemination bulls. We analysed 26,090 ejaculates of 794 Brown Swiss bulls to assess ejaculate volume, sperm concentration, sperm motility, sperm head and tail anomalies and insemination success. The heritability of the six semen traits was between 0 and 0.26. Genome-wide association testing on 607,511 SNPs revealed a QTL on bovine chromosome 6 that was associated with sperm motility (P = 2.5 x 10⁻²⁷), head (P = 2.0 x 10⁻⁴⁴) and tail anomalies (P = 7.2 x 10⁻⁴⁹) and insemination success (P = 9.9 x 10⁻¹³). The QTL harbors a recessive allele that compromises semen quality and male fertility. We replicated the effect of the QTL on fertility (P = 7.1 x 10⁻³²) in an independent cohort of 2481 Brown Swiss bulls. The analysis of whole-genome sequencing data revealed that a synonymous variant (BTA6:58373887C>T, rs474302732) in WDR19 encoding WD repeat-containing protein 19 was in linkage disequilibrium with the fertility-associated haplotype. WD repeat-containing protein 19 is a constituent of the intraflagellar transport complex that is essential for the physiological function of motile cilia and flagella. Bioinformatic and transcription analyses revealed that the BTA6:58373887 T-allele activates a cryptic exonic splice site that eliminates three evolutionarily conserved amino acids from WDR19. Western blot analysis demonstrated that the BTA6:58373887 T-allele decreases protein expression. We make the remarkable observation that, in spite of negative effects on semen quality and bull fertility, the BTA6:58373887 T-allele has a frequency of 24% in the Brown Swiss population. Our findings are the first to uncover a variant that is associated with quantitative variation in semen quality and male fertility in cattle.

In cattle farming, artificial insemination is the most common method of breeding. To ensure high fertilization rates, ejaculate quality and insemination success are closely monitored in artificial insemination bulls. We analyse semen quality, insemination success and microarray-called genotypes at more than 600,000 genome-wide SNP markers of 794 bulls to identify a recessive allele that compromises semen quality. We take advantage of whole-genome sequencing to pinpoint a variant in the coding sequence of WDR19 encoding WD repeat-containing protein 19 that activates a novel exonic splice site. Our results indicate that cryptic splicing in WDR19 is associated with reduced male reproductive performance. This is the first report of a variant that contributes to quantitative variation in bovine semen quality.

The effects of age, weight, and sire on pregnancy rate in cattle

The goal was to estimate the heritabilities and genetic variances for pregnancy rate (PR) and calving date (CD) in Angus cattle along with the effect of weight, age, and sire on PR and CD. The data consisted of 4,999 records on PR and CD. Statistical models included year as a fixed effect; premating/postmating weight and age as covariates; and sire of embryo, maternal grandsire (MGS), and permanent maternal environmental effects as random effects. The models also included the interactions between herd and weight (weight change). Direct and maternal effects on PR and CD were estimated using sire MGS and animal models in REML. Pregnancy rate increased from age 2 to 6 and decreased from age 7 to 11 (P < 0.01) and this effect was independent of the culling strategy. There was a quadratic effect of premating cow weight independent of age on PR, with lower PR for low weights (P < 0.01). Overall, cows with a premating weight of 550 kg had the greatest PR. Cows that lost weight during mating had lower PR (P < 0.01). The maternal additive heritability for PR was 0.001 ± 0.012 and the direct additive heritability was 0.024 ± 0.020. The ratio of permanent maternal environmental variance to phenotypic variance was significant (0.048 ± 0.017; P < 0.01). This demonstrates that permanent maternal environmental effects play a major role in the repeatability of PR (0.049 ± 0.015; P < 0.01). The maternal additive heritability for CD was 0.040 ± 0.022, and the direct additive heritability was 0.076 ± 0.045. The ratio of permanent maternal environmental variance to phenotypic variance was low (0.014 ± 0.017) and the repeatability for CD was significant (0.0544 ± 0.0180; P < 0.01). This suggests that maternal genetic effects are as important as direct genetic effects on CD. There was a positive quadratic relationship between premating cow weight and CD with delayed calving for low/high weights (P < 0.01). Cows that lost weight over mating also had a later CD (P < 0.01). Comparisons of a weight-selected herd to the control herd showed differences (P < 0.05) in the optimal premating weight for early calving (control, 480 kg, and weight selected, 615 kg). Calving date was also more sensitive to changes in weight over mating in the weight selection herd (P < 0.05). Therefore, the set point in the weight-fertility axis and the sensitivity of fertility to changes in weight both changed in the weight selection herd.

