A genome-wide association study reveals a novel candidate gene for sperm motility in pigs

Methionine Improves Boar Sperm Quality by Promoting Mitochondrial Translation during Liquid Storage

Boar sperm quality serves as an important indicator of reproductive efficiency, playing a direct role in enhancing the output of livestock production. It has been demonstrated that mitochondrial protein translation is present in sperm and plays a crucial role in regulating sperm motility, capacitation and in vitro fertilization rate. The present study aimed to determine whether methionine supplementation enhances mitochondrial translation in boar sperm, thereby improving sperm quality. The results showed a significant elevation in the abundance of mitochondrial methionyl-tRNA formyltransferase (MTFMT), a crucial enzyme for mitochondrial protein translation, and mitochondrial DNA-encoded cytochrome c oxidase subunit 1 (COX1) in boar sperm exhibiting high motility. Both amino acids and methionine supplementation significantly enhanced boar sperm motility during storage. Moreover, methionine supplementation mitigates the loss of acrosomal integrity, enhances the expression of COX1, and boosts mitochondrial activity. Furthermore, the positive impact of methionine was negated in the presence of the mitochondrial translation inhibitor chloramphenicol. Together, these findings suggest that boar sperm may utilize methionine as a protein translation substrate to enhance sperm motility by stimulating mitochondrial protein translation. The supplementation of methionine may enhance the quality of boar sperm, thereby providing guidance for the optimization of diluent formulations for liquid storage and the identification of physiological regulators that regulate sperm motility.

Evaluation of sperm quality and male fertility: The use of molecular markers in boar sperm and seminal plasma

In pig production, the optimization of artificial insemination (AI) efficiency significantly relies on the accurate assessment of semen quality and fertility of boars. Traditional methods such as conventional seminogram techniques, although long-standing, exhibit limited sensitivity in predicting boar fertility, warranting the exploration of novel molecular markers. This review synthesizes the current knowledge on the utilization of molecular markers for semen quality evaluation and male fertility prediction in boars, providing an in-depth examination of molecular markers in this context. Specifically, the present work delves into the potential of OMICs technologies, encompassing genetic and genomic approaches, transcriptomics, proteomics, and metabolomics. A diverse array of molecular markers, including genomic regions associated with sperm quality and male fertility, chromatin integrity, mitochondrial DNA content, mRNA and non-coding RNA signatures, as well as proteins and metabolites in sperm and seminal plasma, are identified as promising molecular markers for fertility prediction in boars. Furthermore, the need of validating biomarkers and their practical implementation in AI centres is here emphasized. Addressing these considerations and integrating molecular markers within the swine breeding field holds the potential to enhance reproductive management practices and optimize productivity in boar breeding programs. This integration can significantly improve overall efficiency within the pig breeding industry.

The combination of RNA-seq transcriptomics and data-independent acquisition proteomics reveal the mechanisms and function of different gooses testicular development at different stages of laying cycle

Egg production performance is an important economic trait in the poultry industry. In previous studies, attention has often been paid to the growth and development of the ovaries, while there has been less research on the testicular tissue of male goose. Due to various factors, there are usually significant differences in the process of testicular spermatogenesis among different goose breeds. The Jilin white goose (JL) is a high-production local goose species in China, domesticated from Anser cygnoides, which has a high egg-laying performance and the egg-laying period can last from February to July. In the production of goose within Jilin Province, the female goose of Jilin White goose is considered as an important maternal parent of synthetic lines, and ganders from Hungarian white goose (HU), Wanxi white goose (WX) and Jilin white goose are the main male parents. Each year, all 3 gander species begin to exhibit breeding capacity in February and reach the peak of reproductive capacity by April, marked by high fertilization rates. With the gradual increase in temperature, the testicular tissue of Hungarian and Wanxi goose gradually diminishes in its ability to produce sperm. the testicular tissue undergoes significant shrinkage by the end of June, resulting in a near loss of sperm production capability, thereby yielding low fertilization rates. However, the Jilin White goose demonstrates the ability to maintain a stable sperm production capacity. Individuals with low sperm motility contribute to increased seed production costs and pose constraints on the industrial development of livestock and poultry varieties. In this study, transcriptomics and proteomics data from gooses testicular of 3 different goose breeds inclouding Jilin white goose, Wanxi white gooseand Hungary white goose sampled in 2 stages, peak of laying cycle (PLC) and end of laying cycle (ELC). In a comparative analysis between PLC and ELC groups (ELC vs. PLC) of 3 breeds, we identified 401,340,6651 differentially expressed genes (DEGs) and 18,225,323 differentially expressed proteins (DEPs), respectively. Differentially expressed genes and proteins were significantly enriched in Gene Ontology (GO) terms such as phosphotransferase activity, cytoskeletal protein binding, microtubule motor activity, channel activity and carbohydrate metabolic process. The KEGG enrichment analysis of the DEGs in testicular showed that most differentially expressed mRNAs participate in the KEGG pathways, including ECM-receptor interaction, MAPK signaling pathway, carbon metabolism, Cell cycle, VEGF signaling pathway, Lipoic acid metabolism and p53 signaling pathway. The differential expression of 4 selected DEGs (SPAG6, NEK2, HSPA4L, SERF1A) was verified by qRT-PCR, and the results were consistent with RNA-seq data. In conclusion, this study reveals the differences in gene expression regulation in testicular tissues of different goose species, and screening candidate genes and proteins related to spermatogenesis.

