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BackgroundMajor advances in selection progress for cattle have been made following the introduction of genomic tools over the past 10-12 years. These tools depend upon the Bos taurus reference genome (UMD3.1.1), which was created using now-outdated technologies and is hindered by a variety of deficiencies and inaccuracies.ResultsWe present the new reference genome for cattle, ARS-UCD1.2, based on the same animal as the original to facilitate transfer and interpretation of results obtained from the earlier version, but applying a combination of modern technologies in a de novo assembly to increase continuity, accuracy, and completeness. The assembly includes 2.7 Gb and is >250× more continuous than the original assembly, with contig N50 >25 Mb and L50 of 32. We also greatly expanded supporting RNA-based data for annotation that identifies 30,396 total genes (21,039 protein coding). The new reference assembly is accessible in annotated form for public use.ConclusionsWe demonstrate that improved continuity of assembled sequence warrants the adoption of ARS-UCD1.2 as the new cattle reference genome and that increased assembly accuracy will benefit future research on this species.

Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed to achieve puberty (i.e., longissimus dorsi muscle, adipose, and liver). These tissues were collected from pre- and post-pubertal Brangus heifers (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) derived from a population of cattle used to identify quantitative trait loci associated with fertility traits (i.e., age of first observed corpus luteum (ACL), first service conception (FSC), and heifer pregnancy (HPG)). In order to exploit the power of complementary omics analyses, pre- and post-puberty co-expression gene networks were constructed by combining the results from genome-wide association studies (GWAS), RNA-Seq, and bovine transcription factors. Eight tissues among pre-pubertal and post-pubertal Brangus heifers revealed 1,515 differentially expressed and 943 tissue-specific genes within the 17,832 genes confirmed by RNA-Seq analysis. The hypothalamus experienced the most notable up-regulation of genes via puberty (i.e., 204 out of 275 genes). Combining the results of GWAS and RNA-Seq, we identified 25 loci containing a single nucleotide polymorphism (SNP) associated with ACL, FSC, and (or) HPG. Seventeen of these SNP were within a gene and 13 of the genes were expressed in uterus or endometrium. Multi-tissue omics analyses revealed 2,450 co-expressed genes relative to puberty. The pre-pubertal network had 372,861 connections whereas the post-pubertal network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, DACH2, PROP1, SIX6, etc.). Results from these multi-tissue omics analyses improve understanding of the number of genes and their complex interactions for puberty in cattle.

Mitochondrial DNA copy number (mtDNA CN) is heritable and easily obtained from low-pass sequencing (LPS). This study investigated the genetic correlation of mtDNA CN with growth and carcass traits in a multi-breed and crossbred beef cattle population. Blood, leucocyte, and semen samples were obtained from 2,371 animals and subjected to LPS that resulted in nuclear DNA (nuDNA) and mtDNA sequence reads. Mitochondrial DNA CN was estimated as the ratio of mtDNA to nuDNA coverages. Variant calling was performed from mtDNA, and 11 single nucleotide polymorphisms (SNP) were identified in the population. Samples were classified in taurine haplogroups. Haplogroup and mtDNA type were further classified based on the 11 segregating SNP. Growth and carcass traits were available for between 7,249 and 60,989 individuals. Associations of mtDNA CN, mtDNA haplogroups, mtDNA types, and mtDNA SNP with growth and carcass traits were estimated with univariate animal models, and genetic correlations were estimated with a bivariate animal model based on pedigree. Mitochondrial DNA CN tended (P -value ≤0.08) to be associated with birth weight and weaning weight. There was no association (P -value >0.10) between mtDNA SNP, haplogroups, or types with growth and carcass traits. Genetic correlation estimates of mtDNA CN were −0.30 ± 0.16 with birth weight, −0.31 ± 0.16 with weaning weight, −0.15 ± 0.14 with post-weaning gain, −0.11 ± 0.19 with average daily dry-matter intake, −0.04 ± 0.22 with average daily gain, −0.29 ± 0.13 with mature cow weight, −0.11 ± 0.13 with slaughter weight, −0.14 ± 0.13 with carcass weight, −0.07 ± 0.14 with carcass backfat, 0.14 ± 0.14 with carcass marbling, and −0.06 ± 0.14 with ribeye area. In conclusion, mtDNA CN was negatively correlated with most traits investigated, and the genetic correlation was stronger with growth traits than with carcass traits. [ABSTRACT FROM AUTHOR]

