The Genome Sequence of Taurine Cattle: A Window to Ruminant Biology and Evolution

Science  24 Apr 2009:
Vol. 324, Issue 5926, pp. 522-528
DOI: 10.1126/science.1169588


To understand the biology and evolution of ruminants, the cattle genome was sequenced to about sevenfold coverage. The cattle genome contains a minimum of 22,000 genes, with a core set of 14,345 orthologs shared among seven mammalian species of which 1217 are absent or undetected in noneutherian (marsupial or monotreme) genomes. Cattle-specific evolutionary breakpoint regions in chromosomes have a higher density of segmental duplications, enrichment of repetitive elements, and species-specific variations in genes associated with lactation and immune responsiveness. Genes involved in metabolism are generally highly conserved, although five metabolic genes are deleted or extensively diverged from their human orthologs. The cattle genome sequence thus provides a resource for understanding mammalian evolution and accelerating livestock genetic improvement for milk and meat production.

Domesticated cattle (Bos taurus and Bos taurus indicus) provide a significant source of nutrition and livelihood to nearly 6.6 billion humans. Cattle belong to a clade phylogenetically distant from humans and rodents, the Cetartiodactyl order of eutherian mammals, which first appeared ~60 million years ago (1). Cattle represent the Ruminantia, which occupy diverse terrestrial environments with their ability to efficiently convert low-quality forage into energy-dense fat, muscle, and milk. These biological processes have been exploited by humans since domestication, which began in the Near East some 8000 to 10,000 years ago (2). Since then, over 800 cattle breeds have been established, representing an important world heritage and a scientific resource for understanding the genetics of complex traits.

The cattle genome was assembled with methods similar to those used for the rat and sea urchin genomes (3, 4). The most recent assemblies, Btau3.1 and Btau4.0, combined bacterial artificial chromosome (BAC) and whole-genome shotgun (WGS) sequences. Btau3.1 was used for gene-specific analyses. Btau4.0, which includes finished sequence data and used different mapping methods to place the sequence on chromosomes, was used for all global analyses other than gene prediction. The contig N50 (50% of the genome is in contigs of this size or greater) is 48.7 kb for both assemblies; the scaffold N50 for Btau4.0 is 1.9 Mb. In the Btau4.0 assembly, 90% of the total genome sequence was placed on the 29 autosomes and X chromosome and validated (3). Of 1.04 million expressed sequence tag (EST) sequences, 95.0% were contained in the assembled contigs. With an equivalent gene distribution in the remaining 5% of the genome, the estimated genome size is 2.87 Gbp. Comparison with 73 finished BACs and single-nucleotide polymorphism (SNP) linkage data (5, 6) confirmed this assembly quality with greater than 92% genomic coverage, and fewer than 0.8% of SNPs were incorrectly positioned at the resolution of these maps (3, 4).

We used the cattle genome to catalog protein-coding genes, microRNA (miRNA) genes, and ruminant-specific interspersed repeats, and we manually annotated over 4000 genes. The consensus protein-coding gene set for Btau3.1 (OGSv1), from six predicted gene sets (4), consists of 26,835 genes with a validation rate of 82% (4). On this basis, we estimate that the cattle genome contains at least 22,000 protein-coding genes. We identified 496 miRNA genes of which 135 were unpublished miRNAs (4). About half of the cattle miRNA occur in 60 genomic miRNA clusters, containing two to seven miRNA genes separated by less than 10 kbp (fig. S2). The overall GC content of the cattle genome is 41.7%, with an observed-to-expected CpG ratio of 0.234, similar to that of other mammals.

The cattle genome has transposable element classes like those of other mammals, as well as large numbers of ruminant-specific repeats (table S4) that compose 27% of its genome. The consensus sequence of Bov-B, a long interspersed nuclear element (LINE) lacked a functional open reading frame (ORF), which suggested that it was inactive (7). However, Bov-B repeats with intact ORF were identified in the genome, and their phylogeny (fig. S4) indicates that some are still actively expanding and evolving. Mapping chromosomal segments of high- and low-density ancient repeat content, L2/MIR [a LINE/SINE (short interspersed nuclear element) pair] and Bov-B, and more recent repeats, Bov-B/ART2A (Bov-B–derived SINE pair), revealed that the genome consists of ancient regions enriched for L2/MIR and recent regions enriched for Bov-B/ART2A (fig. S7). Exclusion of Bov-B/ART2A from contiguous blocks of ancient repeats suggests that evolution of the ruminant or cattle genome experienced invasions of new repeats into regions lacking ancient repeats. Alternatively, older repeats may have been destroyed by insertion of ruminant- or cattle-specific repeats. AGC trinucleotide repeats, the most common simple-sequence repeat (SSR) in artiodactyls (which include cattle, pigs, and sheep), are 90- and 142-fold overrepresented in cattle compared with human and dog, respectively (fig. S10). Of the AGC repeats in the cattle genome, 39% were associated with Bov-A2 SINE elements.

