Analysis of Gwa Genome Wide Association Study for OCD

GWA (Genome-Wide Association ) Study Analysis for OCD

The OCD (Obsessive Compulsive Disorder) is a type of mental disorder that makes people doing the same thing repeatedly. In other words, the OCD is characterized by unreasonable obsessive fear and thoughts that lead to repetitive behaviors. People suffering from OCD are unable to control their activities and thoughts. While the cause of OCD is unknown, however, the risk factors include stress and history of child abuse. (Moran, 2013).The OSD is characterized by the neuropsychiatric disorder with features of compulsion and obsession or combination of both features. (Moran, 2013). Within the last decade, the OCD is increasingly demanding high attention from psychiatric and medical institutions due to the higher rates of its clinical diagnosis. In the United States, people suffering from OCD are approximately between 1 and 5% of the total population, however, the OSD is common among adolescent and adults. Most psychologists believe that OCD is characterized by the neurotic disorder that includes general anxiety, and panic disorder. In essence, severe OCD can affect personal life of individuals in a negative way. However, majority of people suffering from OCD can still perform many essential activities because the disease is different from severe psychotic disease that can make an individual unable to communicate with people.( Murray and Lopez 1996). To generate a clear picture of obsessive-compulsive disorder, it is critical to understand that there are two major components of OCD, which include compulsions and obsessions. An obsession refers to the recurrent persistent ideas, images and thoughts that always intrude into subconscious or conscious of individuals. The obsessions are neither consciously created nor wanted, however, created by an unwelcome outsider. Many people may understand that they are possessed by this obsession; nevertheless, they will try to dispose or resist these features. (Visscher, Brown, McCarthy et al. 2012).

On the other hand, compulsions are repetitive form of behaviors performed based on stereotyped fashion or certain rules. (Stein, Andersen, & Overo 2007). Typically, compulsions refer to repetitive behaviors carried out based on strong feelings to carry out the compulsion. ( Ziegler, 2009). The genetic study has revealed that genes affecting dopaminergic, serotonergic, and serotonergic systems as well as interactions among them play a critical role in the functioning of the circuit. (Zohar, 1999, Zohar, et al. 2012). More importantly, environmental factors that include neurological, psychological and adverse perinatal events can modify the risk genes expression, which can consequently trigger the manifestation of the obsessive and compulsive behaviors. (Pauls et al., 2014).

In essence, the GWAS (Genome-Wide Association Studies) has become a standard and effective approach to address the OSD problem. (Mattheisen, Samuels, Wang, et al. 2014). The GWAS uses the recent advanced in technology to assist in solving the mystery behind a complex genetic disorder characterized by the OSD. (Yang,, Lee, Goddard, et al. 2011, Gibson, 2010).

Objective of this paper is to carry out the analysis of the (Genome-Wide Association Study) that reveals the computation and statistical too to address the GWAS. The next section presents method used to carry out the analysis.

Methods

The method discusses the selection procedure, sampling information and quality control carried out for the research. The aim of the lab report is to use the web-based informatics tools and resources to interpret the GWAS results. The study integrates the statistical tools into biological understanding to deliver accurate results.

Study Selection Procedure

The study carries out the analysis of the GWAS by comparing genomic case control and data collected from the QIMR (Queensland Institute of Medical Research) that comprise of ~2,372,500 SNPs (single nucleotide polymorphisms). The genomic cases are prevalent when tested because they compose of the DNA variants that take the form of SNPs compared to controls. The selection process is relevant to the samples making the study to remove the sample artefacts and design bias.

Study exclusion/inclusion

The inclusion and exclusion are based on the selection of the features of the datasets. The study only includes participants from European ancestry. The strategy is to prevent over-represented of variance across the various ethnic groups. However, the study excludes subjects from Non-European ancestry and subjects with discrepant genetic genders to avoid the sample mix-up. Moreover, the study removes a pair of individuals closely related than a third degree relative.

Raw data

The paper analyzes the extracted information that includes phenotypes, SNP genotypes, and MAF (minor allele frequencies).

