@phdthesis{LopezArboleda2021,
  author    = {L{\´o}pez Arboleda, William Andr{\´e}s},
  title     = {Global Genetic Heterogeneity in Adaptive Traits},
  doi       = {10.25972/OPUS-24246},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-242468},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2021},
  abstract  = {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 Dr{\"o}ge- 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.},
  language  = {en}
}
@phdthesis{Freudenthal2020,
  author    = {Freudenthal, Jan Alexander},
  title     = {Quantitative genetics from genome assemblies to neural network aided omics-based prediction of complex traits},
  doi       = {10.25972/OPUS-19942},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-199429},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {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.},
  subject      = {Genetics},
  language  = {en}
}