A Study on the Advantages of Genetic Counseling as an Advanced Medical Technology

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Genetic counseling is currently considered one of the most complicated advanced medical technologies. It was introduced to the world in 1947 (A Short History of Genetic Counseling, 1975). In the 1970s, the need for genetic counseling increased, which led to the establishment of a new category of genetic counselors (Report of the Genetic Associates Conference, 1979). After that, the American Board of Medical Genetics, which is responsible for setting morals and credentialing for the field, was established (Cohen, 1984). Basically, the term genetic counseling was defined by a committee of the American Society of Human Genetics, (1975), as: ““ a communication process which deals with the human problems associated with the occurrence, or the risk of occurrence, of a genetic disorder in a family””. In addition, genetic counseling is an unusual form of medicine in that it cares for the patients by providing them with information about the genetic abnormalities of their families.

Genetic counseling addresses the needs of parents concerned about their unborn children’s health and adults concerned about the possibilities of developing a genetic disease. Essentially, the genetic counseling process consists of medical diagnosis of the disease, the potential plan, and accessible management; analysis of the risk of the recurrence of the disease in relatives; solutions for dealing with the recurrence; selection of an appropriate course of action; and last, regulating as much as possible the disorder in the affected individual and the risk of recurrence of that disorder.

Although, the genetic counseling is a powerful public health tool by which we are enabled to predict the risk of developing huge rage of genetic diseases. It is being faced by scientific complications involved in providing a high level of efficiency, accuracy, privacy, confidentiality, and equity. So in order to reach the required technical level, huge amount of studies and research was developed to discover more effective genetic testing technologies. And this paper will be presenting complications associated with the accuracy of genetic testing. Essentially, genetic testing accuracy of diseases follows the pattern of simple Mendelian inheritance is much higher than in complex diseases for which a large number of loci contribute to the genetic variance, such as Type II Diabetes and Crohn’s disease.

In the name of accuracy, i will bring the light on genetic testing of the microsatellite instability (MSI). In addition, the approved definition of MSI including unifying measurement standards was established by ‘The International Workshop on Microsatellite Instability and RER Phenotypes in Cancer Detection and Familial Predisposition’, in 1998 (Boland et al., 1998). Basically, microsatellite instability (MSI) is the deletion of DNA microsatellites that is caused by a general failure of the DNA mismatch repair (MMR) system. As the microsatellite instability (MSI) is present in up to 70% of patients with cancers related to the hereditary nonpolyposis colorectal cancer (HNPCC) (Liu et al., 2000), it is an essential genetic marker in determining the risk of developing the hereditary nonpolyposis colorectal cancer (HNPCC), which is the most

common hereditary cancer disease (Lynch and de la Chapelle, 1999).

In fact, the deleterious germline mutation of the mismatch repair (MMR) genes, mainly MSH2 and MLH1, initiates developing hereditary nonpolyposis colorectal cancer (HNPCC) (Vasen et al., 2001; Lin et al., 1998). Since the involvement of MSI with HNPCC was approved, a variety of laboratories developed their own tools for measuring MSI, and related studies indicated that many markers could be used to distinguish MSI tumours in order to develop a reliable MSI testing methodology that provide a respectable level of sensitivity and specificity of identifying germline mutations of mismatch repair genes (MMR) or what so called microsatellite instability (MSI). In my paper, I will be analysing a study, which is based on laboratory experiment, aims to analyse the technical variations of two important methods of MSI testing, which are: a simplified 3-marker assay for MSI and the 5-marker (NCI) assay.


The study majorly aims to determine the ability of a simplified 3-marker assay and the traditional 5-marker assay, to identify MSI tumors their level of accuracy in detecting MMR protein defects, and to determine the clinical and molecular characteristics of patients identified as MSI presented by each. The collection of the samples was obtained between January 1990 and December 2004, and it followed institutional guidelines and a protocol approved by the Institutional Review Board. The samples collected were 357 snap-frozen tissues from patients presented with primary adenocarcinoma of the colon or rectum, and underwent initial treatment. Colon cancer samples were analyzed, along with their corresponding normal tissue, for all 5 microsatellite markers by uniplex PCR (5-marker assay), and for only BAT25, BAT26, and D2S123 using multiplex PCR (3-marker assay). The sample considered microsatellite unstable when 2 of 3 markers showed size instability for the multiplex 3- marker assay, or 2 of 5 for the 5-marker (NCI) assay.

