Güler, Merve Nur, juhendajaChaharna, KaterynaTartu Ülikool. Loodus- ja täppisteaduste valdkondTartu Ülikool. Bioinseneeria instituut2026-07-092026-07-092026https://hdl.handle.net/10062/123316Genotype-phenotype studies often lack known ground truth, making it difficult to evaluate whether statistical or machine-learning methods recover the true causal architecture of a trait. In this thesis, an existing genotype-phenotype simulation codebase was extended and refined into Gen2PhenSim, a configurable Python-based simulator that generates binary and quantitative phenotypes from real genotype data under user-defined genetic architectures. The simulator supports additive SNP effects, dominant and recessive encodings, second- and third-order interaction terms, Gaussian noise, and quantitative-trait heritability control. Besides, it has a feature to control for selecting SNPs based on their distance to select independent SNPs. As a proof of concept, simulated phenotypes were analysed with Regenie and GWAS hits were compared with the true simulated causal SNPs.enAttribution-NonCommercial-NoDerivs 3.0 Estoniahttp://creativecommons.org/licenses/by-nc-nd/3.0/ee/genotypephenotypesimulatorepistasisLDGWASbakalaureusetöödThesis