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

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.

Impact of Sire on Embryo Development and Pregnancy

The use of in vitro embryo production (IVP) has increased globally, particularly in the United States. Although maternal factors influencing embryo development have been extensively studied, the influence of the sire is not well understood. Sperm plays a crucial role in embryo development providing DNA, triggering oocyte maturation, and aiding in mitosis. Current sire fertility measurements do not consistently align with embryo production outcomes. Low-fertility sires may perform well in IVP systems but produce fewer pregnancies. Testing sires in vitro could identify characteristics affecting embryo development and pregnancy loss risk in IVP and embryo transfer programs.

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.

High-precision early warning system for rice cadmium accumulation risk assessment

Rapid global industrialization has resulted in widespread cadmium contamination in agricultural soils and products. A considerable proportion of rice consumers are exposed to Cd levels above the provisional safe intake limit, raising widespread environmental concerns on risk management. Therefore, a generalized approach is urgently needed to enable correct evaluation and early warning of cadmium contaminants in rice products. Combining big data and computer science together, this study developed a system named "SMART Cd Early Warning", which integrated 4 modules including genotype-to-phenotype (G2P) modelling, high-throughput sequencing, G2P prediction and rice Cd contamination risk assessment, for rice cadmium accumulation early warning. This system can rapidly assess the risk of rice cadmium accumulation by genotyping leaves at seeding stage. The parameters including statistical methods, population size, training population-testing population ratio, SNP density were assessed to ensure G2P model exhibited superior performance in terms of prediction precision (up to 0.76 ± 0.003) and computing efficiency (within 2 h). In field trials of cadmium-contaminated farmlands in Wenling and Fuyang city, Zhejiang Province, "SMART Cd Early Warning" exhibited superior capability for identification risk rice varieties, suggesting a potential of "SMART Cd Early-Warning system" in OsGCd risk assessment and early warning in the age of smart.

Transcriptome annotation of 17 porcine tissues using nanopore sequencing technology

The annotation of animal genomes plays an important role in elucidating molecular mechanisms behind the genetic control of economically important traits. Here, we employed long-read sequencing technology, Oxford Nanopore Technology, to annotate the pig transcriptome across 17 tissues from two Yorkshire littermate pigs. More than 9.8 million reads were obtained from a single flow cell, and 69 781 unique transcripts at 50 108 loci were identified. Of these transcripts, 16 255 were found to be novel isoforms, and 22 344 were found at loci that were novel and unannotated in the Ensembl (release 102) and NCBI (release 106) annotations. Novel transcripts were mostly expressed in cerebellum, followed by lung, liver, spleen, and hypothalamus. By comparing the unannotated transcripts to existing databases, there were 21 285 (95.3%) transcripts matched to the NT database (v5) and 13 676 (61.2%) matched to the NR database (v5). Moreover, there were 4324 (19.4%) transcripts matched to the SwissProt database (v5), corresponding to 11 356 proteins. Tissue-specific gene expression analyses showed that 9749 transcripts were highly tissue-specific, and cerebellum contained the most tissue-specific transcripts. As the same samples were used for the annotation of cis-regulatory elements in the pig genome, the transcriptome annotation generated by this study provides an additional and complementary annotation resource for the Functional Annotation of Animal Genomes effort to comprehensively annotate the pig genome.

