Whole-genome scan reveals significant non-additive effects for sire conception rate in Holstein cattle

Estimation of genetic parameters for bull conception rate and its genetic correlations with semen production traits in Japanese Black bulls

The P of achieving pregnancy is an important trait of bull fertility in beef cattle and is defined as the bull conception rate (BCR). This study aimed to clarify and better understand the genetic architecture of the BCR calculated using artificial insemination and pregnancy diagnosis records from a progeny testing program in Japanese Black bulls. In this study, we estimated the genetic parameters of the BCR and their correlation with semen production traits. In addition, we assessed the correlated responses in BCR by considering the selection of semen production traits. Nine hundred and sixteen Japanese Black bulls were selected based on fertility, with 28 869 pregnancy diagnostic records from the progeny testing program. Our results showed that the heritability estimate was 0.04 in the BCR at the first service and 0.14 in BCR for the three services, and an increase in the inbreeding coefficient led to a significant decrease in BCR. The phenotypic trend of BCR remained almost constant over the years, whereas the genetic trend increased. In addition, the changes in the progeny testing year effect showed a similar tendency to the phenotypic trends, suggesting that the phenotypic trends could be mainly due to non-genetic effects, including progeny testing year effects. The estimated genetic correlation of BCR with sperm motility traits was favorably moderate to high (ranging from 0.49 to 0.97), and those with sperm quantity traits such as semen volume were favorably low to moderate (ranging from 0.23 to 0.51). In addition, the correlated responses in BCR at the first service by selection for sperm motility traits resulted in a higher genetic gain than direct selection. This study provides new insights into the genetic factors affecting BCR and the possibility of implementing genetic selection to improve BCR by selecting sperm motility traits in Japanese Black bulls.

Genomics and Dairy Bull Fertility

Current evidence suggests that dairy bull fertility is influenced by genetic factors, and hence, it could be managed and improved by genetic means. There are major mutations that explain about 4% to 8% of the observed differences in conception rate between bulls segregating in most dairy breeds. Research has shown that genomic prediction of bull fertility is possible, and this could be used to make accurate genome-guided selection decisions, such as early culling of predicted subfertile bull calves. Inbreeding negatively influences bull fertility, and the increase in homozygosity seems an important risk factor for dairy bull subfertility.

Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle

Breeding bulls are well suited to investigate inherited variation in male fertility because they are genotyped and their reproductive success is monitored through semen analyses and thousands of artificial inseminations. However, functional data from relevant tissues are lacking in cattle, which prevents fine-mapping fertility-associated genomic regions. Here, we characterize gene expression and splicing variation in testis, epididymis, and vas deferens transcriptomes of 118 mature bulls and conduct association tests between 414,667 molecular phenotypes and 21,501,032 genome-wide variants to identify 41,156 regulatory loci. We show broad consensus in tissue-specific and tissue-enriched gene expression between the three bovine tissues and their human and murine counterparts. Expression- and splicing-mediating variants are more than three times as frequent in testis than epididymis and vas deferens, highlighting the transcriptional complexity of testis. Finally, we identify genes (WDR19, SPATA16, KCTD19, ZDHHC1) and molecular phenotypes that are associated with quantitative variation in male fertility through transcriptome-wide association and colocalization analyses.

Adenylate kinase 9 is essential for sperm function and male fertility in mammals

Despite passing routine laboratory tests for semen quality, bulls used in artificial insemination exhibit significant variation in fertility. Routine analysis of fertility data identified a dairy bull with extreme subfertility (10% pregnancy rate). To characterize the subfertility phenotype, a range of in vitro, in vivo, and molecular assays were carried out. Sperm from the subfertile bull exhibited reduced motility and severely reduced caffeine-induced hyperactivation compared to controls. Ability to penetrate the zona pellucida, cleavage rate, cleavage kinetics, and blastocyst yield after IVF or AI were significantly lower than in control bulls. Whole-genome sequencing from semen and RNA sequencing of testis tissue revealed a critical mutation in adenylate kinase 9 (AK9) that impaired splicing, leading to a premature termination codon and a severely truncated protein. Mice deficient in AK9 were generated to further investigate the function of the gene; knockout males were phenotypically indistinguishable from their wild-type littermates but produced immotile sperm that were incapable of normal fertilization. These sperm exhibited numerous abnormalities, including a low ATP concentration and reduced motility. RNA-seq analysis of their testis revealed differential gene expression of components of the axoneme and sperm flagellum as well as steroid metabolic processes. Sperm ultrastructural analysis showed a high percentage of sperm with abnormal flagella. Combined bovine and murine data indicate the essential metabolic role of AK9 in sperm motility and/or hyperactivation, which in turn affects sperm binding and penetration of the zona pellucida. Thus, AK9 has been found to be directly implicated in impaired male fertility in mammals.

