Growth curve, blood parameters and carcass traits of grass-fed Angus steers

The increasing demand for natural products is currently transforming the meat industry, making grass-fed and finished beef a valuable option for improving profits. However, the transformation of conventional operations to grass-fed systems comprises many modifications, such as logistical, technological, and financial that could be very complex and expensive, involving economic risk. Therefore, in this study, we analyzed the growth curve, critical economic traits, and carcass quality and finished characteristics over several consecutive years in closely related grass-fed and finished Angus steers, to reduce the genetic effect on the results. We found that grass-fed steers require around 188 additional days to reach the market weight (approx. 470 kg) and had approximately 70% less average daily gain compared to the grain-fed and finished steers. Regression analysis demonstrated an interaction between feed and age (P < 0.01); thus, individual regressions were fitted for each regimen style, obtaining almost perfect linear curves for both treatments, which could be straightforwardly used in practical situations due to its simplicity. Six of eight carcass traits were different between grain-fed and grass-fed and finished steers. Hot-carcass weight, dressing, back fat, and quality grade were superior in grain-fed individuals, contrarily to yield grade and ribeye area/carcass ratio, which were better in grass-fed and finished steers (P < 0.05). Interestingly, the meat tenderness was certainly low and similar in both treatments (P = 0.25), indicating the feasibility of producing tender meat with animals under a grass-fed diet. Nevertheless, according to the quality grade analysis, grain-fed carcasses were greater ranked compared to grass-fed bodies (P < 0.01), regardless of their same tenderness. The results will provide valuable information for better understanding beef cattle in grass-feeding finishing systems, especially from weaning to harvest. Additionally, the study will expand the knowledge about the quality of meat obtained from animals that received grass exclusively, becoming relevant information for economic evaluation and management decisions for grass-based cattle operations.

Development of polymorphic markers in the immune gene complex loci of cattle

The addition of cattle health and immunity traits to genomic selection indices holds promise to increase individual animal longevity and productivity, and decrease economic losses from disease. However, highly variable genomic loci that contain multiple immune-related genes were poorly assembled in the first iterations of the cattle reference genome assembly and underrepresented during the development of most commercial genotyping platforms. As a consequence, there is a paucity of genetic markers within these loci that may track haplotypes related to disease susceptibility. By using hierarchical assembly of bacterial artificial chromosome inserts spanning 3 of these immune-related gene regions, we were able to assemble multiple full-length haplotypes of the major histocompatibility complex, the leukocyte receptor complex, and the natural killer cell complex. Using these new assemblies and the recently released ARS-UCD1.2 reference, we aligned whole-genome shotgun reads from 125 sequenced Holstein bulls to discover candidate variants for genetic marker development. We selected 124 SNPs, using heuristic and statistical models to develop a custom genotyping panel. In a proof-of-principle study, we used this custom panel to genotype 1,797 Holstein cows exposed to bovine tuberculosis (bTB) that were the subject of a previous GWAS study using the Illumina BovineHD array. Although we did not identify any significant association of bTB phenotypes with these new genetic markers, 2 markers exhibited substantial effects on bTB phenotypic prediction. The models and parameters trained in this study serve as a guide for future marker discovery surveys particularly in previously unassembled regions of the cattle genome.

Symposium review: Advances in sequencing technology herald a new frontier in cattle genomics and genome-enabled selection

The cattle reference genome assembly has underpinned major innovations in beef and dairy genetics through genome-enabled selection, including removal of deleterious recessive variants and selection for favorable alleles affecting quantitative production traits. The initial reference assemblies, up to and including UMD3.1 and Btau4.1, were based on a combination of clone-by-clone sequencing of bacterial artificial chromosome clones generated from blood DNA of a Hereford bull and whole-genome shotgun sequencing of blood DNA from his inbred daughter/granddaughter named L1 Dominette 01449 (Dominette). The approach introduced assembly gaps, misassemblies, and errors, and it limited the ability to assemble regions that undergo rearrangement in blood cells, such as immune gene clusters. Nonetheless, the reference supported the creation of genotyping tools and provided a basis for many studies of gene expression. Recently, long-read sequencing technologies have emerged that facilitated a re-assembly of the reference genome, using lung tissue from Dominette to resolve many of the problems and providing a bridge to place historical studies in common context. The new reference, ARS-UCD1.2, successfully assembled germline immune gene clusters and improved overall continuity (i.e., reduction of gaps and inversions) by over 250-fold. This reference properly places nearly all of the legacy genetic markers used for over a decade in the industry. In this review, we discuss the improvements made to the cattle reference; remaining issues present in the assembly; tools developed to support genome-based studies in beef and dairy cattle; and the emergence of newer genome assembly methods that are producing even higher-quality assemblies for other breeds of cattle at a fraction of the cost. The new frontier for cattle genomics research will likely include a transition from the individual Hereford reference genome, to a "pan-genome" reference, representing all the DNA segments existing in commonly used cattle breeds, bringing the cattle reference into line with the current direction of human genome research.

