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Computational Analysis of Plasma Glycome and Genotypes

Genome-wide association studies have been used to identify genetic variants underlying specific phenotypes, diseases and traits. Establishing associations for polygenic traits is not a straightforward task due to the complex interplay between several genes involved in producing a specific phenotype. Glycosylation is an important and complex post- translational modification. Glycosilation patterns in human plasma proteins result from a metabolic activity of several genes, shown to be under the control of both heritable and environmental factors. This research is focused on establishing the influence of the genotype component on the glycosylation profiles. The goal is to integrate the large amount of data available from glycomics and genomics and use machine learning algorithms to find the association between the glycosylation profiles and SNPs. Individuals containing similar glycan profiles were grouped using a consensus scoring of pariwise distances between vectors containing measured glycans values. Currently, characteristic SNPs present within the groups are being investigated.

GWAS; glycosylation; SNPs; machine learning

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