ASSOCIATION MAPPING OF COMPLEX TRAITS USING GENOME-WIDE STATISTICAL APPROACHES

Abstract

The research paper examines the genetic basis of a complex trait based on genome-wide association studies (GWAS) and highly-developed statistical modeling strategies. A high-density single nucleotide polymorphism (SNP) dataset of a diverse population was studied after the quality control steps such as minor allele frequency, missing data, and Hardy-Weinberg equilibrium were carefully followed. Handling of population structure and genetic relatedness- Principal component Analysis (PCA) and kinship Matrices were used to reduce false associations. General linear models (GLM) and mixed linear models (MLM) were used to associate and statistical significance was calculated with proper multiple testing corrections with the help of standard bioinformatics packages, i.e., PLINK and GAPIT. Some important SNPs across a number of chromosomes were identified during the analysis, and mixed-model methods were found to be more effective in controlling the false positive than the simple models. The annotation of candidate genes showed that most of the associated loci are connected to important biological pathways of expressing the traits, which illustrates their functional importance. On the whole, this paper has shown that genome-wide statistical methods are pertinent to the discovery of genetic variants of complex phenotypes and can be used as effective tools in understanding their genetic pathophysiology and offer possible future uses in genomics, breeding technologies, and precision medicine.