Single Nucleotide Polymorphism Discovery in Bovine Pituitary Gland Using RNA-Seq Technology

Assessment of Genetic Diversity and Conservation in South African Indigenous Goat Ecotypes: A Review

Goats were amongst the first livestock to be domesticated more than 10,000 years ago for their meat, milk, skin, and fiber. They were introduced to Southern Africa by migrating nations from Central Africa to the south. Due to local adaptation to the different agro-ecological zones and selection, indigenous goats are identified as ecotypes within the indigenous veld goat breed. Their ability to thrive in a resource-limited production system and in challenging environmental conditions makes them valuable animal resources for small-scale and emerging farmers. They play important roles in household agriculture and cultural activities as well as in poverty alleviation. Studies have described the phenotypic and genetic variations in indigenous goats, targeting the major goat-producing regions and the breeds of South Africa. In turn, information is restricted to certain breeds and regions, and the experimental design is often not adequate to inform the conservation status and priorities in changing environments. Advances in genomics technologies have availed more opportunities for the assessment of the biodiversity, demographic histories, and detection regions associated with local adaptation. These technologies are essential for breeding and conservation strategies for sustainable production for food security. This review focuses on the status of indigenous goats in South Africa and the application of genomics technologies for characterization, with emphasis on prioritization for conservation and sustainable utilization.

Differential gene expression and identification of growth-related genes in the pituitary gland of South African goats

Growth and carcass quality are economically important traits in goat production. This study investigated differentially expressed genes from the caprine pituitary gland transcriptome of South African indigenous goat breeds of varying growth performances and carcass quality parameters. Tissues were harvested from the pituitary gland of three South African Boer goats and three village ecotype goats all raised under similar conditions simulating intensive commercial production systems. Three additional tissues were harvested from village ecotype goats that were raised extensively on village farms. Between breed differences were investigated by comparing differential gene expression among three South African Boer and three village goats that were both raised under intensive commercial production system at a research farm. Within-breed differences were investigated by comparing differential gene expression among three village goats raised under extensive conditions (on-farm in Pella, S.A. village farming community) and three village goats raised under intensive commercial production system (at ARC research farm in Pretoria, South Africa. Total RNA was isolated from the pituitary gland of 36-week-old animals (n = 9) and sequenced individually in triplicates. An average of 28,298,512 trimmed, and quality-controlled reads/animal were mapped to the goat genome (Capra_hircus.ARS1.94) using HiSat2 software. Transcript assembly and quantification yielded 104 differentially expressed genes for village goats raised under extensive system and 62 for village goats raised under the intensive production system at the false discovery rate (FRD) of ≤0.05 and a fold change of ≥2. Growth-related genes such as POU3F4 and TSHZ1 were highly expressed within breeds raised under both production systems. Conversely, growth-related genes such as FGFR2 and SMPX genes were highly expressed between breeds raised under similar production systems. Ballgown analysis revealed a high expression of GH1 and IGF1 in the intensively raised compared to extensively raised goats. Both genes were also highly expressed in the village goats when compared to the Boer. The differential gene expression data provided insights into genes and molecular mechanisms associated with growth and growth development in goats.

Age-associated changes in gene expression in the anterior pituitary glands of female Japanese black cattle

Proper functioning of the anterior pituitary (AP) gland is imperative, however, is suppressed by aging via unclear mechanisms. Therefore, we identified differentially expressed genes (DEGs) in the AP glands of Japanese Black young heifers (approximately 22 months old) compared to old cows (approximately 120 months old) via deep sequencing of the transcriptome (RNA-seq) to characterize potentially important pathways. The young and old AP glands expressed 20,171 annotated genes. Of the total transcripts per million, approximately 41.6% and 35.5% were the sum of seven AP hormone genes in young and old AP glands, respectively, with difference observed in the sum between the young and old AP glands (P < 0.05). Moreover, we identified 48 downregulated genes and 218 upregulated genes in old compared to young AP glands (P < 0.01, fold change > 120%). The DEGs included 1 cytokine (AIMP1), 3 growth factors (NRG2, PTN, and TGFB1), 1 receptor-associated protein gene (AGTRAP), and 10 receptor genes, including PRLHR and two orphan G-protein-coupled receptors (GPR156 and GPR176). Metascape analysis of the DEGs revealed "Peptide metabolic process," "Regulation of hormone levels," and "Peptide hormone processing" as enriched pathways. Furthermore, Ingenuity Pathway analysis of the DEGs revealed (1) a network of 24 genes (including GPR156 and PRLHR) named "Neurological disease, organismal injury and abnormalities, and psychological disorders", and (2) two canonical pathways (P < 0.01), namely "Huntington's disease signaling", and "AMPK signaling". Thus, the findings of the current study revealed relevant DEGs, while identifying important pathways that occur during aging in AP glands of female cattle.

