Browsing by Subject "Linkages"

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Genetic approaches to dissect iron efficiency in maize
(2015) Benke, Andreas; Stich, Benjamin
Maize is susceptible to severe Fe-deficiency symptoms when growing on soils with high pH. Therefore, development of Fe-efficient maize genotypes would aid to overcome Fe limitation on these soils. However, Fe-efficiency is a quantitative trait depending on complex mechanism interactions. The determination of these mechanisms would provide a better understanding of the complex trait Fe-efficiency. In the actual study, the determination of Fe-efficiency involved mechanisms were tackled by population and quantitative genetics. In fact, population genetics facilitate the discovery of genes being important to crop improvement based on a comparison of gene evolution and its ancestral genetic material. Linkage mapping and association analyses require both phenotypic variation and polymorphic markers to determine important quantitative trait loci (QTL). The objective of this research was to dissect the genetic architecture of Fe-efficiency in maize by applying different genetic approaches. Artificial selection during domestication and (or) crop improvement can result in limitation of sequence variation at candidate genes that could limit their detection by quantitative genetic approaches. The objectives of our study were to (i) describe patterns of sequence variation of 14 candidate genes for mobilization, uptake, and transport of Fe in maize, as well as regulatory function and (ii) determine if these genes were targets of selection during domestication. This study was based on 14 candidate genes sequences of 27 diverse maize inbreds, 18 teosinte inbreds, and one Zea luxurians strain as an outgroup. The experimental results suggested that the majority of candidate genes for Fe-efficiency examined in this study were not target of artificial selection. Nevertheless, the genes NAAT1, NAS1, and MTK coding for enzymes involved in phytosiderophore production, NRAMP3 responsible for Fe remobilization during germination, and YS1 transporting PS-Fe-complexes into the root showed signatures of selection. These genes might be important for the adaptation of maize to diverse environments with different Fe availabilities. This in turn suggests, that Fe-efficiency was an adaptive trait during maize domestication from teosinte. Identification of QTL provides information on the chromosomal locations contributing to the quantitative variation of complex traits. The benefit of QTL mapping compared to mutant screenings is the possibility to detect multiple genes which may be associated with the phenotypic trait. The objectives of our studies were to (i) identify QTLs for morphological and physiological traits related to Fe homeostasis, (ii) analyze Fe-dependent expression levels of genes known to be involved in Fe homeostasis as well as positional candidate genes from QTL analysis, and (iii) identify QTLs which control the mineral nutrient concentration difference. Our studies were based on experimental data of 85 genotypes from the IBM population cultivated in a hydroponic system. The QTL mapping of morphological and physiological traits provided new putative candidate genes like Ferredoxin 1, putative ferredoxin PETF, MTP4, and MTP8 which complement the genes already known as being responsible for efficient Fe homeostasis at both, deficient and sufficient Fe regime. Furthermore, the candidate gene expression indicated a trans-acting regulation for DMAS1, NAS3, NAS1, FDH1, IDI2, IDI4, and MTK. The mineral element trait QTL confidence intervals comprised candidate genes that sequestrate Cd in vacuoles (HMA3), transport Fe2+into the root cells (ZIP10), protect the cell against oxidative stress (glutaredoxin), ensure micro nutrient homeostasis during sufficient iron regime (NRAMP2), regulate protein activities (PP2C), and prevent deleterious accumulation and interaction of specific elements within cells (PHT1;5, ZIP4). Association mapping is promising to overcome the limitations of low allele diversity and absent recombinations events causing poor resolution in detecting QTL by linkage mapping. In order to unravel the genetic architecture of Fe-efficiency a vast association mapping panel comprising 267 maize inbred lines was used to (i) detect polymorphisms affecting the morphological/physiological trait formation and (ii) fine map QTL confidence intervals determined according to linkage mapping. Some of the SNPs located beyond coding regions of genes that might be important cis-binding-sites for transcription factors. Furthermore, genes detected at the Fe-deficient regime indicate to be involved in universal stress response. However, genes linked to SNPs detected at Fe-sufficient regime might comprise alleles of Fe inefficient genotypes causing inferior trait expression. The combination of several approaches provided a valuable resource of candidate genes which might aid to increase our understanding of the mechanisms of Fe-efficiency in maize and foster the efforts in breeding superior cultivars by applying molecular marker techniques.
The geography and co-location of european technology-specific co-inventorship networks
(2010) Christ, Julian P.
This paper contributes with empirical findings to European co-inventorship location and geographical coincidence of co-patenting networks. Based on EPO co-patenting information for the reference period 2000-2004, we analyze the spatial configuration of 44 technology-specific co-inventorship networks. European co-inventorship (co-patenting) activity is spatially linked to 1259 European NUTS3 units (EU25+CH+NO) and their NUTS1 regions by inventor location. We extract 7.135.117 EPO co-patenting linkages from our own relational database that makes use of the OECD RegPAT (2009) files. The matching between international Patent Classification (IPC) subclasses and 44 technology fields is based on the ISI-SPRU-OST-concordance. We confirm the hypothesis that the 44 co-inventorship networks differ in their overall size (nodes, linkages, self-loops)and that they are dominated by similar groupings of regions. The paper offers statistical evidence for the presence of highly localized European co-inventorship networks for all 44 technology fields, as the majority of linkages between NUTS3 units (counties and districts) are within the same NUTS1 regions. Accordingly, our findings helps to understand general presence of positive spatial autocorrelation in regional patent data. Our analysis explicitly accounts for different network centrality measures (betweenness, degree, eigenvector). Spearman rank correlation coefficients for all 44 technology fields confirm that most co-patenting networks co-locate in those regions that are central in several technology-specfic co-patenting networks. These findings support the hypothesis that leading European regions are indeed multi-field network nodes and that most research collaboration is taking place in dense co-patenting networks.
The geography and co-location of european technology-specific co-inventorship networks
(2010) Christ, Julian P.
This paper contributes with empirical findings to European co-inventorship location and geographical coincidence of co-patenting networks. Based on EPO co-patenting information for the reference period 2000-2004, we analyze the spatial configuration of 44 technology-specific co-inventorship networks. European co-inventorship (co-patenting) activity is spatially linked to 1259 European NUTS3 units (EU25+CH+NO) and their NUTS1 regions by inventor location. We extract 7.135.117 EPO co-patenting linkages from our own relational database that makes use of the OECD RegPAT (2009) files. The matching between International Patent Classification (IPC) subclasses and 44 technology fields is based on the ISI-SPRU-OST-concordance. We confirm the hypothesis that the 44 co-inventorship networks differ in their overall size (nodes, linkages, self-loops) and that they are dominated by similar groupings of regions. The paper offers statistical evidence for the presence of highly localized European co-inventorship networks for all 44 technology fields, as the majority of linkages between NUTS3 units (counties and districts) are within the same NUTS1 regions. Accordingly, our findings helps to understand general presence of positive spatial autocorrelation in regional patent data. Our analysis explicitly accounts for different network centrality measures (betweenness, degree, eigenvector). Spearman rank correlation coefficients for all 44 technology fields confirm that most co-patenting networks co-locate in those regions that are central in several technology-specific co-patenting networks. These findings support the hypothesis that leading European regions are indeed multi-filed network nodes and that most research collaboration is taking place in dense co-patenting networks.