Ethnic and functional differentiation of copy number polymorphisms in Tunisian and HapMap population unveils insights on genome organizational plasticity.

Admixture mapping has been useful in identifying genetic variations linked to phenotypes, adaptation and diseases. Copy number variations (CNVs) represents genomic structural variants spanning large regions of chromosomes reaching several megabases. In this investigation, the "Canary" algorithm was applied to 102 Tunisian samples and 991 individuals from eleven HapMap III populations to genotype 1279 copy number polymorphisms (CNPs). In this present work, we investigate the Tunisian population structure using the CNP makers previously identified among Tunisian. The study revealed that Sub-Saharan African populations exhibited the highest diversity with the highest proportions of allelic CNPs. Among all the African populations, Tunisia showed the least diversity. Individual ancestry proportions computed using STRUCTURE analysis revealed a major European component among Tunisians with lesser contribution from Sub-Saharan Africa and Asia. Population structure analysis indicated the genetic proximity with Europeans and noticeable distance from the Sub-Saharan African and East Asian clusters. Seven genes harbouring Tunisian high-frequent CNPs were identified known to be associated with 9 Mendelian diseases and/or phenotypes. Functional annotation of genes under selection highlighted a noteworthy enrichment of biological processes to receptor pathway and activity as well as glutathione metabolism. Additionally, pathways of potential concern for health such as drug metabolism, infectious diseases and cancers exhibited significant enrichment. The distinctive genetic makeup of the Tunisians might have been influenced by various factors including natural selection and genetic drift, resulting in the development of distinct genetic variations playing roles in specific biological processes. Our research provides a justification for focusing on the exclusive genome organization of this population and uncovers previously overlooked elements of the genome.

Decoding the genetic relationship between Alzheimer's disease and type 2 diabetes: potential risk variants and future direction for North Africa.

Introduction: Alzheimer's disease (AD) and Type 2 diabetes (T2D) are both age-associated diseases. Identification of shared genes could help develop early diagnosis and preventive strategies. Although genetic background plays a crucial role in these diseases, we noticed an underrepresentation tendency of North African populations in omics studies. Materials and methods: First, we conducted a comprehensive review of genes and pathways shared between T2D and AD through PubMed. Then, the function of the identified genes and variants was investigated using annotation tools including PolyPhen2, RegulomeDB, and miRdSNP. Pathways enrichment analyses were performed with g:Profiler and EnrichmentMap. Next, we analyzed variant distributions in 16 worldwide populations using PLINK2, R, and STRUCTURE software. Finally, we performed an inter-ethnic comparison based on the minor allele frequency of T2D-AD common variants. Results: A total of 59 eligible papers were included in our study. We found 231 variants and 363 genes shared between T2D and AD. Variant annotation revealed six single nucleotide polymorphisms (SNP) with a high pathogenic score, three SNPs with regulatory effects on the brain, and six SNPs with potential effects on miRNA-binding sites. The miRNAs affected were implicated in T2D, insulin signaling pathways, and AD. Moreover, replicated genes were significantly enriched in pathways related to plasma protein binding, positive regulation of amyloid fibril deposition, microglia activation, and cholesterol metabolism. Multidimensional screening performed based on the 363 shared genes showed that main North African populations are clustered together and are divergent from other worldwide populations. Interestingly, our results showed that 49 SNP associated with T2D and AD were present in North African populations. Among them, 11 variants located in DNM3, CFH, PPARG, ROHA, AGER, CLU, BDNF1, CST9, and PLCG1 genes display significant differences in risk allele frequencies between North African and other populations. Conclusion: Our study highlighted the complexity and the unique molecular architecture of North African populations regarding T2D-AD shared genes. In conclusion, we emphasize the importance of T2D-AD shared genes and ethnicity-specific investigation studies for a better understanding of the link behind these diseases and to develop accurate diagnoses using personalized genetic biomarkers.

Whole Exome Sequencing allows the identification of two novel groups of Xeroderma pigmentosum in Tunisia, XP-D and XP-E: Impact on molecular diagnosis.

BACKGROUND: Skin cancers (SC) are complex diseases that develop from complex combinations of genetic and environmental risk factors. One of the most severe and rare genetic diseases predisposing to SC is the Xeroderma pigmentosum (XP) syndrome. OBJECTIVES: First, to identify the genetic etiology of XP and to better classify affected patients. Second, to provide early molecular diagnosis for pre-symptomatic patient and finally to offer genetic counseling for related individuals. METHODS: Whole Exome Sequencing (WES) and Run Of Homozygosity (ROH) were performed for two patients belonging to two different multiplex consanguineous families. The identified mutations were confirmed by Sanger sequencing and researched in ten Tunisian families including a total of 25 affected individuals previously suspected as having XP group V (XP-V) form. All patients had mild dermatological manifestations, absence of neurological abnormalities and late onset of skin tumors. RESULTS: Screening for functional variations showed the presence of the ERCC2 p.Arg683Gln in XP14KA-2 patient and a novel mutation, DDB2 p. (Lys381Argfs*2), in XP51-MAH-1 patient. Sanger sequencing and familial segregation showed that the ERCC2 mutation is present at a homozygous state in 10 affected patients belonging to 3 families. The second mutation in DDB2, is present at a homozygous state in 5 affected cases belonging to the same family. These two mutations are absent in the remaining 10 affected patients. The ERCC2 c.2048G > A mutation is present in a medium ROH region (class B) suggesting that it mostly arises from ancient relatedness within individuals. However, the c.1138delG DDB2 mutation is present in a large ROH region (class C) suggesting that it arises from recent relatedness. CONCLUSION: To our knowledge, this is the first study that identifies XP-D and XP-E complementation groups in Tunisia. These two groups are very rare and under-diagnosed in the world and were not reported in North Africa.

Application of Multi-SOM clustering approach to macrophage gene expression analysis.

The production of increasingly reliable and accessible gene expression data has stimulated the development of computational tools to interpret such data and to organize them efficiently. The clustering techniques are largely recognized as useful exploratory tools for gene expression data analysis. Genes that show similar expression patterns over a wide range of experimental conditions can be clustered together. This relies on the hypothesis that genes that belong to the same cluster are coregulated and involved in related functions. Nevertheless, clustering algorithms still show limits, particularly for the estimation of the number of clusters and the interpretation of hierarchical dendrogram, which may significantly influence the outputs of the analysis process. We propose here a multi level SOM based clustering algorithm named Multi-SOM. Through the use of clustering validity indices, Multi-SOM overcomes the problem of the estimation of clusters number. To test the validity of the proposed clustering algorithm, we first tested it on supervised training data sets. Results were evaluated by computing the number of misclassified samples. We have then used Multi-SOM for the analysis of macrophage gene expression data generated in vitro from the same individual blood infected with 5 different pathogens. This analysis led to the identification of sets of tightly coregulated genes across different pathogens. Gene Ontology tools were then used to estimate the biological significance of the clustering, which showed that the obtained clusters are coherent and biologically significant.