Beginning with a large push from consumers to improve meat and carcass quality in the 1990s, through selection, the beef industry has successfully made significant genetic gains towards achieving these improvements. For producers looking to increase the genetic potential of their own herds for carcass quality there are multiple considerations that will inform the time it takes to see improvement. Some of the primary factors influencing the rate of genetic gain for carcass quality phenotypes include: 1) available selection candidates, 2) trait heritability, 3) selection accuracy, and 4) selection intensity. Using the Germplasm Evaluation beef herd at the US Meat Animal Research Center, many of the possible factors influencing this timeline have been evaluated. Collection of carcass quality phenotypes has been an ongoing, long-term objective of the project and has allowed for the estimation of trait heritabilities, breed differences, heterosis effects, and genetic trends over time for this trait complex. Additionally, this herd facilitated the development of decision support tools for creating selection indices and breeding programs, representing the culmination of many of these research outcomes. There are multiple ways to achieve genetic improvement for carcass quality, and our primary objective is to equip producers and other stakeholders with the necessary information to make informed selection decisions. The USDA is an equal opportunity provider and employer [ABSTRACT FROM AUTHOR]

Understanding the genetic relationship between mature cow weight (MWT) and body condition score (BCS) is useful to implement selection programs focused on cow efficiency. The objectives of this study were to estimate genetic parameters, heterosis, and breed effects for MWT and BCS. In total, 25,035 and 24,522 overlapping records were available for MWT and BCS on 6,138 and 6,131 cows, respectively, from the Germplasm Evaluation program, a crossbred beef population at the U.S. Meat Animal Research Center. Pedigree was available for 48,013 individuals. Univariate animal models were used to estimate heritabilities for each trait by parity. Bivariate animal models were used to estimate genetic correlations between parities within a trait and between traits within parities. Bivariate repeatability animal models were used to estimate genetic correlations between traits across parities. Estimates of heritability for different parities ranged from 0.43 ± 0.05 to 0.55 ± 0.07 for MWT and from 0.12 ± 0.03 to 0.25 ± 0.04 for BCS and were lower with the repeatability model at 0.40 ± 0.02 and 0.11 ± 0.01 for MWT and BCS, respectively. Estimates of repeatability were high for MWT (0.67 ± 0.005) and low for BCS (0.22 ± 0.006). Estimates of genetic correlation for MWT and BCS between parities were, in general, high, especially between consecutive parities. Estimates of genetic correlation between MWT and BCS were positive and moderate, ranging from 0.32 ± 0.09 to 0.68 ± 0.14. The direct heterosis estimates were 21.56 ± 3.53 kg (P ≤ 0.001) for MWT and 0.095 ± 0.034 (P ≤ 0.001) for BCS. Ordered by decreasing MWT, the breeds ranked Brahman, Charolais, Angus, Simmental, Salers, Hereford, Santa Gertrudis, Chiangus, Brangus, Red Angus, Shorthorn, Maine-Anjou, Gelbvieh, Beefmaster, Limousin, and Braunvieh. Ordered by decreasing BCS, the breeds ranked Brahman, Red Angus, Charolais, Angus, Hereford, Brangus, Beefmaster, Chiangus, Salers, Simmental, Maine-Anjou, Limousin, Santa Gertrudis, Shorthorn, Gelbvieh, and Braunvieh. Estimates of breed differences for MWT were also adjusted for BCS (AMWT), and in general, AMWT depicted smaller differences between breeds with some degree of re-ranking (r = 0.59). These results suggest that MWT and BCS are at least moderately genetically correlated and that they would respond favorably to selection. Estimates of breed differences and heterotic effects could be used to parameterize multibreed genetic evaluations for indicators of cow maintenance energy requirements. [ABSTRACT FROM AUTHOR]

