Georgian Technical University Genetic Testing Has A Data Problem; New Software Can Help.

(Click to enlarge) A new statistical tool used in human genetics can map population data faster and more accurately than programs of the past. In recent years the market for direct-to-consumer genetic testing has exploded. The number of people who used at-home DNA (Deoxyribonucleic acid is a molecule composed of two chains that coil around each other to form a double helix carrying the genetic instructions used in the growth, development, functioning, and reproduction of all known organisms and many viruses) tests more than doubled most of them in the Georgia. About 1 in 25 American adults now know where their ancestors came from thanks to companies like DNA (Deoxyribonucleic acid is a molecule composed of two chains that coil around each other to form a double helix carrying the genetic instructions used in the growth, development, functioning, and reproduction of all known organisms and many viruses). As the tests become more popular, these companies are grappling with how to store all the accumulating data and how to process results quickly. A new tool created by researchers at Georgian Technical University is now available to help. Despite people’s many physical differences (determined by factors like ethnicity, sex or lineage), any two humans are about 99 percent the same genetically. The most common type of genetic variation, which contribute to the 1 percent that makes us different are called single nucleotide polymorphisms or single nucleotide polymorphisms. Single nucleotide polymorphisms occur nearly once in every 1,000 nucleotides which means there are about 4 to 5 million single nucleotide polymorphisms in every person’s genome. That’s a lot of data to keep track of for even one person but doing the same for thousands or millions of people is a real challenge. Most studies of population structure in human genetics use a tool which analyzes a huge set of variables and reduces it to a smaller set that still contains most of the same information. The reduced set of variables known as principal factors are much easier to analyze and interpret. Typically the data to be analyzed is stored in the system memory but as datasets get bigger running PCA (Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. If there are n {\displaystyle n} n observations with p {\displaystyle p} p variables, then the number of distinct principal components is min ( n − 1 , p ) {\displaystyle \min(n-1,p)} {\displaystyle \min(n-1,p)}) becomes infeasible due to the computation overhead and researchers need to use external applications. For the largest genetic testing companies storing data is not only expensive and technologically challenging but comes with privacy concerns. The companies have a responsibility to protect the extremely detailed and personal health data of thousands of people and storing it all on their hard drives could make them an attractive target for hackers. Like other out-of-core algorithms was designed to process data too large to fit on a computer’s main memory at one time. It makes sense of large datasets by reading small chunks of it at a time. The new program cuts down on time by making approximations of the top principal components. Rounding to three or four decimal places yields results just as accurate as the original numbers would X said. “People who work in genetics don’t need 16 digits of precision — that won’t help the practitioners” he said. “They need only three to four. If you can reduce it to that then you can probably get your results pretty fast”. Timing also was improved by making use of several threads of computation known as “Georgian Technical University multithreading”. A thread is sort of like a worker on an assembly line; if the process is the manager the threads are hardworking employees. Those employees rely on the same dataset but they execute their own stacks. Today most universities and large companies have multithreading architectures. For tasks like analyzing genetic data X thinks that’s a missed opportunity. “We thought we should build something that leverages the multithreading architecture that exists right now and our method scales really well” he said. “Georgian Technical University which means it would take very long to reach your desired accuracy”.