137 research outputs found
Extent of genome-wide linkage disequilibrium in Australian Holstein-Friesian cattle based on a high-density SNP panel
No full textBACKGROUND: The extent of linkage disequilibrium (LD) within a population determines the number of markers that will be required for successful association mapping and marker-assisted selection. Most studies on LD in cattle reported to date are based on microsatellite markers or small numbers of single nucleotide polymorphisms (SNPs) covering one or only a few chromosomes. This is the first comprehensive study on the extent of LD in cattle by analyzing data on 1,546 Holstein-Friesian bulls genotyped for 15,036 SNP markers covering all regions of all autosomes. Furthermore, most studies in cattle have used relatively small sample sizes and, consequently, may have had biased estimates of measures commonly used to describe LD. We examine minimum sample sizes required to estimate LD without bias and loss in accuracy. Finally, relatively little information is available on comparative LD structures including other mammalian species such as human and mouse, and we compare LD structure in cattle with public-domain data from both human and mouse. RESULTS: We computed three LD estimates, D', Dvol and r2, for 1,566,890 syntenic SNP pairs and a sample of 365,400 non-syntenic pairs. Mean D' is 0.189 among syntenic SNPs, and 0.105 among non-syntenic SNPs; mean r2 is 0.024 among syntenic SNPs and 0.0032 among non-syntenic SNPs. All three measures of LD for syntenic pairs decline with distance; the decline is much steeper for r2 than for D' and Dvol. The value of D' and Dvol are quite similar. Significant LD in cattle extends to 40 kb (when estimated as r2) and 8.2 Mb (when estimated as D'). The mean values for LD at large physical distances are close to those for non-syntenic SNPs. Minor allelic frequency threshold affects the distribution and extent of LD. For unbiased and accurate estimates of LD across marker intervals spanning 0.62). For estimation of LD by D' and Dvol with sufficient precision, a sample size of at least 400 is required, whereas for r2 a minimum sample of 75 is adequate
Prawn morphometrics and weight estimation from images using deep learning for landmark localization
No full textAccurate morphometric analyses and weight estimation are useful in aquaculture for optimizing feeding, pre dicting harvest yields, identifying desirable traits for selective breeding, grading processes, and monitoring the health status of production animals. However, the collection of phenotypic data through traditional manual approaches at industrial scales and in real-time is time-consuming, labour-intensive, and prone to errors. Digital imaging of individuals and subsequent training of prediction models using Deep Learning (DL) has the potential to rapidly and accurately acquire phenotypic data from aquaculture species. In this study, we applied a novel DL approach to automate morphometric analysis and weight estimation using the black tiger prawn (Penaeus monodon) as a model crustacean. The DL approach comprises two main components: a feature extraction module that efficiently combines low-level and high-level features using the Kronecker product operation; followed by a landmark localization module that then uses these features to predict the coordinates of key morphological points (landmarks) on the prawn body. Once these landmarks were extracted, weight was estimated using a weight regression module based on the extracted landmarks using a fully connected network. For morphometric analyses, we utilized the detected landmarks to derive five important prawn traits. Principal Component Analysis (PCA) was also used to identify landmark-derived distances, which were found to be highly correlated with shape features such as body length, and width. We evaluated our approach on a large dataset of 8164 images of the Black tiger prawn (Penaeus monodon) collected from Australian farms. Our experimental results demonstrate that the novel DL approach outperforms existing DL methods in terms of accuracy, robustness, and efficiency.Alzayat Saleh, Md Mehedi Hasan, Herman W. Raadsma, Mehar S. Khatkar, Dean R. Jerry, Mostafa Rahimi Azghad
A first-generation metric linkage disequilibrium map of bovine chromosome
No full textWe constructed a metric linkage disequilibrium (LD) map of bovine chromosome 6 (BTA6) on the basis of data from 220 SNPs genotyped on 433 Australian dairy bulls. This metric LD map has distances in LD units (LDUs) that are analogous to centimorgans in linkage maps. The LD map of BTA6 has a total length of 8.9 LDUs. Within the LD map, regions of high LD (represented as blocks) and regions of low LD (steps) are observed, when plotted against the integrated map in kilobases. At the most stringent block definition, namely a set of loci with zero LDU increase over the span of these markers, BTA6 comprises 40 blocks, accounting for 41% of the chromosome. At a slightly lower stringency of block definition (a set of loci covering a maximum of 0.2 LDUs on the LD map), up to 81% of BTA6 is spanned by 46 blocks and with 13 steps that are likely to reflect recombination hot spots. The mean swept radius (the distance over which LD is likely to be useful for mapping) is 13.3 Mb, confirming extensive LD in Holstein-Friesian dairy cattle, which makes such populations ideal for whole-genome association studies
Increased production through parasite control : can ancient breeds of sheep teach us new lessons?
