- E. Xing, Scribes Alok Kothari, Haoyu Wang. 2014. Graphical Induced Structured Input-Output Methods. Abstract: Such differences may also be responsible for the cause of hereditary disease. Genetic Association Hypothesis testing then is the study to find which SNP’s are causal (or associated) vis-a-vis a hereditary disease. Going site to site we try and associate the genotype with a phenotype. Usually these genetic association mappings are very sparse, (1 in 1000) and may represent basic differences in body physiology. So far, a number of standard approaches have been applied to find the causal SNP’s. Some examples of standard approaches are: using the linkage analysis of selected markers, quantitative trait locus (QTL) mapping conducted over one phenotype and one marker genotype at a time, which are then corrected for multiple hypothesis testing. Primitive data mining methods have also been employed, such as the clustering of gene expressions and the high-level descriptive analysis of molecular networks. Such approaches yield crude, usually qualitative characterizations of the study subjects. However, many complex disease syndromes, such as asthma and cancer, consist of a large number of highly related, rather than independent, clinical or molecular phenotypes. They are the effect of many sites of mutation multiple causal SNP’s. Attempting to find the top k SNP’s for complex diseases, using the standard approaches, is not ideal due to interactive effects between the two SNPS’s. Often the correlation between two SNP’s may have been forced as they are bound by physical constraints, (if one mutates the other one has to mutate too). So the way forward is by multiple hypothesis testing. Studying gene expression networks, may tell us what a complex disease like cancer means at a molecular level.
