Massey Research Online - Browsing by Author "Garrick DJ"

Browsing by Author "Garrick DJ"

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Agent-Based Modeling to Improve Beef Production from Dairy Cattle: Model Description and Evaluation
(MDPI (Basel, Switzerland), 2022-10-05) Addis AH; Blair HT; Kenyon PR; Morris ST; Schreurs NM; Garrick DJ
Agent-based modeling (ABM) enables an in silico representation of complex systems and captures agent behavior resulting from interaction with other agents and their environment. This study developed an ABM to represent a pasture-based beef cattle finishing systems in New Zealand (NZ) using attributes of the rearer, finisher, and processor, as well as specific attributes of dairy-origin beef cattle. The model was parameterized using values representing 1% of NZ dairy-origin cattle, and 10% of rearers and finishers in NZ. The cattle agent consisted of 32% Holstein-Friesian, 50% Holstein-Friesian–Jersey crossbred, and 8% Jersey, with the remainder being other breeds. Rearers and finishers repetitively and simultaneously interacted to determine the type and number of cattle populating the finishing system. Rearers brought in four-day-old spring-born calves and reared them until 60 calves (representing a full truck load) on average had a live weight of 100 kg before selling them on to finishers. Finishers mainly attained weaners from rearers, or directly from dairy farmers when weaner demand was higher than the supply from rearers. Fast-growing cattle were sent for slaughter before the second winter, and the remainder were sent before their third winter. The model finished a higher number of bulls than heifers and steers, although it was 4% lower than the industry reported value. Holstein-Friesian and Holstein-Friesian–Jersey-crossbred cattle dominated the dairy-origin beef finishing system. Jersey cattle account for less than 5% of total processed beef cattle. Further studies to include retailer and consumer perspectives and other decision alternatives for finishing farms would improve the applicability of the model for decision-making processes.
Agent-Based Modelling to Improve Beef Production from Dairy Cattle: Young Beef Production
(MDPI (Basel, Switzerland), 2023-04-19) Addis AH; Blair HT; Kenyon PR; Morris ST; Schreurs NM; Garrick DJ; Zaborowicz M; Frankowski J
Approximately 42% of the total calves born in New Zealand’s dairy industry are either euthanized on farms or commercially slaughtered as so-called bobby calves within 2 weeks of age. These practices have perceived ethical issues and are considered a waste of resources because these calves could be grown on and processed for beef. Young beef cattle harvested between 8 and 12 months of age would represent a new class of beef production for New Zealand and would allow for a greater number of calves to be utilized for beef production, reducing bobby calf numbers in New Zealand. However, the acceptance of such a system in competition with existing sheep and beef cattle production systems is unknown. Therefore, the current study employed an agent-based model (ABM) developed for dairy-origin beef cattle production systems to understand price levers that might influence the acceptance of young beef production systems on sheep and beef cattle farms in New Zealand. The agents of the model were the rearer, finisher, and processor. Rearers bought in 4-days old dairy-origin calves and weaned them at approximately 100 kg live weight before selling them to finishers. Finishers managed the young beef cattle until they were between 8 and 12 months of age in contrast to 20 to 30 months for traditional beef cattle. Processing young beef cattle in existing beef production systems without any price premium only led to an additional 5% of cattle being utilized compared to the traditional beef cattle production system in New Zealand. This increased another 2% when both weaner cattle and young beef were sold at a price premium of 10%. In this scenario, Holstein Friesian young bull contributed more than 65% of total young beef cattle. Further premium prices for young beef cattle production systems increased the proportion of young beef cattle (mainly as young bull beef), however, there was a decrease in the total number of dairy-origin cattle processed, for the given feed supply, compared to the 10% premium price. Further studies are required to identify price levers and other alternative young beef production systems to increase the number of young beef cattle as well the total number of dairy-origin beef cattle for beef on sheep and beef cattle farms. Some potential options for investigation are meat quality, retailer and consumer perspectives, and whether dairy farmers may have to pay calf rearers to utilize calves with lower growth potential
Comparison of the genetic characteristics of directly measured and Fourier-transform mid-infrared-predicted bovine milk fatty acids and proteins.
(Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association, 2022-12) Tiplady KM; Lopdell TJ; Sherlock RG; Johnson TJJ; Spelman RJ; Harris BL; Davis SR; Littlejohn MD; Garrick DJ
Fourier-transform mid-infrared (FT-MIR) spectroscopy is a high-throughput and inexpensive methodology used to evaluate concentrations of fat and protein in dairy cattle milk samples. The objective of this study was to compare the genetic characteristics of FT-MIR predicted fatty acids and individual milk proteins with those that had been measured directly using gas and liquid chromatography methods. The data used in this study was based on 2,005 milk samples collected from 706 Holstein-Friesian × Jersey animals that were managed in a seasonal, pasture-based dairy system, with milk samples collected across 2 consecutive seasons. Concentrations of fatty acids and protein fractions in milk samples were directly determined by gas chromatography and high-performance liquid chromatography, respectively. Models to predict each directly measured trait based on FT-MIR spectra were developed using partial least squares regression, with spectra from a random selection of half the cows used to train the models, and predictions for the remaining cows used as validation. Variance parameters for each trait and genetic correlations for each pair of measured/predicted traits were estimated from pedigree-based bivariate models using REML procedures. A genome-wide association study was undertaken using imputed whole-genome sequence, and quantitative trait loci (QTL) from directly measured traits were compared with QTL from the corresponding FT-MIR predicted traits. Cross-validation prediction accuracies based on partial least squares for individual and grouped fatty acids ranged from 0.18 to 0.65. Trait prediction accuracies in cross-validation for protein fractions were 0.53, 0.19, and 0.48 for α-casein, β-casein, and κ-casein, 0.31 for α-lactalbumin, 0.68 for β-lactoglobulin, and 0.36 for lactoferrin. Heritability estimates for directly measured traits ranged from 0.07 to 0.55 for fatty acids; and from 0.14 to 0.63 for individual milk proteins. For FT-MIR predicted traits, heritability estimates were mostly higher than for the corresponding measured traits, ranging from 0.14 to 0.46 for fatty acids, and from 0.30 to 0.70 for individual proteins. Genetic correlations between directly measured and FT-MIR predicted protein fractions were consistently above 0.75, with the exceptions of C18:0 and C18:3 cis-3, which had genetic correlations of 0.72 and 0.74, respectively. The GWAS identified trait QTL for fatty acids with likely candidates in the DGAT1, CCDC57, SCD, and GPAT4 genes. Notably, QTL for SCD were largely absent in the FT-MIR predicted traits, and QTL for GPAT4 were absent in directly measured traits. Similarly, for directly measured individual proteins, we identified QTL with likely candidates in the CSN1S1, CSN3, PAEP, and LTF genes, but the QTL for CSN3 and LTF were absent in the FT-MIR predicted traits. Our study indicates that genetic correlations between directly measured and FT-MIR predicted fatty acid and protein fractions are typically high, but that phenotypic variation in these traits may be underpinned by differing genetic architecture.
Cumulative dairy cow genetic change from selection and crossbreeding over the last 2 decades in New Zealand closely aligns to model-based predictions published in 2000
(Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association, 2021-03) Lopez-Villalobos N; Blair HT; Garrick DJ
A deterministic model was developed in 1998 to evaluate the concurrent effects of selection and crossbreeding on the rate of genetic gain and increases in productivity of New Zealand dairy cattle over the ensuing 25-yr period. The predictions of today's breed composition of the national dairy herd and genetic trends for body weight and lactation yields of milk, fat, and protein are compared with today's actual values. Selection was assumed to use an index that included live weight and lactation yields of milk, fat, and protein. Mating strategies involving the Holstein-Friesian (F), Jersey (J), and Ayrshire (A) breeds were evaluated. Effects of heterosis and age were included to calculate phenotypic live weight and yields of milk, fat, and protein per cow. At the time the model was developed, New Zealand had an across-breed evaluation system, but only straightbred bulls were used after progeny testing based on records of straightbred and crossbred daughters. The model predicted that if crossbred cows and bulls could be considered as bull parents, faster rates of genetic gain may result because of increased selection intensities in the cow to breed bull selection pathway. This scenario transpired, and the best bulls and cows for farm profit were used regardless of breed. Under that mating strategy for the 2018 birthyear, the model predicted the national breed composition would be 11% F, 34% J, 52% F×J, and 2% A; the actual breed composition was 36% F, 9% J, 53% F×J, and 1% A. The model-predicted annual genetic gains would be 16.7 L of milk, 1.2 kg of fat, 1.5 kg of protein, and −0.7 kg of body weight; the realized annual improvements were 13.6 L of milk, 1.31 kg of fat, 1.17 kg of protein, and −0.36 kg of body weight. Predicted long-term responses to selection can closely mirror realized improvements, confirming the value of modeling to inform animal breeding decision making.