Genetic Analysis of Male and Female Fertility After Artificial Insemination in Sheep: Comparison of Single-Trait and Joint Models

The outcome of an insemination depends on male and female fertility. Nevertheless, few studies have incorporated genetic evaluation of these 2 traits jointly. The aim of this work was to compare genetic parameter estimates of male and female fertility defined as success or failure to artificial insemination (AI), using 8 different models. The first 2 models were simple repeatability models studying fertility of one sex and ignoring any information of the other. Models 3 and 4 took into account the information of the other sex by the inclusion of its random permanent environmental effect, whereas models 5 and 6 included fixed effects of the other sex. Models 7 and 8 were joint genetic evaluation models of male and female fertility ignoring or considering genetic correlation. Data were composed of 147,018 AI of the Manech Tête Rousse breed recorded from 2000 to 2004 corresponding to 79,352 ewes and 963 rams. The pedigree file included 120,989 individuals. Variance component estimates from the different models were quite similar; heritabilities varied from 0.050 to 0.053 for female fertility and were near 0.003 for male fertility. Correlations among estimated breeding values in the same sex using different models were higher than 0.99. The genetic correlation between male and female fertility was not significantly different from 0. These results show that for French dairy sheep with extensive use of AI, estimation of breeding values for male and female fertility might be implemented with quite simple models.

Voluntary Waiting Period Management Practices in Dairy Herds Participating in a Progeny Test Program

A survey was mailed to approximately 4,000 herds participating in a young sire progeny test program to estimate the percentage of herds that selectively alter the voluntary waiting period (VWP) for individual cows or groups of cows. Responses were received from 673 herds (17%; 583 Holsteins, 55 Jerseys, 35 other dairy breeds). The mean VWP cited by respondents was 56 +/- 0.6 d (range = 30 to 90 d) and did not differ by breed. Among responding herds, 64% (432/673) indicated the VWP was selectively altered for one or more reasons. The most frequently cited reasons for altering the VWP were postpartum health (50%), season (18%), milk yield (18%), parity (14%), and other reasons (14%). In Holstein herds that altered the VWP based on milk yield, the highest production group averaged 14 more days to first service than the lowest production group (> or =40 vs. <20 kg of energy-corrected milk, respectively). In contrast, days to first service were nearly identical for all production groups in Holstein herds that did not vary the VWP based on milk yield. In conclusion, management decisions to selectively alter the VWP led to differences in days to first service and may have a confounding effect on genetic estimates of daughter fertility. Opportunities to improve the accuracy of daughter pregnancy rate estimates may reside in models that adjust for VWP management decisions on a within-herd basis.

Number of Inseminations to Conception in Holstein Cows Using Censored Records and Time-Dependent Covariates

Three methodologies that accommodate censoring or time-dependent covariates were used to estimate variance components for number of inseminations to conception. Data included 80,071 lactation records and 143,927 artificial inseminations in 47,509 Spanish Holstein cows. Up to 4 inseminations to conception, along with their respective censoring information, were analyzed. An ordinal-censored threshold model (CTM), a sequential threshold model (STM), and a grouped survival analysis via a discrete proportional hazards model (DPH) were implemented. Sire variance estimates on the liability scale were 0.016 and 0.010 for CTM and STM, respectively, and 0.012 for DPH on the logarithmic scale. Heritability estimates on the liability scale were 0.050 and 0.038 with CTM and STM, respectively. All models led to similar rankings of sires, and the strong correlations (0.97 to 0.98) between methodologies suggested robustness in ranking of sires of cows. Service sire variance estimates were 0.021 for both CTM and STM; DPH led to an approximate service sire variance of 0.020. Rankings for service sires between methodologies ranged from 0.76 to 0.90. These lower values are most likely due to differences in the treatment of time-dependent covariates. The STM had greater predictive ability of daughter fertility at first insemination than the other methodologies. However, the CTM predicted daughter fertility more accurately in subsequent inseminations. The DPH and STM had a similar predictive ability of daughter fertility in second and subsequent inseminations.

