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High performance computing for large-scale genomic prediction

Arne De Coninck (2016)
abstract
In the past decades genetics was studied intensively leading to the knowledge that DNA is the molecule behind genetic inheritance and starting from the new millennium next-generation sequencing methods made it possible to sample this DNA with an ever decreasing cost. Animal and plant breeders have always made use of genetic information to predict agronomic performance of new breeds. While this genetic information previously was gathered from the pedigree of the population under study, genomic information of the DNA makes it possible to also deduce correlations between individuals that do not share any known ancestors leading to so-called genomic prediction of agronomic performance. Nowadays, the number of informative samples that can be taken from a genome ranges from one thousand to one million. Using all this information in a breeding context where agronomic performance is predicted and optimized for different environmental conditions is not a straightforward task. Moreover, the number of individuals for which this information is available keeps on growing and thus sophisticated computational methods are required for analyzing these large scale genomic data sets. This thesis introduces some concepts of high performance computing in a genomic prediction context and shows that analyzing phenotypic records of large numbers of genotyped individuals leads to a better prediction accuracy of the agronomic performance in different environments. Finally, it is even shown that the parts of the DNA that influence the agronomic performance under certain environmental conditions can be pinpointed, and this knowledge can thus be used by breeders to select individuals that thrive better in the targeted environment.
Please use this url to cite or link to this publication:
author
promoter
UGent and UGent
organization
alternative title
Hoogperformant computergebruik voor grootschalige genoomwijde voorspellingen
year
type
dissertation
publication status
published
subject
keyword
animal breeding, distributed computing, genomic prediction, plant breeding, High performance computing
pages
XII, 211 pages
publisher
Ghent University. Faculty of Bioscience Engineering
place of publication
Ghent, Belgium
defense location
Gent : Faculteit Bio-ingenieurswetenschappen (A0.030)
defense date
2016-01-29 16:00
ISBN
9789059898622
project
Bioinformatics: from nucleotids to networks (N2N)
language
English
UGent publication?
yes
classification
D1
copyright statement
I have transferred the copyright for this publication to the publisher
id
7061094
handle
http://hdl.handle.net/1854/LU-7061094
date created
2016-01-28 14:24:52
date last changed
2017-03-02 09:23:48
@phdthesis{7061094,
  abstract     = {In the past decades genetics was studied intensively leading to the knowledge that DNA is the molecule behind genetic inheritance and starting from the new millennium next-generation sequencing methods made it possible to sample this DNA with an ever decreasing cost. Animal and plant breeders have always made use of genetic information to predict agronomic performance of new breeds. While this genetic information previously was gathered from the pedigree of the population under study, genomic information of the DNA makes it possible to also deduce correlations between individuals that do not share any known ancestors leading to so-called genomic prediction of agronomic performance. Nowadays, the number of informative samples that can be taken from a genome ranges from one thousand to one million. Using all this information in a breeding context where agronomic performance is predicted and optimized for different environmental conditions is not a straightforward task. Moreover, the number of individuals for which this information is available keeps on growing and thus sophisticated computational methods are required for analyzing these large scale genomic data sets.
This thesis introduces some concepts of high performance computing in a genomic prediction context and shows that analyzing phenotypic records of large numbers of genotyped individuals leads to a better prediction accuracy of the agronomic performance in different environments. Finally, it is even shown that the parts of the DNA that influence the agronomic performance under certain environmental conditions can be pinpointed, and this knowledge can thus be used by breeders to select individuals that thrive better in the targeted environment.},
  author       = {De Coninck, Arne},
  isbn         = {9789059898622},
  keyword      = {animal breeding,distributed computing,genomic prediction,plant breeding,High performance computing},
  language     = {eng},
  pages        = {XII, 211},
  publisher    = {Ghent University. Faculty of Bioscience Engineering},
  school       = {Ghent University},
  title        = {High performance computing for large-scale genomic prediction},
  year         = {2016},
}

Chicago
De Coninck, Arne. 2016. “High Performance Computing for Large-scale Genomic Prediction”. Ghent, Belgium: Ghent University. Faculty of Bioscience Engineering.
APA
De Coninck, A. (2016). High performance computing for large-scale genomic prediction. Ghent University. Faculty of Bioscience Engineering, Ghent, Belgium.
Vancouver
1.
De Coninck A. High performance computing for large-scale genomic prediction. [Ghent, Belgium]: Ghent University. Faculty of Bioscience Engineering; 2016.
MLA
De Coninck, Arne. “High Performance Computing for Large-scale Genomic Prediction.” 2016 : n. pag. Print.