Additionally, identification of Tumors was divided into three essential parts. Firstly, tumors referred to as 3-marker+ MSI, which were identified as MSI by only the multiplex assay. Secondly, the 3-marker- MSI tumors refer to those were identified only by the 5-marker assay and not the 3 marker assay. Lastly, those that were scored by both assays are referred to as the MSS tumors. On the other hand, this study used three other genetic analyzing techniques to compare their outcome with the results received from the assays. The analyzing techniques firstly included Histopathologic Studies using hematoxylin and eosin stained 3 Im sections taken from paraffin blocks. Second technique is the KRAS and BRAF mutation analysis using PCR/Ligase Detection Reaction (LDR) techniques. Last of all is the Array Comparative Genomic Hybridization (aCGH) which was done on Agilent 44 K arrays by the Genomic Core Laboratory of Sloan-Kettering Institute.


The genetic testing of the microsatellite instability (MSI) divided the whole number of the samples involved into three types. First, the 3-marker+ MSI, which was detected as microsatellite instable (MSI) by both assays, it is approximately 56 cancers (16%) and referred to as the 3-marker+ MSI. Second, the microsatellite instable (MSI) samples which were only detected by the 5-marker assay as MSI, they present 11% of the whole numbers (40 samples), and named the 3-marker- MSI. The rest 261 samples (73%) were detected MSS by the both assays. In most cases, 3-marker+ MSI tumors shared specific characteristics, such as the common location in the right colon and showing high proportion of poorly differentiated cancers along with high rate of survival. In contrast, those characteristics are completely absent in the other two groups. In addition, 14% of patients identified as 3-marker+ MSI met the slandered determination of the hereditary nonpolyposis colorectal cancer (HNPCC), compered less than 1% of patients from the other two groups. Another comparison, the tumors were developed before the age of 50 in 21% of 3-marker- MSI tumors whereas the other two showed low percentage (3%). Essentially, the Results of this study proved that 3-marker assay is much more accurate than the 5-marker assay, because the results of 3-marker assay matched the results of the other proven tests and research more than the 5-marker assays’.


  1. BIESECKER, B. (2001). GOALS OF GENETIC COUNSELING. CLINICAL GENETICS, 60(5), 323-330. DOI:10.1034/J.1399-0004.2001.600501.X Cao, Y. C., Shao, Y. B., Tian, S. L., & Cai, Z. Q. (2013). A dynamic genetic algorithm for clustering problems. Applied Mechanics and Materials, 411-414, 1884. doi:http://dx.doi.org/10.4028/www.scientific.net/AMM.411-414.1884 Chen, S. (2005). Accuracy of MSI testing in predicting germline mutations of MSH2 and MLH1: A case study in bayesian meta-analysis of diagnostic tests without a gold standard. Biostatistics, 6(3), 450-64. Retrieved from http://search.proquest.com/docview/204432952?accountid=136258

  2. Esemuede, I., M.D., Forslund, A., PhD., Khan, S. A., M.D., Qin, L., PhD., Gimbel, M. I., M.D., Nash, G. M., M.D., . . . Paty, P. B., M.D. (2010). Improved testing for microsatellite instability in colorectal cancer using a simplified 3-marker assay. Annals of Surgical Oncology, 17(12), 3370-8. doi:http://dx.doi.org/10.1245/s10434-010-1147-4

  3. Fusi, N., Stegle, O., & Lawrence, N. D. (2012). Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies. PLoS Computational Biology, 8(1), e1002330. doi:http://dx.doi.org/10.1371/journal.pcbi.1002330

  4. Ismkhan, H., & Zamanifar, K. (2013). Study of some recent crossovers effects on speed and accuracy of genetic algorithm, using symmetric travelling salesman


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