An imputation-based genome-wide association study on traits related to male reproduction in a White Duroc × Erhualian F2 population

An imputation-based genome-wide association study for growth and fatness traits in Sujiang pigs

Sujiang pigs are a synthetic breed derived from Jiangquhai, Fengjing, and Duroc pigs. In this study, we sequenced the genome of 62 pigs with a coverage depth of 10× to 20×, including 27 Sujiang and 35 founder breed pigs, and we collected 360 global pigs' genome sequence data from public databases including 39 Duroc pigs. We obtained a high-quality variant dataset of 365 Sujiang pigs by imputing the porcine 80 K single nucleotide polymorphism (SNP) Beadchip to the whole-genome scale with a total of 422 pigs as a reference panel. A dataset of 365 imputated Sujiang pigs was used to perform single-trait genome-wide association study (GWAS) and meta-analyses for growth and fatness traits. Single-trait GWAS identified 1 907, 18, and 14 SNPs surpassing the suggestively significant threshold for backfat thickness, chest circumference, and chest width, respectively. Meta-analyses identified 2 400 genome-wide significant SNPs and 520 suggestively significant SNPs for backfat thickness and chest circumference, and 719 genome-wide significant SNPs and 1 225 suggestively significant SNPs for all seven traits. According to the meta-analysis of backfat thickness and chest circumference, a remarkable region of 2.69 Mb on Sus scrofa chromosome 4 containing FAM110B, IMPAD1, LYN, MOS, PENK, PLAG1, SDR16C5 and XKR4 was identified as a candidate region. The haplotype heat map of the 2.69 Mb region verified that Sujiang pigs were derived from Duroc and Chinese indigenous pigs, especially Jiangquhai pigs. The Kruskal-Wallis test showed that haplotypes of the 2.69 Mb region significantly affected backfat thickness and chest circumference traits. We then focused on PLAG1, an important growth-related gene, and identified two synonymous SNPs with obvious differences among different breeds in the PLAG1 gene. We then performed genotyping of 365 Sujiang, 150 Duroc, 95 Jiangquhai, and 100 Fengjing pigs to confirm the above result and verified that the two variants significantly affected phenotypes of growth and fatness traits. Our findings not only provide insights into the genetic architecture of porcine growth and fatness traits but also provide potential markers for selective breeding of these traits in Sujiang pigs.

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.

Impact of merging commercial breeding lines on the genetic diversity of Landrace pigs

BACKGROUND

The pig breeding industry has undergone a large number of mergers in the past decades. Various commercial lines were merged or discontinued, which is expected to reduce the genetic diversity of the pig species. The objective of the current study was to investigate the genetic diversity of different former Dutch Landrace breeding lines and quantify their relationship with the current Dutch Landrace breed that originated from these lines.

RESULTS

Principal component analysis clearly divided the former Landrace lines into two main clusters, which are represented by Norwegian/Finnish Landrace lines and Dutch Landrace lines. Structure analysis revealed that each of the lines that are present in the Dutch Gene bank has a unique genetic identity. The current Dutch Landrace breed shows a high level of admixture and is closely related to the six former lines. The Dumeco N-line, which is conserved in the Dutch Gene bank, is poorly represented in the current Dutch Landrace. All seven lines (the six former and the current line) contribute almost equally to the genetic diversity of the Dutch Landrace breed. As expected, the current Dutch Landrace breed comprises only a small proportion of unique genetic diversity that was not present in the other lines. The genetic diversity level, as measured by Eding's core set method, was equal to 0.89 for the current Dutch Landrace breed, whereas total genetic diversity across the seven lines, measured by the same method, was equal to 0.99.

CONCLUSIONS

The current Dutch Landrace breed shows a high level of admixture and is closely related to the six former Dutch Landrace lines. Merging of commercial Landrace lines has reduced the genetic diversity of the Landrace population in the Netherlands, although a large proportion of the original variation is maintained. Thus, our recommendation is to conserve breeding lines in a gene bank before they are merged.

Novel SNPs in the SPAG11 gene and association with testicular biometric variables in Boer goats and application of the levelled-container technique

Testicular volume (TV) is one of the most important traits used in evaluation of the reproductive capacity of male animals. The levelled-container used in the present study was found to be reliable instrument to measure TV, based on a water displacement method. Sperm-associated antigen 11 (SPAG11) is an important gene that affects male reproductive performance. An objective of the present study, therefore, was to determine if single nucleotide polymorphisms (SNPs) in a fragment of the SPAG11 gene could be used to determine associations with values of testicular biometric variables in Boer goats. Primers were designed to amplify the full length of the first two exons of SPAG11. The targeted fragment was generated using a molecular cloning technique. As the result, four SNPs, [g.1256A > G(ss19199134542), g.1270C > T(ss19199134541), g.1325A > G(ss19199134540) and g.1327 G > A (ss19199134543)], were detected using a single-base extension (SBE) method. Two of these SNPs were synonymous (ss19199134540 and ss19199134542). The other two SNPs were nonsynonymous, thus, there were changes in amino acid in the resulting protein: threonine to isoleucine (for ss19199134541) and arginine to glutamine (for ss19199134543). The SNP ss19199134543 was the only locus detected that was associated with TV (P = 0.002). None of the testes dimensions nor TW were associated with detected SPAG11 gene SNPs. Most likely, the ss19199134543 locus affects tissue structures adjacent to the testes, causing the change in TV. In conclusion, among the studied testicular biometric variables, TV had the greatest potential for preselecting of bucks with desirable semen quality. The use of the levelled-container as a TV measurement approach was an accurate and reliable method.

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.

Identification of loci affecting teat number by genome-wide association studies on three pig populations

OBJECTIVE

Three genome-wide association studies (GWAS) and a meta-analysis of GWAS were conducted to explore the genetic mechanisms underlying variation in pig teat number.

METHODS

We performed three GWAS and a meta-analysis for teat number on three pig populations, including a White Duroc×Erhualian F₂ resource population (n = 1,743), a Chinese Erhualian pig population (n = 320) and a Chinese Sutai pig population (n = 383).

RESULTS

We detected 24 single nucleotide polymorphisms (SNPs) that surpassed the genome-wide significant level on Sus Scrofa chromosomes (SSC) 1, 7, and 12 in the F₂ resource population, corresponding to four loci for pig teat number. We highlighted vertnin (VRTN) and lysine demethylase 6B (KDM6B) as two interesting candidate genes at the loci on SSC7 and SSC12. No significant associated SNPs were identified in the meta-analysis of GWAS.

CONCLUSION

The results verified the complex genetic architecture of pig teat number. The causative variants for teat number may be different in the three populations.