Genome-Wide Association Study Reveals Novel Candidate Genes Influencing Semen Traits in Landrace Pigs

Artificial insemination plays a crucial role in pig production, particularly in enhancing the genetic potential of elite boars. To accelerate genetic progress for semen traits in pigs, it is vital to understand and identify the underlying genetic markers associated with desirable traits. Herein, we genotyped 1238 Landrace boars with GeneSeek Porcine SNP50 K Bead chip and conducted genome-wide association studies to identify genetic regions and candidate genes associated with 12 semen traits. Our study identified 38 SNPs associated with the analyzed 12 semen traits. Furthermore, we identified several promising candidate genes, including HIBADH, DLG1, MED1, APAF1, MGST3, MTG2, and ZP4. These candidate genes have the potential function to facilitate the breeding of boars with improved semen traits. By further investigating and understanding the roles of these genes, we can develop more effective breeding strategies that contribute to the overall enhancement of pig production. The results of our study provide valuable insights for the pig-breeding industry and support ongoing research efforts to optimize genetic selection for superior semen traits.

An Assessment of the Genetic Parameters of Boars' Reproductive Traits

The aim of this study was to estimate genetic parameters for the reproductive traits of boars based on single-nucleotide polymorphism data. A total of 109,836 semen samples from 2249 boars were collected between 2010 and 2022. Five basic traits were assessed: sperm volume, sperm concentration, motility, number of abnormal sperm, and, for the first time for the local population, libido. In addition, two derived traits were assessed: total sperm count and number of functional sperm. Genetic parameters were estimated using the single-step genomic best linear unbiased prediction method (ssGBLUP). Dam and sire breeds were evaluated separately. The five basic traits were evaluated using five-trait models, while the two derived traits were evaluated using single-trait models. The heritability coefficients had lower values for all sperm quality traits with both methods. For the basic traits, the heritability ranged from 0.099 to 0.342. The greatest difference between dam and sire breeds was observed for the heritability of the sperm concentration trait (0.099 and 0.271, respectively). The heritability of the libido trait was twice as high for boars of sire breeds as it was for boars of dam breeds. The genetic parameters estimated with ssGBLUP can be used in routine genetic evaluations to improve the pig breeding process.

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 in Bull Semen Production Traits: An Information Approach Review

Semen quality plays a crucial role in the successful implementation of breeding programs, especially where artificial insemination (AI) is practiced. Bulls with good semen traits have good fertility and can produce a volume of high semen per ejaculation. The aim of this review is to use an information approach to highlight candidate genes and their relation to bull semen production traits. The use of genome-wide association studies (GWAS) has been demonstrated to be successful in identifying genomic regions and individual variations associated with production traits. Studies have reported over 40 genes associated with semen traits using Illumina BeadChip single-nucleotide polymorphism (SNPs).

Infertility due to defective sperm flagella caused by an intronic deletion in DNAH17 that perturbs splicing

Artificial insemination in pig (Sus scrofa domesticus) breeding involves the evaluation of the semen quality of breeding boars. Ejaculates that fulfill predefined quality requirements are processed, diluted and used for inseminations. Within short time, eight Swiss Large White boars producing immotile sperm that had multiple morphological abnormalities of the sperm flagella were noticed at a semen collection center. The eight boars were inbred on a common ancestor suggesting that the novel sperm flagella defect is a recessive trait. Transmission electron microscopy cross-sections revealed that the immotile sperm had disorganized flagellar axonemes. Haplotype-based association testing involving microarray-derived genotypes at 41,094 SNPs of six affected and 100 fertile boars yielded strong association (P = 4.22 × 10-15) at chromosome 12. Autozygosity mapping enabled us to pinpoint the causal mutation on a 1.11 Mb haplotype located between 3,473,632 and 4,587,759 bp. The haplotype carries an intronic 13-bp deletion (Chr12:3,556,401-3,556,414 bp) that is compatible with recessive inheritance. The 13-bp deletion excises the polypyrimidine tract upstream exon 56 of DNAH17 (XM_021066525.1: c.8510-17_8510-5del) encoding dynein axonemal heavy chain 17. Transcriptome analysis of the testis of two affected boars revealed that the loss of the polypyrimidine tract causes exon skipping which results in the in-frame loss of 89 amino acids from DNAH17. Disruption of DNAH17 impairs the assembly of the flagellar axoneme and manifests in multiple morphological abnormalities of the sperm flagella. Direct gene testing may now be implemented to monitor the defective allele in the Swiss Large White population and prevent the frequent manifestation of a sterilizing sperm tail disorder in breeding boars.

