Hungarian Genomic Data Warehouse supporting the healthy ageing research / A Magyar Genomikai Egészségtárház az egészséges hosszú élet kutatásának szolgálatában.

Összefoglaló. A fejlett társadalmak egészségügyi rendszereinek legnagyobb kihívását az öregedéssel összefüggo, korfüggo betegségek jelentik. Annak megértéséhez, hogy az egyes genetikai variánsoknak mi a szerepük egy korfüggo 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 tuzte ki célul, idoskoruk mellett is egészséges önkéntesek teljesgenom-szekvenciáinak és kapcsolódó fenotípusadatainak katalogizálásával és elemzésével, létrehozva az elso magyar teljes genomi referencia-adatbázist. Fontos szempont volt, hogy a kutatás az egészséges öregedést vizsgáló nemzetközi projektekhez is kapcsolódást biztosítson, így lehetoséget teremtve a különbözo országokból származó adatok harmonizálására és közös elemzésére. A kutatás résztvevoinek 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 nok között. A résztvevok csaknem fele (46%) egyedül él. Magas a felsofokú végzettséguek aránya (46%), a résztvevok 61%-a hosszú idon 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 elsoként tervez hozzáférést biztosítani egy magyar teljes genomi referencia-adatbázishoz, amely a genetikusan meghatározott betegségek és fenotípusok kutatásában és a klinikai gyakorlatban is alapveto fontosságú. A projekt bioinformatikai fejlesztései a genetikai/genomikai információk többszintu elérését támogatják a személyes adatok védettségét megorzo 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.

VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering.

BACKGROUND: The low concordance between different variant calling methods still poses a challenge for the wide-spread application of next-generation sequencing in research and clinical practice. A wide range of variant annotations can be used for filtering call sets in order to improve the precision of the variant calls, but the choice of the appropriate filtering thresholds is not straightforward. Variant quality score recalibration provides an alternative solution to hard filtering, but it requires large-scale, genomic data. RESULTS: We evaluated germline variant calling pipelines based on BWA and Bowtie 2 aligners in combination with GATK UnifiedGenotyper, GATK HaplotypeCaller, FreeBayes and SAMtools variant callers, using simulated and real benchmark sequencing data (NA12878 with Illumina Platinum Genomes). We argue that these pipelines are not merely discordant, but they extract complementary useful information. We introduce VariantMetaCaller to test the hypothesis that the automated fusion of measurement related information allows better performance than the recommended hard-filtering settings or recalibration and the fusion of the individual call sets without using annotations. VariantMetaCaller uses Support Vector Machines to combine multiple information sources generated by variant calling pipelines and estimates probabilities of variants. This novel method had significantly higher sensitivity and precision than the individual variant callers in all target region sizes, ranging from a few hundred kilobases to whole exomes. We also demonstrated that VariantMetaCaller supports a quantitative, precision based filtering of variants under wider conditions. Specifically, the computed probabilities of the variants can be used to order the variants, and for a given threshold, probabilities can be used to estimate precision. Precision then can be directly translated to the number of true called variants, or equivalently, to the number of false calls, which allows finding problem-specific balance between sensitivity and precision. CONCLUSIONS: VariantMetaCaller can be applied to small target regions and whole exomes as well, and it can be used in cases of organisms for which highly accurate variant call sets are not yet available, therefore it can be a viable alternative to hard filtering in cases where variant quality score recalibration cannot be used. VariantMetaCaller is freely available at http://bioinformatics.mit.bme.hu/VariantMetaCaller .

Dynamic Contrast-Enhanced MRI Parameters as Biomarkers in Assessing Head and Neck Lesions After Chemoradiotherapy Using a Wide-Bore 3 Tesla Scanner.

