scholarly journals Identification of Ancestry Informative Marker (AIM) Panels to Assess Hybridisation between Feral and Domestic Sheep

Animals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 582
Author(s):  
Elisa Somenzi ◽  
Paolo Ajmone-Marsan ◽  
Mario Barbato
Keyword(s):  
Snp Array ◽  
Ovis Aries ◽  
Supervised Machine Learning ◽  
Conservation Strategies ◽  
Diagnostic Tools ◽  
Domestic Sheep ◽  
Medium Density ◽  
Ancestry Informative Marker ◽  
Informative Marker ◽  
Machine Learning Classification

Hybridisation of wild populations with their domestic counterparts can lead to the loss of wildtype genetic integrity, outbreeding depression, and loss of adaptive features. The Mediterranean island of Sardinia hosts one of the last extant autochthonous European mouflon (Ovis aries musimon) populations. Although conservation policies, including reintroduction plans, have been enforced to preserve Sardinian mouflon, crossbreeding with domestic sheep has been documented. We identified panels of single nucleotide polymorphisms (SNPs) that could act as ancestry informative markers able to assess admixture in feral x domestic sheep hybrids. The medium-density SNP array genotyping data of Sardinian mouflon and domestic sheep (O. aries aries) showing pure ancestry were used as references. We applied a two-step selection algorithm to this data consisting of preselection via Principal Component Analysis followed by a supervised machine learning classification method based on random forest to develop SNP panels of various sizes. We generated ancestry informative marker (AIM) panels and tested their ability to assess admixture in mouflon x domestic sheep hybrids both in simulated and real populations of known ancestry proportions. All the AIM panels recorded high correlations with the ancestry proportion computed using the full medium-density SNP array. The AIM panels proposed here may be used by conservation practitioners as diagnostic tools to exclude hybrids from reintroduction plans and improve conservation strategies for mouflon populations.

scholarly journals Variation in recombination rate and its genetic determinism in sheep (Ovis Aries) populations from combining multiple genome-wide datasets

2017 ◽  
Author(s):  
Morgane Petit ◽  
Jean-Michel Astruc ◽  
Julien Sarry ◽  
Laurence Drouilhet ◽  
Stéphane Fabre ◽  
...  
Keyword(s):  
Meiotic Recombination ◽  
Individual Variation ◽  
Recombination Rate ◽  
Snp Array ◽  
Genetic Determinism ◽  
Ovis Aries ◽  
Domestic Sheep ◽  
Recombination Rates ◽  
Male Recombination ◽  
Genome Wide

AbstractRecombination is a complex biological process that results from a cascade of multiple events during meiosis. Understanding the genetic determinism of recombination can help to understand if and how these events are interacting. To tackle this question, we studied the patterns of recombination in sheep, using multiple approaches and datasets. We constructed male recombination maps in a dairy breed from the south of France (the Lacaune breed) at a fine scale by combining meiotic recombination rates from a large pedigree genotyped with a 50K SNP array and historical recombination rates from a sample of unrelated individuals genotyped with a 600K SNP array. This analysis revealed recombination patterns in sheep similar to other mammals but also genome regions that have likely been affected by directional and diversifying selection. We estimated the average recombination rate of Lacaune sheep at 1.5 cM/Mb, identified about 50,000 crossover hotspots on the genome and found a high correlation between historical and meiotic recombination rate estimates. A genome-wide association study revealed two major loci affecting inter-individual variation in recombination rate in Lacaune, including the RNF212 and HEI10 genes and possibly 2 other loci of smaller effects including the KCNJ15 and FSHR genes. Finally, we compared our results to those obtained previously in a distantly related population of domestic sheep, the Soay. This comparison revealed that Soay and Lacaune males have a very similar distribution of recombination along the genome and that the two datasets can be combined to create more precise male meiotic recombination maps in sheep. Despite their similar recombination maps, we show that Soay and Lacaune males exhibit different heritabilities and QTL effects for inter-individual variation in genome-wide recombination rates.

