Within- and across-breed imputation of high-density genotypes in dairy and beef cattle from medium- and low-density genotypes

peer-reviewedFinancial support of the Irish Department of Agriculture Research Stimulus Fund (RSF-06-0353; RSF-06-0428; 11/SF/311), Science Foundation Ireland (09/IN.1/B2642) and the Irish dairy and beef industry are gratefully acknowledged.The objective of this study was to evaluate, using three different genotype density panels, the accuracy of imputation from lower- to higher-density genotypes in dairy and beef cattle. High-density genotypes consisting of 777 962 single-nucleotide polymorphisms (SNP) were available on 3122 animals comprised of 269, 196, 710, 234, 719, 730 and 264 Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental bulls, respectively. Three different genotype densities were generated: low density (LD; 6501 autosomal SNPs), medium density (50K; 47 770 autosomal SNPs) and high density (HD; 735 151 autosomal SNPs). Imputation from lower- to higher-density genotype platforms was undertaken within and across breeds exploiting population-wide linkage disequilibrium. The mean allele concordance rate per breed from LD to HD when undertaken using a single breed or multiple breed reference population varied from 0.956 to 0.974 and from 0.947 to 0.967, respectively. The mean allele concordance rate per breed from 50K to HD when undertaken using a single breed or multiple breed reference population varied from 0.987 to 0.994 and from 0.987 to 0.993, respectively. The accuracy of imputation was generally greater when the reference population was solely comprised of the breed to be imputed compared to when the reference population comprised of multiple breeds, although the impactDepartment of Agriculture, Food and the MarineScience Foundation Irelan

Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle

peer-reviewedBackground Four traits related to carcass performance have been identified as economically important in beef production: carcass weight, carcass fat, carcass conformation of progeny and cull cow carcass weight. Although Holstein-Friesian cattle are primarily utilized for milk production, they are also an important source of meat for beef production and export. Because of this, there is great interest in understanding the underlying genomic structure influencing these traits. Several genome-wide association studies have identified regions of the bovine genome associated with growth or carcass traits, however, little is known about the mechanisms or underlying biological pathways involved. This study aims to detect regions of the bovine genome associated with carcass performance traits (employing a panel of 54,001 SNPs) using measures of genetic merit (as predicted transmitting abilities) for 5,705 Irish Holstein-Friesian animals. Candidate genes and biological pathways were then identified for each trait under investigation. Results Following adjustment for false discovery (q-value  0.5) with at least one of the four traits. In total, 557 unique bovine genes, which mapped to 426 human orthologs, were within 500kbs of QTL found associated with a trait using the Bayesian approach. Using this information, 24 significantly over-represented pathways were identified across all traits. The most significantly over-represented biological pathway was the peroxisome proliferator-activated receptor (PPAR) signaling pathway. Conclusions A large number of genomic regions putatively associated with bovine carcass traits were detected using two different statistical approaches. Notably, several significant associations were detected in close proximity to genes with a known role in animal growth such as glucagon and leptin. Several biological pathways, including PPAR signaling, were shown to be involved in various aspects of bovine carcass performance. These core genes and biological processes may form the foundation for further investigation to identify causative mutations involved in each trait. Results reported here support previous findings suggesting conservation of key biological processes involved in growth and metabolism

Imputation of ungenotyped parental genotypes in dairy and beef cattle from progeny genotypes