SNPs in Mammary Gland Epithelial Cells Unraveling Potential Difference in Milk Production Between Jersey and Kashmiri Cattle Using RNA Sequencing

Deep RNA sequencing experiment was employed to detect putative single nucleotide polymorphisms (SNP) in mammary epithelial cells between two diverse cattle breeds (Jersey and Kashmiri) to understand the variations in the coding regions that reflect differences in milk production traits. The low milk-producing Kashmiri cattle are being replaced by crossbreeding practices with Jersey cattle with the aim of improving milk production. However, crossbred animals are prone to infections and various other diseases resulting in unsustainable milk production. In this study, we tend to identify high-impact SNPs from Jersey and Kashmiri cows (utilizing RNA-Seq data) to delineate key pathways mediating milk production traits in both breeds. A total of 607 (442 SNPs and 169 INDELs) and 684 (464 SNPs and 220 INDELs) high-impact variants were found specific to Jersey and Kashmir cattle, respectively. Based on our results, we conclude that in Jersey cattle, genes with high-impact SNPs were enriched in nucleotide excision repair pathway, ABC transporter, and metabolic pathways like glycerolipid metabolism, pyrimidine metabolism, and amino acid synthesis (glycine, serine, and threonine). Whereas, in Kashmiri cattle, the most enriched pathways include endocytosis pathway, innate immunity pathway, antigen processing pathway, insulin resistance pathway, and signaling pathways like TGF beta and AMPK which could be a possible defense mechanism against mammary gland infections. A varied set of SNPs in both breeds, suggests a clear differentiation at the genomic level; further analysis of high-impact SNPs are required to delineate their effect on these pathways.

Allele Specific Expression (ASE) analysis between Bos Taurus and Bos Indicus cows using RNA-Seq data at SNP level and gene level

In the current study, allele specific expression analysis was performed in two subspecies cows (Bos taurus and Bos indicus) at SNP and gene levels. RNA-Seq data of 21,078,477 and 20940063 paired end reads from pooling of whole blood samples (Leukocyte) from 40 US Holstein (Bos Taurus) and 45 Cholistani cows (Bos indicus) obtained from SRA database in NCBI. Quality control and trimming of row RNA-Seq data were processed by FASTQC and Trimmomatic softwares. The transcriptome was assembled by TopHat2 software in two cow's population by aligning and mapping the RNA-Seq reads on bovine reference genome. The SNPs were discovered by Samtools software and ASE analysis was performed by Chi-square test. Results showed that 50183 and 137954 SNPs were discovered on the assembled transcriptome of Holstein and Cholistani cow samples, respectively, and 15308 SNPs were common in both breeds. 10158 SNPs from 50183 (20%) in Holstein and 31523 SNPs from 137954 (23%) in Cholistani cows were identified as ASE-SNPs. Reference allele and alternative allele count in Holstein and Cholistani cows were 3041 and 7155, respectively. Among 131 discovered SNPs in 41 genes with different expression in Holstein and Cholistani cows, 31 ASE-SNPs (5 in Holstein; 26 in Cholistani cows) were discovered.

Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencing

The identification of functional genetic variants and associated candidate genes linked to feed efficiency may help improve selection for feed efficiency in dairy cattle, providing economic and environmental benefits for the dairy industry. This study used RNA-sequencing data obtained from liver tissue from 9 Holstein cows [n = 5 low residual feed intake (RFI), n = 4 high RFI] and 10 Jersey cows (n = 5 low RFI, n = 5 high RFI), which were selected from a single population of 200 animals. Using RNA-sequencing, 3 analyses were performed to identify: (1) variants within low or high RFI Holstein cattle; (2) variants within low or high RFI Jersey cattle; and (3) variants within low or high RFI groups, which are common across both Holstein and Jersey cattle breeds. From each analysis, all variants were filtered for moderate, modifier, or high functional effect, and co-localized quantitative trait loci (QTL) classes, enriched biological processes, and co-localized genes related to these variants, were identified. The overlapping of the resulting genes co-localized with functional SNP from each analysis in both breeds for low or high RFI groups were compared. For the first two analyses, the total number of candidate genes associated with moderate, modifier, or high functional effect variants fixed within low or high RFI groups were 2,810 and 3,390 for Holstein and Jersey breeds, respectively. The major QTL classes co-localized with these variants included milk and reproduction QTL for the Holstein breed, and milk, production, and reproduction QTL for the Jersey breed. For the third analysis, the common variants across both Holstein and Jersey breeds, uniquely fixed within low or high RFI groups were identified, revealing a total of 86,209 and 111,126 functional variants in low and high RFI groups, respectively. Across all 3 analyses for low and high RFI cattle, 12 and 31 co-localized genes were overlapping, respectively. Among the overlapping genes across breeds, 9 were commonly detected in both the low and high RFI groups (INSRR, CSK, DYNC1H1, GAB1, KAT2B, RXRA, SHC1, TRRAP, PIK3CB), which are known to play a key role in the regulation of biological processes that have high metabolic demand and are related to cell growth and regeneration, metabolism, and immune function. The genes identified and their associated functional variants may serve as candidate genetic markers and can be implemented into breeding programs to help improve the selection for feed efficiency in dairy cattle.

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.

Effect direction meta-analysis of GWAS identifies extreme, prevalent and shared pleiotropy in a large mammal

In genome-wide association studies (GWAS), variants showing consistent effect directions across populations are considered as true discoveries. We model this information in an Effect Direction MEta-analysis (EDME) to quantify pleiotropy using GWAS of 34 Cholesky-decorrelated traits in 44,000+ cattle with sequence variants. The effect-direction agreement between independent bull and cow datasets was used to quantify the false discovery rate by effect direction (FDRed) and the number of affected traits for prioritised variants. Variants with multi-trait p < 1e–6 affected 1∼22 traits with an average of 10 traits. EDME assigns pleiotropic variants to each trait which informs the biology behind complex traits. New pleiotropic loci are identified, including signals from the cattle FTO locus mirroring its bystander effects on human obesity. When validated in the 1000-Bull Genome database, the prioritized pleiotropic variants consistently predicted expected phenotypic differences between dairy and beef cattle. EDME provides robust approaches to control GWAS FDR and quantify pleiotropy.

Xiang et al. developed an Effect Direction Meta-analysis (EDME) approach to identify true pleiotropy. They used Cholesky-transformation to decorrelate the traits and identified many pleiotropic variants that consistently predicted phenotypic differences in cattle.

The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments

The Genome Analysis Toolkit (GATK) is a popular set of programs for discovering and genotyping variants from next-generation sequencing data. The current GATK recommendation for RNA sequencing (RNA-seq) is to perform variant calling from individual samples, with the drawback that only variable positions are reported. Versions 3.0 and above of GATK offer the possibility of calling DNA variants on cohorts of samples using the HaplotypeCaller algorithm in Genomic Variant Call Format (GVCF) mode. Using this approach, variants are called individually on each sample, generating one GVCF file per sample that lists genotype likelihoods and their genome annotations. In a second step, variants are called from the GVCF files through a joint genotyping analysis. This strategy is more flexible and reduces computational challenges in comparison to the traditional joint discovery workflow. Using a GVCF workflow for mining SNP in RNA-seq data provides substantial advantages, including reporting homozygous genotypes for the reference allele as well as missing data. Taking advantage of RNA-seq data derived from primary macrophages isolated from 50 cows, the GATK joint genotyping method for calling variants on RNA-seq data was validated by comparing this approach to a so-called “per-sample” method. In addition, pair-wise comparisons of the two methods were performed to evaluate their respective sensitivity, precision and accuracy using DNA genotypes from a companion study including the same 50 cows genotyped using either genotyping-by-sequencing or with the Bovine SNP50 Beadchip (imputed to the Bovine high density). Results indicate that both approaches are very close in their capacity of detecting reference variants and that the joint genotyping method is more sensitive than the per-sample method. Given that the joint genotyping method is more flexible and technically easier, we recommend this approach for variant calling in RNA-seq experiments.

