Dairy Futures Co operative Research Center

Bundoora, Australia

Dairy Futures Co operative Research Center

Bundoora, Australia
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Kemper K.E.,University of Melbourne | Reich C.M.,Australian Department of Primary Industries and Fisheries | Bowman P.J.,Australian Department of Primary Industries and Fisheries | Vander Jagt C.J.,Australian Department of Primary Industries and Fisheries | And 7 more authors.
Genetics Selection Evolution | Year: 2015

Background: Genomic 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. Results: BayesR 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. Conclusions: QTL 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. © 2015 Kemper et al.; licensee BioMed Central.

Lin Z.,University of Melbourne | Macleod I.,University of Melbourne | Macleod I.,Australian Department of Primary Industries and Fisheries | Pryce J.E.,Australian Department of Primary Industries and Fisheries | Pryce J.E.,Dairy Futures Co operative Research Center
Journal of Dairy Science | Year: 2013

Data from a 2-yr feeding trial of Holstein-Friesian heifers (n = 842) were used to examine the heritability of feeding behavior traits and their relationships with residual feed intake (RFI), a measure of feed efficiency. Five traits were assessed: number of meals, feeding duration, dry matter intake (DMI), eating rate, and average meal size. For estimating genetic parameters, all traits were simultaneously fitted in a multivariate model with a genomic relationship matrix calculated from heifers' high-density genotype data. All 5 traits were moderately heritable (0.45-0.50), which was slightly higher than the estimate for RFI (0.40 ± 0.09). Two traits had modest genetic correlations with RFI (DMI and feeding duration; 0.45 ± 0.13 and 0.27 ± 0.15, respectively), and 2 traits had modest phenotypic correlations with RFI (DMI and eating rate; 0.52 ± 0.03 and 0.23 ± 0.04, respectively). The results indicate that feeding behavior (1) may differ between efficient and inefficient animals and (2) may be useful for selecting animals with better feed efficiency. However, the limitation is that measurements on DMI are still essential. It is therefore possible that a more efficient selection tool for RFI may be the use of high-density DNA markers to make direct genomic predictions for RFI. © 2013 American Dairy Science Association.

Raven L.-A.,Australian Department of Primary Industries and Fisheries | Raven L.-A.,La Trobe University | Raven L.-A.,Dairy Futures Co operative Research Center | Cocks B.G.,Australian Department of Primary Industries and Fisheries | And 8 more authors.
Genetics Selection Evolution | Year: 2013

Background: Identification of the processes and mutations responsible for the large genetic variation in milk production among dairy cattle has proved challenging. One approach is to identify a biological process potentially involved in milk production and to determine the genetic influence of all the genes included in the process or pathway. Angiogenin encoded by angiogenin, ribonuclease, RNase A family 5 (RNASE5) is relatively abundant in milk, and has been shown to regulate protein synthesis and act as a growth factor in epithelial cells in vitro. However, little is known about the role of angiogenin in the mammary gland or if the polymorphisms present in the bovine RNASE5 gene are associated with lactation and milk production traits in dairy cattle. Given the high economic value of increased protein in milk, we have tested the hypothesis that RNASE5 or genes in the RNASE5 pathway are associated with milk production traits. First, we constructed a "RNASE5 pathway" based on upstream and downstream interacting genes reported in the literature. We then tested SNP in close proximity to the genes of this pathway for association with milk production traits in a large dairy cattle dataset. Results: The constructed RNASE5 pathway consisted of 11 genes. Association analysis between SNP in 1 Mb regions surrounding these genes and milk production traits revealed that more SNP than expected by chance were associated with milk protein percent (P < 0.05 significance). There was no significant association with other traits such as milk fat content or fertility. Conclusions: These results support a role for the RNASE5 pathway in milk production, specifically milk protein percent, and indicate that polymorphisms in or near these genes explain a proportion of the variation for this trait. This method provides a novel way of understanding the underlying biology of lactation with implications for milk production and can be applied to any pathway or gene set to test whether they are responsible for the variation of complex traits. © 2013 Raven et al.; licensee BioMed Central Ltd.

