Naslov
Computational analysis of plasma glycome and genotypes in human population

Autori
Tica, Jelena

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, diplomski

Fakultet
Prirodoslovno-matematički fakultet

Mjesto
Zagreb

Datum
10.11

Godina
2011

Stranica
56

Mentor
Vlahoviček, Kristian

Ključne riječi
glycolisation; GWAS; SNP; bioinformatics; machine learning

Sažetak
Clinical diseases are characterized by distinct phenotypes. To identify disease-related gene or to develop appropriate diagnostic tests, it is necessary to elucidate the gene-phenotype relationships. Genome/wide association studies (GWAS) are used for identifying genetic associations with pheonotypic traits by analyzing a set of single nucleotide polymorphisms (SNPs) as the genetic markers. SNPs arise from point mutations in DNA and are the major source of diversity among individuals, Phenotypic trait that can be analyzed in a context of genetic changes is glyocosylation. This process involves the addition of glycans (sugar chains) to both proteins and lipids, and is the most complex and abundant post- translational modification. The goal of this research is to find the possible relationships between glycosylation profiles and SMPs in isolated human populations by using bioinformatics tools and machine learning algorithms, and to develop an analysis pipeline that would be reproducible and statistically relevant. I have developed a method and a set of computational tools to analyze and visualize correlations between glycans and genotypes based on hierarchical clustering of glycan profile distance data and identity by descent (IBD) values in genotypes. Analysis performed on two distinct datasets from two isolated populations in Croatia and Scotland show that the method is able to identify distinct glycan profiles in subpopulations and find their correlation to genotype results.

Izvorni jezik
Engleski

Znanstvena područja
Biologija

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