Identification of new semen trait-related candidate genes in Duroc boars through genome-wide association and weighted gene co-expression network analyses

Semen traits are crucial in commercial pig production since semen from boars is widely used in artificial insemination for both purebred and crossbred pig production. Revealing the genetic architecture of semen traits potentially promotes the efficiencies of improving semen traits through artificial selection. This study is aimed to identify candidate genes related to the semen traits in Duroc boars. First, we identified the genes that were significantly associated with three semen traits, including sperm motility (MO), sperm concentration (CON), and semen volume (VOL) in a Duroc boar population through a genome-wide association study (GWAS). Second, we performed a weighted gene co-expression network analysis (WGCNA). A total of 2, 3, and 20 single-nucleotide polymorphisms were found to be significantly associated with MO, CON, and VOL, respectively. Based on the haplotype block analysis, we identified one genetic region associated with MO, which explained 6.15% of the genetic trait variance. ENSSSCG00000018823 located within this region was considered as the candidate gene for regulating MO. Another genetic region explaining 1.95% of CON genetic variance was identified, and, in this region, B9D2, PAFAH1B3, TMEM145, and CIC were detected as the CON-related candidate genes. Two genetic regions that accounted for 2.23% and 2.48% of VOL genetic variance were identified, and, in these two regions, WWC2, CDKN2AIP, ING2, TRAPPC11, STOX2, and PELO were identified as VOL-related candidate genes. WGCNA analysis showed that, among these candidate genes, B9D2, TMEM145, WWC2, CDKN2AIP, TRAPPC11, and PELO were located within the most significant module eigengenes, confirming these candidate genes' role in regulating semen traits in Duroc boars. The identification of these candidate genes can help to better understand the genetic architecture of semen traits in boars. Our findings can be applied for semen traits improvement in Duroc boars.

Genome-wide association studies for sperm traits in Assaf sheep breed

Sperm quality traits routinely collected by artificial insemination (AI) center for rams progeny test are related with the capacity to produce sperm doses for AI and, in more or less grade, with males' fertility. Low-quality ejaculates are unuseful to perform AI sperm doses, which suppose high economic loses for the AI center. Moreover, sperm quality traits have low heritability values which make traditional genetic selection little efficient to its improvement. In this work, a genome-wide association study (GWAS) was conducted by using sperm quality traits data and 50 K Affymetrix custom chip genotypes of 429 rams of Assaf breed from OVIGEN AI centre. Furthermore, 47 of these rams were also genotyped with the Illumina HD Ovine BeadChip, and therefore HD genotypes were imputed for all rams with phenotype data. Previous to the GWAS, a linear regression model was fitted including sperm traits as dependent variables; the flock of origin, date of sperm collection, and jump number as fixed effects; rams age at collection in months as covariate; and ram permanent effect as random. Pseudo-phenotypes obtained from this model were used as input for GWAS. Associations at the chromosome-wise level (FDR 10%) of 76 single-nucleotide polymorphisms (SNPs) in 4 chromosomes for ejaculate concentration (CON), 20 SNPs in 3 chromosomes for ejaculate volume (VOL), 32 SNPs in 1 chromosome for ejaculate number of spermatozoa (SPZ), and 23 SNPs for spermatozoa mass motility (MOT) in 17 chromosomes were found. Only SNPs associated with MOT overcame the genome-wide significance level. Some candidate genes for sperm traits variability were SLC9C1 (OAR1), TSN (OAR2), and FUT10 (OAR26) for MOT;. DOCK2, CPLANE1, SPEF2, and RAI14 (OAR16) for CON; SCAPER and PSMA4 (OAR18) for VOL; and PARM1 and LOC101110593 (OAR6) for SPZ. SNPs associated with sperm traits were not found to be correlated with milk production genetic variation; however, the high frequencies of some SNPs with negative effect over sperm traits found in animals at the top milk yield estimated breeding values (EBVs) ranking would allow to exert some selective presure to improve rams sperm performances. Effects and frequencies of some of the SNPs detected over sperm quality traits make these variants good candidates to be used in marker-assisted selection to improve sperm characteristics of Assaf rams and AI center efficiency to produce sperm doses.