A comparative analysis examined the rate of protein evolution and the conservation of gene repertoires among orthologs in the genomes of dog, human, mouse, and rat (representing placental mammals); opossum (marsupial); and platypus (monotreme). Orthology was resolved for >75% of cattle and >80% of human genes (Fig. 1A). There were 14,345 orthologous groups with representatives in human, cattle, or dog; mouse or rat; and opossum or platypus, which represent 16,749 cattle and 16,177 human genes, respectively, of which 12,592 are single-copy orthologs. We also identified 1217 placental mammal–specific orthologous groups with genes present in human, cattle, or dog; mouse or rat; but not opossum or platypus. About 1000 orthologs shared between rodents and laurasiatherians (cattle and dog), many of which encode G protein–coupled receptors, appear to have been lost or may be misannotated in the human genome (Fig. 1B). Gene repertoire conservation among these mammals correlates with conservation at the amino acid–sequence level (Fig. 1C). The elevated rate of evolution in rodents relative to other mammals (8) was supported by the higher amino acid sequence identity between human and dog or cattle proteins relative to that between human and rodent proteins. However, maximum-likelihood analysis of amino acid substitutions in single-copy orthologs supports the accepted sister lineage relation of primates and rodents (1) (Fig. 1D).

Fig. 1

Protein orthology comparison among genomes of cattle, dog, human, mouse, and rat (Bos taurus, Canis familiaris, Homo sapiens, Mus musculus, Rattus norvegicus, representing placental mammals), opossum (Monodelphis domestica, marsupial), and platypus (Ornithorhynchus anatinus, monotreme). (A) The majority of mammalian genes are orthologous, with more than half preserved as single copies (dark blue); a few thousand have species-specific duplications (blue); another few thousand have been lost in specific lineages (orange). We also show those lacking confident orthology assignment (green), and those that are apparently lineage specific [unique (white)]. Placental-specific orthologs are shown in pink. Single- or multiple-copy genes were defined on the basis of representatives in human, bovine, or dog; mouse or rat; and opossum or platypus. (B) Venn diagram showing shared orthologous groups (duplicated genes were counted as one) between laurasiatherians (cattle and dog), human, rodents (mouse and rat), and nonplacental mammals (opossum and platypus) on the basis of the presence of a representative gene in at least one of the grouped species [as in (A)]. (C) Distribution of ortholog protein identities between human and the other species for a subset of strictly conserved single-copy orthologs. (D) A maximum likelihood phylogenetic tree using all single-copy orthologs supports the accepted phylogeny and quantifies the relative rates of molecular evolution expressed as the branch lengths.

Alternative splicing is a major mechanism for transcript diversification (9), yet the extent of its evolutionary conservation and functional impact remain unclear. We used the cattle genome to analyze the conservation of the most common form of alternative splicing, exon skipping, defined as a triplet of exons in which the middle exon is absent in some transcripts, in a set of 1930 exon-skipping events across human, mouse, dog, and cattle (4). We examined 277 cases, with different conservation patterns between human and mouse, in 16 different cattle tissues with reverse transcription polymerase chain reaction (4). These splicing events were divided into a shared set (163 in both human and mouse) and a nonshared set (114 in human but not in mouse). Of the 277, we detected exon-skipping for 188 cases in cattle (table S5), which suggested that the majority of genes with exon-skipping in human were present and regulated in cattle and that, if an event is shared between human and mouse, it was more likely to be found in cattle. It was estimated that at most 40% of exon-skipping is conserved among mammals; thus, our data agree with the upper bound from previous analyses with human and rodents [e.g., (10)].