Beta-defensins as marker for male fertility: a comprehensive review†

Bovine male fertility in animals has a direct impact on the productivity of dairy herds. The epididymal sperm maturations involve extensive sperm surface modifications to gain the fertilizing ability, especially by absorptions of the plethora of biomolecules, including glycoprotein beta-defensins (BDs), enzymes, organic ions, protein, and phospholipids. Defensins are broad-range nonspecific antimicrobial peptides that exhibit strong relations with innate and adaptive immunity, but their roles in male fertility are relatively recently identified. In the course of evolution, BD genes give rise to different clusters with specific functions, especially reproductive functions, by undergoing duplications and nonsynonymous mutations. BD polymorphisms have been reported with milk compositions, disease resistance, and antimicrobial activities. However, in recent decades, the link of BD polymorphisms with fertility has emerged as an appealing improvement of reproductive performance such as sperm motility, membrane integrity, cervical mucus penetration, evading of uterus immunosurveillance, oviduct cell attachment, and egg recognition. The reproductive-specific glycosylated BD class-A BDs (CA-BDs) have shown age- and sex-specific expressions in male reproductive organs, signifying their physiological pleiotropism, especially in the sperm maturation and sperm transport in the female reproductive tract. By considering adult male reproductive organ-specific BD expressions, importance in sperm functionalities, and bioinformatic analysis, we have selected two bovine BBD126 and BBD129 genes as novel potential biomarkers of bovine male fertility. Despite the importance of BDs, however, genomic characterization of most BD genes across most livestock and nonmodel organisms remains predictive/incomplete. The current review discusses our understanding of BD pleiotropic functions, polymorphism, and genomic structural attributes concerning the fertilizability of the male gamete in dairy animals.

Genomic selection for survival under naturally occurring Saprolegnia oomycete infection in farmed European whitefish Coregonus lavaretus

Saprolegnia oomycete infection causes serious economic losses and reduces fish health in aquaculture. Genomic selection based on thousands of DNA markers is a powerful tool to improve fish traits in selective breeding programs. Our goal was to develop a single nucleotide polymorphism (SNP) marker panel and to test its use in genomic selection for improved survival against Saprolegnia infection in European whitefish Coregonus lavaretus, the second most important farmed fish species in Finland. We used a double digest restriction site associated DNA (ddRAD) genotyping by sequencing method to produce a SNP panel, and we tested it analyzing data from a cohort of 1,335 fish, which were measured at different times for mortality to Saprolegnia oomycete infection and weight traits. We calculated the genetic relationship matrix (GRM) from the genome-wide genetic data, integrating it in multivariate mixed models used for the estimation of variance components and genomic breeding values (GEBVs), and to carry out Genome-Wide Association Studies for the presence of quantitative trait loci (QTL) affecting the phenotypes in analysis. We identified one major QTL on chromosome 6 affecting mortality to Saprolegnia infection, explaining 7.7% to 51.3% of genetic variance, and a QTL for weight on chromosome 4, explaining 1.8% to 5.4% of genetic variance. Heritability for mortality was 0.20 to 0.43 on the liability scale, and heritability for weight was 0.44 to 0.53. The QTL for mortality showed an additive allelic effect. We tested whether integrating the QTL for mortality as a fixed factor, together with a new GRM calculated excluding the QTL from the genetic data, would improve the accuracy estimation of GEBVs. This test was done through a cross-validation approach, which indicated that the inclusion of the QTL increased the mean accuracy of the GEBVs by 0.28 points, from 0.33 to 0.61, relative to the use of full GRM only. The area under the curve of the receiver-operator curve for mortality increased from 0.58 to 0.67 when the QTL was included in the model. The inclusion of the QTL as a fixed effect in the model increased the correlation between the GEBVs of early mortality with the late mortality, compared to a model that did not include the QTL. These results validate the usability of the produced SNP panel for genomic selection in European whitefish and highlight the opportunity for modeling QTLs in genomic evaluation of mortality due to Saprolegnia infection.

Analysis of amplification and association polymorphisms in the bovine beta-defensin 129 (BBD129) gene revealed its function in bull fertility