Calling known variants and identifying new variants while rapidly aligning sequence data

Whole-genome sequencing studies can identify causative mutations for subsequent use in genomic evaluations. Speed and accuracy of sequence alignment can be improved by accounting for known variant locations during alignment instead of calling the variants after alignment as in previous programs. The new programs Findmap and Findvar were compared with alignment using Burrows-Wheeler alignment (BWA) or SNAP and variant identification using Genome Analysis ToolKit (GATK) or SAMtools. Findmap stores the reference map and any known variant locations while aligning reads and counting reference and alternate alleles for each DNA source. Findmap also outputs potential new single nucleotide variant, insertion, and deletion alleles. Findvar separates likely true variants from read errors and outputs genotype probabilities. Strategies were tested using cattle, human, and a completely random reference map and simulated or actual data. Most tests simulated 10 bulls, each with 10× simulated sequence reads containing 39 million variants from the 1000 Bull Genomes Project. With 10 processors, clock times for processing 100× data were 105 h for BWA, 25 h for GATK, and 11 h for SAMtools but only about 4 h for SNAP, 3 h for Findmap, and 1 h for Findvar. Alignment programs required about the same total memory; BWA used 46 GB (4.6 GB/processor), whereas >10 processors can share the same memory in SNAP and Findmap, which used 40 and 46 GB, respectively. Findmap correctly mapped 92.9% of reads (compared with 92.6% from SNAP and 90.5% from BWA) and had high accuracy of calling alleles for known variants. For new variants, Findvar found 99.8% of single nucleotide variants, 79% of insertions, and 67% of deletions; GATK found 99.4, 95, and 90%, respectively; and SAMtools found 99.8, 12, and 16%, respectively. False positives (as percentages of true variants) were 11% of single nucleotide variants, 0.4% of insertions, and 0.3% of deletions from Findvar; 12, 8.4, and 2.9%, respectively, from GATK; and 37, 1.3, and 0.4%, respectively, from SAMtools. Advantages of Findmap and Findvar are fast processing, precise alignment, more useful data summaries, more compact output, and fewer steps. Calling known variants during alignment allows more efficient and accurate sequence-based genotyping.

Determination of quantitative trait nucleotides by concordance analysis between quantitative trait loci and marker genotypes of US Holsteins

Experimental designs that exploit family information can provide substantial predictive power in quantitative trait nucleotide discovery projects. Concordance between quantitative trait locus genotype as determined by the a posteriori granddaughter design and marker genotype was determined for 30 trait-by-chromosomal segment effects segregating in the US Holstein population with probabilities of <10^-20 to accept the null hypotheses of no segregating gene affecting the trait within the chromosomal segment. Genotypes for 83 grandsires and 17,217 sons were determined by either complete sequence or imputation for 3,148,506 polymorphisms across the entire genome; 471 Holstein bulls had a complete genome sequence, including 64 of the grandsires. Complete concordance was obtained only for stature on chromosome 14 and daughter pregnancy rate on chromosome 18. For each quantitative trait locus, effects of the 30 polymorphisms with highest concordance scores for the analyzed trait were computed by stepwise regression for predicted transmitting abilities of 26,750 bulls with progeny test and imputed genotypes. Effects for stature on chromosome 11, daughter pregnancy rate on chromosome 18, and protein percentage on chromosome 20 met 3 criteria: complete or almost complete concordance, nominal significance of the polymorphism effect after correction for all other polymorphisms, and marker coefficient of determination >40% of total multiple-regression coefficient of determination for the 30 polymorphisms with highest concordance. An intronic variant marker on chromosome 5 at 93,945,738 bp explained 7% of variance for fat percentage and 74% of total multiple-marker regression variance but was concordant for only 24 of 30 families. The missense polymorphism Phe279Tyr in GHR at 31,909,478 bp on chromosome 20 was confirmed as the causative mutation for fat and protein concentration. For effect on fat percentage on chromosome 14, 12 additional missense polymorphisms were found that had almost complete concordance with the suggested causative polymorphism (missense mutation Ala232Glu in DGAT1). The only polymorphism found likely to improve predictive power for genomic evaluation of dairy cattle was on chromosome 15; that polymorphism had a frequency of 0.45 for the allele with economically positive effects on all production traits.

Reducing animal sequencing redundancy by preferentially selecting animals with low-frequency haplotypes

Many studies leverage targeted whole-genome sequencing (WGS) experiments to identify rare and causal variants within populations. As a natural consequence of their experimental design, many of these surveys tend to sequence redundant haplotype segments due to their high frequency in the base population, and the variants discovered within sequencing data are difficult to phase. We propose a new algorithm, called inverse weight selection (IWS), that preferentially selects individuals based on the cumulative presence of rare frequency haplotypes to maximize the efficiency of WGS surveys. To test the efficacy of this method, we used genotype data from 112,113 registered Holstein bulls derived from the US national dairy database. We demonstrate that IWS is at least 6.8% more efficient than previously published methods in selecting the least number of individuals required to sequence all haplotype segments ≥4% frequency in the US Holstein population. We also suggest that future surveys focus on sequencing homozygous haplotype segments as a first pass to achieve a 50% reduction in cost with an added benefit of phasing variant calls efficiently. Together, this new selection algorithm and experimental design suggestion significantly reduce the overall cost of variant discovery through WGS experiments, making surveys for causal variants influencing disease and production even more efficient.