A systems biology framework integrating GWAS and RNA-seq to shed light on the molecular basis of sperm quality in swine

BACKGROUND

Genetic pressure in animal breeding is sparking the interest of breeders for selecting elite boars with higher sperm quality to optimize ejaculate doses and fertility rates. However, the molecular basis of sperm quality is not yet fully understood. Our aim was to identify candidate genes, pathways and DNA variants associated to sperm quality in swine by analysing 25 sperm-related phenotypes and integrating genome-wide association studies (GWAS) and RNA-seq under a systems biology framework.

RESULTS

By GWAS, we identified 12 quantitative trait loci (QTL) associated to the percentage of head and neck abnormalities, abnormal acrosomes and motile spermatozoa. Candidate genes included CHD2, KATNAL2, SLC14A2 and ABCA1. By RNA-seq, we identified a wide repertoire of mRNAs (e.g. PRM1, OAZ3, DNAJB8, TPPP2 and TNP1) and miRNAs (e.g. ssc-miR-30d, ssc-miR-34c, ssc-miR-30c-5p, ssc-miR-191, members of the let-7 family and ssc-miR-425-5p) with functions related to sperm biology. We detected 6128 significant correlations (P-value ≤ 0.05) between sperm traits and mRNA abundances. By expression (e)GWAS, we identified three trans-expression QTL involving the genes IQCJ, ACTR2 and HARS. Using the GWAS and RNA-seq data, we built a gene interaction network. We considered that the genes and interactions that were present in both the GWAS and RNA-seq networks had a higher probability of being actually involved in sperm quality and used them to build a robust gene interaction network. In addition, in the final network we included genes with RNA abundances correlated with more than four semen traits and miRNAs interacting with the genes on the network. The final network was enriched for genes involved in gamete generation and development, meiotic cell cycle, DNA repair or embryo implantation. Finally, we designed a panel of 73 SNPs based on the GWAS, eGWAS and final network data, that explains between 5% (for sperm cell concentration) and 36% (for percentage of neck abnormalities) of the phenotypic variance of the sperm traits.

CONCLUSIONS

By applying a systems biology approach, we identified genes that potentially affect sperm quality and constructed a SNP panel that explains a substantial part of the phenotypic variance for semen quality in our study and that should be tested in other swine populations to evaluate its relevance for the pig breeding sector.

Gene Polymorphisms in Boar Spermatozoa and Their Associations with Post-Thaw Semen Quality

Genetic markers have been used to assess the freezability of semen. With the advancement in molecular genetic techniques, it is possible to assess the relationships between sperm functions and gene polymorphisms. In this study, variant calling analysis of RNA-Seq datasets was used to identify single nucleotide polymorphisms (SNPs) in boar spermatozoa and to explore the associations between SNPs and post-thaw semen quality. Assessment of post-thaw sperm quality characteristics showed that 21 boars were considered as having good semen freezability (GSF), while 19 boars were classified as having poor semen freezability (PSF). Variant calling demonstrated that most of the polymorphisms (67%) detected in boar spermatozoa were at the 3’-untranslated regions (3’-UTRs). Analysis of SNP abundance in various functional gene categories showed that gene ontology (GO) terms were related to response to stress, motility, metabolism, reproduction, and embryo development. Genomic DNA was isolated from sperm samples of 40 boars. Forty SNPs were selected and genotyped, and several SNPs were significantly associated with motility and membrane integrity of frozen-thawed (FT) spermatozoa. Polymorphism in SCLT1 gene was associated with significantly higher motility and plasma membrane integrity of FT spermatozoa from boars of the GSF group compared with those of the PSF group. Likewise, polymorphisms in MAP3K20, MS4A2, and ROBO1 genes were significantly associated with reduced cryo-induced lipid peroxidation and DNA damage of FT spermatozoa from boars of the GSF group. Candidate genes with significant SNP associations, including APPL1, PLBD1, FBXO16, EML5, RAB3C, OXSR1,PRICKLE1, and MAP3K20 genes, represent potential markers for post-thaw semen quality, and they might be relevant for future improvement in the selection procedure of boars for cryopreservation. The findings of this study provide evidence indicating that polymorphisms in genes expressed in spermatozoa could be considered as factors associated with post-thaw semen quality.

Weighted single-step GWAS identified candidate genes associated with semen traits in a Duroc boar population

BACKGROUND

In the pig production industry, artificial insemination (AI) plays an important role in enlarging the beneficial impact of elite boars. Understanding the genetic architecture and detecting genetic markers associated with semen traits can help in improving genetic selection for such traits and accelerate genetic progress. In this study, we utilized a weighted single-step genome-wide association study (wssGWAS) procedure to detect genetic regions and further candidate genes associated with semen traits in a Duroc boar population. Overall, the full pedigree consists of 5284 pigs (12 generations), of which 2693 boars have semen data (143,113 ejaculations) and 1733 pigs were genotyped with 50 K single nucleotide polymorphism (SNP) array.