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.

Genome-wide association study reveals candidate markers related to field fertility and semen quality traits in Holstein-Friesian bulls

In vitro assessment of bull semen quality is routinely used in bull semen processing centres in order to ensure that semen destined to be used in the field has passed minimum standards. Despite these stringent quality control checks, individual bulls that pass the quality control checks can still vary in field fertility by up to 25%. A genome-wide association study was undertaken to determine genetic markers associated with prefreeze and post-thaw bull sperm quality traits as well as field fertility. Genome-wide association analysis was performed using a single nucleotide polymorphism (SNP) regression mixed linear model in WOMBAT. Genes within a 250 Kb span of a suggestive (P ≤ 1 × 10^-5) SNP were considered as candidate genes. One SNP was associated with adjusted pregnancy rate, and 21 SNPs were associated across the seven semen quality traits (P ≤ 1 × 10^-5). Functional candidate genes include SIPA1L2 which was associated with adjusted pregnancy rate. This encodes a Rap GTPase-activating protein involved in Rap1 signalling pathway and was previously found to play a role in the process of sperm differentiation. Gene ontology (GO) analysis also identified significantly enriched biological processes involved protein tyrosine kinase activity including genes such as DYRK1A, TEC and TXK that were associated with sperm motility prior to freezing. Another candidate gene associated with post-thaw sperm motility was FHDC1 which coordinates actin filament and microtubule dynamics. The induced 11 GO terms in the ejaculates rejected after freezing trait were related to ATPase, phosphatase and hydrolase activity. These results reveal novel specific genomic regions and candidate genes associated with economically important phenotypes such as field fertility and semen quality traits.

A potent allele marker related to low bull conception rate in Japanese Black bulls

Over the years, there has been considerable variation in the bull conception rate (BCR) of Japanese Black cattle; moreover, several Japanese Black bulls with a low BCR of ≤10% have been identified. However, the alleles responsible for the low BCR are not determined yet. Therefore, in this study, we aimed to identify single-nucleotide polymorphisms (SNPs) for predicting low BCR. To this end, the genome of Japanese Black bulls was comprehensively examined by a genome-wide association study with whole-exome sequencing (WES), and the effect of the identified marker regions on BCR was determined. The WES analysis of six sub-fertile bulls with a BCR of ≤10% and 73 normal bulls with a BCR of ≥40% identified a homozygous genotype for low BCR in Bos taurus autosome 5 in the region between 116.2 and 117.9 Mb. The g.116408653G > A SNP in this region had the most significant effect on the BCR (P-value = 1.0 × 10^-23), and the GG (55.4 ± 11.2%) and AG (54.4 ± 9.4%) genotypes in the SNP had a higher phenotype than the AA (9.5 ± 6.1%) genotype for the BCR. The mixed model analysis revealed that g.116408653G > A was related to approximately 43% of the total genetic variance. In conclusion, the AA genotype of g.116408653G > A is a useful index for identifying sub-fertile Japanese Black bulls. Some positive and negative effects of SNP on the BCR were presumed to identify the causative mutations, which can help evaluate bull fertility.

Within- and between-Breed Selection Signatures in the Original and Improved Valachian Sheep

This study explored the genomic diversity and selection signatures in two Slovakian national breeds, the Original Valachian and the Improved Valachian sheep. As they are an important animal genetic resource within the country, but with decreasing population size, our aim is to identify potentially valuable genomic regions. A total of 97 sheep (18 male and 79 female) from the Original Valachian, and 69 sheep (25 male and 44 female) from the Improved Valachian populations were genotyped using the GeneSeek GGP Ovine 50 K chip. The inbreeding levels were assessed with runs of homozygosity (ROH). The selection signatures within breeds were identified based on the top 1% of most homozygous regions within the breed, the so-called ROH islands. The selection signatures between breeds were assessed based on variance in linkage disequilibrium. Overall, we have identified selection signatures with quantitative trait loci (QTL) and genes pointing towards all three production purposes of the Valachian sheep, milk, meat, and wool, including their quality characteristics. Another group with apparent large importance was the various traits related to health and resistance to parasites, which is well in line with the sturdy nature of this breed.

