A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds
Authors
Keywords
-
Journal
Animal
Volume -, Issue -, Pages 1-10
Publisher
Cambridge University Press (CUP)
Online
2019-10-11
DOI
10.1017/s1751731119002167
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Genome-wide association studies for 30 haematological and blood clinical-biochemical traits in Large White pigs reveal genomic regions affecting intermediate phenotypes
- (2019) Samuele Bovo et al. Scientific Reports
- Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach
- (2018) A. Jacobs et al. Scientific Reports
- Detection of Selection Signatures in Chinese Landrace and Yorkshire Pigs Based on Genotyping-by-Sequencing Data
- (2018) Kai Wang et al. Frontiers in Genetics
- Genomic analysis reveals genes affecting distinct phenotypes among different Chinese and western pig breeds
- (2018) Zhe Zhang et al. Scientific Reports
- Pedigree reconstruction from SNP data: parentage assignment, sibship clustering and beyond
- (2017) Jisca Huisman Molecular Ecology Resources
- Random forest estimation of genomic breeding values for disease susceptibility over different disease incidences and genomic architectures in simulated cow calibration groups
- (2016) S. Naderi et al. JOURNAL OF DAIRY SCIENCE
- Authentication of “mono-breed” pork products: Identification of a coat colour gene marker in Cinta Senese pigs useful to this purpose
- (2016) L. Fontanesi et al. Livestock Science
- Principal component analysis: a review and recent developments
- (2016) Ian T. Jolliffe et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Twenty years of artificial directional selection have shaped the genome of the Italian Large White pig breed
- (2015) G. Schiavo et al. ANIMAL GENETICS
- PLAG1 and NCAPG-LCORL in livestock
- (2015) Akiko Takasuga ANIMAL SCIENCE JOURNAL
- Combined use of principal component analysis and random forests identify population-informative single nucleotide polymorphisms: application in cattle breeds
- (2015) F. Bertolini et al. JOURNAL OF ANIMAL BREEDING AND GENETICS
- A genome-wide scan for signatures of selection in Chinese indigenous and commercial pig breeds
- (2014) Songbai Yang et al. BMC GENETICS
- Genomewide association for a dominant pigmentation gene in sheep
- (2013) J.W. Kijas et al. JOURNAL OF ANIMAL BREEDING AND GENETICS
- Selection of SNP from 50K and 777K arrays to predict breed of origin in cattle1
- (2013) B. Hulsegge et al. JOURNAL OF ANIMAL SCIENCE
- Genomic analyses identify distinct patterns of selection in domesticated pigs and Tibetan wild boars
- (2013) Mingzhou Li et al. NATURE GENETICS
- Genetic Diversity, Linkage Disequilibrium and Selection Signatures in Chinese and Western Pigs Revealed by Genome-Wide SNP Markers
- (2013) Huashui Ai et al. PLoS One
- Signatures of Diversifying Selection in European Pig Breeds
- (2013) Samantha Wilkinson et al. PLoS Genetics
- Development of a genetic tool for product regulation in the diverse British pig breed market
- (2012) Samantha Wilkinson et al. BMC GENOMICS
- Evaluation of approaches for identifying population informative markers from high density SNP Chips
- (2011) Samantha Wilkinson et al. BMC GENETICS
- Performance of random forest when SNPs are in linkage disequilibrium
- (2009) Yan A Meng et al. BMC BIOINFORMATICS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now