“10 001 Dalmatians:” Croatia Launches Its National Biobank

Rudan, Igor; Marušić, Ana; Janković, Stipan; Rotim, Krešimir; Boban, Mladen; Lauc, Gordan; Grković, Ivica; Đogaš, Zoran; Zemunik, Tatijana; Vatavuk, Zoran; Benčić, Goran; Rudan, Diana; Mulić, Rosanda; Krželj, Vjekoslav; Terzić, Janoš; Stojanović, Dražen; Puntarić, Dinko; Bilić, Ervina; Ropac, Darko; Vorko-Jović, Ariana; Znaor, Ariana; Stevanović, Ranko; Biloglav, Zrinka; Polašek, Ozren

doi:10.3325/cmj.2009.50.4

Cited by 107 publications

(81 citation statements)

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“…One such study reported increased odds of nephrolithiasis in isolated populations (25), which was attributed to the existence of consanguinity in some of the island populations (18,(26)(27)(28)(29)(30). Subsequent efforts in gene mapping in two of the Croatian Adriatic islands (Vis and Korčula) yielded a number of interesting results, including findings on genetic influence on height determination (31,32), lung function (33), chronic kidney disease (34), and levels of fasting glucose (35), serum lipids (36)(37)(38), creatinine (39), and plasma N-glycans (40), supporting the initial proposal that isolated populations present a very valuable and fundamentally advantageous research resource (17,18,41).…”

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confidence: 69%

Predictive Value of 8 Genetic Loci for Serum Uric Acid Concentration

et al. 2010

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AimTo investigate the value of genomic information in prediction of individual serum uric acid concentrations.Methods Three population samples were investigated: from isolated Adriatic island communities of Vis (n = 980) and Korčula (n = 944), and from general population of the city of Split (n = 507). Serum uric acid concentration was correlated with the genetic risk score based on 8 previously described genes: PDZK1, GCKR, SLC2A9, ABCG2, LR-RC16A, SLC17A1, SLC16A9, and SLC22A12, represented by a total of 16 single-nucleotide polymorphisms (SNP). The data were analyzed using classification and regression tree (CART) and general linear modeling. ResultsThe most important variables for uric acid prediction with CART were genetic risk score in men and age in women. The percent of variance for any single SNP in predicting serum uric acid concentration varied from 0.0%-2.0%. The use of genetic risk score explained 0.1%-2.5% of uric acid variance in men and 3.9%-4.9% in women. The highest percent of variance was obtained when age, sex, and genetic risk score were used as predictors, with a total of 30.9% of variance in pooled analysis. ConclusionDespite overall low percent of explained variance, uric acid seems to be among the most predictive human quantitative traits based on the currently available SNP information. The use of genetic risk scores is a valuable approach in genetic epidemiology and increases the predictability of human quantitative traits based on genomic information compared with single SNP approach. Uric acid is the end product of purine metabolism in humans and higher primates and was initially believed to be a highly potent risk factor for disease development (1). This was confirmed in different studies that reported increased levels of serum uric acid in metabolic disorders and cancer (2-4). However, recent studies have reported possible beneficial effects of uric acid, especially in cardiovascular and neurological diseases of late onset (5,6). Possible protective effects are due to a strong antioxidant properties of uric acid (7). All these findings make uric acid a highly interesting research target in both epidemiology and genetics.Efforts in the search for the genetic basis of uric acid concentrations have been focused on several candidate genes (8,9) and the recently described SLC2A9 has been found to be a gene with a major effect in humans, especially in women (10,11). SLC2A9, also known as Glut 9, is a member of the SLC2A facilitative glucose transporter family, which has an important role in the sugars metabolism (12). The gene encodes a putative transporter of 540 amino acids, which is closely related to several more members of the same gene family, with 44% and 38% resemblance to Glut5 and Glut1, respectively (12). The protein consists of 12 transmembrane domains, sugar motifs, and other signatures which facilitate sugars transport (13). The long form of this protein in humans is most strongly expressed in basolateral membranes of proximal renal tubular cells, the liver, and placenta, w...

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Predictive Value of 8 Genetic Loci for Serum Uric Acid Concentration

et al. 2010

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“…Blood samples were collected from unselected examinees from the Croatian Adriatic island Korčula as part of a larger genetic epidemiology program. [39][40][41] Participants were recruited on the basis of the official voting registers and contacts with the local stakeholders and religious communities. Sampling took place in 2007.…”

Section: Methodsmentioning

confidence: 99%

Epigenetic silencing ofHNF1Aassociates with changes in the composition of the human plasmaN-glycome

Zoldoš¹,

Horvat²,

Novokmet³

et al. 2012

Epigenetics

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“…In many publications isolated aspects of information technology are combined with organizational, ethical and data security aspects. Conceptually IT implementations vary between three different levels of biobanking, which are single institution/project biobanks, biobanks of research networks as well as national and international biobanking collaboration scenarios [24][25][26][27][28]. It has then been decided to primarily focus on requirements for the basic management of samples, storage locations and clinical annotations as well as the processes related with sample acquisition, transportation, storage, quality assurance measures and clinical phenotype documentation.…”

Section: Methodsmentioning

confidence: 99%

IT Infrastructure Components for Biobanking

Prokosch

Beck²,

Ganslandt³

et al. 2010

Appl Clin Inform

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KeywordsRequirement specification, biobanking, translational research information technology infrastructure SummaryObjective: Within translational research projects in the recent years large biobanks have been established, mostly supported by homegrown, proprietary software solutions. No general requirements for biobanking IT infrastructures have been published yet. This paper presents an exemplary biobanking IT architecture, a requirements specification for a biorepository management tool and exemplary illustrations of three major types of requirements. Methods: We have pursued a comprehensive literature review for biobanking IT solutions and established an interdisciplinary expert panel for creating the requirements specification. The exemplary illustrations were derived from a requirements analysis within two university hospitals. Results: The requirements specification comprises a catalog with more than 130 detailed requirements grouped into 3 major categories and 20 subcategories. Special attention is given to multitenancy capabilities in order to support the project-specific definition of varying research and biobanking contexts, the definition of workflows to track sample processing, sample transportation and sample storage and the automated integration of preanalytic handling and storage robots. Conclusion: IT support for biobanking projects can be based on a federated architectural framework comprising primary data sources for clinical annotations, a pseudonymization service, a clinical data warehouse with a flexible and user-friendly query interface and a biorepository management system. Flexibility and scalability of all such components are vital since large medical facilities such as university hospitals will have to support biobanking for varying monocentric and multicentric research scenarios and multiple medical clients.

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“10 001 Dalmatians:” Croatia Launches Its National Biobank

Cited by 107 publications

References 14 publications

Predictive Value of 8 Genetic Loci for Serum Uric Acid Concentration

Predictive Value of 8 Genetic Loci for Serum Uric Acid Concentration

Epigenetic silencing ofHNF1Aassociates with changes in the composition of the human plasmaN-glycome

IT Infrastructure Components for Biobanking

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