Impact of genetic selection on management of boar replacement

Boars in an artificial insemination centre have been selected for their superior genetic potential, with 'superior' being defined as having traits the customer wants transmitted to his herd. The ability to meet the customers' needs depends on the heritability of the trait, the geneticist's success in devising a selection scheme for the trait in balance with other economically important traits, and the boar's ability to produce sperm that can fertilise oocytes. Genetic evaluation research over the past 20 years has greatly increased the number of traits for which a boar can be selected: currently in the Canadian national program, these include age at 100 kg, backfat at 100 kg, feed efficiency, lean yield and litter size. In the near future, traits that are very likely to be added to this selection list include piglet survival, marbling, loin eye area and structure traits. In Canada, sires are ranked on two estimated breeding value (EBV) indices; one, focused on development of terminal sire lines, is based on the growth and yield traits and another, primarily focused on maternal line development, de-emphasises these traits and incorporates litter size. Boars that are in Canadian AI centres because of their excellent growth traits are typically in the top 5-10% of the national population for terminal sire line index, but they may be only average or substandard for litter size. Conversely, boars selected to be in the top 5-10% for conveying such reproductive traits as litter size may only be in the top 33% for growth traits. The more offspring from a superior boar in either of these indices, the faster the population average for the trait improves. The original sire gets knocked out of the elite group, is culled and replaced by a higher ranked young boar from the now improved general population. Although genetic superiority should govern an AI centre's selection and culling of boars, decision-making in real life is seldom that simple. Selection criteria may be contradictory as above, or a boar with truly superior traits may be excluded because a newly-developed molecular genetics test determines he carries an undesirable gene such as PSS, RN or others being developed. Selection for terminal sire or maternal line traits can ignore important practical factors that affect an AI centre--boars with superior genetics may not produce good semen because skeletal or penile problems prevent ejaculation, or because sperm production is poor due to a genetic flaw, disease, or some other cause. Interestingly, selection pressure for one trait may inadvertently select for a trait that is linked but whose linkage is unrecognised, and such unintentionally selected genes could benefit, harm, or have no effect on production traits. An AI centre serving a variety of customers must select boars in anticipation of their customers' needs (including new, foreign and niche markets). A centre should also review its genetic evaluation results and progeny records, both to critique its own selection success and to try to detect unexpected linkages. Finally, an AI centre needs to predict its own future, selecting not just for production traits for the swine producer, but also for factors that enhance the centre's efficiency including boar conformation and temperament, and sperm quantity, quality and hardiness. Can we select for efficiency? Our colleagues in dairy cattle AI evaluate bull performance--should the swine industry consider evaluation of male fertility traits?

Post-testicular sperm environment and fertility

When mammalian spermatozoa exit the testis, they show a highly specialized morphology; however, they are not yet able to carry out their task: to fertilize an oocyte. This property, that includes the acquisition of motility and the ability to recognize and to fuse with the oocyte investments, is gained only after a transit through the epididymis during which the spermatozoa from the testis travel to the vas deferens. The exact molecular mechanisms that turn these cells into fertile gametes still remain mysterious, but surface-modifying events occurring in response to the external media are key steps in this process. Our laboratory has established cartographies of secreted (secretomes) and present proteins (proteomes) in the epididymal fluid of different mammals and have shown the regionalized variations in these fluid proteins along the epididymis. We have found that the main secreted proteins are common in different species and that enzymatic activities, capable of controlling the sperm surface changes, are present in the fluid. Our studies also indicate that the epididymal fluid is more complex than previously thought; it contains both soluble and particulate compartments such as exosome-like vesicles (epididymosomes) and certainly specific glycolipid-protein micelles. Understanding how these different compartments interplay to modify sperm components during their transit will be a necessary step if one wants to control and to ameliorate sperm quality and to obtain valuable fertility markers helpful to establish a male fertility based genetic selection.

Computing mating bull fertility from DHI nonreturn data

Animal model methodology was used to compute yearly measures of relative fertility of Holstein AI mating bulls based upon 70-d nonreturn of first breedings as reported to U.S. DHIA from 1988 through 1997. Estimated Relative Conception Rates (ERCR) were computed for bulls with a minimum of 50 first breedings in a single year using variance ratios 45.5 for mating bull, 45.5 for animal genetic effects, and 31 for permanent environment. The model assumed repeatability across lactations of 0.05 and included fixed effects of herd-year-month bred and classes of parity, early lactation energy-corrected milk and days open when bred. Estimates of fertility were greater for breedings to cows that were young, had low early lactation production, and were in late stages of lactation. ERCR were expressed as difference in nonreturn from the average AI mating bull of herdmates. Values ranged from -18 to +13. For ERCR computed from a minimum of 1000 breedings, 90% were within four units of zero. Early ERCR computed from a few breedings in a single year were tested for ability to predict later ERCR computed from a minimum of 1000 different breedings. Early ERCR computed from 300 or more matings accurately predicted later independent ERCR. For yearly estimates each based upon a minimum of 1000 breedings, 8% changed more than three units, and 4% declined more than three units. Correlations between ERCR and predicted transmitting abilities protein and type production index were significant but accounted for little variance. Correlations between ERCR and other traits were not significant.