β-defensins are adsorbable on the sperm surface in the male reproductive tract (MRT) and enhance sperm functional characteristics. The beta-defensin 129 (DEFB129) antimicrobial peptide is involved in sperm maturation, motility, and fertilization. However, its role in bovine fertility has not been well investigated. This study examines the relationship between the bovine BBD129 gene and Bos indicus x Bos taurus bull fertility. The complete coding sequence of BBD129 mRNA was identified by RNA Ligase Mediated-Rapid Amplification of cDNA End (RLM-RACE) and Sanger sequencing methodologies. It consisted of 582 nucleotides (nts) including 5' untranslated region (UTR) (46nts) and 3'UTR (23nts). It conserves all beta-defensin-like features. The expression level of BBD129 was checked by RT-qPCR and maximal expression was detected in the corpus-epididymis region compared to other parts of MRT. Polymorphism in BBD129 was also confirmed by Sanger sequencing of 254 clones from 5 high fertile (HF) and 6 low fertile (LF) bulls at two positions, 169 T > G and 329A > G, which change the S57A and N110S in the protein sequence respectively. These two mutations give rise to four types of BBD129 haplotypes. The non-mutated TA-BBD129 (169 T/329A) haplotype was substantially more prevalent among high-fertile bulls (P < 0.005), while the double-site mutated GG-BBD129 (169 T > G/329A > G) haplotype was significantly more prevalent among low-fertile bulls (P < 0.005). The in silico analysis confirmed that the polymorphism in BBD129 results in changes in mRNA secondary structure, protein conformations, protein stability, extracellular-surface availability, post-translational modifications (O-glycosylation and phosphorylation), and affects antibacterial and immunomodulatory capabilities. In conclusion, the mRNA expression of BBD129 in the MRT indicates its region-specific dynamics in sperm maturation. BBD129 polymorphisms were identified as the deciding elements accountable for the changed proteins with impaired functionality, contributing to cross-bred bulls' poor fertility.

Integrated multi-omics analyses reveals molecules governing sperm metabolism potentially influence bull fertility

Bull fertility is of paramount importance in bovine industry because semen from a single bull is used to breed several thousands of cows; however, so far, no reliable test is available for bull fertility prediction. In the present study, spermatozoa from high- and low-fertility bulls were subjected to high-throughput transcriptomic, proteomic and metabolomic analysis. Using an integrated multi-omics approach the molecular differences between high- and low-fertility bulls were identified. We identified a total of 18,068 transcripts, 5041 proteins and 3704 metabolites in bull spermatozoa, of which the expression of 4766 transcripts, 785 proteins and 33 metabolites were dysregulated between high- and low-fertility bulls. At transcript level, several genes involved in oxidative phosphorylation pathway were found to be downregulated, while at protein level genes involved in metabolic pathways were significantly downregulated in low-fertility bulls. We found that metabolites involved in Taurine and hypotaurine metabolism were significantly downregulated in low-fertility bulls. Integrated multi-omics analysis revealed the interaction of dysregulated transcripts, proteins and metabolites in major metabolic pathways, including Butanoate metabolism, Glycolysis and gluconeogenesis, Methionine and cysteine metabolism, Phosphatidyl inositol phosphate, pyrimidine metabolism and saturated fatty acid beta oxidation. These findings collectively indicate that molecules governing sperm metabolism potentially influence bull fertility.

Deciphering the genetic basis of male fertility in Italian Brown Swiss dairy cattle

Improving reproductive performance remains a major goal in dairy cattle worldwide. Service sire has been recognized as an important factor affecting herd fertility. The main objective of this study was to reveal the genetic basis of male fertility in Italian Brown Swiss dairy cattle. Dataset included 1102 Italian Brown Swiss bulls with sire conception rate records genotyped with 454k single nucleotide polymorphisms. The analysis included whole-genome scans and gene-set analyses to identify genomic regions, individual genes and genetic mechanisms affecting Brown Swiss bull fertility. One genomic region on BTA1 showed significant additive effects. This region harbors gene RABL3 which is implicated cell proliferation and motility. Two genomic regions, located on BTA6 and BTA26, showed marked non-additive effects. These regions harbor genes, such as WDR19 and ADGRA1, that are directly involved in male fertility, including sperm motility, acrosome reaction, and embryonic development. The gene-set analysis revealed functional terms related to cell adhesion, cellular signaling, cellular transport, immune system, and embryonic development. Remarkably, a gene-set analysis also including Holstein and Jersey data, revealed significant processes that are common to the three dairy breeds, including cell migration, cell–cell interaction, GTPase activity, and the immune function. Overall, this comprehensive study contributes to a better understanding of the genetic basis of male fertility in cattle. In addition, our findings may guide the development of novel genomic strategies for improving service sire fertility in Brown Swiss cattle.

