ORCID Profile
0000-0001-8605-1738
Current Organisations
University of New England
,
University of York
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Publisher: Springer Science and Business Media LLC
Date: 09-02-2012
Publisher: Springer Science and Business Media LLC
Date: 03-06-2022
DOI: 10.1186/S12711-022-00734-6
Abstract: Selection of livestock based on their robustness or sensitivity to environmental variation could help improve the efficiency of production systems, particularly in the light of climate change. Genetic variation in robustness arises from genotype-by-environment (G × E) interactions, with genotypes performing differently when animals are raised in contrasted environments. Understanding the nature of this genetic variation is essential to implement strategies to improve robustness. In this study, our aim was to explore the genetics of robustness in Australian sheep to different growth environments using linear reaction norm models (RNM), with post-weaning weight records of 22,513 lambs and 60 k single nucleotide polymorphisms (SNPs). The use of scale-corrected genomic estimated breeding values (GEBV) for the slope to account for scale-type G × E interactions was also investigated. Additive genetic variance was observed for the slope of the RNM, with genetic correlations between low- and high-growth environments indicating substantial re-ranking of genotypes (0.44–0.49). The genetic variance increased from low- to high-growth environments. The heritability of post-weaning body weight ranged from 0.28 to 0.39. The genetic correlation between intercept and slope of the reaction norm for post-weaning body weight was low to moderate when based on the estimated (co)variance components but was much higher when based on back-solved SNP effects. An initial analysis suggested that a region on chromosome 11 affected both the intercept and the slope, but when the GEBV for the slope were conditioned on the GEBV for the intercept to remove the effect of scale-type G × E interactions on SNP effects for robustness, a single genomic region on chromosome 7 was found to be associated with robustness. This region included genes previously associated with growth traits and disease susceptibility in livestock. This study shows a significant genetic variation in the slope of RNM that could be used for selecting for increased robustness of sheep. Both scale-type and rank-type G × E interactions contributed to variation in the slope. The correction for scale effects of GEBV for the slope should be considered when analysing robustness using RNM. Overall, robustness appears to be a highly polygenic trait.
Publisher: CSIRO Publishing
Date: 21-07-2021
DOI: 10.1071/AN21107
Abstract: Context Improving meat quality traits such as marbling is a well established breeding objective for many beef producers. More recently, the inclusion of feed efficiency is being considered. The main driving factors being the direct feed cost, as well as consumer concerns related to environmental sustainability of beef production. Aims The main aim of this study was to examine modifying the definition of residual feed intake (RFI), by including an adjustment for intramuscular fat (IMF). The secondary aim was to further understand the genetic relationships between feed intake and a range of carcass traits. Methods Using a population of 4034 Australian Angus animals, feed intake and carcass traits, along with pedigree and fixed effects, were analysed. This included the calculation of three definitions of RFI, being the standard definition, accounting for average daily gain and metabolic mid-weight, and two amended versions accounting for ultrasound IMF (RFIu), or carcass IMF (RFIi). Variance components, heritabilities, and genetic and phenotypic correlations were estimated and compared. Key results All three definitions of RFI were moderately heritable (0.30–0.32) and highly correlated, both genetically (0.99) and phenotypically (0.99). Unfavourable genetic correlations were observed between RFI and carcass IMF (CIMF), and between RFIu and CIMF at 0.29 and 0.24 respectively. Similarly, there were unfavourable genetic correlations between RFI and ultrasound IMF (UIMF), between RFIi and UIMF, and between RFIu and UIMF at 0.30, 0,21 and 0.23 respectively. Conclusions RFI can be redefined to account for traits, other than average daily gain and metabolic mid-weight, such as IMF. However due to limitations of phenotypic linear regression, and only small amounts of variation in feed intake being explained by the IMF traits, the redefinition of RFI was a suboptimal approach to breeding candidate selection. Furthermore, the present study confirmed the challenges with selecting for both feed efficiency and meat quality traits as they are generally genetically antagonist. Implications For beef cattle breeding programs, the investigation of alternative selection approaches is warranted. This may include further understanding the genetic correlations among traits in the breeding objective and, according to their economic value, optimally weighting the related estimated breeding value.