RESULTS

Results show that the most significant genetic regions (0.4 Mb windows) explained approximately 2%~ 6% of the total genetic variances for the studied traits. Totally, the identified significant windows (windows explaining more than 1% of total genetic variances) explained 28.29, 35.31, 41.98, and 20.60% of genetic variances (not phenotypic variance) for number of sperm cells, sperm motility, sperm progressive motility, and total morphological abnormalities, respectively. Several genes that have been previously reported to be associated with mammal spermiogenesis, testes functioning, and male fertility were detected and treated as candidate genes for the traits of interest: Number of sperm cells, TDRD5, QSOX1, BLK, TIMP3, THRA, CSF3, and ZPBP1; Sperm motility, PPP2R2B, NEK2, NDRG, ADAM7, SKP2, and RNASET2; Sperm progressive motility, SH2B1, BLK, LAMB1, VPS4A, SPAG9, LCN2, and DNM1; Total morphological abnormalities, GHR, SELENOP, SLC16A5, SLC9A3R1, and DNAI2.

CONCLUSIONS

In conclusion, candidate genes associated with Duroc boars' semen traits, including the number of sperm cells, sperm motility, sperm progressive motility, and total morphological abnormalities, were identified using wssGWAS. KEGG and GO enrichment analysis indicate that the identified candidate genes were enriched in biological processes and functional terms may be involved into spermiogenesis, testes functioning, and male fertility.

Identifying candidate genes associated with sperm morphology abnormalities using weighted single-step GWAS in a Duroc boar population

Artificial insemination (AI) has been used as a routine technology globally in the pig production industry since 1930. One of the preferable advantages of AI technology is that the semen of elite boars can be disseminated to the commercial sow population rapidly. Understanding the genetic background of semen traits may help in developing genetic improvement programs of boars by including these traits into the selection index. In this study, we utilized weighted single-step genome-wide association study (wssGWAS) to identify genetic regions and further candidate genes associated with sperm morphology abnormalities (proximal droplet, distal droplet, bent tail, coiled tail, and distal midpiece reflex) in a Duroc boar population. Several genomic regions explained 2.76%-9.22% of the genetic variances for sperm morphology abnormalities were identified. The first three detected QTL regions together explained about 7.65%-25.10% of the total genetic variances of the studied traits. Several genes were detected and considered as candidate genes for each of the traits under study: coiled tail, HOOK1, ARSA, SYCE3, SOD3, GMNN, RBPJ, STIL, and FGF1; bent tail, FGF1, ADIPOR1, ARPC5, FGFR3, PANX1, IZUMO1R, ANKRD49, and GAL; proximal droplet, NSF, WNT3, WNT9B, LYZL6, FGFR1OP, RNASET2, FYN, LRRC6, EPC1, DICER1, FNDC3A, and PFN1; distal droplet, ARSA, SYCE3, MOV10L1, CBR1, KDM6B, TP53, PTBP2, UBR7, KIF18A, ADAM15, FAAH, TEKT3, and SRD5A1; and distal midpiece reflex, OMA1, PFN1, PELP1, BMP2, GPR18, TM9SF2, and SPIN1. GO and KEGG enrichment analysis revealed the potential function of the identified candidate genes in spermatogenesis, testis functioning, and boar spermatozoa plasma membrane activating and maintenance. In conclusion, we detected candidate genes associated with the coiled tail, bent tail, proximal droplet, distal droplet, and distal midpiece reflex in a Duroc boar population using wssGWAS. Overall, these novel results reflect the polygenic genetic architecture of the studied sperm morphology abnormality traits, which may provide knowledge for conducting genomic selection on these traits. The detected genetic regions can be used in developing trait-specific marker assisted selection models by assigning higher genetic variances to these regions.

Genome-wide association study reveals new loci for yield-related traits in Sichuan wheat germplasm under stripe rust stress

BACKGROUND

As one of the most important food crops in the world, increasing wheat (Triticum aestivum L.) yield is an urgent task for global food security under the continuous threat of stripe rust (caused by Puccinia striiformis f. sp. tritici) in many regions of the world. Molecular marker-assisted breeding is one of the most efficient ways to increase yield. Here, we identified loci associated to multi-environmental yield-related traits under stripe rust stress in 244 wheat accessions from Sichuan Province through genome-wide association study (GWAS) using 44,059 polymorphic markers from the 55 K single nucleotide polymorphism (SNP) chip.