Differential Gene Expression Associated with Soybean Oil Level in the Diet of Pigs
(MDPI (Basel, Switzerland), 2022-06-25) Fanalli SL; da Silva BPM; Gomes JD; de Almeida VV; Freitas FAO; Moreira GCM; Silva-Vignato B; Afonso J; Reecy J; Koltes J; Koltes D; de Almeida Regitano LC; Garrick DJ; de Carvalho Balieiro JC; Meira AN; Freitas L; Coutinho LL; Fukumasu H; Mourão GB; de Alencar SM; Luchiari Filho A; Cesar ASM; Hernandez P
The aim of this study was to identify the differentially expressed genes (DEG) from the skeletal muscle and liver samples of animal models for metabolic diseases in humans. To perform the study, the fatty acid (FA) profile and RNA sequencing (RNA-Seq) data of 35 samples of liver tissue (SOY1.5, n = 17 and SOY3.0, n = 18) and 36 samples of skeletal muscle (SOY1.5, n = 18 and SOY3.0, n = 18) of Large White pigs were analyzed. The FA profile of the tissues was modified by the diet, mainly those related to monounsaturated (MUFA) and polyunsaturated (PUFA) FA. The skeletal muscle transcriptome analysis revealed 45 DEG (FDR 10%), and the functional enrichment analysis identified network maps related to inflammation, immune processes, and pathways associated with oxidative stress, type 2 diabetes, and metabolic dysfunction. For the liver tissue, the transcriptome profile analysis revealed 281 DEG, which participate in network maps related to neurodegenerative diseases. With this nutrigenomics study, we verified that different levels of soybean oil in the pig diet, an animal model for metabolic diseases in humans, affected the transcriptome profile of skeletal muscle and liver tissue. These findings may help to better understand the biological mechanisms that can be modulated by the diet.
Estimating Heritabilities and Breeding Values From Censored Phenotypes Using a Data Augmentation Approach.
(Frontiers Media S.A., 2022-07-25) Stephen MA; Cheng H; Pryce JE; Burke CR; Steele NM; Phyn CVC; Garrick DJ; Cánovas A
Time-dependent traits are often subject to censorship, where instead of precise phenotypes, only a lower and/or upper bound can be established for some of the individuals. Censorship reduces the precision of phenotypes but can represent compromise between measurement cost and animal ethics considerations. This compromise is particularly relevant for genetic evaluation because phenotyping initiatives often involve thousands of individuals. This research aimed to: 1) demonstrate a data augmentation approach for analysing censored phenotypes, and 2) quantify the implications of phenotype censorship on estimation of heritabilities and predictions of breeding values. First, we simulated uncensored phenotypes, representing fine-scale "age at puberty" for each individual in a population of some 5,000 animals across 50 herds. Analysis of these uncensored phenotypes provided a gold-standard control. We then produced seven "test" phenotypes by superimposing varying degrees of left, interval, and/or right censorship, as if herds were measured on only one, two or three occasions, with a binary measure categorized for animals at each visit (either pre or post pubertal). We demonstrated that our estimates of heritabilities and predictions of breeding values obtained using a data augmentation approach were remarkably robust to phenotype censorship. Our results have important practical implications for measuring time-dependent traits for genetic evaluation. More specifically, we suggest that data collection can be designed with relatively infrequent repeated measures, thereby reducing costs and increasing feasibility across large numbers of animals.
Genetic and phenotypic relationships between ewe reproductive performance and wool and growth traits in Uruguayan Ultrafine Merino sheep.
