P.J. Bowman
La Trobe University
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Featured researches published by P.J. Bowman.
Journal of Dairy Science | 2009
Ben J. Hayes; P.J. Bowman; Amanda J. Chamberlain; Michael E. Goddard
A new technology called genomic selection is revolutionizing dairy cattle breeding. Genomic selection refers to selection decisions based on genomic breeding values (GEBV). The GEBV are calculated as the sum of the effects of dense genetic markers, or haplotypes of these markers, across the entire genome, thereby potentially capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. The QTL effects, inferred from either haplotypes or individual single nucleotide polymorphism markers, are first estimated in a large reference population with phenotypic information. In subsequent generations, only marker information is required to calculate GEBV. The reliability of GEBV predicted in this way has already been evaluated in experiments in the United States, New Zealand, Australia, and the Netherlands. These experiments used reference populations of between 650 and 4,500 progeny-tested Holstein-Friesian bulls, genotyped for approximately 50,000 genome-wide markers. Reliabilities of GEBV for young bulls without progeny test results in the reference population were between 20 and 67%. The reliability achieved depended on the heritability of the trait evaluated, the number of bulls in the reference population, the statistical method used to estimate the single nucleotide polymorphism effects in the reference population, and the method used to calculate the reliability. A common finding in 3 countries (United States, New Zealand, and Australia) was that a straightforward BLUP method for estimating the marker effects gave reliabilities of GEBV almost as high as more complex methods. The BLUP method is attractive because the only prior information required is the additive genetic variance of the trait. All countries included a polygenic effect (parent average breeding value) in their GEBV calculation. This inclusion is recommended to capture any genetic variance not associated with the markers, and to put some selection pressure on low-frequency QTL that may not be captured by the markers. The reliabilities of GEBV achieved were significantly greater than the reliability of parental average breeding values, the current criteria for selection of bull calves to enter progeny test teams. The increase in reliability is sufficiently high that at least 2 dairy breeding companies are already marketing bull teams for commercial use based on their GEBV only, at 2 yr of age. This strategy should at least double the rate of genetic gain in the dairy industry. Many challenges with genomic selection and its implementation remain, including increasing the accuracy of GEBV, integrating genomic information into national and international genetic evaluations, and managing long-term genetic gain.
Journal of Dairy Science | 2010
J.E. Pryce; S. Bolormaa; Amanda J. Chamberlain; P.J. Bowman; K. Savin; Michael E. Goddard; Ben J. Hayes
Genome-wide association studies (GWAS) were used to discover genomic regions explaining variation in dairy production and fertility traits. Associations were detected with either single nucleotide polymorphism (SNP) markers or haplotypes of SNP alleles. An across-breed validation strategy was used to narrow the genomic interval containing causative mutations. There were 39,048 SNP tested in a discovery population of 780 Holstein sires and validated in 386 Holsteins and 364 Jersey sires. Previously identified mutations affecting milk production traits were confirmed. In addition, several novel regions were identified, including a putative quantitative trait loci for fertility on chromosome 18 that was detected only using haplotypes greater than 3 SNP long. It was found that the precision of quantitative trait loci mapping increased with haplotype length as did the number of validated haplotypes discovered, especially across breed. Promising candidate genes have been identified in several of the validated regions.
