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The research institute in Martonvásár is one of the largest agricultural research institutes in Hungary and in Central Europe. For many years now, the accumulated data on the extensive wheat breeding stocks has been handled and analysed using programs developed in the institute. The information system that has been elaborated and constantly improved can be used for keeping records of breeding stock, for planning field and laboratory experiments, for site-plant performance evaluation, for automated data collection, for the rapid evaluation of the results and for effective management of the pedigree, seed exchange and the institute’s cereal gene bank.The demand for the storage of molecular data and their use in breeding has increased parallel with the development of new, PCR-based markers. For this reason, informatics tools (data structure and software) suited to the design of marker-assisted selection experiments and the interpretation of the results have been developed as part of the existing Martonvásár wheat breeding information system. The aim was to link molecular data to the phenotypic information already available in the database and to make the results available to wheat breeders and geneticists.The interpretation of molecular data related to specific genotypes is of assistance in clarifying the genetic background of economically important phenotypic traits, in identifying markers linked to the useful genes or agronomic traits to be found in the genomics database, and in the selection of satisfactory parental partners for breeding. Marker assisted selection coupled with traditional breeding activities enables the breeder to make plant selections based on the presence of target genes. Conventional wheat breeding with the integrated molecular component allows breeders to more accurately and efficiently select defined sets of genes in segregating generations.The molecular data are stored in a relational database, the central element of which is the [DNASource] entity. This is used to collect and store information on gene sources arising during breeding. It is therefore linked both to the phenotypic data stored in the traditional breeding system (measurements, observations, laboratory data) and to the component parts of the new, molecular data structure ([PrimerBank], [Marker], [Allele] and [Gene]).

In recent years an information system has been elaborated and constantly improved in Martonvásár, making it possible to handle the 3–4 million identification, observation, measurement, pedigree and other data generated for a total of almost 100,000 experimental plots each year. The extremely rapid development of biotechnology has made breeders interested in integrating molecular breeding methods into the conventional phenotype-pedigree system. The aim is to improve the competitiveness of breeding programmes through the intensive use of this new technology, with particular emphasis on determining how marker-assisted selection can be utilised. The present paper outlines not only a new data structure introduced to accommodate the new data elements of data categories such as gene sources, primer bank, primer combinations, markers, genes and alleles, but also data management tools and a standalone software interface to combine both molecular and phenotypic data. The integration of the molecular genomic data (GENETECH) with the information from the existing databases: pedigree (PEDIGREE), gene bank (GENEBANK) and germplasm exchange (GERMPEXCHG), ensures that biotechnological data generated at no little cost can be harnessed in ways that are important for breeders in decision-making. This is achieved through: (i) identification and centralization in uniform sources of the molecular data, and their matching with specific phenotypes, with special regard to those of importance for marker-assisted selection, (ii) integration and compliance with existing information system data, (iii) facilitation of decision-making based on the above (e.g. grouping of selection/crossing partners).

Összefoglaló. A fejlett társadalmak egészségügyi rendszereinek legnagyobb kihívását az öregedéssel összefüggő, korfüggő betegségek jelentik. Annak megértéséhez, hogy az egyes genetikai variánsoknak mi a szerepük egy korfüggő betegség kialakulásában, meg kell ismerkednünk magával az öregedési folyamattal, az egészséges hosszú élettel asszociált, valamint az adott populációra jellegzetes variánsokkal is. A Semmelweis Egyetem Genomikai Medicina és Ritka Betegségek Intézete a Nemzeti Bionika Program keretén belül a Magyar Genomikai Egészségtárház felállítását tűzte ki célul, időskoruk mellett is egészséges önkéntesek teljesgenom-szekvenciáinak és kapcsolódó fenotípusadatainak katalogizálásával és elemzésével, létrehozva az első magyar teljes genomi referencia-adatbázist. Fontos szempont volt, hogy a kutatás az egészséges öregedést vizsgáló nemzetközi projektekhez is kapcsolódást biztosítson, így lehetőséget teremtve a különböző országokból származó adatok harmonizálására és közös elemzésére. A kutatás résztvevőinek 49%-a 70–80 éves, 36%-a 81–90 éves, 14%-uk pedig 90 év feletti; a nemek aránya 44/56%-os megoszlást mutatott a férfiak és a nők között. A résztvevők csaknem fele (46%) egyedül él. Magas a felsőfokú végzettségűek aránya (46%), a résztvevők 61%-a hosszú időn át sportolt, 70%-uk sosem dohányzott. A vizsgálati alanyok szülei is magas életkort éltek meg, az édesapáknál 74,3, az édesanyák esetében pedig 80,47 év volt a halálozáskori átlagéletkor. Adattárházunk elsőként tervez hozzáférést biztosítani egy magyar teljes genomi referencia-adatbázishoz, amely a genetikusan meghatározott betegségek és fenotípusok kutatásában és a klinikai gyakorlatban is alapvető fontosságú. A projekt bioinformatikai fejlesztései a genetikai/genomikai információk többszintű elérését támogatják a személyes adatok védettségét megőrző statisztikai elemzési és mesterségesintelligencia-eljárások segítségével. Orv Hetil. 2021; 162(27): 1079–1088.

Summary. Genetics has proven to be a a successful approach in the study of ageing. To understand the role of each genetic variant in the development of an age-dependent disease, we need to become familiar with the ageing process itself and with the population-specific variants. The Institute of Genomic Medicine and Rare Disorders of the Semmelweis University within the framework of the National Bionics Program set up a data collection, the Hungarian Genomic Data Warehouse, by cataloging and analyzing complete genome sequences and related phenotype data of healthy volunteers, which also serves as a reference national Hungarian genomic database. The structure of the data warehouse allows interoperability with the most important international research projects on ageing. 49% of the participants in the Hungarian Genomic Data Warehouse were 70–80 years old, 36% were 81–90, 14% over 90 years old. The gender ratio was 44/56% between men and women. The proportion of people with higher education is high (46%), 61% of the participants played sports for a long time, and 70% never smoked. The parents of the participants also lived a high age, with an average age at death of 74.3 years for fathers and 80.47 years for mothers. The Hungarian Genomic Data Warehouse can provide vital and timely support in personalized medicine, especially in the research and diagnosis of genetically inherited disorders. The long-term goal of these bioinformatic developments is to provide access at multiple levels to the genomic data using privacy-preserving data analysis methods in genomics. Orv Hetil. 2021; 162(27): 1079–1088.