Publisher: Oxford University Press (OUP)
Date: 07-2021
DOI: 10.1093/TAS/TXAB126
Abstract: Information on body weight and average daily gain (ADG) of growing animals is key not only to monitoring performance, but also for use in genetic evaluations in the pursuit of achieving sustainable genetic gain. Accurate calculation of ADG, however, requires serial measures of body weight over at least 70 days. This can be resource intensive and thus alternative approaches to predicting in idual animal ADG warrant investigation. One such approach is the use of continuously collected in idual animal partial body weights. The objective of the present study was to determine the utility of partial body weights in predicting both body weight and ADG a secondary objective was to deduce the appropriate length of test to determine ADG from partial body weight records. The dataset used consisted of partial body weights, predicted body weights and recorded body weights recorded for 8,972 growing cattle from a range of different breed types in 35 contemporary groups. The relationships among partial body weight, predicted body weight and recorded body weight at the beginning and end of the performance test were determined and calculated ADG per animal from each body weight measure were also compared. On average, partial body weight explained 90.7 ± 2.0% of the variation in recorded body weight at the beginning of the postweaning gain test and 87.9 ± 2.9% of the variation in recorded body weight at its end. The GrowSafe proprietary algorithm to predict body weight from the partial body weight strengthened these coefficients of determination to 95.1 ± 0.9% and 94.9 ± 0.8%, respectively. The ADG calculated from the partial body weight or from the predicted body weight were very strongly correlated (r = 0.95) correlations between these ADG values with those calculated from the recorded body weights were weaker at 0.81 and 0.78, respectively. For some applications, ADG may be measured with sufficient accuracy with a test period of 50 days using partial body weights. The intended inference space is to in idual trials which have been represented in this study by contemporary groups of growing cattle from different genotypes.
Publisher: Springer Science and Business Media LLC
Date: 2013
Publisher: Springer Netherlands
Date: 05-11-2010
Publisher: CSIRO Publishing
Date: 05-11-2022
DOI: 10.1071/AN20659
Abstract: Context Genomic prediction is the use of genomic data in the estimation of genomic breeding values (GEBV) in animal breeding. In beef cattle breeding programs, genomic prediction increases the rates of genetic gain by increasing the accuracy of selection at earlier ages. Aims The objectives of the study were to examine the effect of single-nucleotide polymorphism (SNP) density and to evaluate the effect of using SNPs preselected from imputed whole-genome sequence for genomic prediction. Methods Genomic and phenotypic data from 2110 Hanwoo steers were used to predict GEBV for marbling score (MS), meat texture (MT), and meat colour (MC) traits. Three types of SNP densities including 50k, high-density (HD), and whole-genome sequence data and preselected SNPs from genome-wide association study (GWAS) were used for genomic prediction analyses. Two scenarios (independent and dependent discovery populations) were used to select top significant SNPs. The accuracy of GEBV was assessed using random cross-validation. Genomic best linear unbiased prediction (GBLUP) was used to predict the breeding values for each trait. Key results Our result showed that very similar prediction accuracies were observed across all SNP densities used in the study. The prediction accuracy among traits ranged from 0.29 ± 0.05 for MC to 0.46 ± 0.04 for MS. Depending on the studied traits, up to 5% of prediction accuracy improvement was obtained when the preselected SNPs from GWAS analysis were included in the prediction analysis. Conclusions High SNP density such as HD and the whole-genome sequence data yielded a similar prediction accuracy in Hanwoo beef cattle. Therefore, the 50K SNP chip panel is sufficient to capture the relationships in a breed with a small effective population size such as the Hanwoo cattle population. Preselected variants improved prediction accuracy when they were included in the genomic prediction model. Implications The estimated genomic prediction accuracies are moderately accurate in Hanwoo cattle and for searching for SNPs that are more productive could increase the accuracy of estimated breeding values for the studied traits.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 24-11-2015
