1,290 research outputs found
Author Lev Raphael reads from his work at the Michigan Writers Series
No full textInternationally acclaimed author and Greater Lansing resident, Lev Raphael, reads from his memoir "My Germany". He recounts his travels to the NAZI labor camp where his mother was held during World War II and coming to terms with his mother's traumatic past. Introduced by Michigan State University Librarian Michael Rodriguez at an event held at the MSU Main Library. Part of the Michigan State University Libraries' Michigan Writers Series
Across breed genomic evaluation in cattle
Full text linkGenomic evaluation techniques have been a huge success in the dairy cattle industry,
as they allow accurate enough estimation of breeding values at a young age to allow
selection decisions to be made at an earlier stage, thereby increasing the rate of
genetic progress per annum. The success of genomic selection techniques relies on
the existence of linkage disequilibrium (LD) between markers and quantitative trait
loci (QTL) across the population of interest; LD persists across larger distances
within breeds than across breeds. Therefore, most success so far has been for
selection within breeds, but the industry is keen for “across breed” evaluations to be
developed, both in a multi-breed scenario which would allow evaluations for breeds
that are numerically too small to carry out evaluations within breeds, and also for the
evaluation of crossbred animals.
This thesis investigates the potential for applying genomic selection techniques in
both the multi-breed and crossbred scenarios. Chapter 2 examines the potential for a
multi-breed reference population to improve the accuracy of genomic evaluation for
a numerically small breed, for a range of production and non-production traits. The
results provide evidence that forming a multi-breed reference population for two
closely related breeds (Holstein and Friesian) results in a higher accuracy of GEBVs
for the smaller breed, particularly when more phenotypic records are added via the
single-step GBLUP method, and when a higher density SNP chip is used. Chapter 3
examines the crossbred scenario, whereby GEBVs are calculated for crossbred
individuals based on a crossbred reference population. The population used for
analysis was a highly crossbred African population, and GEBVs were calculated for
three groups of animals chosen according to whether they had a high or low
proportion of imported dairy genetics. Accuracy of prediction was higher than
expected, and provided proof of concept for applying genomic selection techniques
in crossbred African cattle populations. Chapter 4 investigates the potential for using
novel SNPs derived from sequence data in order to estimate genomic relationships
across cattle breeds, deploying data from two closely related breeds, Fleckvieh and
Simmental, and a further distant European breed, the Brown Swiss. Novel SNPs
were selected from sequence based on their putative impact on the genome, with
impacts being inferred by SNP annotation software snpEff. Results showed that
genomic relationships calculated using novel SNPs have a high correlation with
genomic relationships calculated using SNPs common to the Illumina BovineHD
SNP chip, though between-breed correlations were lower than those within breeds.
The results presented in this thesis demonstrate that utilising a multi-breed reference
population can improve the accuracy of prediction for a numerically small breed, and
that genomic prediction of highly crossbred individuals is also feasible. However,
differences between breeds and also types of crossbred animal suggest that no one
solution can be used for all across-breed evaluations, and further research will be
needed to allow commercial implementation in further populations
Application of random regression models to study growth curves and genotype x environment interactions in sheep
Full text linkModelling sheep growth and producing estimated breeding values (EBV) for growth traits are
widely used to optimize sheep production. The methods available model growth traits as a
function of age, often with a set of fixed or random environmental effects.
Current methods to
map growth rely on two prominent assumptions that: (1) The mean and covariance structure of
the growth trait remains constant with time or age, and (2) each measurement of the growth
trait is genetically different from, but correlated to, all other measurements of the growth trait.
However, this methodology is problematic. It has three main issues: (1) impracticality; (2)
neglecting wide random variability in environmental effects; and (3) overparameterization.
Thus, this project used a random regression model to describe growth more accurately in sheep,
thereby producing better selection criteria to choose the best breeding stock. These models
included genetic effects, maternal genetic effects, and a host of conformation and meat quality
data. However, random regression models can struggle to provide accurate results when data
are sparse or unevenly distributed. Thus, the use of commercial data in this project allows
investigation of model performance and assessment of the applicability of random regression
models in commercial environments. Overall, the project found random regression models are
highly sensitive to the distribution and variability of records across the age distribution. This
results in high correlations between the parameters of the regression model which can result in
inaccurate genetic parameters. In Chapter 2 random regression models were constructed for
growth in Charollais and Suffolk sheep with constrained correlations resulting in heritabilities
between (0.18-0.49) and (0.20-0.50) respectively. The inclusion of the constrained correlation
was validated using a novel procedure. In Chapter 3, carcass information was incorporated
using commercial mixed breed and Scottish Blackface research data with similar numbers of
records. A selection index and a random regression model were compared.
Model convergence
for the random regression model was achieved by constraining the correlation. Genetic
parameters could be calculated between weights and fat class or conformation. The selection
index offered accurate information for slaughter weight and carcass weight. Chapter 4 assessed
GxE effects in the RamCompare project using a sire model with a sire by flock interaction and
a reaction norm model using a phenotypic deviation. It indicated GxE effects were present for
birth weight, scan weight, weaning weight and muscle depth. It also showed that an assessment
of GxE effects in commercial flocks can be conducted in datasets that lack environmental data.
The project contributes to a growing body of research on how to best model heritable traits and
provide genetic information to commercial sheep flocks
Deep machine learning of topological states of quantum matter
No full textauthor: Raphael KaubrueggerMasterarbeit Universität Innsbruck 201
Deep machine learning of topological states of quantum matter
No full textauthor: Raphael KaubrueggerMasterarbeit Universität Innsbruck 201
Deep machine learning of topological states of quantum matter
No full textauthor: Raphael KaubrueggerMasterarbeit Universität Innsbruck 201
Vocabulario Portuguez & Latino, Antico ... : Tomo VIII
No full textContén ademáis: Diccionario Castellano y Portuguez para facilitar ... la noticia de la lengua Latina ... Author el P. D. Raphael Blutea
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