Comparative Analysis of the Liver Transcriptome among Cattle Breeds Using RNA-seq

Global gene expression in liver transcriptome varies among cattle breeds. The present investigation was aimed to identify the differentially expressed genes (DEGs), metabolic gene networks and metabolic pathways in bovine liver transcriptome of young bulls. In this study, we comparatively analyzed the bovine liver transcriptome of dairy (Polish Holstein Friesian (HF); n = 6), beef (Hereford; n = 6), and dual purpose (Polish-Red; n = 6) cattle breeds. This study identified 895, 338, and 571 significant (p < 0.01) differentially expressed (DE) gene-transcripts represented as 745, 265, and 498 hepatic DE genes through the Polish-Red versus Hereford, Polish-HF versus Hereford, and Polish-HF versus Polish-Red breeds comparisons, respectively. By combining all breeds comparisons, 75 hepatic DE genes (p < 0.01) were identified as commonly shared among all the three breed comparisons; 70, 160, and 38 hepatic DE genes were commonly shared between the following comparisons: (i) Polish-Red versus Hereford and Polish-HF versus Hereford; (ii) Polish-Red versus Hereford and Polish-HF versus Polish-Red; and (iii) Polish-HF versus Hereford and Polish-HF versus Polish-Red, respectively. A total of 440, 82, and 225 hepatic DE genes were uniquely observed for the Polish-Red versus Hereford, Polish-HF versus Hereford, and Polish-Red versus Polish-HF comparisons, respectively. Gene ontology (GO) analysis identified top-ranked enriched GO terms (p < 0.01) including 17, 16, and 31 functional groups and 151, 61, and 140 gene functions that were DE in all three breed liver transcriptome comparisons. Gene network analysis identified several potential metabolic pathways involved in glutamine family amino-acid, triglyceride synthesis, gluconeogenesis, p38MAPK cascade regulation, cholesterol biosynthesis (Polish-Red versus Hereford); IGF-receptor signaling, catecholamine transport, lipoprotein lipase, tyrosine kinase binding receptor (Polish-HF versus Hereford), and PGF-receptor binding, (Polish-HF versus Polish-Red). Validation results showed that the relative expression values were consistent to those obtained by RNA-seq, and significantly correlated between the quantitative reverse transcription PCR (RT-qPCR) and RNA-seq (Pearson’s r  > 0.90). Our results provide new insights on bovine liver gene expressions among dairy versus dual versus beef breeds by identifying the large numbers of DEGs markers submitted to NCBI gene expression omnibus (GEO) accession number GSE114233, which can serve as useful genetic tools to develop the gene assays for trait-associated studies as well as, to effectively implement in genomics selection (GS) cattle breeding programs in Poland.

PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy

Principal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs) β * ^ were in most cases larger than the single-trait model estimations ( β 1 ^ and β 2 ^ ). Analysis using the simulated data showed that PCA-based multiple-trait GWAS has improved statistical power for detecting QTL compared to single-trait GWAS. For the minor allele frequency (MAF), when the MAF of QTNs was greater than 0.2, the PCA-based model had a significant advantage in detecting the pleiotropic QTNs, but when its MAF was reduced from 0.2 to 0, the advantage began to disappear. In addition, as the linkage disequilibrium (LD) of the pleiotropic QTNs decreased, its detection ability declined in the co-localization effect model. Furthermore, on the real data of 1141 Simmental cattle, we applied the PCA model to the multiple-trait GWAS analysis and identified a QTL that was consistent with a candidate gene, MCHR2, which was associated with presoma muscle development in cattle. In summary, PCA-based multiple-trait GWAS is an efficient model for exploring pleiotropic QTNs in quantitative traits.

Multitrait meta-analysis identified genomic regions associated with sexual precocity in tropical beef cattle