Publisher: CSIRO Publishing
Date: 2017
DOI: 10.1071/AN15440
Abstract: Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro fertilisation and embryo transfer (JIVET) can produce multiple offspring per mating in sheep and cattle. In breeding programs this allows for higher female selection intensity and, in the case of JIVET, a reduction in generation interval, resulting in higher rates of genetic gain. Low selection accuracy of young females entering JIVET has often dissuaded producers from using this technology. However, genomic selection (GS) could increase selection accuracy of candidates at a younger age to help increase rates of genetic gain. This increase might vary for different traits in multiple trait breeding programs depending on genetic parameters and the practicality of recording, particularly for hard to measure traits. This study used both stochastic (animals) and deterministic (GS) simulation to evaluate the effect of reproductive technologies on the genetic gain for various traits in sheep breeding programs, both with and without GS. Optimal contribution selection was used to manage inbreeding and to optimally assign reproductive technologies to in idual selection candidates. Two Australian sheep industry indexes were used – a terminal sire index that focussed on growth and carcass traits (the ‘Lamb 2020’ index) and a Merino index that focuses on wool traits, bodyweight, and reproduction (MP+). We observed that breeding programs using artificial insemination or natural mating (AI/N) + MOET, compared with AI/N alone, yielded an extra 39% and 27% genetic gain for terminal and Merino indexes without GS, respectively. However, the addition of JIVET to AI/N + MOET without GS only yielded an extra 1% genetic gain for terminal index and no extra gain in the Merino index. When GS was used in breeding programs, we observed AI/N + MOET + JIVET outperformed AI/N + MOET by 21% and 33% for terminal and Merino indexes, respectively. The implementation of GS increased genetic gain where reproductive technologies were used by 9–34% in Lamb 2020 and 37–98% in MP+. In idual trait response to selection varied in each breeding program. The combination of GS and reproductive technologies allowed for greater genetic gain in both indexes especially for hard to measure traits, but had limited effect on the traits that already had a large amount of early age records.
Publisher: CSIRO Publishing
Date: 2019
DOI: 10.1071/AN17720
Abstract: The present study assessed the effectiveness and cost–benefit of several genotyping strategies for breeding poll Merino sheep in a closed nucleus with different initial allele frequencies and assuming a single-gene responsible for the horn or poll phenotype. We assumed that selection was based on phenotypes or genotypes for a single gene conferring polledness via a complete-dominance model. Under such a model, a complete fixation of the ‘polled allele’ (P) requires genotyping of the ewe-selection candidates. Testing a higher proportion of female candidates resulted in a faster fixation of the P-allele. Fixation ranged from 1 year of selection with a high starting P-allele frequency of 0.9, to 7 years for low starting P-allele frequencies of 0.3. When premiums of AU$50 or AU$100 were paid for rams with a PP genotype, breeding for PP genotypes was not profitable when the starting P-allele frequency was below 0.7. If the starting allele frequency was above 0.7, net profitability was positive over 10 years when premiums of AU$200 were paid for known PP-genotype rams. While fixing the P-allele, genetic gain for production traits was slowed down in the first 5 years of selection by up to 23% and 3% for initial P allele-frequencies of 0.3 and 0.9 respectively. Lost genetic gain due to fixing the P-allele, which can never be recovered in a closed nucleus, incurred 200–800% higher costs than the DNA testing costs. Rates of genetic gain recovered to pre-P-allele selection level rates of genetic gain once the P-allele was fixed. Testing a maximum of 25% ewe-selection candidates was the least expensive strategy across all starting allele frequencies and premiums. To avoid large losses of genetic gain in a closed nucleus with low P-allele starting frequencies, opening the nucleus should be considered to increase starting P-allele frequencies and also to potentially increase rates of genetic gain to offset the economic loss caused by P-selection.