(Oxford University Press on behalf of the American Society of Animal Science, 2023-03-07) Ramos Z; Garrick DJ; Blair HT; De Barbieri I; Ciappesoni G; Montossi F; Kenyon PR
This study reports genetic parameters for yearling and adult wool and growth traits, and ewe reproductive performance. Data were sourced from an Uruguayan Merino flock involved in a long-term selection program focused on reduced fiber diameter (FD), and increased clean fleece weight (CFW) and live weight (LW). Pedigree and performance data from approximately 5,700 mixed-sex yearling lambs and 2,000 mixed-age ewes born between 1999 and 2019 were analyzed. The number of records ranged from 1,267 to 5,738 for yearling traits, and from 1,931 to 7,079 for ewe productive and reproductive performance. Data on yearling and adult wool traits, LW and body condition score (BCS), yearling eye muscle area (Y_EMA), and fat thickness (Y_FAT), and several reproduction traits were analyzed. The genetic relationships between FD and reproduction traits were not different from zero. Moderate unfavorable genetic correlations were found between adult CFW and ewe lifetime reproduction traits (-0.34 ± 0.08 and -0.33 ± 0.09 for the total number of lambs weaned and total lamb LW at weaning, respectively). There were moderate to strong positive genetic correlations between yearling LW and all reproduction traits other than ewe-rearing ability (-0.08 ± 0.11) and pregnancy rate (0.18 ± 0.08). The genetic correlations between Y_EMA and reproduction traits were positive and ranged from 0.15 to 0.49. Moderate unfavorable genetic correlations were observed between yearling FD and Y_FAT and between adult FD and BCS at mating (0.31 ± 0.12 and 0.23 ± 0.07, respectively). The genetic correlations between adult fleece weight and ewe BCS at different stages of the cycle were negative, but generally not different from zero. This study shows that selection for reduced FD is unlikely to have any effect on reproduction traits. Selection for increased yearling LW and Y_EMA will improve ewe reproductive performance. On the other hand, selection for increased adult CFW will reduce ewe reproductive performance, whereas selection for reduced FD will negatively impact body fat levels. Although unfavorable genetic relationships between wool traits and both FAT and ewe reproductive performance existed, simultaneous improvements in the traits would occur using appropriately designed indexes.
Genetic Parameters for Growth, Ultrasound and Carcass Traits in New Zealand Beef Cattle and Their Correlations with Maternal Performance
(MDPI, (Basel, Switzerland), 2021-12-23) Weik F; Hickson RE; Morris ST; Garrick DJ; Archer JA
Research has shown that enhancing finishing performance in beef cows is feasible; however, any adverse impact of selection strategies for finishing performance on the performance of the maternal herd should be taken into account. The aim of this research was to examine the inheritance of growth, ultrasound and carcass traits in finishing beef cattle and to evaluate their correlations with maternal performance traits. Data were collected from a nationwide progeny test on commercial New Zealand hill country farms comprising a total of 4473 beef cows and their progeny. Most finishing traits were moderately to highly heritable (0.28-0.58) with the exception of meat or fat colour and ossification (0.00-0.12). Ultrasound scan traits had high genetic correlations with corresponding traits measured at slaughter (rg = 0.53-0.95) and may be used as a selection tool for improved genetic merit of the beef carcass. Fat content determined via ultrasound scanning in the live animal or at slaughter in finishing cattle is positively genetically correlated with rebreeding performance (rg = 0.22-0.39) in female herd replacements and negatively correlated with mature cow live weight (rg = -0.40 to -0.19). Low-magnitude associations were observed between the genetic merit for carcass fat traits with body condition in mature cows.
Genetic Parameters for Maternal Performance Traits in Commercially Farmed New Zealand Beef Cattle
(MDPI (Basel, Switzerland), 2021-08-26) Weik F; Hickson RE; Morris ST; Garrick DJ; Archer JA; Michael E D
Maternal performance is a major driver of profitability in cow-calf beef cattle enterprises. The aim of this research was to evaluate the inheritance of maternal performance traits and examine the intercorrelation among reproduction, live weight, hip height, body condition and maternal contribution to calf weaning weight in 15-month-old heifers, 2-year-old cows and mature cows in New Zealand beef herds. Data were collected on a total of 14,241 cows and their progeny on five commercial New Zealand hill country farms. Heritabilities were low for reproductive traits in heifers and mature cows (0-0.06) but were greater in 2-year-old cows (0.12-0.21). Body condition scores were lowly (0.15-0.26) and live weights (0.42-0.48) and hip heights (0.47-0.65) highly heritable in heifers, 2-year-old cows and mature cows. Results indicate that 2-year-old cows with higher genetic potential for rebreeding ability may have greater genetic merit for live weight, hip height and body condition as heifers (rg = 0.19-0.54) but are unlikely to be larger cows at maturity (rg = -0.27--0.10). The maternal genetic effect on weaning weight had a heritability of 0.20 and was negatively genetically correlated with body condition score in lactating cows (rg = -0.55--0.40) but positively genetically correlated with rebreeding performance (rg = 0.48).