RESULTS

A total of 13 stable quantitative trait loci (QTLs) were found to be highly associating to yield-related traits, including 6 for spike length (SL), 3 for thousand-kernel weight (TKW), 2 for kernel weight per spike (KWPS), and 2 for both TKW and KWPS, in at least two test environments under stripe rust stress conditions. Of them, ten QTLs were overlapped or very close to the reported QTLs, three QTLs, QSL.sicau-1AL, QTKW.sicau-4AL, and QKWPS.sicau-4AL.1, were potentially novel through the physical location comparison with previous QTLs. Further, 21 candidate genes within three potentially novel QTLs were identified, they were mainly involved in the regulation of phytohormone, cell division and proliferation, meristem development, plant or organ development, and carbohydrate transport.

CONCLUSIONS

QTLs and candidate genes detected in our study for yield-related traits under stripe rust stress will facilitate elucidating genetic basis of yield-related trait and could be used in marker-assisted selection in wheat yield breeding.

Significance testing and genomic inflation factor using high-density genotypes or whole-genome sequence data

Significance testing for genome‐wide association study (GWAS) with increasing SNP density up to whole‐genome sequence data (WGS) is not straightforward, because of strong LD between SNP and population stratification. Therefore, the objective of this study was to investigate genomic control and different significance testing procedures using data from a commercial pig breeding scheme. A GWAS was performed in GCTA with data of 4,964 Large White pigs using medium density, high density or imputed whole‐genome sequence data, fitting a genomic relationship matrix based on a leave‐one–chromosome‐out approach to account for population structure. Subsequently, genomic inflation factors were assessed on whole‐genome level and the chromosome level. To establish a significance threshold, permutation testing, Bonferroni corrections using either the total number of SNPs or the number of independent chromosome fragments, and false discovery rates (FDR) using either the Benjamini–Hochberg procedure or the Benjamini and Yekutieli procedure were evaluated. We found that genomic inflation factors did not differ between different density genotypes but do differ between chromosomes. Also, the leave‐one‐chromosome‐out approach for GWAS or using the pedigree relationships did not account appropriately for population stratification and gave strong genomic inflation. Regarding different procedures for significance testing, when the aim is to find QTL regions that are associated with a trait of interest, we recommend applying the FDR following the Benjamini and Yekutieli approach to establish a significance threshold that is adjusted for multiple testing. When the aim is to pinpoint a specific mutation, the more conservative Bonferroni correction based on the total number of SNPs is more appropriate, till an appropriate method is established to adjust for the number of independent tests.

H2AFZ, RNF4 and NR4A1 loci are associated with boar semen quality by population association studies

Spermatogenesis is a complex process regulated by many genes. In this study, H2AFZ, RNF4 and NR4A1 genes were selected as candidate genes for boar semen quality traits based on their functions during spermatogenesis, and the associations of three loci (H2AFZ c.192 + 210-192 + 213delCGAT, RNF4 c.374 + 358 T > C and NR4A1 c.956 + 796 A > G) with sperm quality traits were analyzed in Duroc (n = 185), Large White (n = 87) and Landrace (n = 49) pig populations. The results showed H2AFZ c.192 + 210-192 + 213delCGAT AA boars produced 1.52% lower abnormal sperm rate (ASR) than AB boars in Landrace pigs (p < 0.05); RNF4 c.374 + 358 TC boars produced 0.31 × 10⁸/ml higher sperm concentration (SCON) than CC boars (p < 0.05) in Large White pigs; NR4A1 c.956 + 796 A > G was associated with ASR in Duroc and Large White pigs and was associated with sperm motility (MOT) in Large White and Landrace pigs. This study indicated the H2AFZ, RNF4 and NR4A1 loci were the potential molecular markers for improving the semen quality traits in boars.

Effects of alleles in crossbred pigs estimated for genomic prediction depend on their breed-of-origin

BACKGROUND

This study investigated if the allele effect of a given single nucleotide polymorphism (SNP) for crossbred performance in pigs estimated in a genomic prediction model differs depending on its breed-of-origin, and how these are related to estimated effects for purebred performance.