Publisher: Springer Science and Business Media LLC
Date: 17-05-2011
Publisher: Korean Society of Animal Science and Technology
Date: 13-04-2023
Publisher: American Psychological Association (APA)
Date: 2008
DOI: 10.1037/A0014397
Abstract: A postcompletion error (PCE) is a specific kind of cognitive slip that involves omitting a final task step after the main goal of the task is accomplished. It is notoriously difficult to provoke (and hence study) slips under experimental conditions. In this paper, the authors present an experimental task paradigm that has been shown to be effective for studying PCEs in routine procedural tasks. Two studies were carried out to examine the effect of interruption position and task structure on the prevalence of PCEs. It was found that significantly more PCEs were obtained when an interruption occurred just before the PC step than when an interruption occurred at any other position in the task. The authors account for this effect in terms of Altmann and Trafton's activation-based goal memory model. The same interruption effect was obtained for some, but not all, other procedural errors the authors discuss the nature of these errors and likely explanations for the differences.
Publisher: Wageningen Academic Publishers
Date: 31-12-2023
Publisher: CSIRO Publishing
Date: 23-09-2021
DOI: 10.1071/AN21098
Abstract: Context Genotype imputation is an effective method to increase the number of SNP markers available for an animal and thereby increase the overall power of genome-wide associations and accuracy of genomic predictions. It is also the key to achieve a common set of markers for all in iduals when the original genotypes are obtained using multiple genotyping platforms. High accuracy of imputed genotypes is crucial to their utility. Aims In this study, we propose a method for the construction of a common set of medium density markers for imputation, which relies on keeping as much information as possible. We also investigated the impact of changing marker coordinates on the basis of the new bovine genome assembly, ARS-UCD 1.2, on imputation accuracy. Methods In total, 49 754 animals with 45 364 single nucleotide polymorphism markers were used in a 10-fold cross-validation to compare four different imputation scenarios. The four scenarios were based on two alternative designs for the reference datasets. (1) A traditional reference panel that was created using the overlapping SNP from five medium density arrays and (2) a composite reference panel created by combining SNPs across the five arrays. Each of the reference datasets was used to test imputation accuracy when the SNPs were aligned on the basis of two genome assemblies (UMD 3.1 and ARS-UCD 1.2). Key results Our results showed that a composite reference panel can achieve higher imputation accuracies than does a traditional overlap reference. Incorporating mapping information on the basis of the recent genome build slightly improved the imputation accuracies, especially for lower density chips. Conclusions Markers with unreliable mapping information and animals with low connectedness to the imputation reference dataset benefited the most from the ARS-UCD 1.2 assembly and composite reference respectively. Implications The presented method is straightforward and can be used to setup an optimal imputation for accurate inference of genotypes in Australian Angus cattle.
