@article{SerenGrimmFitzetal.2016, author = {Seren, {\"U}mit and Grimm, Dominik and Fitz, Joffrey and Weigel, Detlef and Nordborg, Magnus and Borgwardt, Karsten and Korte, Arthur}, title = {AraPheno: a public database for Arabidopsis thaliana phenotypes}, series = {Nucleic Acids Research}, volume = {45}, journal = {Nucleic Acids Research}, number = {D1}, doi = {10.1093/nar/gkw986}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147909}, pages = {D1054-D1059}, year = {2016}, abstract = {Natural genetic variation makes it possible to discover evolutionary changes that have been maintained in a population because they are advantageous. To understand genotype-phenotype relationships and to investigate trait architecture, the existence of both high-resolution genotypic and phenotypic data is necessary. Arabidopsis thaliana is a prime model for these purposes. This herb naturally occurs across much of the Eurasian continent and North America. Thus, it is exposed to a wide range of environmental factors and has been subject to natural selection under distinct conditions. Full genome sequencing data for more than 1000 different natural inbred lines are available, and this has encouraged the distributed generation of many types of phenotypic data. To leverage these data for meta analyses, AraPheno (https://arapheno.1001genomes.org) provide a central repository of population-scale phenotypes for A. thaliana inbred lines. AraPheno includes various features to easily access, download and visualize the phenotypic data. This will facilitate a comparative analysis of the many different types of phenotypic data, which is the base to further enhance our understanding of the genotype-phenotype map.}, language = {en} } @article{NaglerNaegeleGillietal.2018, author = {Nagler, Matthias and N{\"a}gele, Thomas and Gilli, Christian and Fragner, Lena and Korte, Arthur and Platzer, Alexander and Farlow, Ashley and Nordborg, Magnus and Weckwerth, Wolfram}, title = {Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field}, series = {Frontiers in Plant Science}, volume = {9}, journal = {Frontiers in Plant Science}, number = {1556}, issn = {1664-462X}, doi = {10.3389/fpls.2018.01556}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-189560}, year = {2018}, abstract = {Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.}, language = {en} } @article{LopezArboledaReinertNordborgetal.2021, author = {Lopez-Arboleda, William Andres and Reinert, Stephan and Nordborg, Magnus and Korte, Arthur}, title = {Global genetic heterogeneity in adaptive traits}, series = {Molecular Biology and Evolution}, volume = {38}, journal = {Molecular Biology and Evolution}, number = {11}, doi = {10.1093/molbev/msab208}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270410}, pages = {4822-4831}, year = {2021}, abstract = {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.}, language = {en} } @article{TogninalliSerenMengetal.2018, author = {Togninalli, Matteo and Seren, {\"U}mit and Meng, Dazhe and Fitz, Joffrey and Nordborg, Magnus and Weigel, Detlef and Borgwardt, Karsten and Korte, Arthur and Grimm, Dominik G.}, title = {The AraGWAS Catalog: a curated and standardized Arabidopsis thaliana GWAS catalog}, series = {Nucleic Acids Research}, volume = {46}, journal = {Nucleic Acids Research}, number = {D1}, doi = {10.1093/nar/gkx954}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158727}, pages = {D1150-D1156}, year = {2018}, abstract = {The abundance of high-quality genotype and phenotype data for the model organism Arabidopsis thaliana enables scientists to study the genetic architecture of many complex traits at an unprecedented level of detail using genome-wide association studies (GWAS). GWAS have been a great success in A. thaliana and many SNP-trait associations have been published. With the AraGWAS Catalog (https://aragwas.1001genomes.org) we provide a publicly available, manually curated and standardized GWAS catalog for all publicly available phenotypes from the central A. thaliana phenotype repository, AraPheno. All GWAS have been recomputed on the latest imputed genotype release of the 1001 Genomes Consortium using a standardized GWAS pipeline to ensure comparability between results. The catalog includes currently 167 phenotypes and more than 222 000 SNP-trait associations with P < 10\(^{-4}\), of which 3887 are significantly associated using permutation-based thresholds. The AraGWAS Catalog can be accessed via a modern web-interface and provides various features to easily access, download and visualize the results and summary statistics across GWAS.}, language = {en} }