No full textWith a rising world population and economic development, the global demand for meat, milk and other animal products is increasing dramatically. Controlling parasitic diseases in livestock, in particular helminth infections, could rapidly improve productivity and resource utilization. There is a growing interest in indigenous ruminant breeds because these animals have adapted to survive with minimal maintenance in the presence of high exposure to parasite infection. Recent findings on the mechanisms of parasite resistance in indigenous breeds are discussed, and the possibility that such studies may lead to new insight into the immunity and control of parasites proposed. These findings have important implications for the preservation of poorly characterized local indigenous breeds
Effects of Yerba Mate (Ilex paraguariensis) supplementation on the productive performance of dairy cows during mid-lactation
No full textYerba Mate (Ilex paraguariensis), a tea known for its high antioxidant content, was fed (250 g/cow.day) to 8 of 16 Holstein cows for 6 weeks to assess its effect on their performance. Cows were weighed and blood samples were taken on Weeks 0, 3 and 6. Blood samples were centrifuged and plasma was analysed for reactive oxygen metabolites, biological antioxidant potential, advanced oxidation protein products and non-esterified fatty acids. Cows were milked two times daily and milk yields were recorded daily for individual cows. On Weeks 0 and 6, individual milk samples were collected from two consecutive milkings, composited, and analysed for somatic cell counts, fat and true protein concentrations. Plasma concentrations of reactive oxygen metabolites, biological antioxidant potential and non-esterified fatty acids were not affected by Yerba Mate supplementation. Similarly, no effect of Yerba Mate supplementation was noted on milk fat and protein content and on somatic cell counts. This study indicates that supplementation of dairy cows’ diet with Yerba Mate during mid lactation seems to improve milk yield when cows are fed with maize silage; however, even if the effect on milk yield was significant it was quite small and needs to be validated with further studies. Cows’ oxidative status was not affected by Yerba Mate supplementation indicating that the effect of Yerba Mate on their productive performances is not mediated by changes in redox status.</jats:p
Detection of Pseudomonas aeruginosa from ovine fleece washings by PCR amplification of 16S ribosomal RNA
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NetView: a high-definition network-visualization approach to detect fine-scale population structures from genome-wide patterns of variation.
No full textHigh-throughput sequencing and single nucleotide polymorphism (SNP) genotyping can be used to infer complex population structures. Fine-scale population structure analysis tracing individual ancestry remains one of the major challenges. Based on network theory and recent advances in SNP chip technology, we investigated an unsupervised network clustering method called Super Paramagnetic Clustering (Spc). When applied to whole-genome marker data it identifies the natural divisions of groups of individuals into population clusters without use of prior ancestry information. Furthermore, we optimised an analysis pipeline called NetView, a high-definition network visualization, starting with computation of genetic distance, followed clustering using Spc and finally visualization of clusters with Cytoscape. We compared NetView against commonly used methodologies including Principal Component Analyses (PCA) and a model-based algorithm, Admixture, on whole-genome-wide SNP data derived from three previously described data sets: simulated (2.5 million SNPs, 5 populations), human (1.4 million SNPs, 11 populations) and cattle (32,653 SNPs, 19 populations). We demonstrate that individuals can be effectively allocated to their correct population whilst simultaneously revealing fine-scale structure within the populations. Analyzing the human HapMap populations, we identified unexpected genetic relatedness among individuals, and population stratification within the Indian, African and Mexican samples. In the cattle data set, we correctly assigned all individuals to their respective breeds and detected fine-scale population sub-structures reflecting different sample origins and phenotypes. The NetView pipeline is computationally extremely efficient and can be easily applied on large-scale genome-wide data sets to assign individuals to particular populations and to reproduce fine-scale population structures without prior knowledge of individual ancestry. NetView can be used on any data from which a genetic relationship/distance between individuals can be calculated
Multi-species comparative analysis of the equine ACE gene identifies a highly conserved potential transcription factor binding site in intron 16.