Genomic Regions Associated with Wool, Growth and Reproduction Traits in Uruguayan Merino Sheep
(MDPI (Basel, Switzerland), 2023-01-07) Ramos Z; Garrick DJ; Blair HT; Vera B; Ciappesoni G; Kenyon PR
The aim of this study was to identify genomic regions and genes associated with the fiber diameter (FD), clean fleece weight (CFW), live weight (LW), body condition score (BCS), pregnancy rate (PR) and lambing potential (LP) of Uruguayan Merino sheep. Phenotypic records of approximately 2000 mixed-age ewes were obtained from a Merino nucleus flock. Genome-wide association studies were performed utilizing single-step Bayesian analysis. For wool traits, a total of 35 genomic windows surpassed the significance threshold (PVE ≥ 0.25%). The proportion of the total additive genetic variance explained by those windows was 4.85 and 9.06% for FD and CFW, respectively. There were 42 windows significantly associated with LWM, which collectively explained 43.2% of the additive genetic variance. For BCS, 22 relevant windows accounted for more than 40% of the additive genetic variance, whereas for the reproduction traits, 53 genomic windows (24 and 29 for PR and LP, respectively) reached the suggestive threshold of 0.25% of the PVE. Within the top 10 windows for each trait, we identified several genes showing potential associations with the wool (e.g., IGF-1, TGFB2R, PRKCA), live weight (e.g., CAST, LAP3, MED28, HERC6), body condition score (e.g., CDH10, TMC2, SIRPA, CPXM1) or reproduction traits (e.g., ADCY1, LEPR, GHR, LPAR2) of the mixed-age ewes.
Identification of Genomic Regions Associated with Concentrations of Milk Fat, Protein, Urea and Efficiency of Crude Protein Utilization in Grazing Dairy Cows
(MDPI (Basel, Switzerland), 2021-03-23) Ariyarathne HBPC; Correa-Luna M; Blair HT; Garrick DJ; Lopez-Villalobos N
Abstract The objective of this study was to identify genomic regions associated with milk fat percentage (FP), crude protein percentage (CPP), urea concentration (MU) and efficiency of crude protein utilization (ECPU: ratio between crude protein yield in milk and dietary crude protein intake) using grazing, mixed-breed, dairy cows in New Zealand. Phenotypes from 634 Holstein Friesian, Jersey or crossbred cows were obtained from two herds at Massey University. A subset of 490 of these cows was genotyped using Bovine Illumina 50K SNP-chips. Two genome-wise association approaches were used, a single-locus model fitted to data from 490 cows and a single-step Bayes C model fitted to data from all 634 cows. The single-locus analysis was performed with the Efficient Mixed-Model Association eXpedited model as implemented in the SVS package. Single nucleotide polymorphisms (SNPs) with genome-wide association p-values ≤ 1.11 × 10−6 were considered as putative quantitative trait loci (QTL). The Bayes C analysis was performed with the JWAS package and 1-Mb genomic windows containing SNPs that explained > 0.37% of the genetic variance were considered as putative QTL. Candidate genes within 100 kb from the identified SNPs in single-locus GWAS or the 1-Mb windows were identified using gene ontology, as implemented in the Ensembl Genome Browser. The genes detected in association with FP (MGST1, DGAT1, CEBPD, SLC52A2, GPAT4, and ACOX3) and CPP (DGAT1, CSN1S1, GOSR2, HERC6, and IGF1R) were identified as candidates. Gene ontology revealed six novel candidate genes (GMDS, E2F7, SIAH1, SLC24A4, LGMN, and ASS1) significantly associated with MU whose functions were in protein catabolism, urea cycle, ion transportation and N excretion. One novel candidate gene was identified in association with ECPU (MAP3K1) that is involved in post-transcriptional modification of proteins. The findings should be validated using a larger population of New Zealand grazing dairy cows.