Kemper K.E.,University of Melbourne | Hayes B.J.,Australian Department of Primary Industries and Fisheries | Hayes B.J.,La Trobe University | Hayes B.J.,Dairy Futures Co operative Research Center | And 4 more authors.
Journal of Animal Breeding and Genetics | Year: 2015

The mutations that cause genetic variation in quantitative traits could be old and segregate across many breeds or they could be young and segregate only within one breed. This has implications for our understanding of the evolution of quantitative traits and for genomic prediction to improve livestock. We investigated the age of quantitative trait loci (QTL) for milk production traits identified as segregating in Holstein dairy cattle. We use a multitrait method and found that six of 11 QTL also segregate in Jerseys. Variants identified as Holstein-only QTL were fixed or rare [minor allele frequency (MAF) < 0.05] in Jersey. The age of the QTL mutations appears to vary from perhaps 2000 to 50 000 generations old. The older QTL tend to have high derived allele frequencies and often segregate across both breeds. Holstein-only QTL were often embedded within longer haplotypes, supporting the conclusion that they are typically younger mutations that have occurred more recently than QTL that segregate in both breeds. A reference population for genomic prediction using both Holsteins and Jersey cattle incorrectly predicted a QTL in Jersey cattle when the QTL only segregates in Holsteins. Overcoming this error should help to make genomic prediction more accurate in smaller breeds. © 2015 Blackwell Verlag GmbH.

Liu Z.,Australian Department of Primary Industries and Fisheries | Liu Z.,Dairy Futures Co operative Research Center | Rochfort S.,Australian Department of Primary Industries and Fisheries | Rochfort S.,Dairy Futures Co operative Research Center | Rochfort S.,La Trobe University
Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences | Year: 2013

Current liquid chromatography (LC) based methods for the analysis of polar plant metabolites require multiple runs using complex mobile phases and a combination of different columns. Here we describe a fast liquid chromatography-mass spectrometry (LC-MS) method for the determination of major polar metabolites in plants that requires only a single run using a single column. The method takes advantage of the ability to acquire both positive and negative data in an ion trap mass spectrometer (MS) and also the accurate mass capability of the orbitrap MS. The separation of polar compounds is achieved with a polar, reversed-phase column (Synergi Hydro-RP). A single analysis with a 25. min runtime is able to reliably determine the level of nearly all essential amino acids, several major organic acids and several major sugars in plant materials, as exemplified by analysis of a perennial ryegrass extract. The level of detection on column was below 0.1. ng (average 0.03. ng) for most amino acids, below 5. ng (average 2.3. ng) for organics acids and below 1. ng (average 0.64. ng) for sugars. The levels of quantified metabolites in ryegrass varied from 22μg/g dry weight for histidine to 41. mg/g dry weight for sucrose. © 2012.

Liu Z.,Australian Department of Primary Industries and Fisheries | Liu Z.,Dairy Futures Co operative Research Center | Rochfort S.,Australian Department of Primary Industries and Fisheries | Rochfort S.,Dairy Futures Co operative Research Center | Rochfort S.,La Trobe University
Journal of Integrative Plant Biology | Year: 2014

Metabolite analysis or metabolomics is an important component of systems biology in the postgenomic era. Although separate liquid chromatography (LC) methods for quantification of the major classes of polar metabolites of plants have been available for decades, a single method that enables simultaneous determination of hundreds of polar metabolites is possible only with gas chromatography-mass spectrometry (GC-MS) techniques. The rapid expansion of new LC stationary phases in the market and the ready access of mass spectrometry in many laboratories provides an excellent opportunity for developing LC-MS based methods for multitarget quantification of polar metabolites. Although various LC-MS methods have been developed over the last 10 years with the aim to quantify one or more classes of polar compounds in different matrices, currently there is no consensus LC-MS method that is widely used in plant metabolomics studies. The most promising methods applicable to plant metabolite analysis will be reviewed in this paper and the major problems encountered highlighted. The aim of this review is to provide plant scientists, with limited to moderate experience in analytical chemistry, with uptodate and simplified information regarding the current status of polar metabolite analysis using LC-MS techniques. © 2014 Institute of Botany, Chinese Academy of Sciences.