Genome-wide detection of non-additive quantitative trait loci for semen production traits in beef and dairy bulls

Semen production traits are important aspects of bull fertility, because semen quantity leads to direct profits for artificial insemination centres, and semen quality is associated with the probability of achieving a pregnancy. Most genome-wide association studies (GWASs) for semen production traits have assumed that each quantitative trait locus (QTL) has an additive effect. However, GWASs that account for non-additive effects are also important in fitness traits, such as bull fertility. Here, we performed a GWAS using models that accounted for additive and non-additive effects to evaluate the importance of non-additive effects on five semen production traits in beef and dairy bulls. A total of 65 463 records for 615 Japanese Black bulls (JB) and 50 734 records for 873 Holstein bulls (HOL), which were previously genotyped using the Illumina BovineSNP50 BeadChip, were used to estimate genetic parameters and perform GWAS. The heritability estimates were low (ranged from 0.11 to 0.23), and the repeatability estimates were low to moderate (ranged from 0.28 to 0.45) in both breeds. The estimated repeatability was approximately twice as high as the estimated heritability for all traits. In this study, only one significant region with an additive effect was detected in each breed, but multiple significant regions with non-additive effects were detected for each breed. In particular, the region at approximately 64 Mbp on Bos taurus autosome 17 had the highest significant non-additive effect on four semen production traits in HOL. The rs41843851 single nucleotide polymorphism (SNP) in the region had a much lower P-value for the non-additive effect (P-value = 1.1 × 10^-31) than for the additive effect (P-value = 1.1 × 10^-8) in sperm motility. The AA and AB genotypes on the SNP had a higher phenotype than the BB genotype in HOL, and there was no bull with the BB genotype in JB. Our results showed that non-additive QTLs affect semen production traits, and a novel QTL accounting for non-additive effects could be detected by GWAS. This study provides new insights into non-additive QTLs that affect fitness traits, such as semen production traits in beef and dairy bulls.

Evaluation of bull fertility in Italian Brown Swiss dairy cattle using cow field data

Dairy bull fertility is traditionally evaluated using semen production and quality traits; however, these attributes explain only part of the differences observed in fertility among bulls. Alternatively, bull fertility can be directly evaluated using cow field data. The main objective of this study was to investigate bull fertility in the Italian Brown Swiss dairy cattle population using confirmed pregnancy records. The data set included a total of 397,926 breeding records from 1,228 bulls and 129,858 lactating cows between first and fifth lactation from 2000 to 2019. We first evaluated cow pregnancy success, including factors related to the bull under evaluation, such as bull age, bull inbreeding, and AI organization, and factors associated with the cow that receives the dose of semen, including herd-year-season, cow age, parity, and milk yield. We then estimated sire conception rate using only factors related to the bull. Model predictive ability was evaluated using 10-fold cross-validation with 10 replicates. Interestingly, our analyses revealed that there is a substantial variation in conception rate among Brown Swiss bulls, with more than 20% conception rate difference between high-fertility and low-fertility bulls. We also showed that the prediction of bull fertility is feasible as our cross-validation analyses achieved predictive correlations equal to 0.30 for sire conception rate. Improving reproduction performance is one of the major challenges of the dairy industry worldwide, and for this, it is essential to have accurate predictions of service sire fertility. This study represents the foundation for the development of novel tools that will allow dairy producers, breeders, and artificial insemination companies to make enhanced management and selection decisions on Brown Swiss male fertility.

Targeted sequencing reveals candidate causal variants for dairy bull subfertility