We constructed a cattle-human Oxford grid (fig. S12) (4) to conduct synteny-based chromosomal comparisons, which reinforced that human genome organization is more similar to cattle's than rodents' because most cattle chromosomes primarily correspond to part of one human chromosome, albeit with multiple rearrangements [e.g., (11)]. In contrast, the cattle-mouse Oxford grid shows poorer chromosomal correspondence. Lineage-specific evolutionary breakpoints were identified for cattle, artiodactyls, and ferungulates (a group encompassing artiodactyls and carnivores, represented by cattle, pig, and dog) and are shown with cattle (fig. S11) and human sequence coordinates (Fig. 2) (4). Primate, dog, rodent, mouse, and rat lineage-specific breakpoint positions were similarly identified. A total of 124 evolutionary breakpoint regions (EBRs) were identified in the cattle lineage, of which 100 were cattle- or ruminant-specific and 24 were artiodactyl-specific (e.g., Fig. 2). Nine additional EBRs represent presumptive ferungulate-specific rearrangements. Bos taurus chromosome 16 (BTA16) is populated with four ferungulate-specific EBRs, which suggests that this region was rearranged before the Artiodactyla and Carnivora divergence (Fig. 2). Such conserved regions demonstrate that many inversions that occurred before the divergence of the carnivores and artiodactyls have probably been retained in the ancestral form within the human genome. In contrast to the cattle genome, a pig physical map identified only 77 lineage-specific EBRs. Interchromosomal rearrangements and inversions characterize most of the lineage-specific rearrangements observed in the cattle, dog, and pig genomes.

Fig. 2

Examples of EBRs. Ferungulate-, artiodactyl-, and primate-specific EBRs on HSA1 at 175 to 247 Mbp (other lineage-specific EBRs not shown). Homologous synteny blocks constructed for the macaque, chimp, cattle, dog, mouse, rat, and pig genomes were used for pairwise comparisons (4). White areas correspond to EBRs. Arrows to the right of the chromosome ideogram indicate positions of representative cattle-specific; artiodactyl-specific (specific to the chromosomes of pigs and cattle); ferungulate-specific (cattle, dog, and pig); primate-specific (human, macaque, and chimp); and hominoid-specific (human and chimp) rearrangements. Opossum is shown as an outgroup to the eutherian clade, which allows classification of ferungulate-specific EBRs.

An examination of repeat families and individual transposable elements within cattle-, artiodactyl- and ferungulate-specific EBRs showed a significantly higher density of LINE-L1 elements and the ruminant-specific LINE-RTE repeat family (12) in cattle-specific EBRs relative to the remainder of the cattle genome (table S6). In contrast, the SINE-BovA repeat family and the more ancient tRNAGlu-derived SINE repeats (13) were present in lower density in cattle-specific EBRs, similar to other LINEs and SINEs (table S7). The differences in repeat densities were generally consistent in cattle-, artiodactyl- and ferungulate-specific EBRs, with the exception of the tRNAGlu-derived and LTR-ERVL repeats, which are at higher densities in artiodactyl EBRs compared with the rest of the genome.

The tRNAGlu-derived SINEs originated in the common ancestor of Suina (pigs and peccaries), Ruminantia, and Cetacea (whales) (13), which suggests that tRNAGlu-derived SINEs were involved in ancestral artiodactyl chromosome rearrangements. Furthermore, the lower density of the more ancient repeat families in cattle-specific EBRs suggests that either more recently arising repeat elements were inserted into regions lacking ancient repeats or that older repeats were destroyed by this insertion (table S7). The repeat elements differing in density in EBRs were also found in regions of homologous synteny, which suggests that repeats may promote evolutionary rearrangements (see below). Differences in repeat density in cattle-specific EBRs are thus unlikely to be caused by the accumulation of repeats in EBRs after such rearrangements occur. We identified a cattle-specific EBR associated with a bidirectional promoter (figs. S14 and S15) that may affect control of the expression of the CYB5R4 gene, which has been implicated in human diabetes and, therefore, may be important in the regulation of energy flow in cattle (4).

We identified 1020 segmental duplications (SDs) corresponding to 3.1% (94.4 Mbp) of the cattle genome (4). Duplications assigned to a chromosome showed a bipartite distribution with respect to length and percent identity (fig. S16), and interchromosomal duplications were shorter (median length 2.5 kbp) and more divergent (<94% identity) relative to intrachromosomal duplications (median length 20 kbp, ~97% identity) and tended to be locally clustered (fig. S17). Twenty-one of these duplications were >300 kbp and located in regions enriched for tandem duplications (e.g., BTA18) (fig. S18). This pattern is reminiscent of the duplication pattern of the dog, rat, and mouse but different from that of primate and great-ape genomes (14, 15). On average, cattle SDs >10 kbp represent 11.7% of base pairs in 10-kbp intervals located within cattle-specific EBRs and 23.0% of base pairs located within the artiodactyl-specific EBRs. By contrast, in the remainder of the genome sequence assigned to chromosomes the fraction of SDs was 1.7% (P < 1 × 10−12). These data indicate that SDs play a role in promoting chromosome rearrangements by nonallelic homologous recombination [e.g., (16)] and suggest that either a significant fraction of the SDs observed in cattle occurred before the Ruminant-Suina split, and/or that the sites for accumulation of SDs are nonrandomly distributed in artiodactyl genomes.