RESULTS

SNP-allele substitution effects were estimated for a commonly used SNP panel using a genomic best linear unbiased prediction model with breed-specific partial relationship matrices. Estimated breeding values for purebred and crossbred performance were converted to SNP-allele effects by breed-of-origin. Differences between purebred and crossbred, and between breeds-of-origin were evaluated by comparing percentage of variance explained by genomic regions for back fat thickness (BF), average daily gain (ADG), and residual feed intake (RFI). From ten regions explaining most additive genetic variance for crossbred performance, 1 to 5 regions also appeared in the top ten for purebred performance. The proportion of genetic variance explained by a genomic region and the estimated effect of a haplotype in such a region were different depending upon the breed-of-origin. To illustrate underlying mechanisms, we evaluated the estimated effects across breeds-of-origin for haplotypes associated to the melanocortin 4 receptor (MC4R) gene, and for the MC4Rsnp itself which is a missense mutation with a known effect on BF and ADG. Although estimated allele substitution effects of the MC4Rsnp mutation were very similar across breeds, explained genetic variance of haplotypes associated to the MC4R gene using a SNP panel that does not include the mutation, was considerably lower in one of the breeds where the allele frequency of the mutation was the lowest.

CONCLUSIONS

Similar regions explaining similar additive genetic variance were observed across purebred and crossbred performance. Moreover, there was some overlap across breeds-of-origin between regions that explained relatively large proportions of genetic variance for crossbred performance; albeit that the actual proportion of variance deviated across breeds-of-origin. Results based on a missense mutation in MC4R confirmed that even if a causal locus has similar effects across breeds-of-origin, estimated effects and explained variance in its region using a commonly used SNP panel can strongly depend on the allele frequency of the underlying causal mutation.

Weighted single-step GWAS and gene network analysis reveal new candidate genes for semen traits in pigs

Background

In recent years, there has been increased interest in the study of the molecular processes that affect semen traits. In this study, our aim was to identify quantitative trait loci (QTL) regions associated with four semen traits (motility, progressive motility, number of sperm cells per ejaculate and total morphological defects) in two commercial pig lines (L1: Large White type and L2: Landrace type). Since the number of animals with both phenotypes and genotypes was relatively small in our dataset, we conducted a weighted single-step genome-wide association study, which also allows unequal variances for single nucleotide polymorphisms. In addition, our aim was also to identify candidate genes within QTL regions that explained the highest proportions of genetic variance. Subsequently, we performed gene network analyses to investigate the biological processes shared by genes that were identified for the same semen traits across lines.

Results

We identified QTL regions that explained up to 10.8% of the genetic variance of the semen traits on 12 chromosomes in L1 and 11 chromosomes in L2. Sixteen QTL regions in L1 and six QTL regions in L2 were associated with two or more traits within the population. Candidate genes SCN8A, PTGS2, PLA2G4A, DNAI2, IQCG and LOC102167830 were identified in L1 and NME5, AZIN2, SPATA7, METTL3 and HPGDS in L2. No regions overlapped between these two lines. However, the gene network analysis for progressive motility revealed two genes in L1 (PLA2G4A and PTGS2) and one gene in L2 (HPGDS) that were involved in two biological processes i.e. eicosanoid biosynthesis and arachidonic acid metabolism. PTGS2 and HPGDS were also involved in the cyclooxygenase pathway.

Conclusions

We identified several QTL regions associated with semen traits in two pig lines, which confirms the assumption of a complex genetic determinism for these traits. A large part of the genetic variance of the semen traits under study was explained by different genes in the two evaluated lines. Nevertheless, the gene network analysis revealed candidate genes that are involved in shared biological pathways that occur in mammalian testes, in both lines.

Electronic supplementary material

The online version of this article (10.1186/s12711-018-0412-z) contains supplementary material, which is available to authorized users.

The identification of novel regions for reproduction trait in Landrace and Large White pigs using a single step genome-wide association study

OBJECTIVE

The purpose of this study was to investigate a single step genome-wide association study (ssGWAS) for identifying genomic regions affecting reproductive traits in Landrace and Large White pigs.

METHODS

The traits included the number of pigs weaned per sow per year (PWSY), the number of litters per sow per year (LSY), pigs weaned per litters (PWL), born alive per litters (BAL), non-productive day (NPD) and wean to conception interval per litters (W2CL). A total of 321 animals (140 Landrace and 181 Large White pigs) were genotyped with the Illumina Porcine SNP 60k BeadChip, containing 61,177 single nucleotide polymorphisms (SNPs), while multiple traits single-step genomic BLUP method was used to calculate variances of 5 SNP windows for 11,048 Landrace and 13,985 Large White data records.

RESULTS

The outcome of ssGWAS on the reproductive traits identified twenty-five and twenty-two SNPs associated with reproductive traits in Landrace and Large White, respectively. Three known genes were identified to be candidate genes in Landrace pigs including retinol binding protein 7, and ubiquitination factor E4B genes for PWL, BAL, W2CL, and PWSY and one gene, solute carrier organic anion transporter family member 6A1, for LSY and NPD. Meanwhile, five genes were identified to be candidate genes in Large White, two of which, aldehyde dehydrogenase 1 family member A3 and leucine rich repeat kinase 1, associated with all of six reproduction traits and three genes; retrotransposon Gag like 4, transient receptor potential cation channel subfamily C member 5, and LHFPL tetraspan subfamily member 1 for five traits except W2CL.