Non-additive QTL mapping of lactation traits in 124,000 cattle reveals novel recessive loci
(BioMed Central Ltd, 2022-12) Reynolds EGM; Lopdell T; Wang Y; Tiplady KM; Harland CS; Johnson TJJ; Neeley C; Carnie K; Sherlock RG; Couldrey C; Davis SR; Harris BL; Spelman RJ; Garrick DJ; Littlejohn MD
BACKGROUND: Deleterious recessive conditions have been primarily studied in the context of Mendelian diseases. Recently, several deleterious recessive mutations with large effects were discovered via non-additive genome-wide association studies (GWAS) of quantitative growth and developmental traits in cattle, which showed that quantitative traits can be used as proxies of genetic disorders when such traits are indicative of whole-animal health status. We reasoned that lactation traits in cattle might also reflect genetic disorders, given the increased energy demands of lactation and the substantial stresses imposed on the animal. In this study, we screened more than 124,000 cows for recessive effects based on lactation traits. RESULTS: We discovered five novel quantitative trait loci (QTL) that are associated with large recessive impacts on three milk yield traits, with these loci presenting missense variants in the DOCK8, IL4R, KIAA0556, and SLC25A4 genes or premature stop variants in the ITGAL, LRCH4, and RBM34 genes, as candidate causal mutations. For two milk composition traits, we identified several previously reported additive QTL that display small dominance effects. By contrasting results from milk yield and milk composition phenotypes, we note differing genetic architectures. Compared to milk composition phenotypes, milk yield phenotypes had lower heritabilities and were associated with fewer additive QTL but had a higher non-additive genetic variance and were associated with a higher proportion of loci exhibiting dominance. CONCLUSIONS: We identified large-effect recessive QTL which are segregating at surprisingly high frequencies in cattle. We speculate that the differences in genetic architecture between milk yield and milk composition phenotypes derive from underlying dissimilarities in the cellular and molecular representation of these traits, with yield phenotypes acting as a better proxy of underlying biological disorders through presentation of a larger number of major recessive impacts.
Pregnancy status predicted using milk mid-infrared spectra from dairy cattle
(Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association, 2022-04) Tiplady KM; Trinh M-H; Davis SR; Sherlock RG; Spelman RJ; Garrick DJ; Harris BL
Accurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status.
Regression of traits-other-than-production phenotypes for cows milked once a day on estimated breeding values obtained from cows milked twice a day
(The New Zealand Society of Animal Production (Inc), 2019-06-17) Sneddon N; Lopez-Villalobos N; Garrick DJ; Rocha JF; Donaghy D
The use of traits other than production (TOP) to aid selection for conformation fit-for-purpose offspring becomes more important as once-a-day (OAD) milking for whole or part season becomes more common. In 2013 Massey University Dairy 1 farm transitioned their mixed-breed herd milked twice a day (TAD) to an OAD spring-calving system. All cows on the farm were scored for 14 inspector TOP traits and four farmer-scored traits for a total of 1,163 records over five seasons (168 to 254 cows per year). The scores were adjusted for age of the cow and season. The adjusted phenotypes for all cows for each trait were then regressed on their corresponding nationally-produced TAD estimated breeding values (EBV), which had been calculated without using these phenotypes. The four farmer-scored traits had the lowest relationships with EBV. Estimates of regression coefficients near one were observed for the udder traits (udder support, rear udder, front teats, udder overall), dairy conformation and body capacity. This supports the use of national EBVs in OAD herds derived mostly from records on daughters in TAD herds.
Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle
(BioMed Central Ltd, 2021-07-20) Tiplady KM; Lopdell TJ; Reynolds E; Sherlock RG; Keehan M; Johnson TJJ; Pryce JE; Davis SR; Spelman RJ; Harris BL; Garrick DJ; Littlejohn MD
BACKGROUND: Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk. RESULTS: Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk. CONCLUSIONS: This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.
Wool shedding and lamb fleece weights: first-cross and backcross Wiltshire–Romney sheep scored at lamb, hogget and two-tooth ages
(Taylor and Francis Group, 2024-05-03) Sneddon NW; Handcock RC; Corner-Thomas RA; Kenyon PR; Burnham DL; Garrick DJ; Littlejohn MD; Blair HT; Morris ST
With decreasing wool values, interest is increasing regarding shedding sheep. To investigate this, two long-term studies introducing Wiltshire genes into Romney flocks were initiated. Data from these two studies provide phenotypic relationships between a range of shedding scores at different ages. The data included shedding scores (on a 0–5 scale) repeated on lambs (∼5 months), hoggets (∼14–18 months) and two-tooths (∼27 months), and lamb fleece weights. Positive relationships between shedding scores on the same animals were observed. Lamb fleece weight was negatively correlated with all shedding scores. Lamb shedding score in February had a correlation of 0.54 (P < 0.001) with the February score as a hogget at Riverside farm. Scoring wool shedding is a laborious activity requiring individual animals to be scored in the shearing position. Therefore, lamb fleece weight was investigated for its relationship with shedding scores, as a potentially easier alternative. Lamb fleece weight had a greater correlation with February hogget shedding score than with the lamb shedding score (−0.76 vs −0.52, P < 0.001). This study indicated that February scores are an accurate predictor of future shedding phenotypes, and when used in conjunction with fleece weight, are a good predictor of phenotypes expressed at later ages.

Browsing by Author "Garrick DJ"

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