Bull fertility is a key factor for successful reproductive performance in dairy cattle. Since the semen from a single bull can be used to inseminate hundreds of cows, one subfertile bull could have a major impact on herd reproductive efficiency. We have previously identified five genomic regions, located on BTA8 (72.2 Mb), BTA9 (43.7 Mb), BTA13 (60.2 Mb), BTA17 (63.3 Mb), and BTA27 (34.7 Mb), that show large dominance effects on bull fertility. Each of these regions explained about 5–8% of the observed differences in sire conception rate between Holstein bulls. Here, we aimed to identify candidate causal variants responsible for this variation using targeted sequencing (10 Mb per region). For each genomic region, two DNA pools were constructed from n≈20 high‐fertility and n≈20 low‐fertility Holstein bulls. The DNA‐sequencing analysis included reads quality control (using FastQC), genome alignment (using BWA and ARS‐UCD1.2), variant calling (using GATK) and variant annotation (using Ensembl). The sequencing depth per pool varied from 39× to 51×. We identified a set of nonsense mutations, missense mutations, and frameshift variants carried by low‐fertility bulls. Notably, some of these variants were classified as strong candidate causal variants, i.e., mutations with deleterious effects located on genes exclusively/highly expressed in testis. Genes affected by these candidate causal variants include AK9, TTLL9, TCHP, and FOXN4. These results could aid in the development of novel genomic tools that allow early detection and culling of subfertile bull calves.

Autosomal recessive loci contribute significantly to quantitative variation of male fertility in a dairy cattle population

BACKGROUND

Cattle are ideally suited to investigate the genetics of male fertility. Semen from individual bulls is used for thousands of artificial inseminations for which the fertilization success is monitored. Results from the breeding soundness examination and repeated observations of semen quality complement the fertility evaluation for each bull.

RESULTS

In a cohort of 3881 Brown Swiss bulls that had genotypes at 683,609 SNPs, we reveal four novel recessive QTL for male fertility on BTA1, 18, 25, and 26 using haplotype-based association testing. A QTL for bull fertility on BTA1 is also associated with sperm head shape anomalies. All other QTL are not associated with any of the semen quality traits investigated. We perform complementary fine-mapping approaches using publicly available transcriptomes as well as whole-genome sequencing data of 125 Brown Swiss bulls to reveal candidate causal variants. We show that missense or nonsense variants in SPATA16, VWA3A, ENSBTAG00000006717 and ENSBTAG00000019919 are in linkage disequilibrium with the QTL. Using whole-genome sequence data, we detect strong association (P = 4.83 × 10^- 12) of a missense variant (p.Ile193Met) in SPATA16 with male fertility. However, non-coding variants exhibit stronger association at all QTL suggesting that variants in regulatory regions contribute to variation in bull fertility.

CONCLUSION

Our findings in a dairy cattle population provide evidence that recessive variants may contribute substantially to quantitative variation in male fertility in mammals. Detecting causal variants that underpin variation in male fertility remains difficult because the most strongly associated variants reside in poorly annotated non-coding regions.

Host genetics exerts lifelong effects upon hindgut microbiota and its association with bovine growth and immunity

The gut microbiota is a complex ecological community that plays multiple critical roles within a host. Known intrinsic and extrinsic factors affect gut microbiota structure, but the influence of host genetics is understudied. To investigate the role of host genetics upon the gut microbiota structure, we performed a longitudinal study in which we evaluated the hindgut microbiota and its association with animal growth and immunity across life. We evaluated three different growth stages in an Angus-Brahman multibreed population with a graduated spectrum of genetic variation, raised under variable environmental conditions and diets. We found the gut microbiota structure was changed significantly during growth when preweaning, and fattening calves experienced large variations in diet and environmental changes. However, regardless of the growth stage, we found gut microbiota is significantly influenced by breed composition throughout life. Host genetics explained the relative abundances of 52.2%, 40.0%, and 37.3% of core bacterial taxa at the genus level in preweaning, postweaning, and fattening calves, respectively. Sutterella, Oscillospira, and Roseburia were consistently associated with breed composition at these three growth stages. Especially, butyrate-producing bacteria, Roseburia and Oscillospira, were associated with nine single-nucleotide polymorphisms (SNPs) located in genes involved in the regulation of host immunity and metabolism in the hindgut. Furthermore, minor allele frequency analysis found breed-associated SNPs in the short-chain fatty acids (SCFAs) receptor genes that promote anti-inflammation and enhance intestinal epithelial barrier functions. Our findings provide evidence of dynamic and lifelong host genetic effects upon gut microbiota, regardless of growth stages. We propose that diet, environmental changes, and genetic components may explain observed variation in critical hindgut microbiota throughout life.

