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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. Source


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. Source


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. Source


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. Source


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. Source

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