Characteristics of miRNAs Present in Bovine Sperm and Associations With Differences in Fertility

Small non-coding RNAs have been linked to different phenotypes in bovine sperm, however attempts to identify sperm-borne molecular biomarkers of male fertility have thus far failed to identify a robust profile of expressed miRNAs related to fertility. We hypothesized that some differences in bull fertility may be reflected in the levels of different miRNAs in sperm. To explore such differences in fertility that are not due to differences in visible metrics of sperm quality, we employed Next Generation Sequencing to compare the miRNA populations in Bos taurus sperm from bulls with comparable motility and morphology but varying Sire Conception Rates. We identified the most abundant miRNAs in both populations (miRs -34b-3p; -100-5p; -191-5p; -30d-4p; -21-5p) and evaluated differences in the overall levels and specific patterns of isomiR expression. We also explored correlations between specific pairs of miRNAs in each population and identified 10 distinct pairs of miRNAs that were positively correlated in bulls with higher fertility and negatively correlated in comparatively less fertile individuals. Furthermore, 8 additional miRNA pairs demonstrated the opposite trend; negatively correlated in high fertility animals and positively correlated in less fertile bulls. Finally, we performed pathway analysis to identify potential roles of miRNAs present in bull sperm in the regulation of specific genes that impact spermatogenesis and embryo development. Together, these results present a comprehensive picture of the bovine sperm miRNAome that suggests multiple potential roles in fertility.

Identifying pleiotropic variants and candidate genes for fertility and reproduction traits in Holstein cattle via association studies based on imputed whole-genome sequence genotypes

Background

Genetic progress for fertility and reproduction traits in dairy cattle has been limited due to the low heritability of most indicator traits. Moreover, most of the quantitative trait loci (QTL) and candidate genes associated with these traits remain unknown. In this study, we used 5.6 million imputed DNA sequence variants (single nucleotide polymorphisms, SNPs) for genome-wide association studies (GWAS) of 18 fertility and reproduction traits in Holstein cattle. Aiming to identify pleiotropic variants and increase detection power, multiple-trait analyses were performed using a method to efficiently combine the estimated SNP effects of single-trait GWAS based on a chi-square statistic.

Results

There were 87, 72, and 84 significant SNPs identified for heifer, cow, and sire traits, respectively, which showed a wide and distinct distribution across the genome, suggesting that they have relatively distinct polygenic nature. The biological functions of immune response and fatty acid metabolism were significantly enriched for the 184 and 124 positional candidate genes identified for heifer and cow traits, respectively. No known biological function was significantly enriched for the 147 positional candidate genes found for sire traits. The most important chromosomes that had three or more significant QTL identified are BTA22 and BTA23 for heifer traits, BTA8 and BTA17 for cow traits, and BTA4, BTA7, BTA17, BTA22, BTA25, and BTA28 for sire traits. Several novel and biologically important positional candidate genes were strongly suggested for heifer (SOD2, WTAP, DLEC1, PFKFB4, TRIM27, HECW1, DNAH17, and ADAM3A), cow (ANXA1, PCSK5, SPESP1, and JMJD1C), and sire (ELMO1, CFAP70, SOX30, DGCR8, SEPTIN14, PAPOLB, JMJD1C, and NELL2) traits.

Conclusions

These findings contribute to better understand the underlying biological mechanisms of fertility and reproduction traits measured in heifers, cows, and sires, which may contribute to improve genomic evaluation for these traits in dairy cattle.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08555-z.

Bull fertility and semen quality are not correlated with dairy and production traits in Brown Swiss cattle