Genome-wide scan reveals important additive and non-additive genetic effects associated with resistance to Haemonchus contortus in Florida Native sheep

Florida Native sheep is among the sheep breeds best adapted to humid and hot climatic conditions such as those of Florida, USA, and have shown a superior ability to regulate nematode burdens. This is one of the oldest sheep breeds in North America and is an endangered species. To ensure genetic diversity and long-term survival of the breed, protection of the current genetic stock is critical and conservation efforts are required to promote its breeding and production. The objective of the present study was to investigate the importance of additive and non-additive genetic effects on resistance to natural Haemonchus contortus infections in Florida Native sheep using a whole genome scan. A total of 200 sheep were evaluated in the present study. Phenotypic records included faecal egg count (FEC, eggs/gram), FAMACHA® score, packed cell volume (PCV, %), body condition score and average daily gain (ADG, kg). Sheep were genotyped using the GGP Ovine 50K SNP chip and 45.2 k single nucleotide polymorphism (SNP) markers spanning the entire genome were available for quality control procedures. Mixed models were used to analyse the response variables and included the identity by state matrix to control for population structure. Bonferroni correction was used to control for multiple testing and a second arbitrary threshold (0.1 × 10^-3) was used. Fifteen SNPs with additive and non-additive genetic effects and located in Ovis aries chromosome OAR1, 2, 3, 6, 8, 10, 11, 12, 13 and 21 were associated with FEC, FAMACHA® score, PCV and ADG. These SNPs could be potential genetic markers for resistance to natural H. contortus exposure in Florida Native sheep.

Marker-assisted selection vis-à-vis bull fertility: coming full circle-a review

Bull fertility is considered an indispensable trait, as far as farm economics is concerned since it is the successful conception in a cow that provides calf crop, along with the ensuing lactation. This ensures sustainability of a dairy farm. Traditionally, bull fertility did not receive much attention by the farm managers and breeding animals were solely evaluated based on phenotypic predictors, namely, sire conception rate and seminal parameters in bull. With the advent of the molecular era in animal breeding, attempts were made to unravel the genetic complexity of bull fertility by the identification of genetic markers related to the trait. Marker-Assisted Selection (MAS) is a methodology that aims at utilizing the genetic information at markers and selecting improved populations for important traits. Traditionally, MAS was pursued using a candidate gene approach for identifying markers related to genes that are already known to have a physiological function related to the trait but this approach had certain shortcomings like stringent criteria for significance testing. Now, with the availability of genome-wide data, the number of markers identified and variance explained in relation to bull fertility has gone up. So, this presents a unique opportunity to revisit MAS by selection based on the information of a large number of genome-wide markers and thus, improving the accuracy of selection.

Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes

Background

Transforming large amounts of genomic data into valuable knowledge for predicting complex traits has been an important challenge for animal and plant breeders. Prediction of complex traits has not escaped the current excitement on machine-learning, including interest in deep learning algorithms such as multilayer perceptrons (MLP) and convolutional neural networks (CNN). The aim of this study was to compare the predictive performance of two deep learning methods (MLP and CNN), two ensemble learning methods [random forests (RF) and gradient boosting (GB)], and two parametric methods [genomic best linear unbiased prediction (GBLUP) and Bayes B] using real and simulated datasets.

Methods

The real dataset consisted of 11,790 Holstein bulls with sire conception rate (SCR) records and genotyped for 58k single nucleotide polymorphisms (SNPs). To support the evaluation of deep learning methods, various simulation studies were conducted using the observed genotype data as template, assuming a heritability of 0.30 with either additive or non-additive gene effects, and two different numbers of quantitative trait nucleotides (100 and 1000).

Results

In the bull dataset, the best predictive correlation was obtained with GB (0.36), followed by Bayes B (0.34), GBLUP (0.33), RF (0.32), CNN (0.29) and MLP (0.26). The same trend was observed when using mean squared error of prediction. The simulation indicated that when gene action was purely additive, parametric methods outperformed other methods. When the gene action was a combination of additive, dominance and of two-locus epistasis, the best predictive ability was obtained with gradient boosting, and the superiority of deep learning over the parametric methods depended on the number of loci controlling the trait and on sample size. In fact, with a large dataset including 80k individuals, the predictive performance of deep learning methods was similar or slightly better than that of parametric methods for traits with non-additive gene action.

Conclusions

For prediction of traits with non-additive gene action, gradient boosting was a robust method. Deep learning approaches were not better for genomic prediction unless non-additive variance was sizable.