Abstract Background In a previously reported genome-wide association study based on a high-density bovine SNP genotyping array, 8 SNP were nominally associated (P ≤ 0.003) with average daily gain (ADG) and 3 of these were also associated (P ≤ 0.002) with average daily feed intake (ADFI) in a population of crossbred beef cattle. The SNP were clustered in a 570 kb region around 38 Mb on the draft sequence of bovine chromosome 6 (BTA6), an interval containing several positional and functional candidate genes including the bovine LAP3, NCAPG, and LCORL genes. The goal of the present study was to develop and examine additional markers in this region to optimize the ability to distinguish favorable alleles, with potential to identify functional variation. Results Animals from the original study were genotyped for 47 SNP within or near the gene boundaries of the three candidate genes. Sixteen markers in the NCAPG-LCORL locus displayed significant association with both ADFI and ADG even after stringent correction for multiple testing (P ≤ 005). These markers were evaluated for their effects on meat and carcass traits. The alleles associated with higher ADFI and ADG were also associated with higher hot carcass weight (HCW) and ribeye area (REA), and lower adjusted fat thickness (AFT). A reduced set of markers was genotyped on a separate, crossbred population including genetic contributions from 14 beef cattle breeds. Two of the markers located within the LCORL gene locus remained significant for ADG (P ≤ 0.04). Conclusions Several markers within the NCAPG-LCORL locus were significantly associated with feed intake and body weight gain phenotypes. These markers were also associated with HCW, REA and AFT suggesting that they are involved with lean growth and reduced fat deposition. Additionally, the two markers significant for ADG in the validation population of animals may be more robust for the prediction of ADG and possibly the correlated trait ADFI, across multiple breeds and populations of cattle.

Abstract Background In large genomics projects involving many different types of analyses of bacterial artificial chromosomes (BACs), such as fingerprinting, end sequencing (BES) and full BAC sequencing there are many opportunities for the identities of BACs to become confused. However, by comparing the results from the different analyses, inconsistencies can be identified and a set of high integrity BACs preferred for future research can be defined. Results The location of each bovine BAC in the BAC fingerprint-based genome map and in the genome assembly were compared based on the reported BESs, and for a smaller number of BACs the full sequence. BACs with consistent positions in all three datasets, or if the full sequence was not available, for both the fingerprint map and BES-based alignments, were deemed to be correctly positioned. BACs with consistent BES-based and fingerprint-based locations, but with conflicting locations based on the fully sequenced BAC, appeared to have been misidentified during sequencing, and included a number of apparently swapped BACs. Inconsistencies between BES-based and fingerprint map positions identified thirty one plates from the CHORI-240 library that appear to have suffered substantial systematic problems during the end-sequencing of the BACs. No systematic problems were identified in the fingerprinting of the BACs. Analysis of BACs overlapping in the assembly identified a small overrepresentation of clones with substantial overlap in the library and a substantial enrichment of highly overlapping BACs on the same plate in the CHORI-240 library. More than half of these BACs appear to have been present as duplicates on the original BAC-library plates and thus should be avoided in subsequent projects. Conclusion Our analysis shows that ~95% of the bovine CHORI-240 library clones with both a BAC fingerprint and two BESs mapping to the genome in the expected orientations (~27% of all BACs) have consistent locations in the BAC fingerprint map and the genome assembly. We have developed a broadly applicable methodology for checking the integrity of BAC-based datasets even where only incomplete and partially assembled genomic sequence is available.

Abstract Background Genome assemblies rely on the existence of transcript sequence to stitch together contigs, verify assembly of whole genome shotgun reads, and annotate genes. Functional genomics studies also rely on transcript sequence to create expression microarrays or interpret digital tag data produced by methods such as Serial Analysis of Gene Expression (SAGE). Transcript sequence can be predicted based on reconstruction from overlapping expressed sequence tags (EST) that are obtained by single-pass sequencing of random cDNA clones, but these reconstructions are prone to errors caused by alternative splice forms, transcripts from gene families with related sequences, and expressed pseudogenes. These errors confound genome assembly and annotation. The most useful transcript sequences are derived by complete insert sequencing of clones containing the entire length, or at least the full protein coding sequence (CDS) portion, of the source mRNA. While the bovine genome sequencing initiative is nearing completion, there is currently a paucity of bovine full-CDS mRNA and protein sequence data to support bovine genome assembly and functional genomics studies. Consequently, the production of high-quality bovine full-CDS cDNA sequences will enhance the bovine genome assembly and functional studies of bovine genes and gene products. The goal of this investigation was to identify and characterize the full-CDS sequences of bovine transcripts from clones identified in non-full-length enriched cDNA libraries. In contrast to several recent full-length cDNA investigations, these full-CDS cDNAs were selected, sequenced, and annotated without the benefit of the target organism's genomic sequence, by using comparison of bovine EST sequence to existing human mRNA to identify likely full-CDS clones for full-length insert cDNA (FLIC) sequencing. Results The predicted bovine protein lengths, 5' UTR lengths, and Kozak consensus sequences from 954 bovine FLIC sequences (bFLICs; average length 1713 nt, representing 762 distinct loci) are all consistent with previously sequenced mammalian full-length transcripts. Conclusion In most cases, the bFLICs span the entire CDS of the genes, providing the basis for creating predicted bovine protein sequences to support proteomics and comparative evolutionary research as well as functional genomics and genome annotation. The results demonstrate the utility of the comparative approach in obtaining predicted protein sequences in other species.

