A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype information in large outbreeding populations with complex pedigrees. The method accounts for background genes by estimating polygenic effects. The basic equations used are very similar to the usual linear mixed model equations for polygenic models, and segregation analysis was used to estimate the probabilities of the QTL genotypes for each animal. Method R was used to estimate the polygenic heritability simultaneously with the QTL effects. Also, initial allele frequencies were estimated. The method was tested in a simulated data set of 10,000 animals evenly distributed over 10 generations, where 0, 400 or 10,000 animals were genotyped for a ...
Many biological traits are discretely distributed in phenotype but continuously distributed in genet...
There is a growing need for the development of statistical techniques capable of mapping quantitativ...
Background Genomic selection is particularly beneficial for difficult or expensive to measure traits...
A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype ...
Methodology for mapping quantitative trait loci (QTL) has focused primarily on treating the QTL as a...
Studies involving the effects of single genes on quantitative traits may involve closed populations,...
Studies involving the effects of single genes on quantitative traits may involve closed populations,...
Quantitative trait locus (QTL) mapping studies often employ segregating generations derived from a c...
In this paper a method is presented to determine pleiotropic quantitative trait loci (QTL) or closel...
A mixture model approach is employed for the mapping of quantitative trait loci (QTL) for the situat...
Meta-analysis of information from quantitative trait loci (QTL) mapping experiments was used to deri...
BACKGROUND: In pedigreed populations with a major gene segregating for a quantitative trait, it is n...
Methodology is developed for Quantitative Trait Loci (QTL) analysis in F2 and backcross designed exp...
Two models that estimated genomic estimated breeding values (EBVs) were applied: one used constructe...
A new approach for Quantitative Trait Loci (QTL) analysis in designed experiments is investigated u...
Many biological traits are discretely distributed in phenotype but continuously distributed in genet...
There is a growing need for the development of statistical techniques capable of mapping quantitativ...
Background Genomic selection is particularly beneficial for difficult or expensive to measure traits...
A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype ...
Methodology for mapping quantitative trait loci (QTL) has focused primarily on treating the QTL as a...
Studies involving the effects of single genes on quantitative traits may involve closed populations,...
Studies involving the effects of single genes on quantitative traits may involve closed populations,...
Quantitative trait locus (QTL) mapping studies often employ segregating generations derived from a c...
In this paper a method is presented to determine pleiotropic quantitative trait loci (QTL) or closel...
A mixture model approach is employed for the mapping of quantitative trait loci (QTL) for the situat...
Meta-analysis of information from quantitative trait loci (QTL) mapping experiments was used to deri...
BACKGROUND: In pedigreed populations with a major gene segregating for a quantitative trait, it is n...
Methodology is developed for Quantitative Trait Loci (QTL) analysis in F2 and backcross designed exp...
Two models that estimated genomic estimated breeding values (EBVs) were applied: one used constructe...
A new approach for Quantitative Trait Loci (QTL) analysis in designed experiments is investigated u...
Many biological traits are discretely distributed in phenotype but continuously distributed in genet...
There is a growing need for the development of statistical techniques capable of mapping quantitativ...
Background Genomic selection is particularly beneficial for difficult or expensive to measure traits...