Gene mapping and genomic prediction of bull fertility using sex chromosome markers

Service sire has been recognized as an important factor affecting dairy herd fertility. Our group has reported promising results on gene mapping and genomic prediction of dairy bull fertility using autosomal SNP markers. Little is known, however, about the genetic contribution of sex chromosomes, which are enriched in genes related to sexual development and reproduction. As such, the main goal of this study was to investigate the effect of SNP markers on X and Y chromosomes (BTAX and BTAY, respectively) on sire conception rate (SCR) in US Holstein bulls. The analysis included a total of 5,014 bulls with SCR records and genotypes for roughly 291k SNP located on the autosomes, 1.5k SNP located on the pseudoautosomal region (PAR), 13.7k BTAX-specific SNP, and 24 BTAY-specific SNP. We first performed genomic scans of the sex chromosomes, and then we evaluated the genomic prediction of SCR including BTAX SNP markers in the predictive models. Two markers located on PAR and 3 markers located on the X-specific region showed significant associations with sire fertility. Interestingly, these regions harbor genes, such as FAM9B, TBL1X, and PIH1D3, that are directly implicated in testosterone concentration, spermatogenesis, and sperm motility. On the other hand, BTAY showed very low genetic variability, and none of the segregating markers were associated with SCR. Notably, model predictive ability was largely improved by including BTAX markers. Indeed, the combination of autosomal with BTAX SNP delivered predictive correlations around 0.343, representing an increase in accuracy of about 7.5% compared with the standard whole autosomal genome approach. Overall, this study provides evidence of the importance of both PAR and X-specific regions in male fertility in dairy cattle. These findings may help to improve conception rates in dairy herds through accurate genome-guided decisions on bull fertility.

Association of α/β-Hydrolase D16B with Bovine Conception Rate and Sperm Plasma Membrane Lipid Composition

We have identified a Holstein sire named Tarantino who had been approved for artificial insemination that is based on normal semen characteristics (i.e., morphology, thermoresistance, motility, sperm concentration), but had no progeny after 412 first inseminations, resulting in a non-return rate (NRdev) of -29. Using whole genome association analysis and next generation sequencing, an associated nonsense variant in the α/β-hydrolase domain-containing 16B gene (ABHD16B) on bovine chromosome 13 was identified. The frequency of the mutant allele in the German Holstein population was determined to be 0.0018 in 222,645 investigated cattle specimens. The mutant allele was traced back to Whirlhill Kingpin (bornFeb. 13th, 1959) as potential founder. The expression of ABHD16B was detected by Western blotting and immunohistochemistry in testis and epididymis of control bulls. A lipidome comparison of the plasma membrane of fresh semen from carriers and controls showed significant differences in the concentration of phosphatidylcholine (PC), diacylglycerol (DAG), ceramide (Cer), sphingomyelin (SM), and phosphatidylcholine (-ether) (PC O-), indicating that ABHD16B plays a role in lipid biosynthesis. The altered lipid contents may explain the reduced fertilization ability of mutated sperms.

Selection for bull fertility: a review

Fertility is a critically important factor in cattle production because it directly relates to the ability to produce the offspring necessary to offset costs in production systems. Female fertility has received much attention and has been enhanced through assisted reproductive technologies, as well as genetic selection; however, improving bull fertility has been largely ignored. Improvements in bull reproductive performance are necessary to optimize the efficiency of cattle production. Selection and management to improve bull fertility not only have the potential to increase conception rates but also have the capacity to improve other economically relevant production traits. Bull fertility has reportedly been genetically correlated with traits such as average daily gain, heifer pregnancy, and calving interval. Published studies show that bull fertility traits are low to moderately heritable, indicating that improvements in bull fertility can be realized through selection. Although female fertility has continued to progress according to increasing conception rates, the reported correlation between male and female fertility is low, indicating that male fertility cannot be improved by selection for female fertility. Correlations between several bull fertility traits, such as concentration, number of spermatozoa, motility, and number of spermatozoa abnormalities, vary among studies. Using male fertility traits in selection indices would provide producers with more advanced selection tools. The objective of this review was to discuss current beef bull fertility measurements and to discuss the future of genetic evaluation of beef bull fertility and potential genetic improvement strategies.

