الفهرس 
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Abstract
Recent technological advancements allowing for large-scale sequencing efforts present an exciting opportunity to uncover the genetic underpinnings of complex diseases. In an attempt to characterize these genetic contributors to disease, investigators have embarked in multitude on what are commonly referred to as population-based genetic association studies. Three cases were studied applying statistical linear and generalized linear models. Case study (I) is proposed for analyzing SNP quantitative trait association using linear regression with different coding schemes and one-way ANOVA while case study (II) is proposed for analyzing SNP qualitative trait association using logistic regression with two coding schemes (nominal and ordinal) in a segregating population. The model and method of producing simulation genetic data will be discussed and also part of the data has presented with definition of data variables.
Our results and concluding remarks are summarized as the following:-
I. Case Study (1): Single and multiple SNP association analysis
Both one-way ANOVA and regression with dummy coded variables give the same results. They show only wither there is statistically significant difference in means between genotypes i.e. there is sig, association between SNP and trait of interest. On the other hand they fail to estimate neither the additive nor the dominance effect of the SNP.
• Regression with FM coding method allows to estimate and test both additive and dominance effect of a SNP at specific locus.