Genome Wide Association Study Analysis for OCD Complications Essay

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Genome-Wide Association Study for OCD Complications

The OCD (Obsessive -- compulsive disorder) is referred as repetitive behaviors and thoughts experienced by individuals. (Visscher, Brown, McCarthy, et al. (2012). Typically, the genes' characteristics of twins and families have revealed that the OCD has the feature of multifactorial familial condition involving both environmental and polygenic factors. (Moran, 2013). Genetic studies have revealed that the interaction of the glutamatergic, serotonergic, and dopaminergic systems and genes affecting them play a crucial role in functioning of the circuit. (Yang, Lee, Goddard, Mand et al. 2011). Meanwhile, the environmental factors that include psychological trauma, adverse perinatal effects and neurological trauma may modify the risk genes, which can consequently manifest the compulsive-obsessive behaviors. (Visscher, Brown, McCarthy et al. 2012). The OCD is a frequent and, relative common debilitating neuropsychiatric disorder affecting 2% of the U.S. population. (Arnold, Sicard, Burroughs, et al. (2006). Typically, the OCD is obsessions of repetitive behaviors and thoughts experienced as unwanted. (Baxter, Scott, Vos, et al. 2012). In other words, the OCD is a health and clinically heterogeneous disorder consisting of different types of symptomatic expression. (Murray, and Lopez, 1996). In the United States, the OCD can affect people in early childhood and adulthood and, between 30% and 50% of the U.S. population suffer from OCD in their early childhood often with children below the 10 years of age. (Pauls, Abramovitch, Rauch et al. 2014).

The risk factors associated to the OSD include the stress as well as the history of child abuse. In the healthcare environment, the OCD has continued to demand the psychiatric attention because of its high OSD diagnosis rate. (Murray and Lopez 1996).

Effectively, the GWAS (Genome-Wide Association Studies) is a clinical approach to address the OSD complication. (Ziegler, 2009, Zohar, 1999).

"The premise of the GWAS design is that extensive common variation in the human genome, as exhibited by SNPs with frequencies greater than 1%, is responsible for the risk of most genetically complex disorders." (Cantor, et al., 2009 p 6).

In essence, the GWAS are the standard method for a discovery of genes. The genotyping technologies and computational statistical analysis have become the standard method to analyze the GWA to address the OSD problem. (Barrett, Healy-Farrell, L. & March, 2004).

The goal of this research is to carry out the analysis of GSA using the computation and statistical tool to understand OSD complications.

Methods

Objective of the lab report is to use statistical and computation tools to analyze the GWAS to deliver effective method to understand the strategy to address the OSD complications. The sampling information, selection procedures and quality control are used to understand the effectiveness of GWAS.

Study Selection Procedure

The study collects data from the QIMR (Queensland Institute of Medical Research) for the analysis of GWAS in order to compare the case control and data from QIMR comprising the ~2,372,500 SNPs (single nucleotide polymorphisms). The selection process is very essential to the sample to address the problems of design bias.

Study exclusion/inclusion

The study selects all subjects from European ancestry to address the problem of overrepresentation of variance that occurs across ethnic groups. The study removes individuals who are more closely paired. More important, subjects who are non-Europeans are removed. The subjects with genetic genders discrepancies are also removed to avoid sample mix-up.

Raw data

The extracted data consist of SNP genotypes, phenotypes, and MAF (minor allele frequencies).

Quality control

The standard and statistical QC (quality control) is performed on the QIMR genotype data to enhance the quality of SNP sample data. The final dataset of the OCD is able to match the sample case size where the control sample sizes (n=500). The control sample sizes are able to match the male and female subjects (n=750). However, excessive missing values of (>20%) were dropped to remove the potential errors, which consequently set both cases and control ratio to 50:50 to reduce the signal noise ratio. Moreover, the paper removes the SNPs having low MAFs (10%). The SNPs are also dropped if both cases and controls are less than 10% of sample MAFs groups (

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The study also stimulates the OCD phenotypes with the aid of the GCTA software to deliver the genome plausible association. The statistical tool such as descriptive statistics is used to summarize the raw mass data to provide the median, P-value and Chi-Square. The outcome of the analysis assists in presentation of the results in graphical form.

Results

The data analysis has delivered the results to enhance a greater understanding of the contribution of the GWAS in reducing the OCD complications.

Figure 1: Mahattan Plot

The figure 1 presents the Manhattan Plot revealing that the red dotted line does not surpass the genome-wide threshold.

Fig 2: QQ Plot

The Figure 2 provides the QQ Plot where QQ is - .99 Plots of the GWAS that consequently delivers no minimal inflation. The QQ Plots also reveals that the lambda = inflation and deviation of GWAS is based on the polygenetic architecture leading to some bias that inflates the test statistics. More importantly, the line is able to deviate the null (line), where the median expected is equal to median observed that results to 1.

The Q-Q plot assists in verifying and authenticating the quality of association carrying out after the case control analysis that are not confounded with the unaccounted variables such as population stratification. The illustration also presents the small number of SNPs that deviates from the remaining SNPs association. The graphical illustration shows that the moderate p

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