Predicting male fertility in dairy cattle using markers with large effect and functional annotation data

Background

Fertility is among the most important economic traits in dairy cattle. Genomic prediction for cow fertility has received much attention in the last decade, while bull fertility has been largely overlooked. The goal of this study was to assess genomic prediction of dairy bull fertility using markers with large effect and functional annotation data. Sire conception rate (SCR) was used as a measure of service sire fertility. Dataset consisted of 11.5 k U.S. Holstein bulls with SCR records and about 300 k single nucleotide polymorphism (SNP) markers. The analyses included the use of both single-kernel and multi-kernel predictive models fitting either all SNPs, markers with large effect, or markers with presumed functional roles, such as non-synonymous, synonymous, or non-coding regulatory variants.

Results

The entire set of SNPs yielded predictive correlations of 0.340. Five markers located on chromosomes BTA8, BTA9, BTA13, BTA17, and BTA27 showed marked dominance effects. Interestingly, the inclusion of these five major markers as fixed effects in the predictive models increased predictive correlations to 0.403, representing an increase in accuracy of about 19% compared with the standard model. Single-kernel models fitting functional SNP classes outperformed their counterparts using random sets of SNPs, suggesting that the predictive power of these functional variants is driven in part by their biological roles. Multi-kernel models fitting all the functional SNP classes together with the five major markers exhibited predictive correlations around 0.405.

Conclusions

The inclusion of markers with large effect markedly improved the prediction of dairy sire fertility. Functional variants exhibited higher predictive ability than random variants, but did not outperform the standard whole-genome approach. This research is the foundation for the development of novel strategies that could help the dairy industry make accurate genome-guided selection decisions on service sire fertility.

Genomic prediction of bull fertility in US Jersey dairy cattle

Service sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included the use of linear and Gaussian kernel-based models fitting either all the SNP or subsets of markers with presumed functional roles, such as SNP significantly associated with SCR or SNP located within or close to annotated genes. Model predictive ability was evaluated using 5-fold cross-validation with 10 replicates. The entire SNP set exhibited predictive correlations around 0.30. Interestingly, either SNP marginally associated with SCR or genic SNP achieved higher predictive abilities than their counterparts using random sets of SNP. Among alternative SNP subsets, Gaussian kernel models fitting significant SNP achieved the best performance with increases in predictive correlation up to 7% compared with the standard whole-genome approach. Notably, the use of a multi-breed reference population including the entire US Holstein SCR data set (11.5k bulls) allowed us to achieve predictive correlations up to 0.315, gaining 8% in accuracy compared with the standard model fitting a pure Jersey reference set. Overall, our findings indicate that genomic prediction of Jersey bull fertility is feasible. The use of Gaussian kernels fitting markers with relevant roles and the inclusion of Holstein records in the training set seem to be promising alternatives to the standard whole-genome approach. These results have the potential to help the dairy industry improve US Jersey sire fertility through accurate genome-guided decisions.

Genetic dissection of bull fertility in US Jersey dairy cattle

The service sire has been recognized as an important factor affecting herd fertility in dairy cattle. Recent studies suggest that genetic factors explain part of the difference in fertility among Holstein sires. The main objective of this study was to dissect the genetic architecture of sire fertility in US Jersey cattle. The dataset included 1.5 K Jersey bulls with sire conception rate (SCR) records and 96 K single nucleotide polymorphism (SNP) markers spanning the whole genome. The analysis included whole‐genome scans for both additive and non‐additive effects and subsequent functional enrichment analyses using KEGG Pathway, Gene Ontology (GO) and Medical Subject Headings (MeSH) databases. Ten genomic regions located on eight different chromosomes explained more than 0.5% of the additive genetic variance for SCR. These regions harbor genes, such as PKDREJ,EPB41L2,PDGFD,STX2,SLC25A20 and IP6K1, that are directly implicated in testis development and spermatogenesis, sperm motility and the acrosome reaction. In addition, the genomic scan for non‐additive effects identified two regions on BTA11 and BTA25 with marked recessive effects. These regions harbor three genes—FER1L5,CNNM4 and DNAH3—with known roles in sperm biology. Moreover, the gene‐set analysis revealed terms associated with calcium regulation and signaling, membrane fusion, sperm cell energy metabolism, GTPase activity and MAPK signaling. These gene sets are directly implicated in sperm physiology and male fertility. Overall, this integrative genomic study unravels genetic variants and pathways affecting Jersey bull fertility. These findings may contribute to the development of novel genomic strategies for improving sire fertility in Jersey cattle.