Invited review: A perspective on the future of genomic selection in dairy cattle

Revolutionize livestock breeding in the future: an animal embryo-stem cell breeding system in a dish

Meat and milk production needs to increase ~ 70–80% relative to its current levels for satisfying the human needs in 2050. However, it is impossible to achieve such genetic gain by conventional animal breeding systems. Based on recent advances with regard to in vitro induction of germ cell from pluripotent stem cells, herein we propose a novel embryo-stem cell breeding system. Distinct from the conventional breeding system in farm animals that involves selecting and mating individuals, the novel breeding system completes breeding cycles from parental to offspring embryos directly by selecting and mating embryos in a dish. In comparison to the conventional dairy breeding scheme, this system can rapidly achieve 30–40 times more genetic gain by significantly shortening generation interval and enhancing selection intensity. However, several major obstacles must be overcome before we can fully use this system in livestock breeding, which include derivation and mantaince of pluripotent stem cells in domestic animals, as well as in vitro induction of primordial germ cells, and subsequent haploid gametes. Thus, we also discuss the potential efforts needed in solving the obstacles for application this novel system, and elaborate on their groundbreaking potential in livestock breeding. This novel system would provide a revolutionary animal breeding system by offering an unprecedented opportunity for meeting the fast-growing meat and milk demand of humans.

Single nucleotide polymorphisms in candidate genes associated with milk yield in Argentinean Holstein and Holstein x Jersey cows

BACKGROUND

Research on loci influencing milk production traits of dairy cattle is one of the main topics of investigation in livestock. Many genomic regions and polymorphisms associated with dairy production have been reported worldwide. In this context, the purpose of this study was to identify candidate loci associated with milk yield in Argentinean dairy cattle. A database of candidate genes and single nucleotide polymorphisms (SNPs) for milk production and composition was developed. Thirty-nine SNPs belonging to 22 candidate genes were genotyped on 1643 animals (Holstein and Holstein x Jersey). The genotypes obtained were subjected to association studies considering the whole population and discriminating the population by Holstein breed percentage. Phenotypic data consisted of milk production values recorded during the first lactation of 1156 Holstein and 462 Holstein x Jersey cows from 18 dairy farms located in the central dairy area of Argentina. From these records, 305-day cumulative milk production values were predicted.

RESULTS

Eight SNPs (rs43375517, rs29004488, rs132812135, rs137651874, rs109191047, rs135164815, rs43706485, and rs41255693), located on six Bos taurus autosomes (BTA4, BTA6, BTA19, BTA20, BTA22, and BTA26), showed suggestive associations with 305-day cumulative milk production (under Benjamini-Hochberg procedure with a false discovery rate of 0.1). Two of those SNPs (rs43375517 and rs135164815) were significantly associated with milk production (Bonferroni adjusted p-values < 0.05) when considering the Holstein population.

CONCLUSIONS

The results obtained are consistent with previously reported associations in other Holstein populations. Furthermore, the SNPs found to influence bovine milk production in this study may be used as possible candidate SNPs for marker-assisted selection programs in Argentinean dairy cattle.

Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture

Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of genomic selection. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. We compared DL methods with densely connected network architecture to one of the most often used genome-enabled prediction models: Genomic Best Linear Unbiased Prediction (GBLUP). We used nine published real genomic data sets to compare a fraction of all possible deep learning models to obtain a "meta picture" of the performance of DL methods with densely connected network architecture. In general, the best predictions were obtained with the GBLUP model when genotype×environment interaction (G×E) was taken into account (8 out of 9 data sets); when the interactions were ignored, the DL method was better than the GBLUP in terms of prediction accuracy in 6 out of the 9 data sets. For this reason, we believe that DL should be added to the data science toolkit of scientists working on animal and plant breeding. This study corroborates the view that there are no universally best prediction machines.

Genome-wide study to detect single nucleotide polymorphisms associated with visceral and subcutaneous fat deposition in Holstein dairy cows

Excessive abdominal fat might be associated with more severe metabolic disorders in Holstein cows. Our hypothesis was that there are genetic differences between cows with low and high abdominal fat deposition and a normal cover of subcutaneous adipose tissue. The objective of this study was to assess the genetic basis for variation in visceral adiposity in US Holstein cows. The study included adult Holstein cows sampled from a slaughterhouse (Green Bay, WI, USA) during September 2016. Only animals with a body condition score between 2.75 and 3.25 were considered. The extent of omental fat at the level of the insertion of the lesser omentum over the pylorus area was assessed. A group of 100 Holstein cows with an omental fold &lt;5 mm in thickness and minimum fat deposition throughout the entire omentum, and the second group of 100 cows with an omental fold ⩾20 mm in thickness and with a marked fat deposition observed throughout the entire omentum were sampled. A small piece of muscle from the neck was collected from each cow into a sterile container for DNA extraction. Samples were submitted to a commercial laboratory for interrogation of genome-wide genomic variation using the Illumina BovineHD Beadchip. Genome-Wide association analysis was performed to test potential associations between fat deposition and genomic variation. A univariate mixed linear model analysis was performed using genome-wide efficient mixed model association to identify single nucleotide polymorphisms (SNPs) significantly associated with variation in a visceral fat deposition. The chip heritability was 0.686 and the estimated additive genetic and residual variance components were 0.427 and 0.074, respectively. In total, 11 SNPs defining four quantitative trait locus (QTL) regions were found to be significantly associated with visceral fat deposition (P&lt;0.00001). Among them, two of the QTL were detected with four and five significantly associated SNPs, respectively; whereas, the QTLs detected on BTA12 and BTA19 were each detected with only one significantly associated SNP. No enriched gene ontology terms were found within the gene networks harboring these genes when supplied to DAVID using either the Bos taurus or human gene ontology databases. We conclude that excessive omental fat in Holstein cows with similar body condition scores is not caused by a single Mendelian locus and that the trait appears to be at least moderately heritable; consequently, selection to reduce excessive omental fat is potentially possible, but would require the generation of predicted transmitting abilities from larger and random samples of Holstein cattle.

