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Background The gaur (Bos gaurus) is the largest extant wild bovine species, native to South and Southeast Asia, with unique traits, and is listed as vulnerable by the International Union for Conservation of Nature (IUCN). Results We report the first gaur reference genome and identify three biological pathways including lysozyme activity, proton transmembrane transporter activity, and oxygen transport with significant changes in gene copy number in gaur compared to other mammals. These may reflect adaptation to challenges related to climate and nutrition. Comparative analyses with domesticated indicine (Bos indicus) and taurine (Bos taurus) cattle revealed genomic signatures of artificial selection, including the expansion of sperm odorant receptor genes in domesticated cattle, which may have important implications for understanding selection for male fertility. Conclusions Apart from aiding dissection of economically important traits, the gaur genome will also provide the foundation to conserve the species.

Additional file 1. This contains Supplementary Figures. S1–S5.

Additional file 2. This contains Supplementary Tables S1–S14.

Bison are an icon of the American West and an ecologically, commercially, and culturally important species. Despite numbering in the hundreds of thousands today, conservation concerns remain for the species, including the impact on genetic diversity of a severe bottleneck around the turn of the 20th century and genetic introgression from domestic cattle. Genetic diversity and admixture are best evaluated at genome-wide scale, for which a high-quality reference is necessary. Here, we use trio binning of long reads from a bison-Simmental cattle (Bos taurus taurus) male F1 hybrid to sequence and assemble the genome of the American plains bison (Bison bison bison). The male haplotype genome is chromosome-scale, with a total length of 2.65 Gb across 775 scaffolds (839 contigs) and a scaffold N50 of 87.8 Mb. Our bison genome is ~13× more contiguous overall and ~3400× more contiguous at the contig level than the current bison reference genome. The bison genome sequence presented here (ARS-UCSC_bison1.0) will enable new research into the evolutionary history of this iconic megafauna species and provide a new tool for the management of bison populations in federal and commercial herds.

Additional file 5. Additional methods—comparative genomics analysis to infer ancestral and derived ASIP alleles.

Additional file 1: Figure S1. Histogram and summary statistics of average visual scores for darkness of hair coat in 432 Nellore bulls.

Additional file of Genomic characteristics of cattle copy number variations

Authors’ original file for figure 3

Additional file 1:Supplemental Material file. Table S1. Numbers of species, breeds, animals and trios used to call CNVs genotyped by BovineSNP50 assay. Table S2. Btau_4.0 cattle CNV regions and their frequencies. Table S3. Comparison of CNV regions identified on two cattle genome assemblies. Table S4. UMD3 cattle CNV regions and their frequencies. Table S5. The comparison of CNVs from 39 trios using three CNV calling algorithms: individual-calling, posterior-calling and joint-calling. Table S7. Outgroup CNV regions and their frequencies. Table S8. The effects of CNV length and frequency on calling consistances between CNV callings based on SNP array and aCGH. Table S9. qPCR Summary. Table S10. Over/Underrepresentation of PANTHER molecular function, biological process and pathway terms. Table S12. CNVR frequency differences among breeds. Figure S1. Illustration of a typical CNV call with qPCR validation. (DOC 3 MB)

Authors’ original file for figure 2

Authors’ original file for figure 1

In 2008, a consortium led by the Agricultural Research Service (ARS) and the National Institute for Food and Agriculture (NIFA) published the “Blueprint for USDA Efforts in Agricultural Animal Genomics 2008–2017,” which served as a guiding document for research and funding in animal genomics. In the decade that followed, many of the goals set forth in the blueprint were accomplished. However, several other goals require further research. In addition, new topics not covered in the original blueprint, which are the result of emerging technologies, require exploration. To develop a new, updated blueprint, ARS and NIFA, along with scientists in the animal genomics field, convened a workshop titled “Genome to Phenome: A USDA Blueprint for Improving Animal Production” in November 2017, and these discussions were used to develop new goals for the next decade. Like the previous blueprint, these goals are grouped into the broad categories “Science to Practice,” “Discovery Science,” and “Infrastructure.” New goals for characterizing the microbiome, enhancing the use of gene editing and other biotechnologies, and preserving genetic diversity are included in the new blueprint, along with updated goals within many genome research topics described in the previous blueprint. The updated blueprint that follows describes the vision, current state of the art, the research needed to advance the field, expected deliverables, and partnerships needed for each animal genomics research topic. Accomplishment of the goals described in the blueprint will significantly increase the ability to meet the demands for animal products by an increasing world population within the next decade.

