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

Abstract

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.

Candidate Gene and Genome-Wide Association Studies for Circulating Leptin Levels Reveal Population and Sex-Specific Associations in High Cardiovascular Risk Mediterranean Subjects

“…These results contribute, on one hand, to emphasizing the essential need to study the homogeneity or heterogeneity per sex of genetic associations (in this case, the SNPs associated with leptin levels) in order to obtain fewer biased results; thus contributing to more profound knowledge for future precision medicine [62,63]. On the other hand, these results also underscore the importance of studying specific populations instead of extrapolating results of gene variants obtained in studies undertaken in other populations [38,64,65,66,67]. Several publications have pointed out that, for precision medicine, disease risk is likely to be miscalculated if GWAS results obtained in one population are naively used to compute GRS for a geographically different population [38,66].…”

SOMmelier—Intuitive Visualization of the Topology of Grapevine Genome Landscapes Using Artificial Neural Networks

“…SOM-portrayal of SNP data was performed as described previously [ 19 ]. In short: our SOM implementation used a ternary code with the values 0, 1 and 2 for major homozygous, heterozygous and minor heterozygous genotypes, respectively, as introduced above.…”

“…Here, we apply SOM (self-organizing maps) portrayal [ 12 ], a neural network-based machine learning method with strong visualization capabilities to genome-wide Single nucleotide polymorphism (SNP) data of nearly eight hundred grapevine cultivars collected from Middle Asia in the East to Iberian peninsula in the West and from overseas regions [ 11 ]. SOM portrayal has been developed by us for the detailed analysis of high-dimensional omics data, including diversity and developmental issues, feature selection, knowledge mining and phenotype association of transcriptomic [ 13 , 14 , 15 ], epigenetic [ 16 , 17 ], proteomics [ 18 ] and genetic data [ 19 ], and of combinations of them [ 20 ]. In the context of plants, SOM-portrayal has been previously applied by us for the typing of algae of the genus Prototheca [ 21 ] and by others for studying early seed maturation in garden pea [ 22 ].…”

“…These personalized omics-portraits provide options for the intuitive evaluation of feature space, and for mutual comparisons between the individual accessions. The recent application of SOM-portrayal to population-level distributions of disease-related human SNP-variants demonstrated its capability to extract genetic features and to describe genetic diversity, based on the topology of the SNP-landscapes [ 19 ].…”

Portrayal of the human blood transcriptome of 3,388 adults and its relation to ageing and health

“…Previously, we have developed an omics 'portrayal' methodology based on self-organizing maps (SOM) machine learning [20,21]. It has been applied to a series of data types and diseases [22][23][24][25][26], among them a study about footprints of pneumonia in the blood transcriptome [8]. SOM-portrayal takes into account the multidimensional nature of gene regulation and pursues a modular view on coexpression, reduces dimensionality and supports visual perception by delivering 'personalized', casespecific transcriptome portraits.…”