Livestock Production Science | 2003
M Haile-Mariam; P.J. Bowman; Michael E. Goddard
Abstract Data from Australian Holstein–Friesian cattle on calving interval (CI), survival (Surv), mean milk yield (MY), mean fat yield (FY), mean log e somatic cell count (LnSCC) and the regression of MY, FY and LnSCC on days in milk (called persistency for MY and FY and slope for LnSCC) were analysed to estimate heritability (h 2 ), genetic and environmental correlations using a multi-trait sire model. The dataset consisted of 87,942 and 50,469 cows with milk yield records in the first and second parity, respectively. Among the traits, the highest h 2 (0.32) was estimated for mean MY in the first parity and the lowest (0.02) was for Surv (parity 1 and 2). The h 2 estimates for persistency of MY were 0.09 and 0.11 while those for CI were 0.04 and 0.03 in the first and second parity, respectively. The antagonistic genetic correlation between mean MY and CI increased from 0.43 in the first to 0.58 in the second parity while that of persistency of MY (parity 1 and 2) with CI (0.04 to 0.18) and Surv (−0.06 to 0.18) were close to zero. In general, environmental correlations between traits were lower than genetic correlations. The across parity genetic correlations (e.g. CI in parity 1 with Surv in 2) were similar to the within parity correlations. Fitting day of gestation as a covariate when analysing milk yield reduced the environmental correlation between persistency of MY and CI from 0.16 to near zero. It is suggested that fertility be included in the breeding goal to halt or minimise further deterioration in fertility due to selection for MY. However, selection for persistency of MY may not improve fertility or survival.
Genetics Research | 2009
Klara L. Verbyla; Ben J. Hayes; P.J. Bowman; Michael E. Goddard
Genomic selection describes a selection strategy based on genomic breeding values predicted from dense single nucleotide polymorphism (SNP) data. Multiple methods have been proposed but the critical issue is how to decide whether an SNP should be included in the predictive set to estimate breeding values. One major disadvantage of the traditional Bayes B approach is its high computational demands caused by the changing dimensionality of the models. The use of stochastic search variable selection (SSVS) retains the same assumptions about the distribution of SNP effects as Bayes B, while maintaining constant dimensionality. When Bayesian SSVS was used to predict genomic breeding values for real dairy data over a range of traits it produced accuracies higher or equivalent to other genomic selection methods with significantly decreased computational and time demands than Bayes B.
Livestock Production Science | 1994
Peter M. Visscher; P.J. Bowman; Michael E. Goddard
The aim of the study was to derive economic weights for milk production traits (milk, fat, and protein yield), survival, and mature body size for pasture based production systems. Economic weights were derived using a herd model and differentiating a profit function with respect to the traits of interest. Scaling was taken into account by assuming that the total feed supply per farm was constant and at an economically optimum level. Relative economic weights were expressed in genetic standard deviations. To investigate the robustness of the method to derive economic weights, economic weights were calculated for 11 different sets of herd parameters, varying production levels, average herd life, costs and returns. Protein yield had the highest relative economic weight, followed by survival and mature body size. The latter traits were approximately half as important as protein yield, with the economic weight for body size negative. Fat and milk yield were approximately equally important and 40% as important as protein yield. Milk (volume) yield had a negative economic weight. Economic weights were fairly robust to changes in herd parameters, and it was concluded that the method of calculating economic weights, using a herd model and constraining the total energy supply, was appropriate for pasture based production systems.