Undisturbed reproduction is key for successful breeding of beef and dairy cattle. Improving reproductive ability can be difficult because of antagonistic relationships with other economically relevant traits. In cattle, thorough investigation of female fertility revealed unfavorable genetic correlations with various production phenotypes. However, the correlation between male reproductive ability and production traits remains poorly understood. Here, we investigated the genetic relationships among and between male fertility characteristics and economically relevant traits in a population of Brown Swiss cattle. We performed GWAS with imputed genotypes at nearly 12 million sequence variants for semen quality (sperm head and tail anomalies, motility, concentration, and volume), male fertility, and 57 production phenotypes. Allele substitution effects were then correlated on a trait-by-trait basis to estimate genetic correlations. Correlations between male reproductive characteristics and traits of economic value were small and ranged from -0.0681 to 0.0787. Among the semen quality parameters, sperm motility was negatively correlated with anomalies (head: r = -0.7083 ± 0.0002; tail: r = -0.7739 ± 0.0002) and volume (r = -0.1266 ± 0.0003), whereas volume was negatively correlated with concentration (r = -0.3503 ± 0.0002). Sire nonreturn rate was negatively correlated with sperm anomalies (head: r = -0.1640 ± 0.0002; tail: r = -0.1580 ± 0.0002) and positively correlated with motility (r = 0.1598 ± 0.0002). A meta-analysis of male reproductive traits identified 2 quantitative trait loci: a previously described region on chromosome 6 showed pleiotropic effects and a novel region on chromosome 11 was associated with sperm head anomalies. In conclusion, our results suggest that selection for economically important dairy and production phenotypes has little impact on semen quality and fertility of Brown Swiss bulls.

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.

The future of assessing bull fertility: Can the 'omics fields identify usable biomarkers?

Breeding soundness examinations for bulls rely heavily on the subjective, visual assessment of sperm motility and morphology. Although these criteria have the potential to identify infertile males, they cannot be used to guarantee fertility or provide information about varying degrees of bull fertility. Male factor fertility is complex, and the success of the male gamete is not necessarily realized until well after the spermatozoon enters the oocyte. This paper reviews our existing knowledge of the bull’s contribution from a standpoint of the sperm’s cargo and the impact that this can have on fertilization and the development of the embryo. There has been a plethora of recent research characterizing the many molecular attributes that can affect the functional competence of a spermatozoon. A better understanding of the molecular factors influencing fertilization and embryo development in cattle will lead to the identification of biomarkers for the selection of bulls of superior fertility, which will have major implications for livestock production. To see this improvement in reproductive performance, we believe incorporation of modern technology into breeding soundness examinations will be necessary—although many of the discussed technologies are not ready for large-scale field application. Each of the ‘omics fields discussed in this review have shown promise for the identification of biomarkers of fertility, with certain families of biomarkers appearing to be better suited to different evaluations throughout a bull’s lifetime. Further research is needed for the proposed biomarkers to be of diagnostic or predictive value.

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Assessments of genomic prediction accuracies using artificial intelligent (AI) algorithms (i.e., machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a nonlinear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e., ML-KAML) and deep learning (i.e., DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP, and BayesR) were conducted for two main disease resistance traits (i.e., survival status coded as 0 and 1 and survival time, i.e., days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 single nucleotide polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP, and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1–15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3–19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3–5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318–400 SNPs for survival status and 1,362–1,589 SNPs for survival time) were somewhat lower (0.3–15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to E. ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease-resistant traits studied in this population of striped catfish P. hypophthalmus.

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.

Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel

Simple Summary

To reduce the breeding costs and promote the application of genomic selection (GS) in Chinese Simmental beef cattle, we developed a customized low-density single-nucleotide polymorphism (SNP) panel consisting of 30,684 SNPs. When comparing the predictive performance of the low-density SNP panel to that of the BovineHD Beadchip for 13 traits, we found that this ~30 K panel achieved moderate to high prediction accuracies for most traits, while reducing the prediction accuracies of six traits by 0.04–0.09 and decreasing the prediction accuracy of one trait by 0.2. For the remaining six traits, the usage of the low-density SNP panel was associated with a slight increase in prediction accuracy. Our studies suggested that the low-density SNP panel (~30 K) is a feasible and promising tool for cost-effective genomic prediction in Chinese Simmental beef cattle, which may provide breeding organizations with a cheaper option and greater returns on investment.

