Proteome-Wide Analysis of Disease Associated SNPs
Proteome-Wide Analysis of Disease Associated SNPs
Genome-wide association studies (GWAS) is an efficient way of identifying some diseases like Type 1 diabetes. Genomes are a collection of genes that are found on the chromosomes (Saroglia and Liu, 2012). Although it is a good method for showing the relationship between inter-marker linkage disequilibrium and diseases, it does not give a full analysis of the single nucleotide polymorphisms. There are new genes, which have been discovered related to Type 1 diabetes that adds knowledge to the use of GWAS.
An increase in the population of people affected by Type 1 diabetes especially children under the age of five has been noted according to Levy (2011). The rapid development in genetics requires advanced technology to study the changes (Rimoin et al., 2013). Improved technology has made detection of genetics and behavior related to some diseases simple (Pescatello and Roth, 2011). According to research by Wolf (2013), diabetes could be detected by excess urine leakage in the body.
The study involves a series of genome tests undertaken by many people in order to establish genetic variations in them (Ashton, 2012). These genetic variations make them more vulnerable to diseases. Type 1 diabetes is a killer disease, which is complex and affects genetic behavior (Bradfield et al., 2011). Most people, especially young children, require insulin injections as a preventive measure so that they do not contract the disease. Otherwise, they are particularly vulnerable according to research by Hanas (2007).
From studies, conducting a GWAS on many people, yields more accurate signals than conducting the same test on a few people. Research by Best and Swensen (2012) proves that a large sample of data influences the outcome of the statistics. The GWAS should also be able to show the possibility of survival. Credible signals are displayed if there are high levels of the possibility of survival. The numerous tests conducted should, therefore, be significant to the disease. In this case, due to the preventive measures taken on diabetes, the GWAS is not particularly significant in showing survival levels because necessary measures have already been taken to reduce contraction of Type 1 diabetes. This means that the correction factor of the multiple tests will be extremely high. Approximation of some calculations is usually done to get a general genotype score (Zheng et al., 2012). This affects the accuracy of the signal.
Extended hyperglycemia causes insulin impairment in the beta cells. Invest (1998) believed that the cell impairment is a major cause of diabetes. Varying insulin impairment is caused when the insulin gene is not regulated well by the binding proteins. This increases the glucose levels in the beta cells. The main inhibitor of the insulin gene is a leucine factor enhancer of the form CCAAT of the binding protein (Poretsky, 2010). Another effect is that it reduces fatty acids of the pancreatic islets.
It is evident that enhancing the binding protein will reduce the contraction of diabetes. The article shows how the binding factor was improved by increasing its density to balance on the high complexity of the GWAS system. In the end, it improved the signal of the system and thus, meaningful conclusions could be made. The enhancement is a good gesture, but it raises the costs of doing the procedure and conducting the numerous tests. In the event, it could limit the number of people who take the tests, and this could affect the overall signal.
Two pull-down experiments were performed because of the occurrence of transcription factors on either/or both allele. Research shows that some alleles are very essential in developing autoimmunity (Eisenbarth, 2004). This helps in containing autoimmune disorders through grouping each allele into four quadrants. These separated contaminants such as keratin to the lower portion of the left quadrant because the SILAC ratio is low in the two pull-down experiments. Conducting the two pull down experiments is good because it created a two dimensional plot in each experiment to allow proper interpretation of signals. However, it consumes a lot of time as the process is repeated a second time. This also creates tedious readings because according to the experiment, it produced 48 pull down experiments with 12 SNPs.
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