Additional file 1: Figure S1. Alignment of the Brahman X with other mammalian X chromosomes. Figure S2. Alignment of the Angus cattle Y against other mammalian Y chromosomes. Figure S3. Multi-copy genes in Angus Y ampliconic region Table S1. Summary of X and Y chromosome protein-coding genes. Table S2. Copy numbers of the protein-coding genes in the bovine MSY. Table S3. List of PAR genes missing in the current Hereford, sheep, goat and pig assemblies. Table S4. Copy numbers of OBP and BDA20 genes in each species.

Additional file 3: Figure S2-1:87. Details of the overlaps between individual ROH and individual CNV for each animal and each chromosome in the three breeds. (a) the grey line indicates an animal and a black line on the grey line represents ROH for that animal. Below the grey line are the CNV for the animal with the following color codes: light blue and red for copy loss and copy gain according to PennCNV, respectively, and dark red and dark blue for copy loss and copy gain according to SVS. (b) Mean intermarker distance (IMD, black) and proportions of individuals in a ROH (dark green) and in a CNV according to SVS (magenta) or PennCNV (cyan).

Additional file 4: Figure S3. ROH islands and CNVR for each chromosome in Pinzgauer cattle. Each chromosome (dark grey bar) has four lines. Starting from top to bottom within the chromosome, the top line (black) is for ROH islands. The second line is for PennCNV CNV (light blue for copy loss, red for copy gain and light green for both copy loss and copy gain). The third line is for SVS CNV (blue for copy loss, maroon for copy gain and dark green for both copy loss and copy gain). The fourth (last) line is for intermarker distance (IMD, light grey for IMD 9.2365 kb). The magenta rectangles show regions where consensus CNVR overlap with ROH islands.

Table S1. List of RT-qPCR primers sequences, product length and their annealing temperatures. Table S2. GO terms (Biological Process, Cellular Components and Molecular Functions) of all expressed genes of goat mammary epithelial cells during early lactation. Table S3. Top 20 transcripts identified by RNA-seq in goat mammary epithelial cells harvested from milk fat layers during early lactation in goat. (DOCX 29 kb)

Additional file 5: Figure S4. BAF and LRR plot for each of the 57 ROH islands. In each sub-plot, the top panel is for individual ROH (black) and individual CNV (blue and dark red for copy loss and copy gain, respectively, for SVS and light blue and red for copy loss and copy gain, respectively, for PennCNV). The middle panel shows the mean BAF values at each marker while the third panel is for mean LRR at each marker.

Supplementary Figures. Figures for dot plots for each chromosomes of NA12878 for LACHESIS and SALSA scaffolds, dot plots for scaffolds of goat optical map scaffolds and coverage plot for misassembled contig. (DOCX 664 kb)

Example of a Misplaced SNP (MisSNP). It shows the (un)expected linkage disequilibrium between markers when misplaced in the reference genome (PDF 474 kb)

Partially Misassembled Contig (PmisCon). It presents all cases of partially misassembled contigs of the reference genome found in this study (PDF 7832 kb)

Distribution for the percentage of cytosine with 2 to 11 reads. Figure S2. Methylation of different methylation contexts for cattle somatic tissues. (a) CG percentages with different methylation levels; (b) CHG percentages with different methylation levels; (c) CHH percentages with different methylation levels. Note: the error bar represents the standard deviation among the 10 tissues. Figure S3. Correlation analysis of CG and non-CG methylation using 1-Mb non-overlapping windows for oocyte overlapped with the RRBS data. Note: Only the cytosines that overlapped with the RRBS data in oocyte WGBS were used for plotting. Figure S4. Methylation levels for different genomes. Note: the error bar represents the standard deviation among the 10 tissues. Figure S5. Methylation levels for different repetitive sequences. Note: the error bar represents the standard deviation among the 10 tissues. Figure S6. Methylation distributions of the 3 methylation contexts in genic regions and CG islands for oocyte overlapped with the RRBS data. Figure S7. Autocorrelation analysis for different methylation contexts on genome. Chr1 was used to calculate the correlation of different methylation contexts with different distances. Note: all figures for somatic tissues were from the merged data after examining results individually that did not show differences between them. Figure S8. CG island methylations in cattle somatic tissues. (a) Average methylation levels of CG islands, CG island shores and non-CG island regions. (b) Distribution of CG islands at different methylation levels. (c) CG island methylation levels within every window of 10 % length of all chromosomes. The x-axis is the interval of all chromosomes from 5′ to 3′. Figure S9. Clustering of 10 tissues based on 131 tissue-specific DMIs (tDMIs). (PDF 896 kb)

