STATISTICAL APPROACHES FOR MAPPING COMPLEX TRAIT ARCHITECTURE IN BIOLOGICAL SYSTEMS

Abstract

Complex traits are a major challenge in the context of biological systems because they are polygenic and heterogeneous, meaning that they are controlled by multiple genetic loci and interactions between the environment and the organism. Proper mapping of these characteristics should be done to progress genetic prediction, the evaluation of disease risk, and breeding models. The paper will formulate and implement powerful statistical methods that will be used to disseminate the architecture of complex traits with high-dimensional genomic data. To examine genotype phenotype associations we used a mixture of genome wide association studies (GWAS) and mixed linear models (MLM) and Bayesian regression models. The preprocessing of data was quality control filtering, normalization and population structure correction. The model performance was measured through cross-validation and other statistical measures like coefficient of determination (R 2) and the accuracy of prediction. It was compared with each other to determine how efficient and reliable each method was in detecting significant genetic variants. The findings indicated that several important loci in relation to the target characteristics were discovered, and the mixed linear models properly regulate the false positives and the Bayesian methods showing the best results in the detection of the small-effect variants. Also, integrated models were more predictive than single methodology. The results indicate the need of balancing statistical rigor with computational efficiency in analysis of complex traits. To sum up, the study offers a general framework of mapping a complex trait architecture, which can serve as valuable information on genetic interactions and enhance predictive modeling. These methods offer far reaching consequences to genetics, systems biology and precision breeding programs.