The effect of using cow genomic information on accuracy and bias of genomic breeding values in a simulated Holstein dairy cattle population

Using cow data in the training population is attractive as a way to mitigate bias due to highly selected training bulls and to implement genomic selection for countries with no or limited proven bull data. However, one potential issue with cow data is a bias due to the preferential treatment. The objectives of this study were to (1) investigate the effect of including cow genotype and phenotype data into the training population on accuracy and bias of genomic predictions and (2) assess the effect of preferential treatment for different proportions of elite cows. First, a 4-pathway Holstein dairy cattle population was simulated for 2 traits with low (0.05) and moderate (0.3) heritability. Then different numbers of cows (0, 2,500, 5,000, 10,000, 15,000, or 20,000) were randomly selected and added to the training group composed of different numbers of top bulls (0, 2,500, 5,000, 10,000, or 15,000). Reliability levels of de-regressed estimated breeding values for training cows and bulls were 30 and 75% for traits with low heritability and were 60 and 90% for traits with moderate heritability, respectively. Preferential treatment was simulated by introducing upward bias equal to 35% of phenotypic variance to 5, 10, and 20% of elite bull dams in each scenario. Two different validation data sets were considered: (1) all animals in the last generation of both elite and commercial tiers (n = 42,000) and (2) only animals in the last generation of the elite tier (n = 12,000). Adding cow data into the training population led to an increase in accuracy (r) and decrease in bias of genomic predictions in all considered scenarios without preferential treatment. The gain in r was higher for the low heritable trait (from 0.004 to 0.166 r points) compared with the moderate heritable trait (from 0.004 to 0.116 r points). The gain in accuracy in scenarios with a lower number of training bulls was relatively higher (from 0.093 to 0.166 r points) than with a higher number of training bulls (from 0.004 to 0.09 r points). In this study, as expected, the bull-only reference population resulted in higher accuracy compared with the cow-only reference population of the same size. However, the cow reference population might be an option for countries with a small-scale progeny testing scheme or for minor breeds in large counties, and for traits measured only on a small fraction of the population. The inclusion of preferential treatment to 5 to 20% of the elite cows led to an adverse effect on both accuracy and bias of predictions. When preferential treatment was present, random selection of cows did not reduce the effect of preferential treatment.

Invited review: Learning from the future-A vision for dairy farms and cows in 2067

The world's population will reach 10.4 billion in 2067, with 81% residing in Africa or Asia. Arable land available for food production will decrease to 0.15 ha per person. Temperature will increase in tropical and temperate zones, especially in the Northern Hemisphere, and this will push growing seasons and dairy farming away from arid areas and into more northern latitudes. Dairy consumption will increase because it provides essential nutrients more efficiently than many other agricultural systems. Dairy farming will become modernized in developing countries and milk production per cow will increase, doubling in countries with advanced dairying systems. Profitability of dairy farms will be the key to their sustainability. Genetic improvements will include emphasis on the coding genome and associated noncoding epigenome of cattle, and on microbiomes of dairy cattle and farmsteads. Farm sizes will increase and there will be greater lateral integration of housing and management of dairy cattle of different ages and production stages. Integrated sensors, robotics, and automation will replace much of the manual labor on farms. Managing the epigenome and microbiome will become part of routine herd management. Innovations in dairy facilities will improve the health of cows and permit expression of natural behaviors. Herds will be viewed as superorganisms, and studies of herds as observational units will lead to improvements in productivity, health, and well-being of dairy cattle, and improve the agroecology and sustainability of dairy farms. Dairy farmers in 2067 will meet the world's needs for essential nutrients by adopting technologies and practices that provide improved cow health and longevity, profitable dairy farms, and sustainable agriculture.

Symposium review: Building a better cow-The Australian experience and future perspectives

Genomic selection has led to opportunities for developing new breeding values that rely on phenotypes in dedicated reference populations of genotyped cows. In Australia, it has been applied to 2 novel traits: feed efficiency, which was released in 2015 as feed saved breeding values, and heat tolerance genomic breeding values, released for the first time in 2017. Feed saved is already included in the national breeding objective, which is focused on profitability and designed to be in line with farmer preferences. Our future focus is on traits associated with animal health, either directly or in combination with predictor traits, such as mid-infrared spectral data and, into the future, automated data capture. Although it is common for many evaluated traits to have genomic reliabilities ranging between 60 and 75%, many new, genomic information-only traits are likely to have reliabilities of less than 50%. Pooling of phenotype data internationally and investing in maintenance of reference populations is one option to increase the reliability of these traits; the other is to apply improved genomic prediction methods. For example, advances in the use of sequence data, in addition to gene expression studies, can lead to improved persistence of genomic breeding values across breeds and generations and potentially lead to greater reliabilities. Lower genomic reliabilities of novel traits could reduce the overall index reliability. However, provided these traits contribute to the overall breeding objective (e.g., profit), they are worth including. Bull selection tools and personalized genetic trends are already available, but increased access to economic and automatic capture farm data may see even better use of data to improve farm management and selection decisions.