Paleo-Balkan and Slavic Contributions to the Genetic Pool of Moldavians

Moldova has a rich historical and cultural heritage, which may be reflected in the current genetic makeup of its population. To date, no comprehensive studies exist about the population genetic structure of modern Moldavians. To bridge this gap with respect to paternal lineages, we analyzed 37 binary and 17 multiallelic (STRs) polymorphisms on the non-recombining portion of the Y chromosome in 125 Moldavian males. In addition, 53 Ukrainians from eastern Moldova and 54 Romanians from the neighboring eastern Romania were typed using the same set of markers. In Moldavians, 19 Y chromosome haplogroups were identified, the most common being I-M423 (20.8%), R-M17* (17.6%), R-M458 (12.8%), E-v13 (8.8%), RM269* and R-M412* (both 7.2%). In Romanians, 14 haplogroups were found including I-M423 (40.7%), R-M17* (16.7%), RM405 (7.4%), E-v13 and R-M412* (both 5.6%). In Ukrainians, 13 haplogroups were identified including R-M17 (34.0%), I-M423 (20.8%), R-M269* (9.4%), N-M178, R-M458 and R-M73 (each 5.7%). Our results show that a significant majority of the Moldavian paternal gene pool belongs to eastern/central European and Balkan/eastern Mediterranean Y lineages. Phylogenetic and AMOVA analyses based on Y-STR loci also revealed that Moldavians are close to both eastern/central European and Balkan-Carpathian populations. The data correlate well with historical accounts and geographical location of the region and thus allow to hypothesize that extant Moldavian paternal genetic lineages arose from extensive recent admixture between genetically autochthonous populations of the Balkan-Carpathian zone and neighboring Slavic group

Population History of the Dniester-Carpathians: evidence from Alu insertion and Y-chromosome polymorphisms

The Dniester-Carpathian region has attracted much attention from historians, linguists, and anthropologists, but remains insufficiently studied genetically. We have analyzed a set of autosomal polymorphic loci and Y-chromosome markers in six autochthonous Dniester-Carpathian population groups: 2 Moldavian, 1 Romanian, 1 Ukrainian and 2 Gagauz populations. To gain insight into the population history of the region, the data obtained in this study were compared with corresponding data for other populations of Western Eurasia. The analysis of 12 Alu human-specific polymorphisms in 513 individuals from the Dniester-Carpathian region showed a high degree of homogeneity among Dniester-Carpathian as well as southeastern European populations. The observed homogeneity suggests either a common ancestry of all southeastern European populations or a strong gene flow between them. Nevertheless, tree reconstruction and principle component analyses allow the distinction between Balkan-Carpathian (Macedonians, Romanians, Moldavians, Ukrainians and Gagauzes) and Eastern Mediterranean (Turks, Greeks and Albanians) population groups. These results are consistent with those from classical and other DNA markers and are compatible with archaeological and paleoanthropological data. Haplotypes constructed from Y-chromosome markers were used to trace the paternal origin of the Dniester-Carpathian populations. A set of 32 binary and 7 STR Y-chromosome polymorphisms was genotyped in 322 Dniester-Carpathian Y-chromosomes. On this basis, 21 stable haplogroups and 171 combination binary marker/STR haplotypes were identified. The haplogroups E3b1, G, J1, J2, I1b, R1a1, and R1b3, most common in the Dniester-Carpathian region, are also common in European and Near Eastern populations. Ukrainians and southeastern Moldavians show a high proportion of eastern European lineages, while Romanians and northern Moldavians demonstrate a high proportion of western Balkan lineages. The Gagauzes harbor a conspicuous proportion of lineages of Near Eastern origin, comparable to that in Balkan populations. In general, the Dniester-Carpathian populations demonstrate the closest affinities to the neighboring southeastern and eastern European populations. The expansion times were estimated for 4 haplogroups (E3b1, I1b, R1a1, and R1b3) from associated STR diversity. The presence in the studied area of genetic components of different age indicates successive waves of migration from diverse source areas of Western Eurasia. Neither of the genetic systems used in this study revealed any correspondence between genetic and linguistic patterns in the Dniester-Carpathian region or in Southeastern Europe, a fact which suggests either that the ethnic differentiation in these regions was indeed very recent or that the linguistic and other social barriers were not strong enough to prevent genetic flow between populations. In particular, Gagauzes, a Turkic speaking population, show closer affinities not to other Turkic peoples, but to their geographical neighbors