SDs involving genic regions may give rise to new functional paralogs. Seventy-six percent (778 out of 1020) of the cattle SDs correspond to complete or partial gene duplications with high sequence identity (median 98.7%). This suggests that many of these gene duplications are specific to either the artiodactyla or the Bos lineage and tend to encode proteins that often interface with the external environment, particularly immune proteins and sensory and/or olfactory receptors. Several of these gene duplications are also duplicated in other mammalian lineages (e.g., cytochrome P-450, sulfotransferase, ribonuclease A, defensins, and pregnancy-associated glycoproteins). Paralogs located in segmental duplications that are present exclusively in cattle may have functional implications for the unique physiology, environment, and diet of cattle.

An overrepresentation of genes involved in reproduction in cattle SDs (tables S8 and S9) is associated with several gene families expressed in the ruminant placenta. These families encode the intercellular signaling proteins pregnancy-associated glycoproteins (on BTA29), trophoblast Kunitz domain proteins (on BTA13), and interferon tau (IFNT) (on BTA8). A gene family encoding prolactin-related proteins (on BTA23) was only identified in the assembly-dependent analysis of SDs. These genes regulate ruminant-specific aspects of fetal growth, maternal adaptations to pregnancy, and the coordination of parturition (17, 18). Although type I interferon (IFN) genes are primarily involved in host defense (19), IFNT prevents regression of the corpus luteum during early pregnancy, which results in a uterine environment receptive to early conceptus development (20).

Signatures of positive selection (obtained by measurement of their rates of synonymous and nonsynonymous substitutions) identified 71 genes (4), including 10 immune-related genes (i.e., IFNAR2, IFNG, CD34, TREM1, TREML1, FCER1A, IL23R, IL24, IL15, and LEAP2). As previously mentioned, immune genes are overrepresented in SDs (see Table 1 and fig. S20). Examples of genes varying in cattle relative to mouse include a cluster of β-defensin genes, which encode antimicrobial peptides; the antimicrobial cathelicidin genes [which show increased sequence diversity of the mature cathelicidin peptides (21)]; and changes in the numbers of interferon genes (22) and the number and organization of genes involved in adaptive immune responses in cattle compared with human and mouse (4). This extensive duplication and divergence of genes involved in innate immunity may be because of the substantial load of microorganisms present in the rumen of cattle, which increases the risk of opportunistic infections at mucosal surfaces and positive selection for the traits that enabled stronger and more diversified innate immune responses at these locations. Another possibility is that immunity may have been under selection due to the herd structure, which can promote rapid disease transmission. Also, immune function–related duplicated genes have gained nonimmune functions, e.g., IFNT (see above), and the C-class lysozyme genes, which are involved in microbial degradation in the abomasum (see below).

Table 1

Changes in the number of genes in innate immune gene families. Many of the β-defensin genes are present in unassigned scaffolds, i.e., they are not yet part of the current assembly. The exact number of β-defensin genes is uncertain. Interferon subfamily pseudogenes predicted on the basis of frame-shift mutations or stop codons within the first 100 amino acids of the coding sequence have been excluded from the table. The IFNX genes represent a newly discovered subfamily of IFN and are so named for convenience. BPI, Bactericidal and/or permeability-increasing; RNase, ribonuclease; LBP, lipopolysaccharide-binding protein; ULBP, UL16-binding protein.

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There has been substantial reorganization of gene families encoding proteins present in milk. One such rearrangement affecting milk composition involves the histatherin (HSTN) gene within the casein gene cluster on BTA6 (fig. S21). In the cattle genome, HSTN is juxtaposed to a regulatory element (BCE) important (23) for β-casein (CSN2) expression, and as a probable consequence, HSTN is regulated like the casein genes during the lactation cycle. This rearrangement that led to the juxtaposition of HSTN next to the BCE is also the probable cause of deletion of one of the two copies of α-S2–like casein genes (CSN1S2A) present in other mammalian genomes (24). The biological implications of this change in casein gene copy number are not yet clear.

