P-117

Saberi, Pouné; McKenzie, Judith S.; Emmett, Edward A.

doi:10.1097/01.ede.0000417122.87403.c8

Cited by 2 publications

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“…Currently no data is also available about the incidence and prevalence rates of Alzheimer’s disease in Armenia, however, it is accepted that the actual rates are comparable if not higher compared with worldwide rates (Saberi et al, 2012; Tataryan, 2012). Official statistics is also unavailable for obesity; however, the 2013 report by WHO (WHO, 2013) indicated that 55.5% of the adult population in Armenia were overweight and 24.0% were obese.…”

Section: Resultsmentioning

confidence: 99%

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians – A Machine Learning Approach

et al. 2019

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Background: During the last decades a number of genome-wide association studies (GWASs) has identified numerous single nucleotide polymorphisms (SNPs) associated with different complex diseases. However, associations reported in one population are often conflicting and did not replicate when studied in other populations. One of the reasons could be that most GWAS employ a case-control design in one or a limited number of populations, but little attention was paid to the global distribution of disease-associated alleles across different populations. Moreover, the majority of GWAS have been performed on selected European, African, and Chinese populations and the considerable number of populations remains understudied. Aim: We have investigated the global distribution of so far discovered disease-associated SNPs across worldwide populations of different ancestry and geographical regions with a special focus on the understudied population of Armenians. Data and Methods: We have used genotyping data from the Human Genome Diversity Project and of Armenian population and combined them with disease-associated SNP data taken from public repositories leading to a final dataset of 44,234 markers. Their frequency distribution across 1039 individuals from 53 populations was analyzed using self-organizing maps (SOM) machine learning. Our SOM portrayal approach reduces data dimensionality, clusters SNPs with similar frequency profiles and provides two-dimensional data images which enable visual evaluation of disease-associated SNPs landscapes among human populations. Results: We find that populations from Africa, Oceania, and America show specific patterns of minor allele frequencies of disease-associated SNPs, while populations from Europe, Middle East, Central South Asia, and Armenia mostly share similar patterns. Importantly, different sets of SNPs associated with common polygenic diseases, such as cancer, diabetes, neurodegeneration in populations from different geographic regions. Armenians are characterized by a set of SNPs that are distinct from other populations from the neighboring geographical regions. Conclusion: Genetic associations of diseases considerably vary across populations which necessitates health-related genotyping efforts especially for so far understudied populations. SOM portrayal represents novel promising methods in population genetic research with special strength in visualization-based comparison of SNP data.

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Section: Resultsmentioning

confidence: 99%

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians – A Machine Learning Approach

et al. 2019

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“…[130] Notably, a Nidovirus with a genome larger than 40 kb was recently described by Saberi et al, suggesting that maintenance of large RNA genomes by these mechanisms might be possible. [131] Another option to circumvent the context-dependency of ssRNA genomes is to develop dsRNA viruses as platform for synthetic TcRR systems. dsRNA viruses on average possess larger RNA genomes and do not require RNA secondary structure motifs for their replication.…”

Section: Potential and Limitations Of Protein-catalyzed Rna Replicationmentioning

confidence: 99%

Templated Self‐Replication in Biomimetic Systems

et al. 2019

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nanostructures and nanopatterns. [13][14][15] The specificity of the molecular recognitions between replicative units is concomitant with the encoding and transmission of information, with the degree of specificity determining the fidelty and capacity of information transfer against competing background interactions in noisy "chemical" environments. [16] However, the ability to replicate in this manner does not imply the ability to transmit additional heritable information-simple replicators can only replicate information related to recognition and template directed autocatalysis as this information is intrinsically linked to phenotype. In contrast to most artificial autocatalytic replicators, living systems (including viruses) store the key instructions for replication on an inheritable genetic system rather than in the native structures of the individual sub-components. This decoupling of phenotype and genotype enables heredity and open-ended evolution, the possibility of an indefinite increase in complexity, as evolutionary innovations aquired by natural selection are anchored in the genotype only. [17,18] The most prominent self-replicating molecules are nucleic acids, which form the fundamental basis for information storage and propagation in biological systems. The ability of polynucleic acids to store information is self-evident, and based upon the ability to form cognate Watson-Crick base pair interactions. Nucleic acid systems may have also formed the first genetic replicators in biology. The RNA world hypothesis postulates a stage in the origin of life in which RNA proliferated before the emergence of DNA and proteins. To do this, RNA must be able to carry out several key roles: information storage, catalysis, and (self-) replication. Multiple examples of catalytic RNAs (ribozymes) are known both from nature and in vitro selection experiments, [19][20][21][22] but there is no fossil evidence of an RNA capable of (self-)replication without the involvement of proteins.While minimal nucleic acid-only systems with replicationlike properties have been identified by directed evolution and engineering, limitations such as low activity, lack of generality, and poor information capacity limit their usefulness in bottomup synthetic biology, except in origin of life research. For the creation of more complex biological in vitro systems, RNA or DNA replication mechanisms involving more powerful protein enzymes are likely to be necessary. Even so, the achievement of self-sustained in vitro self-replication inspired by the central dogma of molecular biology is currently hampered by the requirement to encode and efficiently express the enormous A key characteristic of living systems is the storage and replication of information, and as such the development of self-replicating systems capable of heredity is of great importance to the fields of synthetic biology and origin of life research. In this review, the design and implementation of self-replicating systems in the context of bottom-up synthetic biology is discusse...

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P-117

Cited by 2 publications

References 0 publications

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians – A Machine Learning Approach

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians – A Machine Learning Approach

Templated Self‐Replication in Biomimetic Systems

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