Candidate genes and sequence variants for susceptibility to mycobacterial infection identified by whole-exome sequencing

Inborn errors of immunity are known to influence susceptibility to mycobacterial infections. The aim of this study was to characterize the genetic profile of nine patients with mycobacterial infections (eight with BCGitis and one with disseminated tuberculosis) from the Republic of Moldova using whole-exome sequencing. In total, 12 variants in eight genes known to be associated with Mendelian Susceptibility to Mycobacterial Disease (MSMD) were detected in six out of nine patients examined. In particular, a novel splice site mutation c.373–2A&gt;C in STAT1 gene was found and functionally confirmed in a patient with disseminated tuberculosis. Trio analysis was possible for seven out of nine patients, and resulted in 23 candidate variants in 15 novel genes. Four of these genes - GBP2, HEATR3, PPP1R9B and KDM6A were further prioritized, considering their elevated expression in immune-related tissues. Compound heterozygosity was found in GBP2 in a single patient, comprising a maternally inherited missense variant c.412G&gt;A/p.(Ala138Thr) predicted to be deleterious and a paternally inherited intronic mutation c.1149+14T&gt;C. Functional studies demonstrated that the intronic mutation affects splicing and the level of transcript. Finally, we analyzed pathogenicity of variant combinations in gene pairs and identified five patients with putative oligogenic inheritance. In summary, our study expands the spectrum of genetic variation contributing to susceptibility to mycobacterial infections in children and provides insight into the complex/oligogenic disease-causing mode.</p

Synergistic effect of genetic polymorphisms in <i>TLR6</i> and <i>TLR10</i> genes on the risk of pulmonary tuberculosis in a Moldavian population

Polymorphisms in genes that control immune function and regulation may influence susceptibility to pulmonary tuberculosis (TB). In this study, 14 polymorphisms in 12 key genes involved in the immune response ( VDR, MR1, TLR1, TLR2, TLR10, SLC11A1, IL1B, IL10, IFNG, TNF, IRAK1, and FOXP3) were tested for their association with pulmonary TB in 271 patients with TB and 251 community-matched controls from the Republic of Moldova. In addition, gene–gene interactions involved in TB susceptibility were analyzed for a total of 43 genetic loci. Single nucleotide polymorphism (SNP) analysis revealed a nominal association between TNF rs1800629 and pulmonary TB (Fisher exact test P = 0.01843). In the pairwise interaction analysis, the combination of the genotypes TLR6 rs5743810 GA and TLR10 rs11096957 GT was significantly associated with an increased genetic risk of pulmonary TB (OR = 2.48, 95% CI = 1.62–3.85; Fisher exact test P value = 1.5 × 10−5, significant after Bonferroni correction). In conclusion, the TLR6 rs5743810 and TLR10 rs11096957 two-locus interaction confers a significantly higher risk for pulmonary TB; due to its high frequency in the population, this SNP combination may serve as a novel biomarker for predicting TB susceptibility. </jats:p

Synergistic Effect of Genetic Polymorphisms in TLR6 and TLR10 Genes on The Risk of Pulmonary Tuberculosis in Moldavian Population