Kemper K.E.,University of Melbourne | Saxton S.J.,University of Melbourne | Bolormaa S.,Australian Department of Primary Industries and Fisheries | Hayes B.J.,Australian Department of Primary Industries and Fisheries | And 4 more authors.
BMC Genomics | Year: 2014

Background: Selection signatures aim to identify genomic regions underlying recent adaptations in populations. However, the effects of selection in the genome are difficult to distinguish from random processes, such as genetic drift. Often associations between selection signatures and selected variants for complex traits is assumed even though this is rarely (if ever) tested. In this paper, we use 8 breeds of domestic cattle under strong artificial selection to investigate if selection signatures are co-located in genomic regions which are likely to be under selection. Results: Our approaches to identify selection signatures (haplotype heterozygosity, integrated haplotype score and FST) identified strong and recent selection near many loci with mutations affecting simple traits under strong selection, such as coat colour. However, there was little evidence for a genome-wide association between strong selection signatures and regions affecting complex traits under selection, such as milk yield in dairy cattle. Even identifying selection signatures near some major loci was hindered by factors including allelic heterogeneity, selection for ancestral alleles and interactions with nearby selected loci. Conclusions: Selection signatures detect loci with large effects under strong selection. However, the methodology is often assumed to also detect loci affecting complex traits where the selection pressure at an individual locus is weak. We present empirical evidence to suggests little discernible 'selection signature' for complex traits in the genome of dairy cattle despite very strong and recent artificial selection. © 2014 Kemper et al.; licensee BioMed Central Ltd.

Raven L.-A.,Australian Department of Primary Industries and Fisheries | Raven L.-A.,La Trobe University | Raven L.-A.,Dairy Futures Co operative Research Center | Cocks B.G.,Australian Department of Primary Industries and Fisheries | And 5 more authors.
BMC Genomics | Year: 2014

Background: Genome wide association studies (GWAS) in most cattle breeds result in large genomic intervals of significant associations making it difficult to identify causal mutations. This is due to the extensive, low-level linkage disequilibrium within a cattle breed. As there is less linkage disequilibrium across breeds, multibreed GWAS may improve precision of causal variant mapping. Here we test this hypothesis in a Holstein and Jersey cattle data set with 17,925 individuals with records for production and functional traits and 632,003 SNP markers.Results: By using a cross validation strategy within the Holstein and Jersey data sets, we were able to identify and confirm a large number of QTL. As expected, the precision of mapping these QTL within the breeds was limited. In the multibreed analysis, we found that many loci were not segregating in both breeds. This was partly an artefact of power of the experiments, with the number of QTL shared between the breeds generally increasing with trait heritability. False discovery rates suggest that the multibreed analysis was less powerful than between breed analyses, in terms of how much genetic variance was explained by the detected QTL. However, the multibreed analysis could more accurately pinpoint the location of the well-described mutations affecting milk production such as DGAT1. Further, the significant SNP in the multibreed analysis were significantly enriched in genes regions, to a considerably greater extent than was observed in the single breed analyses. In addition, we have refined QTL on BTA5 and BTA19 to very small intervals and identified a small number of potential candidate genes in these, as well as in a number of other regions.Conclusion: Where QTL are segregating across breed, multibreed GWAS can refine these to reasonably small genomic intervals. However, such QTL appear to represent only a fraction of the genetic variation. Our results suggest a significant proportion of QTL affecting milk production segregate within rather than across breeds, at least for Holstein and Jersey cattle. © 2014 Raven et al.; licensee BioMed Central Ltd.

