Systematic analysis of disease-associated variations based on the interaction between genetics and epigenetics

Abstract

Non-coding SNP that identified via GWAS which represent genetic methods in disease research is not possible to functional study. Epigenetics known to help understanding the difference in variation of trait that are unexplainable by genetic only. In this thesis, I conducted the identification of disease-associated variants and their targets based on interplay between genetics and epigenetics. At the system level, first, I performed the deep sequencing for open chromatin across the genome of yeast strains and monozygotic twins. While individual OCRs were associated with a handful of specific genetic markers, gene expression levels were associated with many regulatory loci for yeast strains. In twin study, the difference of chromatin accessibility depends on the genotype of a nearby locus. Based on these findings, epigenetic differences can control regulatory variations through interactions with genetic factors. From previous understanding, I performed the analysis of the cause of disease not solve by GWAS using allelic analysis. This approach showed approximately two times greater sensitivity than QTL mapping. In addition, I conducted the random forest analysis for homozygote problem that cannot analyze the allelic imbalance and small sample size.