Additionally, the cattle serum amyloid A (SAA) gene cluster arose from both a laurasiatherian SD and a cattle-specific EBR, which resulted in two mammary gland–expressed SAA3-like genes, SAA3.1 and SAA3.2 on BTA29, and an SAA3-like gene on BTA15 (fig. S21). SAA3.2 has been shown to inhibit microbial growth (25). Two additional milk protein genes were associated with SDs: cathelicidin (CATHL1) and β2-microglobulin (B2M)—part of the neonatal Fc receptor (FcRn) that transfers immunoglobulin IgG across epithelial cells of many tissues including the gut and mammary gland (26, 27). IgG is the predominant immunoglobulin in cow’s milk compared with IgA in human milk (28). Unlike humans, who acquire passive immunity from the mother via placental transfer of immunoglobulins during pregnancy, calves acquire passive immunity by ingestion of IgG in milk (28). B2M is also redistributed in epithelial cells upon calving, and it protects IgG from degradation (26). A genetic variant of B2M has negative effects on passive immune transfer (29). The additional copy of the gene encoding B2M might be associated with the abundance of IgG in cows’ milk and an increased capacity for uptake in the neonatal gut. Considering that the passive transfer of immunity to the calf is one of the important functions of milk, it is striking that lactation-related genes affected by genomic rearrangements often encode immune-related proteins in milk.

Cattle metabolic pathways demonstrated a strong degree of conservation among the comprehensive set of genes involved in core mammalian metabolism (4) and permitted an examination of unique genetic events that may be related to ruminant-specific metabolic adaptations. However, among 1032 genes examined from the human metabolic pathways, five were deleted or extensively diverged in cattle: PLA2G4C (phospholipase A2, group IVC), FAAH2 (fatty acid amide hydrolase 2), IDI2 (isopentenyl-diphosphate delta isomerase 2), GSTT2 (glutathione S-transferase theta 2), and TYMP (thymidine phosphorylase), which may be adaptations that impact on fatty acid metabolism (PLA2G4C and FAAH2); the mevalonate pathway (synthesis of dolichols, vitamins, steroid hormones, and cholesterol) (IDI2); detoxification (GSTT2); and pyrimidine metabolism (TYMP). Phylogenetic analysis shows that PLA2G4C was deleted ~87 to 97 million years ago in the laurasiatherian lineages (fig. S22). Strikingly, ~20% of the sequences from two abomasum (last chamber of the cattle stomach) EST libraries (a total of 2392 sequences) correspond to three C-type lysozyme genes. Lysozyme primarily functions in animals as an antibacterial protein, which suggests that they probably function in the abomasum (similar to the monogastric stomach) to degrade the cell walls of bacteria entering from the foregut (30). The cattle genome contains 10 C-type lysozyme genes (table S14 and fig. S23), and EST evidence (fig. S23) shows that six of the seven remaining C-type lysozyme genes are expressed primarily in the intestinal tract, which suggests additional roles for the encoded proteins in ruminant digestion.

In summary, the biological systems most affected by changes in the number and organization of genes in the cattle lineage include reproduction, immunity, lactation, and digestion. We highlighted the evolutionary activity associated with chromosomal breakpoint regions and their propensity for promoting gene birth and rearrangement. These changes in the cattle lineage probably reflect metabolic, physiologic, and immune adaptations due to microbial fermentation in the rumen, the herd environment and its influence on disease transmission, and the reproductive strategy of cattle. The cattle genome and associated resources will facilitate the identification of novel functions and regulatory systems of general importance in mammals and may provide an enabling tool for genetic improvement within the beef and dairy industries.

The Bovine Genome Sequencing Consortium

Principal Investigator: Richard A. Gibbs1

Analysis project leadership: Christine G. Elsik2,3, Ross L. Tellam4

Sequencing project leadership: Richard A. Gibbs1, Donna M. Muzny1, George M. Weinstock5,1

Analysis group organization: David L. Adelson6, Evan E. Eichler7,8, Laura Elnitski9, Christine G. Elsik2,3, Roderic Guigó10, Debora L. Hamernik11, Steve M. Kappes12, Harris A. Lewin13,14, David J. Lynn15, Frank W. Nicholas16, Alexandre Reymond17, Monique Rijnkels18, Loren C. Skow19, Ross L. Tellam4, Kim C. Worley1, Evgeny M. Zdobnov20,21,22

Sequencing project white paper: Richard A. Gibbs1, Steve M. Kappes12, Lawrence Schook13, Loren C. Skow19, George M. Weinstock5,1, James Womack23