Increasing the number of single nucleotide polymorphisms used in genomic evaluation of dairy cattle

GeneSeek (Neogen Corp., Lexington, KY) designed a new version of the GeneSeek Genomic Profiler HD BeadChip for Dairy Cattle, which originally had >77,000 single nucleotide polymorphisms (SNP). A set of >140,000 SNP was selected that included all SNP on the existing GeneSeek chip, all SNP used in US national genomic evaluations, SNP that were possible functional mutations, and other informative SNP. Because SNP with a lower minor allele frequency might track causative variants better, 30,000 more SNP were selected from the Illumina BovineHD Genotyping BeadChip (Illumina Inc., San Diego, CA) by choosing SNP to maximize differences in minor allele frequency between a SNP being considered for the new chip and the 2 SNP that flanked it. Single-gene tests were included if their location was known and bioinformatics indicated relevance for dairy cattle. To determine which SNP from the new chip should be included in genomic evaluations, genotypes available from chips already in use were used to impute and evaluate the SNP set. Effects for 134,511 usable SNP were estimated for all breed-trait combinations; SNP with the largest absolute values for effects were selected (5,000 for Holsteins, 1,000 for Jerseys, and 500 each for Brown Swiss and Ayrshires for each trait). To increase overlap with the 60,671 SNP currently used for genomic evaluation, 12,094 more SNP with the largest effects were added. After removing SNP with many parent-progeny conflicts, 84,937 SNP remained. Three cutoff studies were conducted with 3 SNP sets to determine reliability gain over that for parent average when evaluations based on August 2011 data were used to predict December 2014 performance. Across all traits, mean Holstein reliability gains were 32.5, 33.4, and 32.0 percentage points for 60,671, 84,937, and 134,511 SNP, respectively. After genotypes from the new chip became available, the proposed set was reduced from 84,937 to 77,321 SNP to remove SNP that were not included during manufacture, reduce computing time, and improve imputation performance. The set of 77,321 SNP was evaluated using August 2011 data to predict April 2015 performance. Reliability gain over 60,671 SNP was 1.4 percentage points across traits for Holsteins. Improvement over 84,937 SNP was partially the result of 4mo of additional data and genotypes from the new chip. Revision of the SNP set used for genomic evaluation is expected to be an ongoing process to increase evaluation accuracy.

Short communication: Use of young bulls in the United States

The availability of genomic evaluations since 2008 has resulted in many changes to dairy cattle breeding programs. One such change has been the increased contribution of young bulls (0.8 to 3.9 yr old) to those programs. The increased use of young bulls was investigated using pedigree data and breeding records obtained from the US national dairy database (Beltsville, MD). The adoption of genotyping was so rapid that by 2009, >90% of all Holstein artificial insemination (AI) service sires and 86% of Jersey AI service sires were genotyped, regardless of age. The percentage of sons sired by young bulls increased by 49 percentage points (10% in 2008 compared with 59% in 2012) due to the onset of genomic evaluations for Holsteins and by 46 percentage points for Jerseys (11 and 57%, respectively). When limiting these data to sons retained for breeding purposes through AI, the increase was even more dramatic, increasing approximately 80 percentage points from 2008 to 2012 for both Holsteins and Jerseys (1, 5, 28, 52, and 81% for Holsteins and 3, 4, 43, 46, and 82% for Jerseys from 2008 through 2012). From US breeding records from 2007 through 2012, 24,580,793 Holstein and 1,494,095 Jersey breedings were examined. Young bulls accounted for 28% and 25% of Holstein and Jersey breedings in 2007, respectively. These percentages increased to 51% of Holstein and 52% of Jersey breedings in 2012, representing 23- and 27-percentage-unit increases, respectively. Matings to genotyped young bulls have rapidly increased while the use of nongenotyped bulls has diminished since the onset of genomics. Mean sire age for Holstein male progeny born in 2012 was 2.7 yr younger than males born in 2006, and 1.3 yr younger for females; corresponding values for Jerseys were 2.3 and 0.9 yr. Holstein male offspring had an increase of 281 kg between 2006 and 2012, compared with 197 kg between 2000 and 2006 for parent averages (PA) for milk, an increase of 84 kg between the 2 periods. Jersey male offspring had an increase of 49 kg between the 2 periods. To demonstrate the economic impact of the differential use of young bulls, herds were grouped by the frequency of their use of young bulls, and average PTA for milk and net merit for cows that were bred in 2003 through 2012 were calculated. In 2012, herds using >75% young bulls created offspring that had a PA of +52 kg for milk and +$58 net merit compared with herds using no young bulls. Jersey herds using >75% young bulls created offspring that had a PA of +142 kg for milk and +$63 for net merit compared with herds using no young bulls. Use of young bulls has greatly reduced the generation interval and improved the rate of genetic gain since the implementation of genomic evaluations.