No full textAngiotensin converting enzyme (ACE) is essential for control of blood pressure. The human ACE gene contains an intronic Alu indel (I/D) polymorphism that has been associated with variation in serum enzyme levels, although the functional mechanism has not been identified. The polymorphism has also been associated with cardiovascular disease, type II diabetes, renal disease and elite athleticism. We have characterized the ACE gene in horses of breeds selected for differing physical abilities. The equine gene has a similar structure to that of all known mammalian ACE genes. Nine common single nucleotide polymorphisms (SNPs) discovered in pooled DNA were found to be inherited in nine haplotypes. Three of these SNPs were located in intron 16, homologous to that containing the Alu polymorphism in the human. A highly conserved 18 bp sequence, also within that intron, was identified as being a potential binding site for the transcription factors Oct-1, HFH-1 and HNF-3β, and lies within a larger area of higher than normal homology. This putative regulatory element may contribute to regulation of the documented inter-individual variation in human circulating enzyme levels, for which a functional mechanism is yet to be defined. Two equine SNPs occurred within the conserved area in intron 16, although neither of them disrupted the putative binding site. We propose a possible regulatory mechanism of the ACE gene in mammalian species which was previously unknown. This advance will allow further analysis leading to a better understanding of the mechanisms underpinning the associations seen between the human Alu polymorphism and enzyme levels, cardiovascular disease states and elite athleticism
Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep
No full textBackground: Discerning the traits evolving under neutral conditions from those traits evolving rapidly because of various selection pressures is a great challenge. We propose a new method, composite selection signals (CSS), which unifies the multiple pieces of selection evidence from the rank distribution of its diverse constituent tests. The extreme CSS scores capture highly differentiated loci and underlying common variants hauling excess haplotype homozygosity in the samples of a target population.Results: The data on high-density genotypes were analyzed for evidence of an association with either polledness or double muscling in various cohorts of cattle and sheep. In cattle, extreme CSS scores were found in the candidate regions on autosome BTA-1 and BTA-2, flanking the POLL locus and MSTN gene, for polledness and double muscling, respectively. In sheep, the regions with extreme scores were localized on autosome OAR-2 harbouring the MSTN gene for double muscling and on OAR-10 harbouring the RXFP2 gene for polledness. In comparison to the constituent tests, there was a partial agreement between the signals at the four candidate loci; however, they consistently identified additional genomic regions harbouring no known genes. Persuasively, our list of all the additional significant CSS regions contains genes that have been successfully implicated to secondary phenotypic diversity among several subpopulations in our data. For example, the method identified a strong selection signature for stature in cattle capturing selective sweeps harbouring UQCC-GDF5 and PLAG1-CHCHD7 gene regions on BTA-13 and BTA-14, respectively. Both gene pairs have been previously associated with height in humans, while PLAG1-CHCHD7 has also been reported for stature in cattle. In the additional analysis, CSS identified significant regions harbouring multiple genes for various traits under selection in European cattle including polledness, adaptation, metabolism, growth rate, stature, immunity, reproduction traits and some other candidate genes for dairy and beef production.Conclusions: CSS successfully localized the candidate regions in validation datasets as well as identified previously known and novel regions for various traits experiencing selection pressure. Together, the results demonstrate the utility of CSS by its improved power, reduced false positives and high-resolution of selection signals as compared to individual constituent tests
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