Abstract

Chinese Simmental beef cattle play a key role in the Chinese beef industry due to their great adaptability and marketability. To achieve efficient genetic gain at a low breeding cost, it is crucial to develop a customized cost-effective low-density SNP panel for this cattle population. Thirteen growth, carcass, and meat quality traits and a BovineHD Beadchip genotyping of 1346 individuals were used to select trait-associated variants and variants contributing to great genetic variance. In addition, highly informative SNPs with high MAF in each 500 kb sliding window and in each genic region were also included separately. A low-density SNP panel consisting of 30,684 SNPs was developed, with an imputation accuracy of 97.4% when imputed to the 770 K level. Among 13 traits, the average prediction accuracy levels evaluated by genomic best linear unbiased prediction (GBLUP) and BayesA/B/Cπ were 0.22–0.47 and 0.18–0.60 for the ~30 K array and BovineHD Beadchip, respectively. Generally, the predictive performance of the ~30 K array was trait-dependent, with reduced prediction accuracies for seven traits. While differences in terms of prediction accuracy were observed among the 13 traits, the low-density SNP panel achieved moderate to high accuracies for most of the traits and even improved the accuracies for some traits.

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.

Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle

Background

Over the past decade, Fourier transform infrared (FTIR) spectroscopy has been used to predict novel milk protein phenotypes. Genomic data might help predict these phenotypes when integrated with milk FTIR spectra. The objective of this study was to investigate prediction accuracy for milk protein phenotypes when heterogeneous on-farm, genomic, and pedigree data were integrated with the spectra. To this end, we used the records of 966 Italian Brown Swiss cows with milk FTIR spectra, on-farm information, medium-density genetic markers, and pedigree data. True and total whey protein, and five casein, and two whey protein traits were analyzed. Multiple kernel learning constructed from spectral and genomic (pedigree) relationship matrices and multilayer BayesB assigning separate priors for FTIR and markers were benchmarked against a baseline partial least squares (PLS) regression. Seven combinations of covariates were considered, and their predictive abilities were evaluated by repeated random sub-sampling and herd cross-validations (CV).

Results

Addition of the on-farm effects such as herd, days in milk, and parity to spectral data improved predictions as compared to those obtained using the spectra alone. Integrating genomics and/or the top three markers with a large effect further enhanced the predictions. Pedigree data also improved prediction, but to a lesser extent than genomic data. Multiple kernel learning and multilayer BayesB increased predictive performance, whereas PLS did not. Overall, multilayer BayesB provided better predictions than multiple kernel learning, and lower prediction performance was observed in herd CV compared to repeated random sub-sampling CV.

Conclusions

Integration of genomic information with milk FTIR spectral can enhance milk protein trait predictions by 25% and 7% on average for repeated random sub-sampling and herd CV, respectively. Multiple kernel learning and multilayer BayesB outperformed PLS when used to integrate heterogeneous data for phenotypic predictions.

Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations

The difficulty in finding causative mutations has hampered their use in genomic prediction. Here, we present a methodology to fine-map potentially causal variants genome-wide by integrating the functional, evolutionary and pleiotropic information of variants using GWAS, variant clustering and Bayesian mixture models. Our analysis of 17 million sequence variants in 44,000+ Australian dairy cattle for 34 traits suggests, on average, one pleiotropic QTL existing in each 50 kb chromosome-segment. We selected a set of 80k variants representing potentially causal variants within each chromosome segment to develop a bovine XT-50K genotyping array. The custom array contains many pleiotropic variants with biological functions, including splicing QTLs and variants at conserved sites across 100 vertebrate species. This biology-informed custom array outperformed the standard array in predicting genetic value of multiple traits across populations in independent datasets of 90,000+ dairy cattle from the USA, Australia and New Zealand.

The Intergenerational Impacts of Paternal Diet on DNA Methylation and Offspring Phenotypes in Sheep

Knowledge of non-genomic inheritance of traits is currently limited. Although it is well established that maternal diet influences offspring inheritance of traits through DNA methylation, studies on the impact of prepubertal paternal diet on DNA methylation are rare. This study aimed to evaluate the impact of prepubertal diet in Polypay rams on complex traits, DNA methylation, and transmission of traits to offspring. A total of 10 littermate pairs of F0 rams were divided so that one ram was fed a control diet, and the other was fed the control diet with supplemental methionine. Diet was associated with earlier age at puberty in treatment vs. control F0 rams. F0 treatment rams tended to show decreased pubertal weight compared to control rams; however, no differences were detected in overall growth. A total of ten F0 rams were bred, and the entire F1 generation was fed a control diet. Diet of F0 rams had a significant association with scrotal circumference (SC) and weight at puberty of F1 offspring. The paternal diet was not significantly associated with F1 ram growth or age at puberty. The DNA methylation of F0 ram sperm was assessed, and genes related to both sexual development (e.g., DAZAP1, CHD7, TAB1, MTMR2, CELSR1, MGAT1) and body weight (e.g., DUOX2, DUOXA2) were prevalent in the data. These results provide novel information about the mechanisms through which the prepubertal paternal diet may alter body weight at puberty and sexual development.

