Uses of Data Science in Genetic Diseases 

Uses of Data Science in Genetic Diseases 

The human genome has about 40,000 genes and is made up of about three million base pairs. The first human genome was sequenced in 2001 and took several years to complete. But in recent years, researchers have sequenced thousands of genomes. It's becoming a relatively simple task for a lab to do this nowadays. This means that genetic data is now produced much faster than it can be organized or analyzed. Indeed, we have a lot of data that is the untapped potential of great use. Let's discuss.

Genetics and the data science

One or more abnormalities in the genome cause a genetic disorder. A mutation in a single gene (monogenic) or multiple genes (polygenic) or a chromosomal abnormality is the main cause of the genetic disorders. As you know, a hereditary disease is a genetic disorder that is inherited from one or both parents. A few things that have been particularly successful in using genetics and the data science of healthcare. Now the risk of the diseases can be predicted through genetics. When most people think of genetics and disease, they think of single-gene causes. An example is a gene for cancer. But things are rarely so simple. 

Polygenic interactions

Most diseases involve many genes, possibly thousands. This is called polygenic interactions or the combined effect. It is possible to detect many of these polygenic effects because of the availability of enormous data. So this combination for instance of 400 genes collectively work to predict a particular disease. There is a unique study on Parkinson's disease conducted as part of the Parkinson's progression markers initiative that was made. Here clinical, genetics, imagination, and demographic data are the basic needs of the researcher. It can be predicted a patient's risk of Parkinson's disease after developing a highly accurate model.

DNA data mining to predict diseases

• Similarly, researchers have made progress on DNA data mining to predict diseases ranging from coronary artery disease to breast cancer. But it's important to remember that DNA is not destiny. 
• Even in identical twins who share the same DNA, diseases don't show up the same. Really, what data scientists do is they give probabilities but not concrete. Because they are based on DNA, they can be administered while the embryo is still in the womb. 
• This raises a huge number of ethical dilemmas. It's important to remember that in something like identical twins have 100% same DNA. 
• That is important to remember because it is not only the tests for things like certain types of cancer or Parkinson's but also, the researchers have been developing tests for things like IQ and mental illness.
• It is important to be a little humble when interpreting the probabilities that come from predictive analytics for diseases through DNA. There is also a need to focus on controllable factors, especially when you look at things like heart disease. 
• There are so many things that a person can do. These are diets and exercises that can help reduce the person's risk even if it never changes their DNA. 

It is bringing the exciting potential for finding and diagnosing diseases. It is also bringing an understanding of their nature through the combination of genetics and data science.