Gene prediction and consensus gene set: Tyler Alioto10, Stylianos E. Antonarakis20, Alex Astashyn24, Charles E. Chapple10, Hsiu-Chuan Chen24, Jacqueline Chrast17, Francisco Câmara10, Christine G. Elsik2,3 (leader), Olga Ermolaeva24, Roderic Guigó10, Charlotte N. Henrichsen17, Wratko Hlavina24, Yuri Kapustin24, Boris Kiryutin24, Paul Kitts24, Felix Kokocinski25, Melissa Landrum24, Donna Maglott24, Kim Pruitt24, Alexandre Reymond17, Victor Sapojnikov24, Stephen M. Searle25, Victor Solovyev26, Alexandre Souvorov24, Catherine Ucla20, George M. Weinstock5,1, Carine Wyss20

Experimental validation of gene set: Tyler Alioto10, Stylianos E. Antonarakis20, Charles E. Chapple10, Jacqueline Chrast17, Francisco Câmara10, Roderic Guigó10 (leader), Charlotte N. Henrichsen17, Alexandre Reymond17, Catherine Ucla20, Carine Wyss20

MicroRNA analysis: Juan M. Anzola3, Daniel Gerlach20,21, Evgeny M. Zdobnov20,21,22 (leader)

GC composition analysis: Eran Elhaik27,28, Christine G. Elsik2,3 (leader), Dan Graur27, Justin T. Reese2

Repeat analysis: David L. Adelson6 (leader), Robert C. Edgar29, John C. McEwan30, Gemma M. Payne30, Joy M. Raison31

Protein ortholog analysis: Thomas Junier19,20, Evgenia V. Kriventseva32, Evgeny M. Zdobnov20,21,22 (leader)

Exon-skipping analysis: Jacqueline Chrast17, Eduardo Eyras33,34, Charlotte N. Henrichsen17, Mireya Plass34, Alexandre Reymond17 (leader)

Evolutionary breakpoint analysis and Oxford grid: Ravikiran Donthu13, Denis M. Larkin13,14, Harris A. Lewin13,14 (leader), Frank W. Nicholas16

Bidirectional promoter analysis: Laura Elnitski9 (leader), Denis M. Larkin13,14, Harris A. Lewin13,14, James Reecy35, Mary Q. Yang9

Segmental duplication analysis: David L. Adelson6, Lin Chen7, Ze Cheng7, Carol G. Chitko-McKown36, Evan E. Eichler7,8 (leader), Laura Elnitski9, Christine G. Elsik2,3, George E. Liu37, Lakshmi K. Matukumalli38,37, Jiuzhou Song39, Bin Zhu39

Analysis of gene ontology in segmental duplications: Christine G. Elsik2,3, David J. Lynn15 (leader), Justin T. Reese2

Adaptive evolution: Daniel G. Bradley40, Fiona S.L. Brinkman15, Lilian P.L. Lau40, David J. Lynn15 (leader), Matthew D. Whiteside15

Innate immunity: Ross L. Tellam4 (leader), Angela Walker41, Thomas T. Wheeler42

Lactation: Theresa Casey43, J. Bruce German44,45, Danielle G. Lemay45, David J. Lynn15, Nauman J. Maqbool46, Adrian J. Molenaar42, Monique Rijnkels18 (leader)

Metabolism: Harris A. Lewin13,14 (leader), Seongwon Seo47, Paul Stothard48

Adaptive immunity: Cynthia L. Baldwin49, Rebecca Baxter50, Candice L. Brinkmeyer-Langford19, Wendy C. Brown51 , Christopher P. Childers2, Timothy Connelley52, Shirley A. Ellis53, Krista Fritz19, Elizabeth J. Glass50, Carolyn T.A. Herzig49, Antti Iivanainen54, Kevin K. Lahmers51, Loren C. Skow19 (leader)

Annotation data management: Anna K. Bennett2, Christopher P. Childers2, C. Michael Dickens3, Christine G. Elsik2,3 (leader), James G.R. Gilbert25, Darren E. Hagen2, Justin T. Reese2, Hanni Salih3

Manual annotation organization: Jan Aerts55, Alexandre R. Caetano56, Brian Dalrymple4, Christine G. Elsik2,3, Jose Fernando Garcia57, Richard A. Gibbs1, Clare A. Gill3,58, Debora L. Hamernik11, Stefan G. Hiendleder59, Erdogan Memili60, Frank W. Nicholas16, James Reecy35, Monique Rijnkels18, Loren C. Skow19, Diane Spurlock35, Paul Stothard48, Ross L. Tellam4, George M. Weinstock5,1, John L. Williams61, Kim C. Worley1

cDNA tissues, libraries, and sequencing: Lee Alexander62, Michael J. Brownstein63, Leluo Guan48, Robert A. Holt64 (leader), Steven J.M. Jones64 (leader), Marco A. Marra64 (leader), Richard Moore64, Stephen S. Moore48 (leader), Andy Roberts62, Masaaki Taniguchi65,48, Richard C. Waterman62