Animal Genetics | 2012
Ben J. Hayes; P.J. Bowman; Hans D. Daetwyler; James W. Kijas; J. H. J. van der Werf
Although genomic selection offers the prospect of improving the rate of genetic gain in meat, wool and dairy sheep breeding programs, the key constraint is likely to be the cost of genotyping. Potentially, this constraint can be overcome by genotyping selection candidates for a low density (low cost) panel of SNPs with sparse genotype coverage, imputing a much higher density of SNP genotypes using a densely genotyped reference population. These imputed genotypes would then be used with a prediction equation to produce genomic estimated breeding values. In the future, it may also be desirable to impute very dense marker genotypes or even whole genome re-sequence data from moderate density SNP panels. Such a strategy could lead to an accurate prediction of genomic estimated breeding values across breeds, for example. We used genotypes from 48 640 (50K) SNPs genotyped in four sheep breeds to investigate both the accuracy of imputation of the 50K SNPs from low density SNP panels, as well as prospects for imputing very dense or whole genome re-sequence data from the 50K SNPs (by leaving out a small number of the 50K SNPs at random). Accuracy of imputation was low if the sparse panel had less than 5000 (5K) markers. Across breeds, it was clear that the accuracy of imputing from sparse marker panels to 50K was higher if the genetic diversity within a breed was lower, such that relationships among animals in that breed were higher. The accuracy of imputation from sparse genotypes to 50K genotypes was higher when the imputation was performed within breed rather than when pooling all the data, despite the fact that the pooled reference set was much larger. For Border Leicesters, Poll Dorsets and White Suffolks, 5K sparse genotypes were sufficient to impute 50K with 80% accuracy. For Merinos, the accuracy of imputing 50K from 5K was lower at 71%, despite a large number of animals with full genotypes (2215) being used as a reference. For all breeds, the relationship of individuals to the reference explained up to 64% of the variation in accuracy of imputation, demonstrating that accuracy of imputation can be increased if sires and other ancestors of the individuals to be imputed are included in the reference population. The accuracy of imputation could also be increased if pedigree information was available and was used in tracking inheritance of large chromosome segments within families. In our study, we only considered methods of imputation based on population-wide linkage disequilibrium (largely because the pedigree for some of the populations was incomplete). Finally, in the scenarios designed to mimic imputation of high density or whole genome re-sequence data from the 50K panel, the accuracy of imputation was much higher (86-96%). This is promising, suggesting that in silico genome re-sequencing is possible in sheep if a suitable pool of key ancestors is sequenced for each breed.
Journal of Dairy Science | 2011
J.E. Pryce; Birgit Gredler; S. Bolormaa; P.J. Bowman; C. Egger-Danner; C. Fuerst; Reiner Emmerling; Johann Sölkner; Michael E. Goddard; Ben J. Hayes
Three breeds (Fleckvieh, Holstein, and Jersey) were included in a reference population, separately and together, to assess the accuracy of prediction of genomic breeding values in single-breed validation populations. The accuracy of genomic selection was defined as the correlation between estimated breeding values, calculated using phenotypic data, and genomic breeding values. The Holstein and Jersey populations were from Australia, whereas the Fleckvieh population (dual-purpose Simmental) was from Austria and Germany. Both a BLUP with a multi-breed genomic relationship matrix (GBLUP) and a Bayesian method (BayesA) were used to derive the prediction equations. The hypothesis tested was that having a multi-breed reference population increased the accuracy of genomic selection. Minimal advantage existed of either GBLUP or BayesA multi-breed genomic evaluations over single-breed evaluations. However, when the goal was to predict genomic breeding values for a breed with no individuals in the reference population, using 2 other breeds in the reference was generally better than only 1 breed.
Genetics Selection Evolution | 2015
Kathryn E. Kemper; C. M. Reich; P.J. Bowman; Christy Vander Jagt; Amanda J. Chamberlain; B. A. Mason; Benjamin J. Hayes; Michael E. Goddard
BackgroundGenomic selection is increasingly widely practised, particularly in dairy cattle. However, the accuracy of current predictions using GBLUP (genomic best linear unbiased prediction) decays rapidly across generations, and also as selection candidates become less related to the reference population. This is likely caused by the effects of causative mutations being dispersed across many SNPs (single nucleotide polymorphisms) that span large genomic intervals. In this paper, we hypothesise that the use of a nonlinear method (BayesR), combined with a multi-breed (Holstein/Jersey) reference population will map causative mutations with more precision than GBLUP and this, in turn, will increase the accuracy of genomic predictions for selection candidates that are less related to the reference animals.ResultsBayesR improved the across-breed prediction accuracy for Australian Red dairy cattle for five milk yield and composition traits by an average of 7% over the GBLUP approach (Australian Red animals were not included in the reference population). Using the multi-breed reference population with BayesR improved accuracy of prediction in Australian Red cattle by 2 – 5% compared to using BayesR with a single breed reference population. Inclusion of 8478 Holstein and 3917 Jersey cows in the reference population improved accuracy of predictions for these breeds by 4 and 5%. However, predictions for Holstein and Jersey cattle were similar using within-breed and multi-breed reference populations. We propose that the improvement in across-breed prediction achieved by BayesR with the multi-breed reference population is due to more precise mapping of quantitative trait loci (QTL), which was demonstrated for several regions. New candidate genes with functional links to milk synthesis were identified using differential gene expression in the mammary gland.ConclusionsQTL detection and genomic prediction are usually considered independently but persistence of genomic prediction accuracies across breeds requires accurate estimation of QTL effects. We show that accuracy of across-breed genomic predictions was higher with BayesR than with GBLUP and that BayesR mapped QTL more precisely. Further improvements of across-breed accuracy of genomic predictions and QTL mapping could be achieved by increasing the size of the reference population, including more breeds, and possibly by exploiting pleiotropic effects to improve mapping efficiency for QTL with small effects.