CONCLUSION

The genomic regions identified in this study provided a start-up point for marker assisted selection and estimating genomic breeding values for improving reproductive traits in commercial pig populations.

Genetic parameters for semen quality and quantity traits in five pig lines

We aimed to estimate genetic parameters for semen quality and quantity traits as well as for within-boar variation of these traits to evaluate their inclusion in breeding goals. Genetic parameters were estimated within line using a multiple-trait (4 × 4) repeatability animal model fitted for 5 pig lines, considering 4 semen traits: sperm motility (MOT), sperm progressive motility (PROMOT), log-transformed number of sperm cells per ejaculate (lnN), and total morphological abnormalities (ABN). The within-boar variation of these traits was analyzed based on a multiple-trait (2 × 2) approach for SD and average (AVG) and a single-trait analysis for CV. The average heritabilities across the 5 lines estimated by multiple-trait analysis were 0.18 ± 0.07 (MOT), 0.22 ± 0.08 (PROMOT), 0.16 ± 0.04 (lnN), and 0.20 ± 0.04 (ABN). The average genetic correlations were favorable between MOT and PROMOT (0.86 ± 0.10), between MOT and ABN (-0.66 ± 0.25), and between PROMOT and ABN (-0.65 ± 0.25). As determined by within-boar variation analysis, AVG exhibited the greatest heritabilities followed by SD and CV, respectively, for the traits MOT and ABN. For PROMOT, average SD heritability was lower than CV heritability, whereas for lnN, they were the same. The average genetic correlations between AVG and SD were favorable for MOT (-0.60 ± 0.13), PROMOT (-0.79 ± 0.14), and ABN (0.78 ± 0.17). The moderate heritabilities indicate the possibility of effective selection of boars based on semen traits. Average and SD are proposed as appropriate traits for selection regarding uniformity.

Boar management and semen handling factors affect the quality of boar extended semen

Artificial insemination (AI) is the preferred method for reproduction in the majority of the intensive pig production systems Worldwide. To this end, fresh extended ready-to-use semen doses are either purchased from AI-centres or produced by boars kept on-farm. For profitable semen production, it is necessary to obtain a maximum amount of high quality semen from each boar. This paper reviews current knowledge on factors that may affect semen quality by influencing the boar or the semen during processing. Genetic markers could be used for early detection of boars with the highest fertility potential. Genetic selection for fast growth might jeopardize semen quality. Early detection of boars no longer fit for semen production might be possible by ultrasonography of the testes. Seasonal variation in sperm quality could be associated with changes in photoperiod and heat stress during summer. Comfortable housing, with appropiate bedding material to avoid locomotion problems is essential. In some areas, cooling systems may be necessary to avoid heat stress. The sperm quality can be manipulated by feeding strategies aiming, for instance, to increase sperm resistance to oxidative stress and extend storage duration. High collection frequency will negatively influence sperm quality. Also, if collection is not hygienically performed it will result in bacterial contamination of the semen doses. The concern over bacterial contamination has risen not only because of its negative effect on semen quality but also due to the detection of antimicrobial resistance in isolates from extended semen. Moreover, bacterial and viral pathogens must be monitored because they affect semen production and quality and constitute a risk of herd infection. During processing, boar sperm are submitted to many stress factors that can cause oxidative stress and capacitation-like changes potentially reducing their fertility potential. Dilution rate or dilution temperature affects the quality of the semen doses. Some packaging might preserve semen better than others and some plastic components might be toxic for sperm. Standard operation procedures and quality assurance systems in AI centres are needed.

Efficient Boar Semen Production and Genetic Contribution: The Impact of Low-Dose Artificial Insemination on Fertility

Diluting semen from high fertile breeding boars, and by that inseminating many sows, is the core business for artificial insemination (AI) companies worldwide. Knowledge about fertility results is the reason by which an AI company can lower the concentration of a dose. Efficient use of AI boars with high genetic merit by decreasing the number of sperm cells per insemination dose is important to maximize dissemination of the genetic progress made in the breeding nucleus. However, a potential decrease in fertility performance in the field should be weighed against the added value of improved genetics and, in general, is not tolerated in commercial production. This overview provides some important aspects that influence the impact of low-dose AI on fertility: (i) the importance of monitoring field fertility, (ii) the need for accurate and precise semen assessment, (iii) the parameters that are taken into account, (iv) the application of information from genetic and genomic selection and (v) the optimization when using different AI techniques. Efficient semen production, processing and insemination in combination with increasing use of genetic and genomic applications result in maximum impact of genetic trend.