Genome sequence production: Joseph Chacko1, Mimi M. Chandrabose1, Andy Cree1 (leader), Marvin Diep Dao1, Huyen H. Dinh1 (leader), Ramatu Ayiesha Gabisi1, Sandra Hines1, Jennifer Hume1 (leader), Shalini N. Jhangiani1, Vandita Joshi1, Christie L. Kovar1 (leader), Lora R. Lewis1, Yih-shin Liu1, John Lopez1, Margaret B. Morgan1, Donna M. Muzny1 (leader), Ngoc Bich Nguyen1, Geoffrey O. Okwuonu1, San Juana Ruiz1, Jireh Santibanez1, Rita A. Wright1

Sequence finishing: Christian Buhay1 (leader), Yan Ding1, Shannon Dugan-Rocha1 (leader), Judith Herdandez1, Michael Holder1, Aniko Sabo1

Automated BAC assembly: Amy Egan1, Jason Goodell1, Katarzyna Wilczek-Boney1

Sequence production informatics: Gerald R. Fowler1 (leader), Matthew Edward Hitchens1, Ryan J. Lozado1, Charles Moen1, David Steffen66,1, James T. Warren1, Jingkun Zhang1

BAC mapping: Readman Chiu64, Steven J.M. Jones64, Marco A. Marra64 (leader), Jacqueline E. Schein64

Genome assembly: K. James Durbin67,1, Paul Havlak68,1, Huaiyang Jiang1, Yue Liu1, Xiang Qin1, Yanru Ren1, Yufeng Shen1,69, Henry Song1, George M. Weinstock5,1, Kim C. Worley1 (leader)

Sequence library production: Stephanie Nicole Bell1, Clay Davis1, Angela Jolivet Johnson1, Sandra Lee1, Lynne V. Nazareth1 (leader), Bella Mayurkumar Patel1, Ling-Ling Pu1, Selina Vattathil1, Rex Lee Williams Jr.1

BAC production: Stacey Curry1, Cerissa Hamilton1, Erica Sodergren5,1 (leader)

Sequence variation detection: Lynne V. Nazareth1, David A. Wheeler1

Markers and mapping: David L. Adelson6, Jan Aerts55, Wes Barris4, Gary L. Bennett36, Brian Dalrymple4, André Eggen70, Clare A. Gill3,58, Ronnie D. Green71, Gregory P. Harhay36, Matthew Hobbs72, Oliver Jann50, Steve M. Kappes12 (leader), John W. Keele36, Matthew P. Kent73, Denis M. Larkin13,14, Harris A. Lewin13,14, Sigbjørn Lien73, John C. McEwan30, Stephanie D. McKay74, Sean McWilliam4, Stephen S. Moore48, Frank W. Nicholas16, Gemma M. Payne30, Abhirami Ratnakumar75,4, Hanni Salih3, Robert D. Schnabel74, Timothy Smith36, Warren M. Snelling36, Tad S. Sonstegard37, Roger T. Stone36, Yoshikazu Sugimoto76, Akiko Takasuga76, Jeremy F. Taylor74, Ross L. Tellam4, Curtis P. Van Tassell37, John L. Williams61

