Dissecting Mouse Anxiety Assay Related Otls By Jointly Using Multiple Traits

It is a challenge to develop efficient statistical methods for mapping genes underlying complex traits. The Mapping software (QTLNetwork V2.2) that integrates the detection of gene-gene interaction and gene-environment interaction for quantitative trait loci (QTLs) based on marker polymorphisms, for quantitative trait nucleotides (QTSs) based on SNPs, and for quantitative trait transcripts (QTTs) based on variation in expression of transcripts. Mixed linear model approaches are used for unbiased prediction of all these genetic main effects, epistasis effects, and gene-environment interaction effects. This dissertation consists of three chapters.Chapter1is an overall introduction.Chapter2QTL mapping based on Marker, SNP and Transcripts data from RIL population of BXD family by new approach were performing. Using506markers, detected8QTLs including three main-effect QTLs (h2=0.27) and three pairs of epistasis QTLs (h2=0.23). By using2,320SNPs,17QTSs were detected for seven main-effect QTSs (h2=0.25) and six pairs of epistasis QTSs (h2=0.22). Using a small mapping population (188individuals in5treatments), and detected only one main QTT (h2=0.07) and two pairs of epistasis QTTs with very small effects. The mapping results by three methods were compared for chromosome1and11. Three QTSs were resided within the flanking marker intervals of three QTLs detected. The QTT is mapped into the QTTs on chromosome1. In conclusion, the new approaches developed here are capable for identifying causal genes associated with complex traits.Chapter3Dissecting mouse anxiety assay related QTLs by jointly using multiple traits. To identify their genetic regulatory network, mice data that consists of528individuals derived from71BXD recombinant inbred strains were analyzed using the multiple trait method implemented in QTLNetwork v2.2. Quantitative trait loci (QTL) that affect the inheritance of complex traits, detect the interaction between any pair of QTL (epistasis) and the QTL and epistasis in different environmental conditions. Analysis of mice data and simulation study were performed to identity that the method proposed increases the statistical power for QTL mappmg and nproves t precision of parameter estimation by the correlated structure of multiple traits. Also, this metho can distinguish QTLs due to pleiotropy or close linkage.