Pilot studies have shown promising results in characterizing head and neck tumors (HNT) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), differentiating between malignant and benign lesions and evaluating changes in response to chemoradiotherapy (CRT). Our aim was to find DCE-MRI parameters, biomarkers in evaluating the post-CRT status. Two hundred and five patients with head and neck lesions were examined with DCE-MRI sequences. The time intensity curves (TIC) were extracted and processed to acquire time-to-peak (TTP), relative maximum enhancement (RME), relative wash-out (RWO), and two new parameters attack and decay. These parameters were analyzed using univariate tests in SPSS (Statistical Package for the Social Sciences, version 17, SPSS Inc. Chicago, USA) to identify parameters that could be used to infer tumor malignancy and post-CRT changes. Multiple parameters of curve characteristics were significantly different between malignant tumors after CRT (MACRT) and changes caused by CRT. The best-performing biomarkers were the attack and the decay. We also found multiple significant (p < 0.05) parameters for both the benign and malignant status as well as pre- and post-CRT status. Our large cohort of data supports the increasing role of DCE-MRI in HNT differentiation, particularly for the assessment of post-CRT status along with accurate morphological imaging.

Novel genes in Human Asthma Based on a Mouse Model of Allergic Airway Inflammation and Human Investigations.

PURPOSE: Based on a previous gene expression study in a mouse model of asthma, we selected 60 candidate genes and investigated their possible roles in human asthma. METHODS: In these candidate genes, 90 SNPs were genotyped using MassARRAY technology from 311 asthmatic children and 360 healthy controls of the Hungarian (Caucasian) population. Moreover, gene expression levels were measured by RT PCR in the induced sputum of 13 asthmatics and 10 control individuals. t-tests, chi-square tests, and logistic regression were carried out in order to assess associations of SNP frequency and expression level with asthma. Permutation tests were performed to account for multiple hypothesis testing. RESULTS: The frequency of 4 SNPs in 2 genes differed significantly between asthmatic and control subjects: SNPs rs2240572, rs2240571, rs3735222 in gene SCIN, and rs32588 in gene PPARGC1B. Carriers of the minor alleles had reduced risk of asthma with an odds ratio of 0.64 (0.51-0.80; P=7×10(-5)) in SCIN and 0.56 (0.42-0.76; P=1.2×10(-4)) in PPARGC1B. The expression levels of SCIN, PPARGC1B and ITLN1 genes were significantly lower in the sputum of asthmatics. CONCLUSIONS: Three potentially novel asthma-associated genes were identified based on mouse experiments and human studies.

Bayesian systems-based genetic association analysis with effect strength estimation and omic wide interpretation: a case study in rheumatoid arthritis.

Rich dependency structures are often formed in genetic association studies between the phenotypic, clinical, and environmental descriptors. These descriptors may not be standardized, and may encompass various disease definitions and clinical endpoints which are only weakly influenced by various (e.g., genetic) factors. Such loosely defined complex intermediate clinical phenotypes are typically used in follow-up candidate gene association studies, e.g., after genome-wide analysis, to deepen the understanding of the associations and to estimate effect strength. This chapter discusses a solid methodology, which is useful in such a scenario, by using probabilistic graphical models, namely, Bayesian networks in the Bayesian statistical framework. This method offers systematically scalable, comprehensive hierarchical hypotheses about multivariate relevance. We discuss its workflow: from data engineering to semantic publication of the results. We overview the construction, visualization, and interpretation of complex hypotheses related to the structural analysis of relevance. Furthermore, we illustrate the use of a dependency model-based relevance measure, which takes into account the structural properties of the model, for quantifying the effect strength. Finally, we discuss the "interpretational" or translational challenge of a genetic association study, with a focus on the fusion of heterogeneous omic knowledge to reintegrate the results into a genome-wide context.

Multivariate analysis of dopaminergic gene variants as risk factors of heroin dependence.

