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- GWAS (2) (entfernen)
Understanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS in the model plant species Arabidopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, whereas others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be extremely sensitive to sampling and population structure.
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.