Koufariotis L.,La Trobe University | Koufariotis L.,Australian Department of Primary Industries and Fisheries | Koufariotis L.,Dairy Futures Co operative Research Center | Chen Y.-P.P.,La Trobe University | And 4 more authors.
BMC Genomics | Year: 2014

Background: In livestock, as in humans, the number of genetic variants that can be tested for association with complex quantitative traits, or used in genomic predictions, is increasing exponentially as whole genome sequencing becomes more common. The power to identify variants associated with traits, particularly those of small effects, could be increased if certain regions of the genome were known a priori to be enriched for associations. Here, we investigate whether twelve genomic annotation classes were enriched or depleted for significant associations in genome wide association studies for complex traits in beef and dairy cattle. We also describe a variance component approach to determine the proportion of genetic variance captured by each annotation class.Results: P-values from large GWAS using 700K SNP in both dairy and beef cattle were available for 11 and 10 traits respectively. We found significant enrichment for trait associated variants (SNP significant in the GWAS) in the missense class along with regions 5 kilobases upstream and downstream of coding genes. We found that the non-coding conserved regions (across mammals) were not enriched for trait associated variants. The results from the enrichment or depletion analysis were not in complete agreement with the results from variance component analysis, where the missense and synonymous classes gave the greatest increase in variance explained, while the upstream and downstream classes showed a more modest increase in the variance explained.Conclusion: Our results indicate that functional annotations could assist in prioritization of variants to a subset more likely to be associated with complex traits; including missense variants, and upstream and downstream regions. The differences in two sets of results (GWAS enrichment depletion versus variance component approaches) might be explained by the fact that the variance component approach has greater power to capture the cumulative effect of mutations of small effect, while the enrichment or depletion approach only captures the variants that are significant in GWAS, which is restricted to a limited number of common variants of moderate effects. © 2014 Koufariotis et al.; licensee BioMed Central Ltd.

Hand M.L.,La Trobe University | Hand M.L.,Dairy Futures Co operative Research Center | Cogan N.O.,La Trobe University | Cogan N.O.,Dairy Futures Co operative Research Center | And 2 more authors.
BMC Genomics | Year: 2012

Background: Single nucleotide polymorphisms (SNPs) provide essential tools for the advancement of research in plant genomics, and the development of SNP resources for many species has been accelerated by the capabilities of second-generation sequencing technologies. The current study aimed to develop and use a novel bioinformatic pipeline to generate a comprehensive collection of SNP markers within the agriculturally important pasture grass tall fescue; an outbreeding allopolyploid species displaying three distinct morphotypes: Continental, Mediterranean and rhizomatous.Results: A bioinformatic pipeline was developed that successfully identified SNPs within genotypes from distinct tall fescue morphotypes, following the sequencing of 414 polymerase chain reaction (PCR) - generated amplicons using 454 GS FLX technology. Equivalent amplicon sets were derived from representative genotypes of each morphotype, including six Continental, five Mediterranean and one rhizomatous. A total of 8,584 and 2,292 SNPs were identified with high confidence within the Continental and Mediterranean morphotypes respectively. The success of the bioinformatic approach was demonstrated through validation (at a rate of 70%) of a subset of 141 SNPs using both SNaPshot™ and GoldenGate™ assay chemistries. Furthermore, the quantitative genotyping capability of the GoldenGate™ assay revealed that approximately 30% of the putative SNPs were accessible to co-dominant scoring, despite the hexaploid genome structure. The sub-genome-specific origin of each SNP validated from Continental tall fescue was predicted using a phylogenetic approach based on comparison with orthologous sequences from predicted progenitor species.Conclusions: Using the appropriate bioinformatic approach, amplicon resequencing based on 454 GS FLX technology is an effective method for the identification of polymorphic SNPs within the genomes of Continental and Mediterranean tall fescue. The GoldenGate™ assay is capable of high-throughput co-dominant SNP allele detection, and minimises the problems associated with SNP genotyping in a polyploid by effectively reducing the complexity to a diploid system. This SNP collection may now be refined and used in applications such as cultivar identification, genetic linkage map construction, genome-wide association studies and genomic selection in tall fescue. The bioinformatic pipeline described here represents an effective general method for SNP discovery within outbreeding allopolyploid species. © 2012 Hand et al.; licensee BioMed Central Ltd.

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