Quality control

The statistical standard (OQ) quality control on the QIMR genotype data were carried out to ensure the best quality of SNP sample data. The OCD final dataset matches the case sample sizes with the control sample sizes (n=500), which consequently match the number of both genders-male and female (n=750). The study dropped the sample of excessive missing values ((>20%). The strategy is to remove the potential errors by setting the sex ratio equal for both control and cases (50:50) in order to reduce the number of signal to noise ratio. Moreover, the research removes the SNPs that have low MAFs (10%) between both cases and controls within sample groups MAFs (

Statistical analyses

The statistical tool is used to test the allele frequency differences between control and cases using the PLINK software. The study stimulates OCD phenotypes using the GCTA software to create one or more genome plausible association. The study uses the descriptive statistics to summarize the mass raw data in a manageable form, and the outcomes of the analysis provide the P-value and, median Chi-Square of the data collected. Moreover, the analysis assists the researcher to present the data in graphical forms to enhance visual presentation of the research findings.

Results

The outcomes of analysis provide the following results. First, none of the red dotted line surpasses the threshold of the genome-wide significant as being revealed in Fig 1, the Manhattan Plot.

Fig 1: Mahattan Plot

The outcome of the analysis also delivers the QQ Plot as being revealed in Fig 2 where QQ - .99 Plots of the GWAS for the Lambda reveals no minimal inflation. Based on the illustration in the Fig 2, the QQ plots (lambda = inflation and GWAS deviation is due to a polygenetic architecture or some sort occurrence of bias, which inflate the test statistics). Moreover, the line deviates the null (line). Moreover, the median expected =median observed that results to 1.

Fig 2: QQ Plot

The Q-Q plot is used to verify and authenticate the quality of association, which is made after the analysis of case control and not confounded with unaccounted variables such as population stratification. The graph shows the presence of small number of SNPs deviating from the remaining unassociated SNPs. The graphs also reveals a moderate p

Figure 3. Regional LD Plot

The fig 3 determines the extent the LCD block is tagged by the SNP. In essence, the LCD plot determines the extent the LD block has been able to tag by the top of SNP. The vertical line of the LCD plot = 25592137-25718520. The analysis in the Table 1 reveals the Chi Square, the SNP, BP and other values.

Table 1: Results of the Analysis

Values

CHR

17

SNP

BP

25596879

AI

Risk allele as viewed on the odd ratio

F_A (all affected people)

0.4175

F_U (unaffected people)

0.3523

CHISQ

27.16

OR

1.318 (1.3 x greater that T allele where cases vs. controls) The Top SNP is equal to Rs1487971

Figure 4. Regional Associations Plot

The regional associations plot reveals the number of SNPs that are collected from the NCBI's gene datasets. The datasets deliver the graph in accordance to the strength of correlation. Based on the effect of the size, the odd ratio (1.318) is not significant because the value is less than the p-value (1.874e-07). The p-value should be 1.874e-08 to be significant. Moreover, the Chi Square (27.16) is not significant because the chi-square needs to be higher than 27.16 to be significant. (See Table 1). However, the gene function is consistence with the GWAS statistical association because the role of BLM hydrolase in normal physiological is unknown.

Discussion

The results demonstrate the results of lab report of the GWA (Genome-Wide Association) Study analysis. (Geller, et al. 2003). In the contemporary health environment, GWAS have become a standard method to discover the disease gene. (Cullen, et al. 2007). While a substantial number of GWAS has been liked to disorder, however, only few GWAS variants are associated and implicated to SNPs that explains a fraction of genetic risk. (Stewart et al. (2007). The statistical tests reveal the aggregation of GWAS association connected to the genes. (Ruscio, Stein, Chiu et al. 2010). In essence, the statistical method is applied to accumulate SNPs association. (Stewart, Mayerfeld, Arnold, et al. (2013). The method involves the permutation tests to address the problem that can lead to a source of bias. The study reveals that the GWAS require three critical and essential elements. (Barrett, Healy-Farrell, & March, 2004). First, the large sample study population is effective to deliver genetic information to address the research objectives. Moreover, polymorphic alleles are the efficient and inexpensive strategy to cover the genome adequately. More importantly, the statistical analytical method is powerful tool "to identify the genetic associations in an unbiased fashion." (Cantor, Lange, and Sinsheimer, 2010 p 9). In essence, each of the three elements has been developed to accomplish thee research objectives. The study formed large study sample using the productive collaborations that lead the samples to deliver a sufficient statistical power to deliver relative small associations having a common variants.

Reference

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