Scientists are getting fascinating new perspectives on the human genome, and it’s all thanks to the advancements made in big data analytics. For years, genes have been studied and mapped, with perhaps the crowning achievement being the completion of the Human Genome Project in the early 2000s, but true understanding of how human genetics work has required more intensive study and more resources. Only recently have scientists been able to look more closely at human genes, and much of this progress comes as they apply big data analytics to the issue.
In many ways, big data is a perfect fit for genetics since genes are all about information. A typical human genome contains more than 20,000 genes, with each made up of millions of base pairs. Simply mapping a genome requires a hundred gigabytes of data, and sequencing multiple genomes and tracking gene interactions multiplies that number many times — hundreds of petabytes in some cases. It is the use of big data analytics that gives researchers further insight into how genes act, in turning changing how we look at them.
One particularly intriguing discovery that came from applying big data analytics techniques comes from scientists at the University of Haifa. In their study, they were able to observe what is called the “social character” of genes. What scientists have long wanted to figure out are the inner workings of complex genetic effects that take part in the creation of complex diseases. This goal has been particularly difficult since genetic expressions of certain diseases usually come from the combination of several genetic markers interacting with each other. So not only would researchers have to comb through an entire genetic sequence, they’d also have to track interactions between multiple different genes. Needless to say, this complex undertaking required the collection of massive amounts of data. But this study was able to significantly narrow down the possibilities from 900 million to only 340,000. Scientists were able to do this using statistical tools commonly found in big data analysis. The result was a greater understanding of how genes interact, displaying certain effects also observed in the social sciences.
Much of the work done with the human genome and big data analytics deals closely with health and medicine. In certain ways, this changes how we view genes in that we look upon them as sort of a road map to better health and treatments. Indeed, this is the main goal of using big data and genetics — creating personalized medicine. By studying the data from a human genome, scientists can create more effective and appropriate treatments for patients. The one-size-fits-all approach can be set aside for medicine that works best with each individual person. This not only results in better outcomes but can also lead to less time needed for treatment. The same applies for the creation of new drugs. By identifying significant genetic variants among patients that respond well to certain new drugs, the number of patients needed for trials would be significantly reduced. That would also mean the trial and error process currently used would be made obsolete.
Big data analytics used for studying genetics can be used for other health causes as well. They can be viewed as health records, which are stored and accessed by doctors whenever the patient is being checked up on. Based off of the analysis of genes, doctors would be able to identify heritable traits that can be passed on to the next generation. This is especially useful when pinpointing people who might be high-risk for certain diseases such as diabetes. Analytics can also unlock the possibilities of survival for specific illnesses. Essentially, proper analysis of human genes gives us a fascinating perspective on health outcomes and likely developments long before they actually happen.
While not a crystal ball by any means, big data analytics helps us have greater understanding of the human genome, in turn helping researchers get a better idea of what may happen in the future. Big data also gives incredible insight into how genes work to make us who we are. While analytics capabilities such as ad hoc analysis or clickstream data have been used in other industries before, science is showing a fresh eagerness to use big data to the advancement of genetic research. With more time, greater discoveries are likely to be made.