Abstract BACKGROUND: Host immunity is essential for efficient recognition and clearance of M. tuberculosis infection. Polymorphisms in genes that regulate immune response have been reported to influence the susceptibility/resistance to pulmonary tuberculosis (TB). Here we evaluated associations between 14 polymorphisms in 12 core genes involved in immune responses and pulmonary TB in Moldavian population, and investigated whether interactions between these and previously analyzed polymorphisms could exist and modulate the risk of pulmonary TB.METHODS: Polymorphisms VDR rs7975232, VDR rs1544410, VDR rs2228570, MR1 rs1052632, TLR1 rs5743618, TLR2 rs111200466, TLR10 rs11096957, SLC11A1 rs2276631, IL1B rs1143643, IL10 rs1800896, IFNG rs2430561, TNF rs1800629, IRAK1 rs1059703, and FOXP3 rs2232365 were genotyped in 271 Moldavian pulmonary TB cases and 251 community-matched healthy controls. Associations were tested using Fisher test and logistic regression. Complemented with the data from our previous study (PMID: 30529560), investigation of gene-gene interactions was performed for a total of 43 loci. Significance level was adjusted by the Bonferroni correction.RESULTS: Single polymorphism analysis revealed a nominal association between TNF rs1800629 and pulmonary TB (Fisher exact test p-value = 0.01843). Marginal differences between cases and controls were observed for haplotypes in the gene cluster TLR1-TLR6-TLR10 and gene TLR2. In the pairwise interaction analysis, the combination of genotypes TLR6 rs5743810 GA and TLR10 rs11096957 GT was significantly associated with an increased genetic risk of pulmonary TB (OR = 2.48, 95% CI = 1.62–3.85; Fisher exact test p-value = 1.5 x 10-5, significant after Bonferroni correction).CONCLUSION: The TLR6 rs5743810 and TLR10 rs11096957 two-locus interaction confers a significantly higher risk for pulmonary TB and has potential as a novel biomarker for predicting TB susceptibility.</jats:p

Synergistic effect of genetic polymorphisms in TLR6 and TLR10 genes on the risk of pulmonary tuberculosis in Moldavian population

Abstract BACKGROUND Host immunity is essential for efficient recognition and clearance of M. tuberculosis infection. Polymorphisms in genes that regulate immune response have been reported to influence the susceptibility/resistance to pulmonary tuberculosis (TB). Here we evaluated associations between 14 polymorphisms in 12 core genes involved in immune responses and pulmonary TB in Moldavian population, and investigated whether interactions between these and previously analyzed polymorphisms could exist and modulate the risk of pulmonary TB. METHODS Polymorphisms VDR rs7975232, VDR rs1544410, VDR rs2228570, MR1 rs1052632, TLR1 rs5743618, TLR2 rs111200466, TLR10 rs11096957, SLC11A1 rs2276631, IL1B rs1143643, IL10 rs1800896, IFNG rs2430561, TNF rs1800629, IRAK1 rs1059703, and FOXP3 rs2232365 were genotyped in 271 Moldavian pulmonary TB cases and 251 community-matched healthy controls. Associations were tested using Fisher test and logistic regression. Complemented with the data from our previous study (PMID: 30529560), investigation of gene-gene interactions was performed for a total of 43 loci. Significance level was adjusted by the Bonferroni correction. RESULTS Single polymorphism analysis revealed a nominal association between TNF rs1800629 and pulmonary TB (Fisher exact test p-value = 0.01843). Marginal differences between cases and controls were observed for haplotypes in the gene cluster TLR1-TLR6-TLR10 and gene TLR2. In the pairwise interaction analysis, the combination of genotypes TLR6 rs5743810 GA and TLR10 rs11096957 GT was significantly associated with an increased genetic risk of pulmonary TB (OR = 2.48, 95% CI = 1.62–3.85; Fisher exact test p-value = 1.5 × 10− 5, significant after Bonferroni correction). CONCLUSION The TLR6 rs5743810 and TLR10 rs11096957 two-locus interaction confers a significantly higher risk for pulmonary TB and has potential as a novel biomarker for predicting TB susceptibility.</jats:p

Additional file 2 of Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes

Additional file 2. R program and examples. The program code of RNAdeNoise in R language, examples of the use