Multitrait meta-analyses are a strategy to produce more accurate genome-wide association studies, especially for complex phenotypes. We carried out a meta-analysis study for traits related to sexual precocity in tropical beef cattle (Nellore and Brahman) aiming to identify important genomic regions affecting these traits. The traits included in the analyses were age at first calving (AFC), early pregnancy (EP), age at first corpus luteum (AGECL), first postpartum anoestrus interval (PPAI), and scrotal circumference (SC). The traits AFC, EP, and SCN were measured in Nellore cattle, while AGECL, PPAI, and SCB were measured in Brahman cattle. Meta-analysis resulted in 108 significant single-nucleotide polymorphisms (SNPs), at an empirical threshold P-value of 1.39 × 10-5 (false discovery rate [FDR] < 0.05). Within 0.5 Mb of the significant SNP, candidate genes were annotated and analyzed for functional enrichment. Most of the closest genes to the SNP with higher significance in each chromosome have been associated with important roles in reproductive function. They are TSC22D2, KLF7, ARHGAP29, 7SK, MAP3K5, TLE3, WDR5, TAF3, TMEM68, PPP1R15B, NR2F2, GALR1, SUFU, and KCNU1. We did not observe any significant SNP in BTA5, BTA12, BTA17, BTA18, BTA19, BTA20, BTA22, BTA23, BTA25, and BTA28. Although the majority of significant SNPs are in BTA14, it was identified significant associations in multiple chromosomes (19 out of 29 autosomes), which is consistent with the postulation that reproductive traits are complex polygenic phenotypes. Five proposed association regions harbor the majority of the significant SNP (76%) and were distributed over four chromosomes (P < 1.39 × 10-5, FDR < 0.05): BTA2 (5.55%) from 95 to 96 Mb, BTA4 (5.55%) from 94.1 to 94.8 Mb, BTA14 (59.26%) from 24 to 25 Mb and 29 to 30 Mb, and BTA21 (5.55%) from 6.7 Mb to 11.4 Mb. These regions harbored key genes related to reproductive function. Moreover, these genes were enriched for functional groups associated with immune response, maternal-fetal tolerance, pregnancy maintenance, embryo development, fertility, and response to stress. Further studies including other breeds and precocity traits could confirm the importance of these regions and identify new candidate regions for sexual precocity in beef cattle.

Single nucleotide polymorphism discovery in bovine liver using RNA-seq technology

BACKGROUND

RNA-seq is a useful next-generation sequencing (NGS) technology that has been widely used to understand mammalian transcriptome architecture and function. In this study, a breed-specific RNA-seq experiment was utilized to detect putative single nucleotide polymorphisms (SNPs) in liver tissue of young bulls of the Polish Red, Polish Holstein-Friesian (HF) and Hereford breeds, and to understand the genomic variation in the three cattle breeds that may reflect differences in production traits.

RESULTS

The RNA-seq experiment on bovine liver produced 107,114,4072 raw paired-end reads, with an average of approximately 60 million paired-end reads per library. Breed-wise, a total of 345.06, 290.04 and 436.03 million paired-end reads were obtained from the Polish Red, Polish HF, and Hereford breeds, respectively. Burrows-Wheeler Aligner (BWA) read alignments showed that 81.35%, 82.81% and 84.21% of the mapped sequencing reads were properly paired to the Polish Red, Polish HF, and Hereford breeds, respectively. This study identified 5,641,401 SNPs and insertion and deletion (indel) positions expressed in the bovine liver with an average of 313,411 SNPs and indel per young bull. Following the removal of the indel mutations, a total of 195,3804, 152,7120 and 205,3184 raw SNPs expressed in bovine liver were identified for the Polish Red, Polish HF, and Hereford breeds, respectively. Breed-wise, three highly reliable breed-specific SNP-databases (SNP-dbs) with 31,562, 24,945 and 28,194 SNP records were constructed for the Polish Red, Polish HF, and Hereford breeds, respectively. Using a combination of stringent parameters of a minimum depth of ≥10 mapping reads that support the polymorphic nucleotide base and 100% SNP ratio, 4,368, 3,780 and 3,800 SNP records were detected in the Polish Red, Polish HF, and Hereford breeds, respectively. The SNP detections using RNA-seq data were successfully validated by kompetitive allele-specific PCR (KASPTM) SNP genotyping assay. The comprehensive QTL/CG analysis of 110 QTL/CG with RNA-seq data identified 20 monomorphic SNP hit loci (CARTPT, GAD1, GDF5, GHRH, GHRL, GRB10, IGFBPL1, IGFL1, LEP, LHX4, MC4R, MSTN, NKAIN1, PLAG1, POU1F1, SDR16C5, SH2B2, TOX, UCP3 and WNT10B) in all three cattle breeds. However, six SNP loci (CCSER1, GHR, KCNIP4, MTSS1, EGFR and NSMCE2) were identified as highly polymorphic among the cattle breeds.

CONCLUSIONS

This study identified breed-specific SNPs with greater SNP ratio and excellent mapping coverage, as well as monomorphic and highly polymorphic putative SNP loci within QTL/CGs of bovine liver tissue. A breed-specific SNP-db constructed for bovine liver yielded nearly six million SNPs. In addition, a KASPTM SNP genotyping assay, as a reliable cost-effective method, successfully validated the breed-specific putative SNPs originating from the RNA-seq experiments.