William M. Muir mainly focuses on Genetics, Selection, Best linear unbiased prediction, Single-nucleotide polymorphism and Biotechnology. His Selection research is multidisciplinary, incorporating perspectives in Animal science, Kin selection and Inheritance. His Sire study in the realm of Animal science connects with subjects such as Third generation and Cage.
His Best linear unbiased prediction research incorporates themes from SNP, Statistics, Quantitative trait locus and Heritability. His Single-nucleotide polymorphism study which covers Genotyping that intersects with Mixed model. He has included themes like Risk analysis and Animal breeding in his Biotechnology study.
His scientific interests lie mostly in Genetics, Selection, Gene, Statistics and Animal science. His Genetics and Single-nucleotide polymorphism, Allele, Allele frequency, Genome and Genetic architecture investigations all form part of his Genetics research activities. His Single-nucleotide polymorphism study integrates concerns from other disciplines, such as Genetic marker and Marek's disease.
The study incorporates disciplines such as Kin selection and Biotechnology in addition to Selection. His work is dedicated to discovering how Statistics, Heritability are connected with Mixed model and other disciplines. His Animal science study incorporates themes from Body weight, Feed conversion ratio, Feather and Genetic stock.
His primary scientific interests are in Genetics, Gene, Selection, Single-nucleotide polymorphism and Allele. His study on Genetics is mostly dedicated to connecting different topics, such as Best linear unbiased prediction. The concepts of his Selection study are interwoven with issues in Quantitative genetics, Statistics, Biotechnology and Inbreeding.
His Statistics research incorporates elements of Ecology and Heritability. He interconnects Genetic marker and Allelic Imbalance in the investigation of issues within Single-nucleotide polymorphism. His Genome-wide association study research includes elements of Association mapping and Genetic association.
William M. Muir mainly investigates Genetics, Selection, Quantitative trait locus, Genome-wide association study and Best linear unbiased prediction. His research in Single-nucleotide polymorphism, Allele, Minor allele frequency, Gene and Phenotype are components of Genetics. His research integrates issues of Statistics, Biotechnology, Inbreeding and Competition in his study of Selection.
His study focuses on the intersection of Quantitative trait locus and fields such as Genetic marker with connections in the field of Data set and Bayes' theorem. His Genome-wide association study study combines topics in areas such as Population stratification, Association mapping, Data mining and Genetic association. The Best linear unbiased prediction study which covers SNP that intersects with Genetic variation.
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Group selection for adaptation to multiple-hen cages: selection program and direct responses
W. M. Muir.
Poultry Science (1996)
Possible ecological risks of transgenic organism release when transgenes affect mating success: sexual selection and the Trojan gene hypothesis.
William M. Muir;Richard D. Howard.
Proceedings of the National Academy of Sciences of the United States of America (1999)
Comparison of genomic and traditional BLUP‐estimated breeding value accuracy and selection response under alternative trait and genomic parameters
Journal of Animal Breeding and Genetics (2007)
Genome-wide association mapping including phenotypes from relatives without genotypes.
H. Wang;I. Misztal;I. Aguilar;A. Legarra.
Genetics Research (2012)
Multilevel Selection 1: Quantitative Genetics of Inheritance and Response to Selection
Piter Bijma;William M. Muir;Johan A. M. Van Arendonk.
Incorporation of Competitive Effects in Forest Tree or Animal Breeding Programs
William M Muir.
A high-density SNP-based linkage map of the chicken genome reveals sequence features correlated with recombination rate.
Martien A.M. Groenen;Per Wahlberg;Mario Foglio;Hans H. Cheng.
Genome Research (2008)
Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds
William M. Muir;Gane Ka-Shu Wong;Yong Zhang;Jun Wang.
Proceedings of the National Academy of Sciences of the United States of America (2008)
Multilevel Selection 2: Estimating the Genetic Parameters Determining Inheritance and Response to Selection
Piter Bijma;William M. Muir;Esther D. Ellen;Jason B. Wolf.
The development and characterization of a 60K SNP chip for chicken
Martien A.M. Groenen;Hendrik Jan Megens;Yalda Zare;Wesley C. Warren.
BMC Genomics (2011)
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