Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data

[1]  H. Soyeurt,et al.  Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds. , 2021, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[2]  N. Ibáñez-Escriche,et al.  Selection for environmental variance of litter size in rabbits involves genes in pathways controlling animal resilience , 2021, Genetics Selection Evolution.

[3]  Shuqi Diao,et al.  Breed identification of meat using machine learning and breed tag SNPs , 2021, Food Control.

[4]  B. Badaoui,et al.  Comparison of three statistical approaches for feature selection for fine-scale genetic population assignment in four pig breeds , 2021, Tropical Animal Health and Production.

[5]  S. McWilliam,et al.  A low-density SNP genotyping panel for the accurate prediction of cattle breeds. , 2020, Journal of animal science.

[6]  Graham J. Etherington,et al.  Whole-genome sequencing of European autochthonous and commercial pig breeds allows the detection of signatures of selection for adaptation of genetic resources to different breeding and production systems , 2020, Genetics Selection Evolution.

[7]  S. Tongsima,et al.  Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique , 2020, BMC Bioinformatics.

[8]  Steven G. Schroeder,et al.  De novo assembly of the cattle reference genome with single-molecule sequencing , 2020, GigaScience.

[9]  G. Galimberti,et al.  A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds. , 2020, Animal : an international journal of animal bioscience.

[10]  B. Bhushan,et al.  Comparative analysis of five different methods to design a breed-specific SNP panel for cattle , 2019, Animal biotechnology.

[11]  Ben J Hayes,et al.  1000 Bull Genomes Project to Map Simple and Complex Genetic Traits in Cattle: Applications and Outcomes. , 2019, Annual review of animal biosciences.

[12]  Shaoxiong Lu,et al.  Genome-Wide and Trait-Specific Markers: A Perspective in Designing Conservation Programs , 2018, Front. Genet..

[13]  Xiao-Lin Wu,et al.  Comparing SNP panels and statistical methods for estimating genomic breed composition of individual animals in ten cattle breeds , 2018, BMC Genetics.

[14]  J. Mank,et al.  Whole-genome resequencing reveals signatures of selection and timing of duck domestication , 2018, GigaScience.

[15]  P. Shaw,et al.  DNA-based techniques for authentication of processed food and food supplements. , 2018, Food chemistry.

[16]  R. Sleator,et al.  Ultra-low-density genotype panels for breed assignment of Angus and Hereford cattle. , 2017, Animal : an international journal of animal bioscience.

[17]  Zhongheng Zhang,et al.  Naïve Bayes classification in R. , 2016, Annals of translational medicine.

[18]  G. Galimberti,et al.  Combined use of principal component analysis and random forests identify population-informative single nucleotide polymorphisms: application in cattle breeds. , 2015, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[19]  Grigorios Tsoumakas,et al.  TRES: Identification of Discriminatory and Informative SNPs from Population Genomic Data. , 2015, The Journal of heredity.

[20]  Sarika,et al.  Locus minimization in breed prediction using artificial neural network approach. , 2014, Animal genetics.

[21]  Carson C Chow,et al.  Second-generation PLINK: rising to the challenge of larger and richer datasets , 2014, GigaScience.

[22]  M. Calus,et al.  Selection of SNP from 50K and 777K arrays to predict breed of origin in cattle. , 2013, Journal of animal science.

[23]  Marek Wesolowski,et al.  Artificial neural networks: theoretical background and pharmaceutical applications: a review. , 2012, Journal of AOAC International.

[24]  Lili Ding,et al.  Comparison of measures of marker informativeness for ancestry and admixture mapping , 2011, BMC Genomics.

[25]  G. L. Bennett,et al.  Predicting breed composition using breed frequencies of 50,000 markers from the US Meat Animal Research Center 2,000 Bull Project. , 2011, Journal of animal science.

[26]  Joshua M. Korn,et al.  Rapid Assessment of Genetic Ancestry in Populations of Unknown Origin by Genome-Wide Genotyping of Pooled Samples , 2010, PLoS genetics.

[27]  Young-Seuk Park,et al.  What do artificial neural networks tell us about the genetic structure of populations? The example of European pig populations. , 2009, Genetics research.

[28]  B. Browning,et al.  A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. , 2009, American journal of human genetics.

[29]  L. Fontanesi Genetic authentication and traceability of food products of animal origin: new developments and perspectives , 2009 .

[30]  M. De Marchi,et al.  Breed assignment test in four Italian beef cattle breeds. , 2008, Meat science.

[31]  B. Browning,et al.  Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. , 2007, American journal of human genetics.

[32]  A. Misra,et al.  SNP genotyping: technologies and biomedical applications. , 2007, Annual review of biomedical engineering.

[33]  G. Luikart,et al.  SNPs in ecology, evolution and conservation , 2004 .

[34]  R. Ward,et al.  Informativeness of genetic markers for inference of ancestry. , 2003, American journal of human genetics.

[35]  P. Taberlet,et al.  Genetic diversity and assignment tests among seven French cattle breeds based on microsatellite DNA analysis. , 2002, Journal of animal science.

[36]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[37]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[38]  B. Rannala,et al.  Detecting immigration by using multilocus genotypes. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[39]  L. Jin,et al.  Ethnic-affiliation estimation by use of population-specific DNA markers. , 1997, American journal of human genetics.

[40]  I. Stirling,et al.  Microsatellite analysis of population structure in Canadian polar bears , 1995, Molecular ecology.

[41]  G. Galimberti,et al.  Preselection statistics and Random Forest classification identify population informative single nucleotide polymorphisms in cosmopolitan and autochthonous cattle breeds. , 2018, Animal : an international journal of animal bioscience.

[42]  M. Groenen,et al.  Development of a genetic tool for product regulation in the diverse British pig breed market , 2012 .

[43]  Robert D Schnabel,et al.  Evaluation of approaches for identifying population informative markers from high density SNP Chips , 2011, BMC Genetics.

[44]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[45]  D. Nen,et al.  New Developments and Perspectives , 2002 .

[46]  Roderick,et al.  Determining the source of individuals: multilocus genotyping in nonequilibrium population genetics. , 1999, Trends in ecology & evolution.

[47]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[48]  S WRIGHT,et al.  Genetical structure of populations. , 1950, Nature.