Crop & Pasture Science | 2004
M. Haile-Mariam; P.J. Bowman; Michael E. Goddard
First and second parity data on calving interval (CI, days), survival to next lactation (Surv, scored 1 or 0), calving to first service interval (CFS, days), 25-day first service non-return rate (FNRR, scored 1 or 0), and insemination or submission rate (InsemR, scored 1 or 0) of Holstein-Friesian cattle were analysed using a sire model to estimate genetic parameters. The estimated genetic parameters were used to obtain predicted transmitting ability (PTA) of sires for fertility traits and Surv, including 6-week pregnancy rate (6-w PR). PTA for 6-w PR was calculated based on an estimated heritability of 0.07 and genetic and environmental correlations with the other fertility traits and Surv. In addition, approximate genetic correlations of fertility traits and Surv with milk yield, type traits, workability (likability, milking speed, temperament), survival index (a measure of survival calculated from estimated breeding values on survival, likability, and type traits), bodyweight, and cell count were estimated. Heritability (h 2 ) of fertility traits was 2-4% in the first parity and 1-2% in the second parity. Genetic correlations between fertility traits were generally higher in magnitude than environmental correlations, particularly in the first parity. The difference in PTA between the best and worst sires was high (21 days in CI and 21% in 6-w PR), showing the scope for selection. Approximate genetic correlations between fertility and most traits that are currently evaluated were low to moderate. Milk, protein and fat yield, body size, overall type, mammary system, udder texture, muzzle width, angularity, body depth, chest width, foot angle, and rear attachment width were unfavourably correlated (0.1-0.5) with most fertility traits. Fat and protein % were favourably correlated with both CI and 6-w PR (~0.2). Pin set was moderately favourably correlated (0.28) with 6-w PR. Surv was favourably (positively) correlated with temperament, likability, and survival index (~0.5). The wide variation in particular in CI and 6-w PR between bulls and the generally unfavourable approximate genetic correlations of fertility traits with most traits for which selection is currently practiced suggest that genetic evaluation for fertility should be introduced. AR Fe rt t t ry M. H am et al
BMC Proceedings | 2010
Klara L. Verbyla; P.J. Bowman; Ben J. Hayes; Michael E. Goddard
Genomic selection describes a selection strategy based on genomic estimated breeding values (GEBV) predicted from dense genetic markers such as single nucleotide polymorphism (SNP) data. Different Bayesian models have been suggested to derive the prediction equation, with the main difference centred around the specification of the prior distributions.MethodsThe simulated dataset of the 13th QTL-MAS workshop was analysed using four Bayesian approaches to predict GEBV for animals without phenotypic information. Different prior distributions were assumed to assess their affect on the accuracy of the predicted GEBV.ConclusionAll methods produced GEBV that were highly correlated with the true breeding values. The models appear relatively insensitive to the choice of prior distributions for QTL-MAS data set and this is consistent with uniformity of performance of different methods found in real data.