Publisher: Oxford University Press (OUP)
Date: 2021
DOI: 10.1093/TAS/TXAB011
Abstract: The improvement of carcass traits is an important breeding objective in beef cattle breeding programs. The most common way of selecting for improvement in carcass traits is via indirect selection using ultrasound scanning of selection candidates which are submitted to genetic evaluation programs. Two systems used to analyze ultrasound images to predict carcass traits are the Pie Medical Esaote Aquila (PIE) and Central Ultrasound Processing (CUP). This study compared the ability of the two systems to predict carcass traits for genetic evaluation in Australian Angus cattle. Genetic and phenotypic parameters were estimated using data from 1,648 Angus steers which were ultrasound scanned twice with both systems, first at feedlot entry and then following 100 d in the feedlot. The traits interpreted from ultrasound scanning included eye muscle area (EMA), rib fat (RIB) rump fat (RUMP), and intramuscular fat (IMF). Abattoir carcass data were collected on all steers following the full feedlot feeding period of 285 d. For all ultrasound scan traits, CUP resulted in higher phenotypic and genetic variances compared to the PIE. For IMF, CUP had higher heritability at feedlot intake (0.51 for CUP compared to 0.37 for PIE) and after 100 d feeding (0.54 for CUP compared to 0.45 PIE). CUP predicted IMF also tended to have stronger correlations with the breeding objective traits of carcass IMF and marbling traits, both genetically (ranging from 0.59 to 0.75 for CUP compared to 0.45–0.63 for PIE) and phenotypically (ranging from 0.27 to 0.43 for CUP compared to 0.19–0.28 for PIE). Ultrasound scan EMA was the only group of traits in which the heritabilities were higher for PIE (0.52 for PIE compared to 0.40 for CUP at feedlot intake and 0.46 for PIE compared to 0.43 for CUP at 100 d of feeding), however with similar relationships to the breeding objective carcass EMA observed. For subcutaneous fat traits of ultrasound RIB and RUMP, the heritabilites and genetic correlations to the related carcass traits were similar, with the exception being the higher heritability observed for CUP predicted RUMP at feedlot intake at 0.52 compared to 0.38 for PIE. The results from this study indicates that the CUP system, compared to PIE, provides an advantage for genetic evaluation of carcass traits in Angus cattle, particularly for the IMF and associated marbling traits.
Publisher: Wageningen Academic Publishers
Date: 31-12-2023
Publisher: CSIRO Publishing
Date: 13-04-2023
DOI: 10.1071/AN22464
Abstract: Context Coping with high levels of cold stress should be beneficial to survival of lambs, given the high mortality rate associated with severe winter storms. The Elsenburg Merino selection experiment involved ergent selection for reproduction. Phenotypic results comparing the positively selected H-Lines and negatively selected L-Lines suggested that cold-stress adaption could have contributed to the favourable genetic trends for survival of H-Line lambs. However, observing the genetic merit of better adapted animals depends on the presence of cold stress at the time of recording. A genotype by environment component (G × E) could, thus, be important when assessing survival/mortality phenotypes. Aim This study proposed the genetic analysis of this possible G × E component and compared the H- and L-Lines in this regard. Methods The sire model allowed the use of progeny phenotypes for neonatal mortality recorded during different levels of cold stress, and the possible G × E could be investigated through the reaction-norm approach. Genetic parameters were evaluated as random regression components by implementing a Gibbs s ling approach. A data set of 5723 in idual lamb records was analysed as the progeny of 213 sires. Results A modelled G × E component played an important role in mortality outcomes, with the mean estimate (and 95% confidence interval) for the slope ( σ s b 2 = 0.113 [ 0.0019 – 0.28 ] AN22464_IE1.gif) only marginally smaller than the corresponding estimate for the intercept ( σ s a 2 = 0.124 [ 0.003 – 0.26 ] AN22464_IE2.gif). The reaction-norm model showed a higher heritability (h2 ± posterior standard deviation) for mortality at 3 days of age during high cold-stress (0.22 ± 0.16 at ~1100 KJm−2h−1) than during mild (0.13 ± 0.10 at ~960 KJm−2h−1) conditions, suggesting a greater ability to discriminate between sires at increasing stress levels. Conclusions Failure to account for this G × E component putatively contributes to the low h2 commonly reported for survival traits. The higher h2 at increased levels of cold stress could have played an important part in the higher survival of the H-Line progeny, who were better at coping with cold, wet and windy conditions. Implications Larger studies representing a wider environmental trajectory are recommended. This should be very feasible since cold stress can be derived from commonly available weather-station data.
