Bibliographic record
Abstract
April 2009 was a watershed month in livestock genomics. Two papers appeared in the journal Science, one described the sequence of the bovine genome (The Bovine Genome Sequencing and Analysis Consortium, Science 324, 522–528), and the second the genetic history of the cow using information derived from Single Nucleotide Polymorphisms (SNP) obtained through the sequencing effort (The Bovine HapMap Consortium, Science 324: 528–532). We now have fine detail of the bovine genome structure and annotation of most of the genes to add to tools such as high density SNP chips and gene expression profiling using new generation sequencing technologies. These advances have already transformed the way we carry out genomics based research in cattle. Although not the first agricultural animal species to be sequenced, chicken was completed in 2004 (Nature 432, 695–716) the cow was the first agriculturally important mammal and the first ruminant. Of course, the cow is just one representative of this highly successful group of animals that have been important to humans in the course of our own history. Sheep, goats, deer, elk, water buffalo, bison, camels, llamas, alpacas and others, have played important roles as a source of food, motive power or fibre over millennia. The bovine genome sequence will form the template for future genome sequencing of many other ruminant species. Already other agricultural species are following hard on the heels of the cow including pig, sheep and even water buffalo. The horse, considered perhaps now as a companion animal, has had a draft sequence release in 2007, http://www.broad.mit.edu/mammals/horse). The concept that this many different species might be sequenced was unthinkable when the cattle sequencing effort began. So what is it we have actually achieved and how will it benefit the science, and people who rely on cattle for their livelihood? We now have a window into the genetic history of one of the most important domestic animals. Cattle have coexisted with humans for over 10 000 years, further they have been subject to selective breeding of one sort or another for much of this time. The genomic ‘footprints’ and signatures resulting from domestication and selection are emerging as an early by-product of the sequencing effort (The Bovine HapMap Consortium, 2009, Science 324: 528–532). An early application of the sequencing effort, that has already been adopted by some livestock-based industries, results from access to thousands of SNPs. The Bov50SNP Chip, developed as a direct result of the sequencing effort (Van Tassell et al. 2008, Nature Methods, 5: 247–252) and now available commercially though Illumina Inc., has already been adopted by the North American Dairy industry for genome selection (J.P. Chenais, personal communication). The rest of the world will follow apace, however, for other breeds of cattle, the situation turns out to be far more complex than we imagined. The success in dairy is largely attributable to the predominance worldwide of one breed (Holstein) and the availability of detailed performance records kept over decades of genetic selection. Indeed, there exists a system for the international exchange on data of dairy bulls through Interbull (http://www-interbull.slu.se/framesida-home.htm). Over 3500 Holsteins were genotyped to provide the basis for the development of algorithms for Genome Selection in North American animals (VanRaden et al., 2009, J Dairy Sci 92: 16–24)). This is in contrast to other dairy breeds and most beef breeds where the availability of sufficient animal numbers with good phenotypes and genotypes does not yet exist. Arguments over whether an even denser SNP chip is required need to be put in perspective given the statistical dilemma resulting from too many genotypes on too few animals, or over-parameterisation. International collaborations will be the only way forward for some breeds to achieve the animal numbers required. Finally, we must remember that only one animal has to date been fully sequenced. The Line 1 Hereford was chosen due to the relatively high level of inbreeding in this line, greatly simplifying the task of genome assembly (The Bovine Genome Sequencing and Analysis Consortium, 2009, Science 324, 522–528). Sequencing costs have dropped by as many as three orders of magnitude since the Bovine Genome Sequencing Project was first launched. Hold onto your hats, as we can expect resequencing efforts on many more animals will provide data on genetic diversity, relationships of breeds, and makeup of cross-bred animals, temporal changes to the genome resulting from intense selection pressure, other forms of mutations such as Copy Number Variation and Insertion/Deletion mutations and causal mutations underlying economically important traits, to name just a few. As the cost of sequencing reduces even further it may not be unreasonable to expect that full genome sequence will be available on most, if not all, key industry animals. The pace of research will provide tremendous challenges to how we carry out genome analysis, and technology transfer to industry at every level. Has the promise been fulfilled? The answer is mixed, and depends more on the nature of the animal populations under study than the power of the technology. We are back to biology after years of technology development, and this would be considered by most to be a huge step in the right direction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".