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.

Across-country genomic prediction of bull fertility in Jersey dairy cattle

The use of information across populations is an attractive approach to increase the accuracy of genomic predictions for numerically small breeds and traits that are time-consuming and difficult to measure, such as male fertility in cattle. This study was conducted to evaluate genomic prediction of Jersey bull fertility using an across-country reference population combining records from the United States and Australia. The data set consisted of 1,570 US Jersey bulls with sire conception rate (SCR) records, 603 Australian Jersey bulls with semen fertility value (SFV) records and SNP genotypes for roughly 90,000 loci. Both SCR and SFV are evaluations of service sire fertility based on cow field data, and both are intended as phenotypic evaluations because the estimates include genetic and nongenetic effects. Within- and across-country genomic predictions were evaluated using univariate and bivariate genomic best linear unbiased prediction models. Predictive ability was assessed in 5-fold cross-validation using the correlation between observed and predicted fertility values and mean squared error of prediction. Within-country genomic predictions exhibited predictive correlations of around 0.28 and 0.02 for the United States and Australia, respectively. The Australian Jersey population is genetically diverse and small in size, so careful selection of the reference population by including only closely related animals (e.g., excluding New Zealand bulls, which is a less-related population) increased the predictive correlations up to 0.20. Notably, the use of bivariate models fitting all US Jersey records and the optimized Australian population resulted in predictive correlations around of 0.24 for SFV values, which is a relative increase in predictive ability of 20%. Conversely, for predicting SCR values, the use of an across-country reference population did not outperform the standard approach using pure US Jersey reference data set. Our findings indicate that genomic prediction of male fertility in dairy cattle is feasible, and the use of an across-country reference population would be beneficial when local populations are small and genetically diverse.

Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle

Various methods have been proposed for genomic prediction (GP) in livestock. These methods have mainly focused on statistical considerations and did not include genome annotation information. In this study, to improve the predictive performance of carcass traits in Chinese Simmental beef cattle, we incorporated the genome annotation information into GP. Single nucleotide polymorphisms (SNPs) were annotated to five genomic classes: intergenic, gene, exon, protein coding sequences, and 3'/5' untranslated region. Haploblocks were constructed for all markers and these five genomic classes by defining a biologically functional unit, and haplotype effects were modeled in both numerical dosage and categorical coding strategies. The first-order epistatic effects among SNPs and haplotypes were modeled using a categorical epistasis model. For all makers, the extension from the SNP-based model to a haplotype-based model improved the accuracy by 5.4-9.8% for carcass weight (CW), live weight (LW), and striploin (SI). For the five genomic classes using the haplotype-based prediction model, the incorporation of gene class information into the model improved the accuracies by an average of 1.4, 2.1, and 1.3% for CW, LW, and SI, respectively, compared with their corresponding results for all markers. Including the first-order epistatic effects into the prediction models improved the accuracies in some traits and genomic classes. Therefore, for traits with moderate-to-high heritability, incorporating genome annotation information of gene class into haplotype-based prediction models could be considered as a promising tool for GP in Chinese Simmental beef cattle, and modeling epistasis in prediction can further increase the accuracy to some degree.