Distribution for the percentage of cytosine with 2 to 11 reads. Figure S2. Methylation of different methylation contexts for cattle somatic tissues. (a) CG percentages with different methylation levels; (b) CHG percentages with different methylation levels; (c) CHH percentages with different methylation levels. Note: the error bar represents the standard deviation among the 10 tissues. Figure S3. Correlation analysis of CG and non-CG methylation using 1-Mb non-overlapping windows for oocyte overlapped with the RRBS data. Note: Only the cytosines that overlapped with the RRBS data in oocyte WGBS were used for plotting. Figure S4. Methylation levels for different genomes. Note: the error bar represents the standard deviation among the 10 tissues. Figure S5. Methylation levels for different repetitive sequences. Note: the error bar represents the standard deviation among the 10 tissues. Figure S6. Methylation distributions of the 3 methylation contexts in genic regions and CG islands for oocyte overlapped with the RRBS data. Figure S7. Autocorrelation analysis for different methylation contexts on genome. Chr1 was used to calculate the correlation of different methylation contexts with different distances. Note: all figures for somatic tissues were from the merged data after examining results individually that did not show differences between them. Figure S8. CG island methylations in cattle somatic tissues. (a) Average methylation levels of CG islands, CG island shores and non-CG island regions. (b) Distribution of CG islands at different methylation levels. (c) CG island methylation levels within every window of 10 % length of all chromosomes. The x-axis is the interval of all chromosomes from 5′ to 3′. Figure S9. Clustering of 10 tissues based on 131 tissue-specific DMIs (tDMIs). (PDF 896 kb)

Misplaced SNPs. It presents all cases of misplaced SNPs found in this study (PDF 3034 kb)

Example of a Misplaced SNP (MisSNP). It shows the (un)expected linkage disequilibrium between markers when misplaced in the reference genome (PDF 474 kb)

Example of a Misassembled Segment (MisSeg). It shows the (un)expected linkage disequilibrium between markers in an specific segment of the genome when wrongly assembled (PDF 295 kb)

Partially Misassembled Contig (PmisCon). It presents all cases of partially misassembled contigs of the reference genome found in this study (PDF 7832 kb)

Misassembled segment (MisSeg). It presents all cases of misassembled segments of the reference genome found in this study (PDF 4176 kb)

Alignment of the protein sequences of KCNJ12 in different species. Figure S2. Boxplot of reliability of the nine body traits. Figure S3. Expression levels of KCNJ12 in 45 tissues. Figure S4. The effect of the CNV100â s copy number on the gene expression of KCNJ12 in muscle tissues. (PDF 678Â kb)

Benchmarking of the r2 calculations. Describes the computational performance for calculating all pair-wise r2 (PDF 301 kb)

Tag marker count criteria. Describes the empirical criteria used for detecting candidate misplaced markers (PDF 229 kb)

Misplaced SNPs. It presents all cases of misplaced SNPs found in this study (PDF 3034 kb)

Misassembled segment (MisSeg). It presents all cases of misassembled segments of the reference genome found in this study (PDF 4176 kb)

Alignment of the protein sequences of KCNJ12 in different species. Figure S2. Boxplot of reliability of the nine body traits. Figure S3. Expression levels of KCNJ12 in 45 tissues. Figure S4. The effect of the CNV100â s copy number on the gene expression of KCNJ12 in muscle tissues. (PDF 678Â kb)

Example of Partially Misassembled Contig (PmisCon). It shows the (un)expected linkage disequilibrium between markers in a partially misassembled contig (PDF 465 kb)

Example of Partially Misassembled Contig (PmisCon). It shows the (un)expected linkage disequilibrium between markers in a partially misassembled contig (PDF 465 kb)

BACKGROUND: The domestic pig (Sus scrofa) is important both as a food source and as a biomedical model given its similarity in size, anatomy, physiology, metabolism, pathology, and pharmacology to humans. The draft reference genome (Sscrofa10.2) of a purebred Duroc female pig established using older clone-based sequencing methods was incomplete, and unresolved redundancies, short-range order and orientation errors, and associated misassembled genes limited its utility. RESULTS: We present 2 annotated highly contiguous chromosome-level genome assemblies created with more recent long-read technologies and a whole-genome shotgun strategy, 1 for the same Duroc female (Sscrofa11.1) and 1 for an outbred, composite-breed male (USMARCv1.0). Both assemblies are of substantially higher (>90-fold) continuity and accuracy than Sscrofa10.2. CONCLUSIONS: These highly contiguous assemblies plus annotation of a further 11 short-read assemblies provide an unprecedented view of the genetic make-up of this important agricultural and biomedical model species. We propose that the improved Duroc assembly (Sscrofa11.1) become the reference genome for genomic research in pigs.

Supplementary figures and methods. Contains all supplementary figures and two supplementary tables. Additionally, contains a brief listing of additional methods and command line code for replicating analysis. (DOCX 3839 kb)

Supplementary figures and methods. Contains all supplementary figures and two supplementary tables. Additionally, contains a brief listing of additional methods and command line code for replicating analysis. (DOCX 3839 kb)