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Genome Wide Association Studies (GWAS) have revolutionized the way on
how genotype-phenotype relations are assessed. In the 20 years long history
of GWAS, multiple challenges from a biological, computational, and statistical
point of view have been faced. The implementation of this technique using
the model plant species Arabidopsis thaliana, has enabled the detection of many
association for multiple traits. Despite a lot of studies implementing GWAS
have discovered new candidate genes for multiple traits, different samples are
used across studies. In many cases, either globally diverse samples or samples
composed of accessions from a geographically restricted area are used. With
the aim of comparing GWAS outcomes between populations from different
geographic areas, this thesis describes the performance of GWAS in different
European samples of A. thaliana. Here, association mapping results for flowering
time were compared. Chapter 2 describes the analyses of random resampling
from this original sample. The aim was to establish reduced subsamples to
later carry out GWAS and compare the outcomes between these subsamples.
In Chapter 3, the European sample was split into eight equally-sized local
samples representing different geographic regions. Next, GWAS was carried
out and an attempt was made to clarify the differences in GWAS outcomes.
Chapter 4 contains the results of a collaboration with Prof. Dr. Wolfgang Dröge-
Laser, in which my mainly task was the analysis of RNAseq data from A.
thaliana plants infected by pathogenic fungi. Finally, Appendix A presents a very
short description of my participation in the GHP Project on Access to Care for
Cardiometabolic Diseases (HPACC) at the university of Heidelberg.
Quantitative genetics is the study of continuously distributed traits and their ge-
netic components. Recent developments in DNA sequencing technologies and
computational systems allow researchers to conduct large scale in silico studies.
However, going from raw DNA reads to genomic prediction of quantitative traits
with the help of neural networks is a long and error-prone process. In the course
of this thesis, many steps involved in this process will be assessed in depth. Chap-
ter 2 will feature a study that compares the landscape of chloroplast genome as-
sembly tools. Chapter 3 will present a software to perform genome-wide associa-
tion studies using modern tools, which allow GWAS-Flow to outperform current
state of the art software packages. Chapter 4 will give an in depth introduc-
tion to machine learning and the nature of quantitative traits and will combine
those to genomic prediction with artificial neural networks and compares the re-
sults to those of algorithms based on linear mixed models. Finally, in Chapter 5
the results from the previous chapters are summarized and used to elucidate the
complex nature of studies concerning quantitative genetics.