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

Identified Candidate Genes of Semen Trait in Three Pig Breeds Through Weighted GWAS and Multi-Tissue Transcriptome Analysis

High-quality semen is an essential factor for the success of artificial insemination, and revealing the genetic structure of pig semen traits helps improve semen quality. This study aimed to identify candidate genes associated with semen traits in three pig breeds (Duroc, Landrace, and Yorkshire) through weighted GWAS and multi-tissue transcriptome analysis. In this study, to identify candidate genes associated with semen traits in Duroc, Landrace, and Yorkshire, we performed weighted GWAS in four traits (sperm motility, sperm progressive motility, sperm abnormality rate, and total sperm count) using 936 pigs and multi-tissue transcriptome analysis using 34 tissues RNA-seq data of 5457 pigs from FarmGTEx. It was found that 16, 9, and 12 significant SNPs associated with semen traits were identified in Duroc, Landrace, and Yorkshire, with corresponding 7, 5, and 7 candidate genes in these three breeds, respectively, which may be involved in mammal spermatogenesis, testicular function, and male fertility. Moreover, we not only found the same candidate gene DNAI2 as in previous studies but also found two new candidate genes PNLDC1 and RSPH3, which were identified simultaneously in both Landrace and Yorkshire. By integrating the GWAS and multi-tissue transcriptome analysis results, we found that candidate genes associated with semen traits of three pig breeds were highly expressed in the testis tissue. The three genotypes of rs320928244 had significant effects on the expression of the DYNLT1 gene in the testis tissue of Landrace. These results together showed that these candidate genes were mainly related to sperm motility defects. This study helps deepen the understanding of the genetic basis of semen traits and provides a theoretical foundation for improving the semen quality of Duroc, Landrace, and Yorkshire breeds.

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.

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.

Investigating the impact of non-additive genetic effects in the estimation of variance components and genomic predictions for heat tolerance and performance traits in crossbred and purebred pig populations

BACKGROUND

Non-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows.

RESULTS

Including non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95-69.59% of the total genetic variance.

CONCLUSIONS

Including non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs.

Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing

BACKGROUND

Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively.

RESULTS

We systematically investigate both dominance-here representing any non-additive within-locus interaction-and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance.

CONCLUSIONS

Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.

QTLs and Candidate Genes Associated with Semen Traits in Merino Sheep

Ram semen traits play a significant role in conception outcomes, which in turn may influence reproductive efficiency and the overall productivity and profitability of sheep enterprises. Since hundreds of ewes may be inseminated from a single ejaculate, it is important to evaluate semen quality prior to use in sheep breeding programs. Given that semen traits have been found to be heritable, genetic variation likely contributes to the variability observed in these traits. Identifying such genetic variants could provide novel insights into the molecular mechanisms underlying variability in semen traits. Therefore, this study aimed to identify quantitative trait loci (QTLs) associated with semen traits in Merino sheep. A genome-wide association study (GWAS) was undertaken using 4506 semen collection records from 246 Merino rams collected between January 2002 and May 2021. The R package RepeatABEL was used to perform a GWAS for semen volume, gross motility, concentration, and percent post-thaw motility. A total of 35 QTLs, located on 16 Ovis aries autosomes (OARs), were significantly associated with either of the four semen traits in this study. A total of 89, 95, 33, and 73 candidate genes were identified, via modified Bonferroni, within the QTLs significantly associated with volume, gross motility, concentration, and percent post-thaw motility, respectively. Among the candidate genes identified, SORD, SH2B1, and NT5E have been previously described to significantly influence spermatogenesis, spermatozoal motility, and high percent post-thaw motility, respectively. Several candidate genes identified could potentially influence ram semen traits based on existing evidence in the literature. As such, validation of these putative candidates may offer the potential to develop future strategies to improve sheep reproductive efficiency. Furthermore, Merino ram semen traits are lowly heritable (0.071-0.139), and thus may be improved by selective breeding.

Candidate genes and proteins regulating bull semen quality: a review

Poor semen profile reflected by suboptimum fertility statistics is a concern in bulls reared for breeding purpose. A critical review of research on candidate genes and proteins associated with semen quality traits will be useful to understand the progress of molecular marker development for bull semen quality traits. Here, we have tabulated and classified candidate genes and proteins associated with bull semen quality based on a literature survey. A total of 175 candidate genes are associated with semen quality traits in various breeds of cattle. Several studies using candidate gene approach have identified 26 genes carrying a total of 44 single nucleotide polymorphisms. Furthermore, nine genome-wide association studies (GWASes) have identified 150 candidate genes using bovine single nucleotide polymorphisms (SNP) chips. Three genes, namely membrane-associated ring-CH-type finger 1 (MARCH1), platelet-derived growth factor receptor beta, and phosphodiesterase type 1, were identified commonly in two GWASes, which, especially MARCH1, are required to explore their regulatory roles in bull semen quality in in-depth studies. With the advancement of high-throughput-omic technologies, more candidate genes associated with bull semen quality may be identified in the future. Therefore, the functional significance of candidate genes and proteins need to be delved further into future investigations to augment bull semen quality.

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.

Genomic dissection of repeatability considering additive and non-additive genetic effects for semen production traits in beef and dairy bulls

The low heritability and moderate repeatability of semen production traits in beef and dairy bulls suggest that non-additive genetic effects, such as dominance and epistatic effects, play an important role in semen production and should therefore be considered in genetic improvement programs. In this study, the repeatability of semen production traits in Japanese Black bulls (JB) as beef bulls and Holstein bulls (HOL) as dairy bulls was evaluated by considering additive and non-additive genetic effects using the Illumina BovineSNP50 BeadChip. We also evaluated the advantage of using more complete models that include non-additive genetic effects by comparing the rank of genotyped animals and the phenotype prediction ability of each model. In total, 65,463 records for 615 genotyped JB and 48,653 records for 845 genotyped HOL were used to estimate additive and non-additive (dominance and epistatic) variance components for semen volume (VOL), sperm concentration (CON), sperm motility (MOT), MOT after freeze-thawing (aMOT), and sperm number (NUM). In the model including both additive and non-additive genetic effects, the broad-sense heritability (0.17-0.43) was more than twice as high as the narrow-sense heritability (0.04-0.11) for all traits and breeds, and the differences between the broad-sense heritability and repeatability were very small for VOL, NUM, and CON in both breeds. A large proportion of permanent environmental variance was explained by epistatic variance. The epistatic variance as a proportion of total phenotypic variance was 0.07-0.33 for all traits and breeds. In addition, heterozygosity showed significant positive relationships with NUM, MOT, and aMOT in JB and NUM in HOL, when the heterozygosity rate was included as a covariate. In a comparison of models, the inclusion of non-additive genetic effects resulted in a re-ranking of the top genotyped bulls for the additive effects. Adjusting for non-additive genetic effects could be expected to produce a more accurate breeding value, even if the models have similar fitting. However, including non-additive genetic effects did not improve the ability of any model to predict phenotypic values for any trait or breed compared with the predictive ability of a model that includes only additive effects. Consequently, although non-additive genetic effects, especially epistatic effects, play an important role in semen production traits, they do not improve prediction accuracy in beef and dairy bulls.