peer-reviewedThe objective of this study was to quantify the accuracy of imputing the genotype of parents using information on the genotype of their progeny and a family-based and population-based imputation algorithm. Two separate data sets were used, one containing both dairy and beef animals (n = 3122) with high-density genotypes (735 151 single nucleotide polymorphisms (SNPs)) and the other containing just dairy animals (n = 5489) with medium-density genotypes (51 602 SNPs). Imputation accuracy of three different genotype density panels were evaluated representing low (i.e. 6501 SNPs), medium and high density. The full genotypes of sires with genotyped half-sib progeny were masked and subsequently imputed. Genotyped half-sib progeny group sizes were altered from 4 up to 12 and the impact on imputation accuracy was quantified. Up to 157 and 258 sires were used to test the accuracy of imputation in the dairy plus beef data set and the dairy-only data set, respectively. The efficiency and accuracy of imputation was quantified as the proportion of genotypes that could not be imputed, and as both the genotype concordance rate and allele concordance rate. The median proportion of genotypes per animal that could not be imputed in the imputation process decreased as the number of genotyped half-sib progeny increased; values for the medium-density panel ranged from a median of 0.015 with a half-sib progeny group size of 4 to a median of 0.0014 to 0.0015 with a half-sib progeny group size of 8. The accuracy of imputation across different paternal half-sib progeny group sizes was similar in both data sets. Concordance rates increased considerably as the number of genotyped half-sib progeny increased from four (mean animal allele concordance rate of 0.94 in both data sets for the medium-density genotype panel) to five (mean animal allele concordance rate of 0.96 in both data sets for the medium-density genotype panel) after which it was relatively stable up to a half-sib progeny group size of eight. In the data set with dairy-only animals, sufficient sires with paternal half-sib progeny groups up to 12 were available and the withinanimal mean genotype concordance rates continued to increase up to this group size. The accuracy of imputation was worst for the low-density genotypes, especially with smaller half-sib progeny group sizes but the difference in imputation accuracy between density panels diminished as progeny group size increased; the difference between high and medium-density genotype panels was relatively small across all half-sib progeny group sizes. Where biological material or genotypes are not available on individual animals, at least five progeny can be genotyped (on either a medium or high-density genotyping platform) and the parental alleles imputed with, on average, ⩾96% accuracy

Learning in the compressed data domain: Application to milk quality prediction

Smart dairy farming has become one of the most exciting and challenging area in cloud-based data analytics. Transfer of raw data from all farms to a central cloud is currently not feasible as applications are generating more data while internet connectivity is lacking in rural farms. As a solution, Fog computing has become a key factor to process data near the farm and derive farm insights by exchanging data between on-farm applications and transferring some data to the cloud. In this context, learning in the compressed data domain, where decompression is not necessary, is highly desirable as it minimizes the energy used for communication/computation, reduces required memory/storage, and improves application latency. Mid-infrared spectroscopy (MIRS) is used globally to predict several milk quality parameters as well as deriving many animal-level phenotypes. Therefore, compressed learning on MIRS data is beneficial both in terms of data processing in the Fog, as well as storing large data sets in the cloud. In this paper, we used principal component analysis and wavelet transform as two techniques for compressed learning to convert MIRS data into a compressed data domain. The study derives near lossless compression parameters for both techniques to transform MIRS data without impacting the prediction accuracy for a selection of milk quality traits

Genetic parameters of ovarian and uterine reproductive traits in dairy cows

peer-reviewedThe objective of the study was to estimate genetic parameters of detailed reproductive traits derived from ultrasound examination of the reproductive tract as well as their genetic correlations with traditional reproductive traits. A total of 226,141 calving and insemination records as well as 74,134 ultrasound records from Irish dairy cows were used. Traditional reproductive traits included postpartum interval to first service, conception, and next calving, as well as the interval from first to last service; number of inseminations, pregnancy rate to first service, pregnant within 42 d of the herd breeding season, and submission in the first 21 d of the herd breeding season were also available. Detailed reproductive traits included resumed cyclicity at the time of ultrasound examination, incidence of multiple ovulations, incidence of early postpartum ovulation, heat detection, ovarian cystic structures, embryo loss, and uterine score; the latter was a subjectively assessed on a scale of 1 (little fluid with normal uterine tone) to 4 (large quantity of fluid with a flaccid uterine tone). Variance (and covariance) components were estimated using repeatability animal linear mixed models. Heritability for all reproductive traits were generally low (0.001–0.05), with the exception of traits related to cyclicity postpartum, regardless if defined traditionally (0.07; calving to first service) or from ultrasound examination [resumed cyclicity at the time of examination (0.07) or early postpartum ovulation (0.10)]. The genetic correlations among the detailed reproductive traits were generally favorable. The exception was the genetic correlation (0.29) between resumed cyclicity and uterine score; superior genetic merit for cyclicity postpartum was associated with inferior uterine score. Superior genetic merit for most traditional reproductive traits was associated with superior genetic merit for resumed cyclicity (genetic correlations ranged from −0.59 to −0.36 and from 0.56 to 0.70) and uterine score (genetic correlations ranged from −0.47 to 0.32 and from 0.25 to 0.52). Genetic predisposition to an increased incidence of embryo loss was associated with both an inferior uterine score (0.24) and inferior genetic merit for traditional reproductive traits (genetic correlations ranged from −0.52 to −0.42 and from 0.33 to 0.80). The results from the present study indicate that selection based on traditional reproductive traits, such as calving interval or days open, resulted in improved genetic merit of all the detailed reproductive traits evaluated in this study. Additionally, greater accuracy of selection for calving interval is expected for a relatively small progeny group size when detailed reproductive traits are included in a multitrait genetic evaluation