BACKGROUND: Heroin dependence is a debilitating psychiatric disorder with complex inheritance. Since the dopaminergic system has a key role in rewarding mechanism of the brain, which is directly or indirectly targeted by most drugs of abuse, we focus on the effects and interactions among dopaminergic gene variants. OBJECTIVE: To study the potential association between allelic variants of dopamine D2 receptor (DRD2), ANKK1 (ankyrin repeat and kinase domain containing 1), dopamine D4 receptor (DRD4), catechol-O-methyl transferase (COMT) and dopamine transporter (SLC6A3) genes and heroin dependence in Hungarian patients. METHODS: 303 heroin dependent subjects and 555 healthy controls were genotyped for 7 single nucleotide polymorphisms (SNPs) rs4680 of the COMT gene; rs1079597 and rs1800498 of the DRD2 gene; rs1800497 of the ANKK1 gene; rs1800955, rs936462 and rs747302 of the DRD4 gene. Four variable number of tandem repeats (VNTRs) were also genotyped: 120 bp duplication and 48 bp VNTR in exon 3 of DRD4 and 40 bp VNTR and intron 8 VNTR of SLC6A3. We also perform a multivariate analysis of associations using Bayesian networks in Bayesian multilevel analysis (BN-BMLA). FINDINGS AND CONCLUSIONS: In single marker analysis the TaqIA (rs1800497) and TaqIB (rs1079597) variants were associated with heroin dependence. Moreover, -521 C/T SNP (rs1800955) of the DRD4 gene showed nominal association with a possible protective effect of the C allele. After applying the Bonferroni correction TaqIB was still significant suggesting that the minor (A) allele of the TaqIB SNP is a risk component in the genetic background of heroin dependence. The findings of the additional multiple marker analysis are consistent with the results of the single marker analysis, but this method was able to reveal an indirect effect of a promoter polymorphism (rs936462) of the DRD4 gene and this effect is mediated through the -521 C/T (rs1800955) polymorphism in the promoter.

Additive effects of serotonergic and dopaminergic polymorphisms on trait impulsivity.

Twin studies suggest 45% heritability of trait impulsivity. Results from candidate gene studies to date are contradictory; impulsivity phenotypes were measured by different behavioral and questionnaire methods related either to the dopaminergic or to the serotonergic system. Here we report an association study of both dopaminergic (COMT rs4680, DRD4 48 bp VNTR, DRD2/ANKK1 rs1800497) and serotonergic (HTR1A rs6925, HTR1B rs13212041, SLC6A4 5-HTTLPR) gene polymorphisms and trait impulsivity assessed with the Barratt Impulsiveness Scale (BIS-11) in a sample of 687 Caucasian young adults. Results showed lower impulsivity in the presence of the DRD4 7-repeat (P = 0.006) and the HTR1B rs13212041 alleles (P = 0.003). These findings stayed significant after Bonferroni correction. A multivariate analysis using Bayesian networks confirmed independent effects of these two polymorphisms and provided a coherent characterization of the system of dependencies with respect to the impulsivity construct as well as its subscales. These results clearly suggest an additive effect of dopaminergic and serotonergic polymorphisms on trait impulsivity.

Haplotyping of putative microRNA-binding sites in the SNAP-25 gene.

Synaptosomal-associated protein 25 (SNAP-25) plays a crucial role in exocitosis. Single nucleotide polymorphisms (rs3746544 and rs1051312) in the 3' un-translated region of the SNAP-25 gene have been described to be in association with attention-deficit hyperactivity disorder. As the disease affects millions of children world-wide, understanding the genetic background of attention-deficit hyperactivity disorder is of crucial importance. Efficient and reliable PCR-RFLP protocols were elaborated for the genotyping of the rs3746544 and rs1051312 SNPs employing a high-throughput capillary electrophoresis method for fragment analysis. A novel real-time PCR-based technique was used applying sequence specific TaqMan probes to haplotype the two SNPs, and the G-C haplotype could not be detected in a large Caucasian population (N=1376). These findings have been confirmed by molecular biology tools as well as by the PHASE Bayesian computational approach. In silico analyses have suggested that the two SNPs might alter microRNA binding and thus have an effect on SNAP-25 production. We have demonstrated that this biological information can be revealed only by direct haplotype analysis emphasizing the importance of our novel molecular haplotye analysis protocol. Results of the study of the two SNPs might shed light on the association of SNAP-25 variants and pathological phenotypes at the molecular level.