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

1-OR: HLA as an ancestry informative marker (AIM)

2007 ◽  
Vol 68 (1) ◽  
pp. S9
Author(s):  
Loren Gragert ◽  
Martin Maiers ◽  
William Klitz
Keyword(s):  
Ancestry Informative Marker ◽  
Informative Marker

​Re-sequencing of Whole Genome Jordanian Awassi Ewes using Hiseq Sequencing Technology: Major Step Towards Sheep Genomic Selection

2021 ◽  
Author(s):  
Hussein Migdadi ◽  
Nizar Haddad ◽  
Ruba AlOmari ◽  
Mohammad Brake ◽  
Mustafa AlShdaifat ◽  
...  
Keyword(s):  
Production Systems ◽  
Ovis Aries ◽  
Domestic Sheep ◽  
Major Step ◽  
Illumina Hiseq ◽  
Structure Variation ◽  
Awassi Sheep ◽  
Wide Range ◽  
Number Variation ◽  
Paired End Sequencing

Background: Jordanian Awassi sheep (Ovis aries) is the dominant fat tail sheep breed that appeals to customers because of its various production systems, including fiber, meat and milk. This report is the first whole ewe genome sequence (WGS) of O. aries submitted in the NCBI database from Jordan. Methods: 64 Paired-end sequencing libraries were constructed and subjected to Illumina Hiseq 2500 sequencing system. High-quality reads were aligned against the reference sheep genome and detecting comprehensive sources (SNPs, InDels, SV, CNVs) of genetic variations. We have deposited data sequences at the NCBI under SRA (sequence reads archives) under the accession numbers SRR11128863, PRJNA574879. Result: Genome resequencing of Jordanian Awassi ewe was carried out with approximately 93.88 Gb with a mapping rate and effective mapping depths were 99.28% and 36.32. Around 19 million SNPs, 3,6 million InDels, 35,180 Structure variation and 13,524 copy number variation among the Jordanian ewe genome were detected. This wide range of genetic variation provides a framework for further genetic studies that will help understand the molecular basis underlying phenotypic variation of economically important traits in sheep and improve intrinsic defects in domestic sheep breeds.

Evaluating disaster-related tweet credibility using content-based and user-based features

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasser Assery ◽  
Yuan (Dorothy) Xiaohong ◽  
Qu Xiuli ◽  
Roy Kaushik ◽  
Sultan Almalki
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Emergency Response ◽  
Learning Model ◽  
Performance Comparison ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Classification

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

Field metabolic rate and water turnover of red kangaroos and sheep in an arid rangeland: an empirically derived dry-sheep-equivalent for kangaroos

2009 ◽  
Vol 57 (1) ◽  
pp. 23 ◽  
Author(s):  
A. J. Munn ◽  
T. J. Dawson ◽  
S. R. McLeod ◽  
D. B. Croft ◽  
M. B. Thompson ◽  
...  
Keyword(s):  
Metabolic Rate ◽  
Grazing Pressure ◽  
Ovis Aries ◽  
Domestic Sheep ◽  
Detailed Knowledge ◽  
Water Turnover ◽  
Field Metabolic Rate ◽  
Red Kangaroo ◽  
Resource Requirements ◽  
Comparative Measure

Sustainable management of pastures requires detailed knowledge of total grazing pressure, but this information is critically lacking in Australia’s rangelands where livestock co-occur with large herbivorous marsupials. We present the first comparative measure of the field metabolic rate (an index of food requirement) of Australia’s largest marsupial, the red kangaroo (Macropus rufus), with that of domestic sheep (Ovis aries; merino breed). We tested the assumption that the grazing pressure of red kangaroos is equivalent to 0.7 sheep, and show this to be a two-fold overestimation of their contribution to total grazing. Moreover, kangaroos had extraordinarily lower rates of water turnover, being only 13% that of sheep. Consequently, our data support arguments that the removal of kangaroos may not markedly improve rangeland capacity for domestic stock. Furthermore, given the low resource requirements of kangaroos, their use in consumptive and non-consumptive enterprises can provide additional benefits for Australia’s rangelands than may occur under traditional rangeland practices.

scholarly journals Microhaplotype: Ability of personal identification and being ancestry informative marker

2017 ◽  
Vol 6 ◽  
pp. e442-e444 ◽  
Author(s):  
Yan Pu ◽  
Peng Chen ◽  
Jing Zhu ◽  
Youjing Jiang ◽  
Qingqing Li ◽  
...  
Keyword(s):  
Personal Identification ◽  
Ancestry Informative Marker ◽  
Informative Marker
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