Effect of feeding colostrum at different volumes and subsequent number of transition milk feeds on the serum immunoglobulin G concentration and health status of dairy calves

peer-reviewedTransfer of sufficient IgG to the newborn calf via colostrum is vital to provide it with adequate immunological protection and resistance to disease. The objectives of the present study were to compare serum IgG concentration and health parameters of calves (1) fed different volumes of colostrum [7, 8.5, or 10% of body weight (BW)] within 2 h of birth and (2) given 0, 2, or 4 subsequent feedings of transition milk (i.e., milkings 2 to 6 postcalving). Ninety-nine dairy calves were fed 7, 8.5, or 10% of BW in colostrum within 2 h of birth and given 0, 2, or 4 subsequent feedings of transition milk. The concentration of IgG in the serum of calves was measured at 24, 48, 72, and 642 h of age by an ELISA. The apparent efficiency of absorption for IgG was determined. Health scores were assigned to calves twice per week and all episodes of disease were recorded. The effect of experimental treatment on calf serum IgG concentration differed by the age of the calf. Calves fed 8.5% of BW in colostrum had a greater mean serum IgG concentration than calves fed 7 or 10% of BW at 24, 48, and 72 h of age. At 642 h of age, serum IgG concentrations of calves fed 8.5% of BW (24.2 g/L) and calves fed 10% of BW (21.6 g/L) did not differ, although the serum IgG concentration of calves fed 8.5% of BW was still greater than that of calves fed 7% of BW (20.7 g/L). No difference in serum IgG concentration existed between calves fed 7% of BW and those fed 10% of BW at any age. No significant effect of number of subsequent feedings of transition milk on calf serum IgG concentration was detected. The apparent efficiency of absorption of calves fed 8.5% of BW in colostrum (38%) was greater than calves fed 7% of BW in colostrum (26%) and tended to be greater than in calves fed 10% of BW (29%). Calves fed further feedings of transition milk after the initial feeding of colostrum had a lower odds (0.62; 95% confidence interval: 0.41 to 0.93) of being assigned a worse eye/ear score (i.e., a more copious ocular discharge or pronounced ear droop) and a lower odds (0.5; 95% confidence interval: 0.32 to 0.79) of being assigned a worse nasal score (i.e., a more copious and purulent nasal discharge) during the study period relative to calves that received no further feedings of transition milk. In conclusion, calves fed 8.5% of BW in colostrum within 2 h of birth achieved a greater concentration of IgG in serum in the first 3 d of life than calves fed either 7 or 10% of BW. Feeding calves transition milk subsequently reduced their odds of being assigned a worse eye/ear and nasal score

A Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture

Distributed analytics facilitate to make the data-driven services smarter for a wider range of applications in many domains, including agriculture. The key to producing services at such level is timely analysis for deriving insights from reliable data. Centralized data analytic services are becoming infeasible due to limitations in the Information and Communication Technologies (ICT) infrastructure, timeliness of the information, and data ownership. Distributed Machine Learning (DML) platforms facilitate efficient data analysis and overcome such limitations effectively. Federated Learning (FL) is a DML methodology, which enables optimizing resource consumption while performing privacy-preserved timely analytics. In order to create such services through FL, there need to be innovative machine learning (ML) models as data complexity as well as application requirements limit the applicability of existing ML models. Even though NN-based models are highly advantageous, use of NN in FL settings is limited with thin clients (with less computational capabilities) and high-dimensional data (with a large number of model parameters). Therefore, in this paper, we propose a novel Neural Network (NN)- and Partial Least Square (PLS) regression-based joint FL model (FL-NNPLS). Its predictive performance is evaluated under sequentially and parallel-updating based FL algorithms in a smart farming context for milk quality analysis. Smart farming is a fast-growing industrial sector which requires effective analytics platforms to enable sustainable farming practices. However, the use of advanced ML techniques is still at an early stage for improving the effectiveness of farming practices. Our FL-NNPLS approach performs and compares well with a centralized approach and demonstrates state-of-the-art performance