Abstract Background Existing linkage maps of the bovine genome primarily contain anonymous microsatellite markers. These maps have proved valuable for mapping quantitative trait loci (QTL) to broad regions of the genome, but more closely spaced markers are needed to fine-map QTL, and markers associated with genes and annotated sequence are needed to identify genes and sequence variation that may explain QTL. Results Bovine expressed sequence tag (EST) and bacterial artificial chromosome (BAC)sequence data were used to develop 918 single nucleotide polymorphism (SNP) markers to map genes on the bovine linkage map. DNA of sires from the MARC reference population was used to detect SNPs, and progeny and mates of heterozygous sires were genotyped. Chromosome assignments for 861 SNPs were determined by twopoint analysis, and positions for 735 SNPs were established by multipoint analyses. Linkage maps of bovine autosomes with these SNPs represent 4585 markers in 2475 positions spanning 3058 cM . Markers include 3612 microsatellites, 913 SNPs and 60 other markers. Mean separation between marker positions is 1.2 cM. New SNP markers appear in 511 positions, with mean separation of 4.7 cM. Multi-allelic markers, mostly microsatellites, had a mean (maximum) of 216 (366) informative meioses, and a mean 3-lod confidence interval of 3.6 cM Bi-allelic markers, including SNP and other marker types, had a mean (maximum) of 55 (191) informative meioses, and were placed within a mean 8.5 cM 3-lod confidence interval. Homologous human sequences were identified for 1159 markers, including 582 newly developed and mapped SNP. Conclusion Addition of these EST- and BAC-based SNPs to the bovine linkage map not only increases marker density, but provides connections to gene-rich physical maps, including annotated human sequence. The map provides a resource for fine-mapping quantitative trait loci and identification of positional candidate genes, and can be integrated with other data to guide and refine assembly of bovine genome sequence. Even after the bovine genome is completely sequenced, the map will continue to be a useful tool to link observable phenotypes and animal genotypes to underlying genes and molecular mechanisms influencing economically important beef and dairy traits.

Abstract Background Bovine chromosome (BTA) 15 contains a quantitative trait loci (QTL) for meat tenderness, as well as several breaks in synteny with human chromosome (HSA) 11. Both linkage and radiation hybrid (RH) maps of BTA 15 are available, but the linkage map lacks gene-specific markers needed to identify genes underlying the QTL, and the gene-rich RH map lacks associations with marker genotypes needed to define the QTL. Integrating the maps will provide information to further explore the QTL as well as refine the comparative map between BTA 15 and HSA 11. A recently developed approach to integrating linkage and RH maps uses both linkage and RH data to resolve a consensus marker order, rather than aligning independently constructed maps. Automated map construction procedures employing this maximum-likelihood approach were developed to integrate BTA RH and linkage data, and establish comparative positions of BTA 15 markers with HSA 11 homologs. Results The integrated BTA 15 map represents 145 markers; 42 shared by both data sets, 36 unique to the linkage data and 67 unique to RH data. Sequence alignment yielded comparative positions for 77 bovine markers with homologs on HSA 11. The map covers approximately 32% of HSA 11 sequence in five segments of conserved synteny, another 15% of HSA 11 is shared with BTA 29. Bovine and human order are consistent in portions of the syntenic segments, but some rearrangement is apparent. Comparative positions of gene markers near the meat tenderness QTL indicate the region includes separate segments of HSA 11. The two microsatellite markers flanking the QTL peak are between defined syntenic segments. Conclusions Combining data to construct an integrated map not only consolidates information from different sources onto a single map, but information contributed from each data set increases the accuracy of the map. Comparison of bovine maps with well annotated human sequence can provide useful information about genes near mapped bovine markers, but bovine gene order may be different than human. Procedures to connect genetic and physical mapping data, build integrated maps for livestock species, and connect those maps to more fully annotated sequence can be automated, facilitating the maintenance of up-to-date maps, and providing a valuable tool to further explore genetic variation in livestock.