Whole-genome homozygosity mapping reveals candidate regions affecting bull fertility in US Holstein cattle

Background

Achieving rapid genetic progress while maintaining adequate genetic diversity is one of the main challenges facing the dairy industry. The increase in inbreeding can be used to monitor the loss of genetic diversity. Inbreeding tends to increase the proportion of homozygous loci, some of which cause homozygosity of recessive alleles that results in reduced performance. This phenomenon is known as inbreeding depression and tends to be most prominent on fitness-related traits, such as male fertility. Traditionally, inbreeding has been monitored using pedigree information, or more recently, genomic data. Alternatively, it can be quantified using runs of homozygosity (ROH), i.e., contiguous lengths of homozygous genotypes observed in an individual’s chromosome.

Results

The objective of this study was to evaluate the association between ROH and sire conception rate. ROH were evaluated using 268 k genetic markers in 11,790 US Holstein bulls. Interestingly, either the sum, mean, or maximum length of ROH were negatively associated with bull fertility. The association analysis between ROH and sire fertility was performed comparing 300 high-fertility vs. 300 low-fertility bulls. Both the average and sum of ROH length were higher in the low-fertility group. The enrichment of ROH regions in bulls with low fertility was assessed using a Fisher’s exact test. Nine regions were significantly enriched in low-fertility compared to high-fertility bulls. Notably, these regions harbor genes that are closely related to sperm biology and male fertility, including genes exclusively or highly expressed in testis.

Conclusions

The results of this study can help not only to manage inbreeding in genomic selection programs by designing custom mating schemes, but also to better understand the mechanisms underlying male fertility in dairy cattle.

Integration of whole-genome DNA methylation data with RNA sequencing data to identify markers for bull fertility

Predicting bull fertility prior to breeding is a current challenge for the dairy industry. The use of molecular biomarkers has been previously assessed. However, the integration of this information has not been performed to extract biologically relevant markers. The goal of this study was to integrate DNA methylation data with previously published RNA-sequencing results in order to identify candidate markers for sire fertility. A total of 1765 differentially methylated cytosines were found between high- and low-fertility sires. Ten genes associated with 11 differentially methylated cytosines were found in a previous study of gene expression between high- and low-fertility sires. Additionally, two of these genes code for proteins found exclusively in bull seminal plasma. Collectively, our results reveal 10 genes that could be used in the future as a panel for predicting bull fertility.

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.

Symposium review: Big data, big predictions: Utilizing milk Fourier-transform infrared and genomics to improve hyperketonemia management

Negative animal health and performance outcomes are associated with disease incidences that can be labor-intensive, costly, and cumbersome for many farms. Amelioration of unfavorable outcomes through early detection and treatment of disease has emphasized the value of improving health monitoring. Although the value is recognized, detecting hyperketonemia (HYK) is still difficult for many farms to do practically and efficiently. Increasing data streams available to farms presents opportunities to use data to better monitor cow and herd health; however, challenges remain with regard to validating, integrating, and interpreting data. During the transition to lactation period, useful data are presented in the form of milk production and composition, milk Fourier-transform infrared (FTIR) wavelength absorbance, cow management records, and genomics, which have been employed to monitor postpartum onset of HYK. Attempts to predict postpartum HYK from test-day milk and performance variables incorporated into multiple linear regression models have demonstrated sufficient accuracy to monitor monthly herd prevalence; however, they lacked the sensitivity and specificity for individual cow diagnostics. Subsequent artificial neural network prediction models employing FTIR data and milk composition variables achieved 83 and 81% sensitivity and specificity for individual cow diagnostics. Although these results fail to reach the diagnostic goals of 90%, they are achieved without individual cow blood samples, which may justify acceptance of lower performance. The caveat is that these models depend on milk analysis, which is traditionally done every 4 weeks. This infrequent sampling allows for a single diagnostic sample for about half of the fresh cows. Benefits to farms are greatly improved if postpartum cows can be milk-tested weekly. Additionally, this allows for close monitoring of somatic cell count and may open the door for use of other herd health monitoring tools. Future improvements in these models may be achievable by maximizing sensitivity at the expense of specificity and may be most economical in disorders for which the cost of treatment is less than that of mistreating (e.g., HYK). Genomic predictions for HYK may be improved by incorporating genome-wide associated SNP and further utilized for precision management of HYK risk groups. Development and validation of HYK prediction models may provide producers with individual cow and herd-level management tools.

Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants

Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed.

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.