Genetic selection to reduce lameness in dairy cattle

The high prevalence of lameness in dairy cattle is a critical issue for the industry. Despite having a low heritability, considerable genetic variability is associated with the risk of lameness; therefore, genetic selection can be used to complement management-based approaches to reduce lameness in dairy herds. The Lameness Advantage genetic index, available for all recorded and evaluated dairy animals in the UK, is an estimate of an animal's genetic predisposition to lameness. It has been shown that cows with higher Lameness Advantage values had a reduced incidence of sole lesions, digital dermatitis, and lameness; therefore, breeding to increase the average Lameness Advantage value of a herd could be beneficial. This can be readily achieved by breeding replacements from bulls with a Lameness Advantage value that is above the herd average; this is a low-cost and effective strategy that all farms could implement almost immediately to improve foot health. It is important to ensure a balanced approach to genetic selection by first selecting the parents of the next generation on their overall profitability index (eg £PLI, £SCI or £ACI), followed by secondary selection criteria to address specific breeding goals. </jats:p

Genetic (co)variances between milk mineral concentration and chemical composition in lactating Holstein-Friesian dairy cows

Milk mineral concentration is important from both the perspective of processing milk into dairy products and its nutritive value for human consumption. Precise estimates of genetic parameters for milk mineral concentration are lacking because of the considerable resources required to collect vast phenotypes quantities. The milk concentration of calcium (Ca), potassium (K), magnesium (Mg), sodium (Na) and phosphorus (P) in the present study was quantified from mid-IR spectroscopy on 12 223 testday records from 1717 Holstein-Friesian cows. (Co)variance components were estimated using random regressions to model both the additive genetic and within-lactation permanent environmental variances of each trait. The coefficient of genetic variation averaged across days-in-milk (DIM) was 6.93%, 3.46%, 6.55%, 5.20% and 6.68% for Ca, K, Mg, Na and P concentration, respectively; heritability estimates varied across lactation from 0.31 ± 0.05 (5 DIM) to 0.67 ± 0.04 (181 DIM) for Ca, from 0.18 ± 0.03 (60 DIM) to 0.24 ± 0.05 (305 DIM) for K, from 0.08 ± 0.03 (15 DIM) to 0.37 ± 0.03 (223 DIM) for Mg, from 0.16 ± 0.03 (30 DIM) to 0.37 ± 0.04 (305 DIM) for Na and from 0.21 ± 0.04 (12 DIM) to 0.57 ± 0.04 (211 DIM) for P. Genetic correlations within the same trait across different DIM were almost unity between adjacent DIM but weakened as the time interval between pairwise compared DIM lengthened; genetic correlations were weaker than 0.80 only when comparing both peripheries of the lactation. The analysis of the geometry of the additive genetic covariance matrix revealed that almost 90% of the additive genetic variation was accounted by the intercept term of the covariance functions for each trait. Milk protein concentration and mineral concentration were, in general, positively genetically correlated with each other across DIM, whereas milk fat concentration was positively genetically correlated throughout the entire lactation with Ca, K and Mg; the genetic correlation with fat concentration changed from negative to positive with Na and P at 243 DIM and 50 DIM, respectively. Genetic correlations between somatic cell score and Na ranged from 0.38 ± 0.21 (5 DIM) to 0.79 ± 0.18 (305 DIM). Exploitable genetic variation existed for all milk minerals, although many national breeding objectives are probably contributing to an indirect positive response to selection in milk mineral concentration