Genetic Analysis 101


Genetic analysis is done through three methods: sequencing, DNA fingerprint, and DNA microarray. Sequencing breaks down the DNA into every piece of information and tells the order of all genetic information. Although it requires a lot of money and time to process all of the information, some companies will look at specific genes that are of concern for a fraction of the cost, like cancer or other genetic disorders. The DNA fingerprint is not used to determine one's health because it only analyzes parts of DNA that are non-gene coding. Instead, it is used for identification purposes such as forensics, paternity tests, disaster identification, and historical investigations. On the other hand, the DNA microarray is how genetic information is run in companies like Ancestry DNA. It is a genetically printed chip that looks for the most common mutations in humans. However, if the mutation is not printed on the chip, it will not be detected in the analysis. Although these chips were initially made for detecting cancer genes and enzyme metabolism, they can be used to access ancestry, heritage, and certain metabolic functions based on common mutations among different ethnic groups.

The gene mutations show one's susceptibility to the environment. For instance, a weaker house made of Styrofoam will have more problems than a stronger one made of brick during a storm. Additionally, understanding how the general biochemistry in one's body works can be complicated. The DNA analysis is not meant to be a diagnosis but to provide clues for future tests and eliminate guesswork as to which supplements and medications will be most beneficial for one's unique body.

Causative vs Correlative Genetic Studies

Causative effects refer to situations where one factor or event directly causes another, while correlative effects describe a relationship where two things are associated, but one does not necessarily cause the other. In DNA studies, causative effects typically involve changes to a specific gene or genes that directly result in a particular outcome, such as a disease or a particular trait. Correlative effects, on the other hand, describe relationships between genetic markers or other factors that are found to be associated with a particular outcome, but may not necessarily be causing the outcome themselves.

For example, a DNA study may identify a genetic marker that is more common in people with a particular disease. This would be a correlative effect, as the genetic marker may be associated with the disease, but it does not necessarily cause the disease. In contrast, if the study identified a specific mutation in a gene that was responsible for causing the disease, this would be a causative effect.

It's important to distinguish between these two types of effects in DNA studies, as identifying causative effects can help researchers better understand the mechanisms behind a particular outcome and develop more targeted treatments or interventions. Correlative effects can be useful for identifying potential risk factors or associations, but further research is typically needed to establish causation.

The Somaticode analysis fundamentally looks at the function of the gene that has been shown to have an actual change in the gene’s behavior. This approach increases confidence that there is a higher causative association rather than correlative.

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