Genomic DNA: Michael D. MacNeil62

Manual annotation: Antonio R.R. Abatepaulo77, Colette A. Abbey3, Jan Aerts55, Virpi Ahola78, Iassudara G. Almeida57, Ariel F. Amadio79, Elen Anatriello77, Suria M. Bahadue2, Cynthia L. Baldwin49, Rebecca Baxter50, Anna K. Bennett2, Fernando H. Biase13, Clayton R. Boldt3, Candice L. Brinkmeyer-Langford19, Wendy C. Brown51, Alexandre R. Caetano56, Jeffery A. Carroll80, Wanessa A. Carvalho77, Theresa Casey43, Eliane P. Cervelatti57, Elsa Chacko81, Jennifer E. Chapin3, Ye Cheng35, Christopher P. Childers2, Jungwoo Choi3, Adam J. Colley82, Timothy Connelley52, Tatiana A. de Campos56, Marcos De Donato83, Isabel K.F. de Miranda Santos56,77, Carlo J.F. de Oliveira77, Heather Deobald84, Eve Devinoy85, C. Michael Dickens3, Kaitlin E. Donohue2, Peter Dovc86, Annett Eberlein87, Shirley A. Ellis53, Carolyn J. Fitzsimmons59, Alessandra M. Franzin77, Krista Fritz19, Gustavo R. Garcia77, Jose Fernando Garcia57, Sem Genini61, J. Bruce German44,45, James G.R. Gilbert25, Clare A. Gill3,58, Cody J. Gladney3, Elizabeth J. Glass50, Jason R. Grant48, Marion L. Greaser88, Jonathan A. Green74, Darryl L. Hadsell18, Darren E. Hagen2, Hatam A. Hakimov89, Rob Halgren43, Jennifer L. Harrow25, Elizabeth A. Hart25, Nicola Hastings90,50, Marta Hernandez91, Carolyn T.A. Herzig49, Stefan G. Hiendleder59, Matthew Hobbs72, Zhi-Liang Hu35, Antti Iivanainen54, Aaron Ingham4, Terhi Iso-Touru78, Catherine Jamis2, Oliver Jann50, Kirsty Jensen50, Dimos Kapetis61, Tovah Kerr51, Sari S. Khalil2, Hasan Khatib92, Davood Kolbehdari48,93, Charu G. Kumar13, Dinesh Kumar94,35, Richard Leach50, Justin C-M Lee2, Danielle G. Lemay45, Changxi Li95,48, George E. Liu37, Krystin M. Logan96, Roberto Malinverni61, Nauman J. Maqbool46, Elisa Marques48, William F. Martin45, Natalia F. Martins56, Sandra R. Maruyama77, Raffaele Mazza97, Kim L. McLean84, Juan F. Medrano98, Erdogan Memili60, Adrian J. Molenaar42, Barbara T. Moreno57, Daniela D. Moré77, Carl T. Muntean3, Hari P. Nandakumar19, Marcelo F.G. Nogueira99, Ingrid Olsaker100, Sameer D. Pant82, Francesca Panzitta61, Rosemeire C.P. Pastor57, Mario A. Poli101, Nathan Poslusny2, Satyanarayana Rachagani35, Shoba Ranganathan81,102, Andrej Razpet86, James Reecy35, Penny K. Riggs3,58, Monique Rijnkels18, Gonzalo Rincon98, Nelida Rodriguez-Osorio60,103, Sandra L. Rodriguez-Zas13, Natasha E. Romero3, Anne Rosenwald2, Lillian Sando4, Sheila M. Schmutz84, Seongwon Seo47, Libing Shen2, Laura Sherman48, Loren C. Skow19, Bruce R. Southey104, Diane Spurlock35, Ylva Strandberg Lutzow4, Jonathan V. Sweedler104, Imke Tammen72, Masaaki Taniguchi65,48, Ross L. Tellam4, Bhanu Prakash V.L. Telugu74, Jennifer M. Urbanski2, Yuri T. Utsunomiya57, Chris P. Verschoor82, Ashley J. Waardenberg4,105, Angela Walker41, Zhiquan Wang48, Robert Ward106, Rosemarie Weikard87, Thomas H. Welsh Jr.3,58, Thomas T. Wheeler42, Stephen N. White51,107, John L. Williams61, Laurens G. Wilming25, Kris R. Wunderlich3, Jianqi Yang108, Feng-Qi Zhao109

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Supporting Online Material

Materials and Methods

Figs. S1 to S23

Tables S1 to S14


  • * All authors with their affiliations and contributions are listed at the end of this paper.

References and Notes

  1. Materials, methods, and additional discussion are available on Science online.
  2. Funded by the National Human Genome Research Institute (NHGRI U54 HG003273); the U.S. Department of Agriculture's Agricultural Research Service (USDA-ARS agreement no. 59-0790-3-196) and Cooperative State Research, Education, and Extension Service National Research Initiative (grant no. 2004-35216-14163); the state of Texas; Genome Canada through Genome British Columbia; the Alberta Science and Research Authority; the Commonwealth Scientific and Industrial Research Organization of Australia (CSIRO); Agritech Investments Ltd., Dairy Insight, Inc., and AgResearch Ltd., all of New Zealand; the Research Council of Norway; the Kleberg Foundation; and the National, Texas, and South Dakota Beef Check-off Funds. The master accession for this WGS sequencing project is AAFC03000000. The individual WGS sequences are AAFC03000001 to AAFC03131728, and the scaffold records are CM000177 to CM000206 (chromosomes) and DS490632 to DS495890 (unplaced scaffolds).
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