Publisher: Wiley
Date: 29-12-2012
DOI: 10.1111/JBG.12020
Abstract: Long-range phasing and haplotype library imputation methodologies are accurate and efficient methods to provide haplotype information that could be used in prediction of breeding value or phenotype. Modelling long haplotypes as independent effects in genomic prediction would be inefficient due to the many effects that need to be estimated and phasing errors, even if relatively low in frequency, exacerbate this problem. One approach to overcome this is to use similarity between haplotypes to model covariance of genomic effects by region or of animal breeding values. We developed a simple method to do this and tested impact on genomic prediction by simulation. Results show that the diagonal and off-diagonal elements of a genomic relationship matrix constructed using the haplotype similarity method had higher correlations with the true relationship between pairs of in iduals than genomic relationship matrices built using unphased genotypes or assumed unrelated haplotypes. However, the prediction accuracy of such haplotype-based prediction methods was not higher than those based on unphased genotype information.
Publisher: Springer Science and Business Media LLC
Date: 14-09-2015
Publisher: Humana Press
Date: 2013
DOI: 10.1007/978-1-62703-447-0_13
Abstract: Genomic best linear unbiased prediction (gBLUP) is a method that utilizes genomic relationships to estimate the genetic merit of an in idual. For this purpose, a genomic relationship matrix is used, estimated from DNA marker information. The matrix defines the covariance between in iduals based on observed similarity at the genomic level, rather than on expected similarity based on pedigree, so that more accurate predictions of merit can be made. gBLUP has been used for the prediction of merit in livestock breeding, may also have some applications to the prediction of disease risk, and is also useful in the estimation of variance components and genomic heritabilities.
Publisher: Wageningen Academic Publishers
Date: 31-12-2022
Publisher: Wageningen Academic Publishers
Date: 31-12-2022
Publisher: Wageningen Academic Publishers
Date: 31-12-2023
Publisher: Wageningen Academic Publishers
Date: 26-08-2019
Publisher: Wageningen Academic Publishers
Date: 31-12-2023
Publisher: Wageningen Academic Publishers
Date: 31-12-2023
Publisher: Wageningen Academic Publishers
Date: 31-12-2023
Publisher: Elsevier BV
Date: 12-2021
DOI: 10.1016/J.ANIMAL.2021.100411
Abstract: Genotype-by-environment interaction is caused by variation in genetic environmental sensitivity (GES), which can be sub ided into macro- and micro-GES. Macro-GES is genetic sensitivity to macro-environments (definable environments often shared by groups of animals), while micro-GES is genetic sensitivity to micro-environments (in idual environments). A combined reaction norm and double hierarchical generalised linear model (RN-DHGLM) allows for simultaneous estimation of base genetic, macro- and micro-GES effects. The accuracy of variance components estimated using a RN-DHGLM has been explicitly studied for balanced data and recommendation of a data size with a minimum of 100 sires with at least 100 offspring each have been made. In the current study, the data size (numbers of sires and progeny) and structure requirements of the RN-DHGLM were investigated for two types of unbalanced datasets. Both datasets had a variable number of offspring per sire, but one dataset also had a variable number of offspring within macro-environments. The accuracy and bias of the estimated macro- and micro-GES effects and the estimated breeding values (EBVs) obtained using the RN-DHGLM depended on the data size. Reasonably accurate and unbiased estimates were obtained with data containing 500 sires with 20 offspring or 100 sires with 50 offspring, regardless of the data structure. Variable progeny group sizes, alone or in combination with an unequal number of offspring within macro-environments, had little impact on the dispersion of the EBVs or the bias and accuracy of variance component estimation, but resulted in lower accuracies of the EBVs. Compared to genetic correlations of zero, a genetic correlation of 0.5 between base genetic, macro- and micro-GES components resulted in a slight decrease in the percentage of replicates that converged out of 100 replicates, but had no effect on the dispersion and accuracy of variance component estimation or the dispersion of the EBVs. The results show that it is possible to apply the RN-DHGLM to unbalanced datasets to obtain estimates of variance due to macro- and micro-GES. Furthermore, the levels of accuracy and bias of variance estimates when analysing macro- and micro-GES simultaneously are determined by average family size, with limited impact from variability in family size and/or cohort size. This creates opportunities for the use of field data from populations with unbalanced data structures when estimating macro- and micro-GES.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sam Clark.