Genetic and phenotypic parameters for 305-day yield, fertility, and survival in Holsteins

A data file containing 122,715 lactation records of Holstein cows distributed across 11,374 herd-year-season groups was analyzed to obtain (co)variance estimates for yield, fertility and cow survival. Milk, fat, and protein yields were adjusted to 305 d. Days open was truncated to 305 d, and the number of services was truncated to 9. Survival of a cow during a full lactation (305 d) was recorded as 0 (died) or 1 (alive). Variance components for the six traits were estimated using a multiple-trait animal model and the REML procedure. The model included herd-year-season, parity (three groups), age (three groups nested within parity), sex of calf (1 = male, 2 = female), and dystocia score (1 = no problem,..., 5 = extreme difficulty) as fixed effects and animal and permanent environment as random effects. Heritabilities for milk, fat, and protein yields, days open, number of services and cow survival were 0.2, 0.18, 0.18, 0.04, 0.03, and 0.002, respectively. The corresponding repeatabilities were 0.42, 0.41, 0.41, 0.12, 0.08, and 0.009, respectively. Genetic and phenotypic correlations between yield traits were high and positive, and correlations between yields and reproduction traits were high and antagonistic. Phenotypic correlations between survival and yields were about 0.1, and their genetic counterparts were slightly negative. Thus, management practices seem to keep mortality rates of high producing cows lower even though those cows have lower genetic potential for survival than do the low producers. Fertility traits showed a slightly undesirable (e.g., 0.1 to 0.3) genetic relationship with survival, but phenotypic correlations were essentially 0. For heifers, yields increased significantly with age. For cows in other parities, the significant changes with age were reduced fertility and reduced survival.

Lack of association between bovine major histocompatibility complex class II polymorphism and production traits

The relationship between bovine major histocompatibility class II polymorphism and fertility, growth, and milk production traits was investigated. A group of 196 young breeding bulls of the Swedish Red and White breed, selected from the yearly batches of bulls undergoing progeny testing, was typed for polymorphism in the class II DQ subregion using restriction fragment length analyses. The fertility traits included the bull's own fertility, measured as relative nonreturn rates, daughter fertility, measured as number of inseminations per service period, and daughters' frequency of veterinary treatment of cystic ovaries. Milk production traits included milk production and percentages of fat and protein measured on daughters during first lactation. In addition, measures of individual growth rate of the bulls were included. Despite the relatively large amount of data analyzed and the accurate measures on milk production traits, no convincing association was revealed between DQ polymorphism and any of the traits investigated. The results suggest that the bovine class II polymorphism is selectively neutral in relation to the analyzed growth, fertility, and milk production traits.

Genetic analysis of fertility traits in Israeli Holsteins by linear and threshold models

Conception rates of Israeli Holstein cows and heifers were analyzed separately by linear and threshold models. Fixed effects for both data files were insemination number, AI institute, geographical region, and calendar month. Analysis of cows also included the fixed effects of parity, calving status, and DIM at insemination. Random effects included in the models were herd-year-season, insemination technician, sire of cow, and service sire. Fixed effect solutions for heifers and cows were not similar. For cows, insemination month had the greatest effect on conception rate. Heritability of conception rate ranged from 2 to 3.5% for heifers and from 1 to 2% for cows. Correlations between corresponding threshold and linear model random effect solutions were all greater than or equal to .99. Correlations between heifer and cow analyses for sire and service sire solutions were less than .4. Analysis with an incorrect herd-year-season variance component affected only the technician solutions.

Relationship of bull fertility with daughter fertility and production traits in Holstein dairy cattle

The phenotypic and genetic correlations between fertility ratings of AI bulls for conception rate and their estimated breeding values for daughters' fertility and production traits were calculated. Genetic correlations between fertility ratings of bulls for conception and heifer fertility traits (age at first breeding, age at last breeding, and number of insemination per conception) were negative and ranged from -.04 to -.23, indicating daughters of bulls with high fertility ratings were younger at first breeding and required fewer services to conceive. In general, genetic correlations between fertility ratings of bulls for conception rate and cow fertility traits (days from calving to first breeding, days open, and number of inseminations per conception) and production traits (breed class average milk and fat and fat percentage) in the first two lactations were also moderate to high and in the favorable direction. Although heritability of both male and female fertility is low, these data indicate that heavy use of sires with high fertility ratings could have a mild positive effect on both male and female fertility. Evidence is also found to indicate that in this breed, selection for increased milk yield should not impair genetic ability of cows to reproduce.